Processing methods, apparatuses, systems, and components for monitoring photovoltaic power generation systems

Abstract

Embodiments of the present application relate to a kind of processing method, device, system and component for monitoring photovoltaic power generation system, the method comprises: the latitude and longitude of the region where, altitude and characteristic meteorological data are acquired;And acquire current month as first month;Meteorological data pre-processing is carried out;Temperature is monitored in advance to future twelve months;Sunlight intensity is monitored in advance to future twelve months;Generation output is monitored in advance to future twelve months;Generation output prediction table is generated and saved to first generation output prediction table. Current month information, time information and real-time power generation power information are acquired every hour from preset starting time every day;And query first generation output prediction table and obtain predicted power;And according to real-time, monitoring state setting is carried out according to predicted power;And at the preset end time of each day, generation output state analysis is carried out according to all monitoring states obtained in the day. Through the present application, photovoltaic power generation system can be personalized, fine management.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a processing method, apparatus, system and components for monitoring photovoltaic power generation systems. Background Technology

[0002] An integrated photovoltaic-storage-charging power station system refers to a small-scale power generation and distribution system, also known as a microgrid, composed of a photovoltaic power generation system, an energy storage system, and charging facilities. The photovoltaic power generation system consists of multiple photovoltaic arrays. The photovoltaic system converts solar energy into electricity through these arrays and outputs the converted electricity as generated power. Currently, conventional photovoltaic power generation systems only focus on the current power output and do not monitor future operating conditions, let alone monitor whether the current operating conditions meet the pre-monitoring results. This conventional approach has too coarse a granular monitoring level, making it impossible to achieve personalized and refined management of the photovoltaic power generation system. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a method, apparatus, system, and components for monitoring photovoltaic (PV) power generation systems. First, based on the PV power generation system's latitude, longitude, altitude, and meteorological data from the region over the past twenty years, the system's power output (i.e., power generation capacity) for the next twelve months is periodically pre-monitored to obtain a corresponding power output forecast table. Then, during system operation, the actual power output level for each time period of the day is monitored in real-time, referring to the pre-monitored power output forecast table, to obtain the corresponding monitoring status. Based on the monitoring status of multiple time periods, the daily power output status of the system is analyzed in real-time to obtain a corresponding daily analysis report. Through this invention, adaptive pre-monitoring processing can be performed based on the personalized data of the PV power generation system, and real-time monitoring of the system can be performed with reference to the pre-monitoring results, thereby achieving the goal of personalized and refined management of the PV power generation system.

[0004] To achieve the above objectives, a first aspect of the present invention provides a method for monitoring a photovoltaic power generation system, the method comprising:

[0005] The system acquires the longitude, latitude, altitude, and characteristic meteorological data of the region where the photovoltaic power generation system is located as the corresponding first longitude, first latitude, first altitude, and first meteorological data set; and acquires the current month as the corresponding first month.

[0006] The first meteorological data set is used to perform meteorological data preprocessing to generate a corresponding second meteorological data set;

[0007] Based on the first month, the first longitude, the first latitude, the first altitude, and the second meteorological data set, a first temperature forecast table is generated by monitoring the temperature for the next twelve months.

[0008] Based on the first month and the second meteorological data set, the first solar intensity prediction table is generated by monitoring the solar intensity for the next twelve months.

[0009] Based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table, the power generation output is pre-monitored for the next twelve months to generate a corresponding first power generation output forecast table, which is then saved.

[0010] Preferably, the first meteorological data set is a set of characteristic meteorological data for the region where the photovoltaic power generation system is located over the past twenty years;

[0011] The first meteorological data set includes multiple first-year meteorological data; the first-year meteorological data includes 365*24 first-hour temperature collection data, 365 first-day sunshine duration collection data, 1 first-year astronomical radiation statistics data, 1 first-year summer astronomical radiation statistics data, 1 first-year winter astronomical radiation statistics data, and 1 first-year sunshine percentage;

[0012] The second meteorological data set includes the first annual average temperature, the first highest temperature, the first lowest temperature, the first annual average astronomical radiation, the first annual average summer astronomical radiation, the first annual average winter astronomical radiation, the first annual average sunshine percentage, and the sunshine duration L for the 12 first months. i ; i is the month index for the next twelve months, 1≤i≤12;

[0013] The first temperature forecast table includes 12 first temperature forecast records; each first temperature forecast record includes one first month field and 12 first time period temperature fields; each first time period temperature field includes first time period data and first temperature data; the first time period data includes 12 two-hour periods, namely 0-2, 2-4, 4-6, 6-8, 8-10, 10-12, 12-14, 14-16, 16-18, 18-20, 20-22, and 22-24.

[0014] The first sunshine intensity prediction table includes 12 first sunshine intensity prediction records; the first temperature prediction record includes one second month field and 12 first time period sunshine intensity fields; the first time period sunshine intensity fields include second time period data and first sunshine intensity data; the second time period data includes 12 single-hour periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19;

[0015] The first power generation forecast table includes 12 first power generation forecast records; the first power generation forecast record includes one third month field and 12 first time period power generation fields; the first time period power generation fields include third time period data and first power generation data; the third time period data includes 12 single time periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19.

[0016] Preferably, the step of generating a corresponding second meteorological data set by performing meteorological data preprocessing based on the first meteorological data set specifically includes:

[0017] The average temperature of the first year is calculated by averaging the 365*24 first hour temperature data of each meteorological data of the first year, and the average temperature of the first year is calculated by averaging all the first year average temperatures.

[0018] From all the temperature data collected in the first hour, select the maximum and minimum values ​​as the corresponding first highest temperature and first lowest temperature;

[0019] The average annual astronomical radiation, the average summer astronomical radiation, and the average winter astronomical radiation of the first year are calculated by averaging all the first annual astronomical radiation statistics, all the first annual summer astronomical radiation statistics, and all the first annual winter astronomical radiation statistics, respectively.

[0020] The average sunshine percentage for the first year is calculated by averaging all the sunshine percentages for the first year.

[0021] The 365 sunshine duration data points collected for each of the first year's meteorological data were divided into 12 sets of first sunshine duration data by month. The sunshine duration data for each set was then summed to obtain the corresponding first month's sunshine duration data. All the obtained first month's sunshine duration data were then divided into 12 sets of first month's sunshine duration data by month. Finally, the average of the sunshine duration data for each set was calculated to obtain the corresponding first natural month's sunshine duration L. z z is a natural month marker, which consists of January to December; the natural month index z is matched with the first month following the first month to obtain the sunshine duration L of the first natural month. z As the corresponding first month's sunshine duration L i＝1 The natural month index z is matched with the sunshine duration L of the first natural month after the first month. z As the corresponding first month's sunshine duration L i＝2 This process continues until the natural month index z is matched with the twelfth month following the first month, and the sunshine duration L of the first natural month is obtained. z As the corresponding first month's sunshine duration L i＝12 until;

[0022] The first annual average temperature, the first maximum temperature, the first minimum temperature, the first annual average astronomical radiation, the first annual average summer astronomical radiation, the first annual average winter astronomical radiation, the first annual average sunshine percentage, and the sunshine duration L of the 12 first months are obtained. i This forms the corresponding second meteorological data set.

[0023] Preferably, the step of generating a corresponding first temperature forecast table by performing temperature forecasting for the next twelve months based on the first month, the first longitude, the first latitude, the first altitude, and the second meteorological data set specifically includes:

[0024] The first diurnal variation coefficient D is generated by estimating the diurnal variation coefficient based on the first latitude and the first altitude. The estimation constant factor a of the daily variation coefficient d,1 The default value is 6100, and the constant factor a is used for estimation. d,2 The default value is 90;

[0025] The first daily temperature difference W is generated by estimating the daily temperature difference based on the first longitude and the first latitude, where W = a w,1 +[a w,2 (First Latitude - a) w,3 )+aw,4 (a w,5 -First longitude)]; Daily temperature difference estimation constant factor a w,1 The default value is 8, and the constant factor a is estimated. w,2 The default value is 0.25, and the constant factor a is used for estimation. w,3 The default value is 20, and the constant factor a is estimated. w,4 The default value is 0.0075, and the estimation constant factor a is... w,5 The default value is 130;

[0026] Substituting the first month, the first diurnal variation coefficient D, the first diurnal temperature variation W, the first longitude, the first highest temperature, the first lowest temperature, and the first annual average temperature into the preset empirical formula for predicted temperature, 12*12 first predicted temperature data T are obtained. i,j j is the index of the two time periods, 1≤j≤12;

[0027] Create a corresponding first temperature forecast table; set 12 first temperature forecast records in the first temperature forecast table; create a first month field and 12 first time period temperature fields in each first temperature forecast record, and create corresponding first time period data and first temperature data in each first time period temperature field; initialize the first month field of the 12 first temperature forecast records sequentially to the first month, second month, third month, and so on until the twelfth month; set the first time period data of the 12 first time period temperature fields in each first temperature forecast record sequentially to the 0-2 time period, 2-4 time period, 4-6 time period, 6-8 time period, 8-10 time period, 10-12 time period, 12-14 time period, 14-16 time period, 16-18 time period, 18-20 time period, 20-22 time period, and 22-24 time period; and based on the 12 first predicted temperature data T with month index i being 1. i＝1,j The first 12 first temperature data points of the first first temperature prediction record are set; and based on the 12 first predicted temperature data points T with month index i being 2. i＝2,j The settings are applied to the 12 first temperature data points of the second first temperature prediction record; and so on, until the 12 first predicted temperature data points T based on the month index i being 12 are obtained. i＝12,j The setup is completed for the 12 first temperature data records of the 12th first temperature prediction record.

[0028] Furthermore, the empirical formula for predicting temperature is:

[0029]

[0030] Tm,n To predict temperature,

[0031] m represents the month and n represents the hour.

[0032] T a The average annual temperature

[0033] T max T min The highest and lowest temperatures,

[0034] W represents the daily temperature difference.

[0035] D is the diurnal variation coefficient.

[0036] Longitude.

[0037] Furthermore, the first month, the first diurnal variation coefficient D, the first daily temperature difference W, the first longitude, the first highest temperature, the first lowest temperature, and the first annual average temperature are substituted into a preset empirical formula for predicting temperature to calculate 12*12 first predicted temperature data T. i,j Specifically, it includes:

[0038] Step 61: Set the first month as the current month; initialize the first index to 1; and set the first longitude as the longitude of the predicted temperature empirical formula. The first highest temperature and the first lowest temperature are used as the highest temperature T in the empirical formula for predicting temperature. max and lowest temperature T min The first annual average temperature is used as the annual average temperature T in the empirical formula for predicting temperature. a The first daily difference coefficient D and the first daily temperature difference W are used as the daily difference coefficient D and daily temperature difference W in the empirical formula for predicting temperature; and the range of the hour n in the empirical formula for predicting temperature is set to [0,2,4,6,8,10,12,14,16,18,20,22].

[0039] Step 62: Take the next month after the current month as the month m in the empirical formula for predicted temperature, and substitute the 1st to 12th values ​​of the hour n into the empirical formula for predicted temperature to calculate the corresponding 12 first predicted temperature data T. i＝第一索引,j ;

[0040] Step 63: Increment the first index by 1; and take the next month of the current month as the new current month; and determine whether the first index is greater than 12; if yes, proceed to step 64, otherwise proceed to step 62.

[0041] Step 64, the obtained 12*12 first predicted temperature data T i,j Output.

[0042] Preferably, the step of generating a corresponding first sunshine intensity forecast table by performing sunshine intensity prediction monitoring for the next twelve months based on the first month and the second meteorological data set specifically includes:

[0043] Based on the first month, the first year's average astronomical radiation, the first year's average summer astronomical radiation, and the first year's average winter astronomical radiation, the monthly astronomical radiation for the next twelve months is estimated to generate 12 corresponding first-month astronomical radiation values ​​S. 1,i ;

[0044] Based on the first month's astronomical radiation S (12) 1,i The first annual average sunshine percentage is used to estimate the monthly local radiation for the next twelve months, generating 12 corresponding first-month local radiation values ​​S. 2,i ;

[0045] Based on the local radiation S of the first month and 12 months of the first month 2,i And 12 of the first month's sunshine duration L i The solar intensity for each solar term during the next twelve months is estimated to generate the corresponding first predicted solar intensity S. i,k k is the index for a single time period, 1≤k≤12;

[0046] Create a corresponding first sunshine intensity prediction table; set 12 first sunshine intensity prediction records in the first sunshine intensity prediction table; create a second month field and 12 first time period sunshine intensity fields in each first sunshine intensity prediction record, and create corresponding second time period data and first sunshine intensity data in each first time period sunshine intensity field; initialize the second month field of the 12 first sunshine intensity prediction records to the first month, second month, third month, and so on until the twelfth month; set the second time period data of the 12 first time period sunshine intensity fields in each first sunshine intensity prediction record to the 7-8 time period, 8-9 time period, 9-10 time period, 10-11 time period, 11-12 time period, 12-13 time period, 13-14 time period, 14-15 time period, 15-16 time period, 16-17 time period, 17-18 time period, and 18-19 time period based on the 12 first predicted sunshine intensities S with month index i being 1. i＝1,k The first 12 solar radiation intensity data of the first solar radiation intensity prediction record are set; and the 12 first predicted solar radiation intensities S based on the month index i being 2 are set.i＝2,k The settings are applied to the 12 first sunshine intensity data points of the second first sunshine intensity prediction record; and so on, until the 12 first predicted sunshine intensities S based on the month index i being 12 are obtained. i＝12,k The process continues until the 12th first solar intensity prediction record is set up.

[0047] Furthermore, the process of estimating the monthly astronomical radiation for the next twelve months based on the first month, the first annual average astronomical radiation, the first annual average summer astronomical radiation, and the first annual average winter astronomical radiation generates 12 corresponding first monthly astronomical radiation values ​​S. 1,i Specifically, it includes:

[0048] The first annual average astronomical radiation is used as parameter S. y And the first annual average summer astronomical radiation is used as parameter S. s And the first annual average winter astronomical radiation is used as parameter S. w And the month numbers from the first month to the twelfth month after the first month are respectively denoted as the corresponding month m. i ; and the parameter S y The parameter S s The parameter S w and each of the months m i Substituting the values ​​into the empirical formula for lunar astronomical radiation, we obtain the corresponding 12 values ​​of the first lunar astronomical radiation S. 1,i ;

[0049] The empirical formula for lunar astronomical radiation is:

[0050] Furthermore, the astronomical radiation S based on the first month of the 12 months... 1,i The first annual average sunshine percentage is used to estimate the monthly local radiation for the next twelve months, generating 12 corresponding first-month local radiation values ​​S. 2,i Specifically, it includes:

[0051] The first annual average daily sunshine percentage is used as parameter p. y ; and the parameter p y and the astronomical radiation S of each of the first months 1,i Substituting the values ​​into the empirical formula for local radiation in the first month, we can calculate the corresponding 12 local radiation values ​​S for the first month. 2,i ;

[0052] The empirical formula for the monthly local radiation is: a, b, c, and d are the preset Bahel model parameters.

[0053] Furthermore, the local radiation S based on the first month and 12 of the first month... 2,i And 12 of the first month's sunshine duration L i The solar intensity for each solar term during the next twelve months is estimated to generate the corresponding first predicted solar intensity S. i,k Specifically, it includes:

[0054] Step 101: Initialize the second index to 1; and set the range of values ​​for hour n in the empirical formula for solar radiation intensity to [7,8,9,10,11,12,13,14,15,16,17,18].

[0055] Step 102, calculate the local radiation level S for the first month. 2,i＝第二索引 And the first month's sunshine duration L i＝第二索引 Substitute the values ​​into the empirical formula for solar radiation intensity, and take the first to 12th values ​​of n within the range of hours. k Substituting these values ​​sequentially into the empirical formula for solar radiation intensity yields the corresponding 12 first predicted solar radiation intensities S. i＝第二索引,k ;

[0056] The empirical formula for solar radiation intensity is:

[0057] Step 103: Increment the second index by 1; and take the next month of the current month as the new current month; and determine whether the second index is greater than 12; if yes, proceed to step 104, otherwise proceed to step 102.

[0058] Step 104, the obtained 12*12 first predicted solar irradiance S i,k Output.

[0059] Preferably, the photovoltaic power generation system includes multiple identical photovoltaic arrays;

[0060] The photovoltaic array corresponds to a set of first standard data, which includes a first standard temperature T. STC First standard solar radiation intensity S STC And at the first standard temperature T STC and the first standard solar radiation intensity S STC Under the condition that the photovoltaic array can output the first maximum power P max .

[0061] Preferably, the step of generating and saving a corresponding first power generation output forecast table based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table for the next twelve months specifically includes:

[0062] Create a corresponding first power generation output forecast table; set 12 first power generation output forecast records in the first power generation output forecast table; create a third month field and 12 first time period power generation output fields in each first power generation output forecast record, and create corresponding third time period data and first power generation output data in each first time period power generation output field; initialize the third month field of the 12 first power generation output forecast records sequentially to the first month, second month, third month after the first month, and so on until the twelfth month; set the third time period data of the 12 first time period power generation output fields in each first power generation output forecast record sequentially to the 7-8 time period, 8-9 time period, 9-10 time period, 10-11 time period, 11-12 time period, 12-13 time period, 13-14 time period, 14-15 time period, 15-16 time period, 16-17 time period, 17-18 time period, and 18-19 time period; initialize the 12 first power generation output data in each first power generation output forecast record to empty;

[0063] The number of photovoltaic arrays in the photovoltaic power generation system is counted as the corresponding first quantity G;

[0064] The system iterates through each of the first power generation output data in the first power generation output prediction table. During the iteration, the currently iterated first power generation output data is taken as the corresponding current power generation output data, and the third time period data corresponding to the current power generation output data is taken as the corresponding current time period. The record index of the first power generation output prediction record corresponding to the current power generation output data is taken as the corresponding current record index. The first temperature prediction record in the first temperature prediction table that corresponds to the current record index is recorded as the corresponding first matching record, and the first sunshine intensity prediction record in the first sunshine intensity prediction table that corresponds to the current record index is recorded as the corresponding second matching record. The first temperature data in the first time period temperature field of the first matching record that matches the current time period data is taken as the corresponding current temperature T. * And take the first solar intensity data of the solar intensity field of the first time period that matches the second time period data in the second matching record with the current time period as the corresponding current solar intensity S. * ; and the current temperature T * Subtract the first standard temperature T STC The temperature difference is taken as the corresponding first temperature difference ΔT, and the current solar radiation intensity S is used as the reference. * Subtract the first standard solar radiation intensity S STCThe difference in solar radiation intensity is taken as the corresponding first intensity difference ΔS; and based on the first quantity G and the first maximum power P max The first temperature difference ΔT, the first intensity difference ΔS, and the current solar radiation intensity S * and the first standard solar radiation intensity S STC Calculate the corresponding first power output P * , α, β, and γ are all preset empirical coefficients; and based on the first power generation P * The current power generation output data in the first power generation output prediction table is set.

[0065] A second aspect of this invention provides a method for monitoring a photovoltaic power generation system, the method comprising:

[0066] Every day, starting from a preset start time, the current month information, time information, and real-time power generation information are obtained every hour as the corresponding second month, first time, and first real-time power; and the corresponding first predicted power is obtained by querying a preset first power generation prediction table based on the second month and the first time; and the corresponding first monitoring status is generated by setting the monitoring status of the photovoltaic power generation system based on the first real-time power and the first predicted power.

[0067] At the preset end time of each day, the power output status of the photovoltaic power generation system is analyzed based on all the first monitoring statuses obtained that day, and a corresponding first analysis report is generated.

[0068] Preferably, the preset start time is no earlier than 7:00 AM each day;

[0069] The preset end time shall not be later than 7 p.m. each day;

[0070] The first power generation forecast table includes 12 first power generation forecast records; the first power generation forecast record includes one third month field and 12 first time period power generation fields; the first time period power generation fields include third time period data and first power generation data; the third time period data includes 12 single time periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19.

[0071] Preferably, the step of obtaining the corresponding first predicted power by querying a preset first power generation output prediction table based on the second month and the first time specifically includes:

[0072] Record the first power generation forecast record in the first power generation forecast table that matches the third month field with the second month as the corresponding matching record; and extract the first power generation data in the first time period field that matches the third time period data with the first time period data in the matching record as the corresponding first predicted power.

[0073] Preferably, the step of generating a corresponding first monitoring state based on the first real-time power and the first predicted power for setting the monitoring state of the photovoltaic power generation system specifically includes:

[0074] The ratio of the first real-time power to the first predicted power is taken as the corresponding first ratio.

[0075] When the first ratio is lower than a preset first power ratio threshold, the corresponding first monitoring state is set to a Class I state; when the first ratio is not lower than the first power ratio threshold but lower than a preset second power ratio threshold, the corresponding first monitoring state is set to a Class II state; when the first ratio is not lower than the second power ratio threshold, the corresponding first monitoring state is set to a Class III state; the first power ratio threshold is less than the second power ratio threshold.

[0076] Preferably, the step of generating a corresponding first analysis report based on the power output status analysis of the photovoltaic power generation system obtained on the same day specifically includes:

[0077] The number of the first monitoring states is counted to generate a corresponding first total; and the number of the first monitoring states during the preset peak power generation period is counted to generate a corresponding second total.

[0078] The number of the first monitoring states, which are respectively Class I and Class III, is statistically analyzed to generate corresponding first and second quantities; and the number of the first monitoring states, which are respectively Class I and Class III, is statistically analyzed during the peak power generation period to generate corresponding third and fourth quantities.

[0079] The ratios of the first and second quantities to the first total quantity are taken as the corresponding first and second ratios; and the ratios of the third and fourth quantities to the second total quantity are taken as the corresponding third and fourth ratios.

[0080] When both the second ratio and the fourth ratio are greater than the preset first ratio threshold, the corresponding first analysis report is set to "excellent power generation output throughout the entire time period and excellent power generation output during the peak period".

[0081] When the second ratio is greater than the first ratio threshold but the fourth ratio is lower than the first ratio threshold, the corresponding first system state is set to good power generation output throughout the day but decreased power generation output during peak hours.

[0082] When the second ratio is lower than the first ratio threshold but the fourth ratio is greater than the first ratio threshold, the corresponding first system state is set to a decrease in power generation output throughout the entire period but good power generation output during peak periods.

[0083] When both the second ratio and the fourth ratio are lower than the first ratio threshold, the corresponding first system state is set to a decrease in power generation output during the entire time period and a decrease in power generation output during the peak period.

[0084] A third aspect of the present invention provides an apparatus for implementing the processing method for monitoring a photovoltaic power generation system as described in the first aspect of the present invention. The apparatus includes: a first acquisition module, a first preprocessing module, a first temperature pre-monitoring module, a first solar radiation intensity pre-monitoring module, and a first power generation output pre-monitoring module.

[0085] The first acquisition module is used to acquire the longitude, latitude, altitude, and characteristic meteorological data of the area where the photovoltaic power generation system is located as the corresponding first longitude, first latitude, first altitude, and first meteorological data set; and to acquire the current month as the corresponding first month;

[0086] The first preprocessing module is used to perform meteorological data preprocessing based on the first meteorological data set to generate a corresponding second meteorological data set;

[0087] The first temperature forecast module is used to generate a corresponding first temperature forecast table by performing temperature forecast monitoring for the next twelve months based on the first month, the first longitude, the first latitude, the first altitude, and the second meteorological data set.

[0088] The first solar intensity pre-monitoring module is used to perform solar intensity pre-monitoring for the next twelve months based on the first month and the second meteorological data set to generate a corresponding first solar intensity forecast table;

[0089] The first power generation output pre-monitoring module is used to perform power generation output pre-monitoring for the next twelve months based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table, generate a corresponding first power generation output forecast table, and save it.

[0090] A fourth aspect of the present invention provides an apparatus for implementing the processing method for monitoring a photovoltaic power generation system as described in the second aspect of the present invention, the apparatus comprising: a first time period monitoring module and a first daily analysis module;

[0091] The first time period monitoring module is used to acquire the current monthly information, time information and real-time power generation information every hour starting from the preset start time each day as the corresponding second month, first time and first real-time power; and to query the preset first power generation prediction table according to the second month and the first time to obtain the corresponding first predicted power; and to set the photovoltaic power generation system monitoring status according to the first real-time power and the first predicted power to generate the corresponding first monitoring status.

[0092] The first daily analysis module is used to perform photovoltaic power generation output status analysis based on all the first monitoring statuses obtained on that day at the preset end time of each day, and generate a corresponding first analysis report.

[0093] The fifth aspect of the present invention provides a processing system for monitoring a photovoltaic power generation system, the system comprising: the apparatus provided in the third aspect of the present invention and the apparatus in the fourth aspect of the present invention.

[0094] A sixth aspect of the present invention provides a processing component for monitoring a photovoltaic power generation system, the component comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a processing method for monitoring a photovoltaic power generation system as described in a first aspect of the present invention, or a processing method for monitoring a photovoltaic power generation system as described in a second aspect of the present invention.

[0095] This invention provides a method, apparatus, system, and components for monitoring photovoltaic (PV) power generation systems. First, based on the PV system's latitude, longitude, altitude, and meteorological data from the region over the past twenty years, the system's power output (i.e., power generation capacity) for the next twelve months is periodically pre-monitored to obtain a corresponding power output forecast table. Then, during system operation, the actual power output level for each time period of the day is monitored in real-time, referring to the pre-monitored power output forecast table, to obtain the corresponding monitoring status. Based on the monitoring status of multiple time periods, the daily power output status of the system is analyzed in real-time to obtain a corresponding daily analysis report. Through this invention, adaptive pre-monitoring processing can be performed based on the personalized data of the PV power generation system, and real-time monitoring of the system can be performed with reference to the pre-monitoring results, improving the personalized and refined management level of the PV power generation system. Attached Figure Description

[0096] Figure 1 This is a schematic diagram of a method for monitoring a photovoltaic power generation system according to Embodiment 1 of the present invention;

[0097] Figure 2 This is a schematic diagram of a method for monitoring a photovoltaic power generation system according to Embodiment 2 of the present invention;

[0098] Figure 3 This is a module structure diagram of a processing device for monitoring a photovoltaic power generation system provided in Embodiment 3 of the present invention;

[0099] Figure 4 This is a module structure diagram of a processing device for monitoring a photovoltaic power generation system provided in Embodiment 4 of the present invention;

[0100] Figure 5 This is a module structure diagram of a processing system for monitoring a photovoltaic power generation system provided in Embodiment 5 of the present invention;

[0101] Figure 6 This is a module structure diagram of a processing component for monitoring a photovoltaic power generation system provided in Embodiment Six of the present invention. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0103] Embodiment 1 of the present invention provides a method for monitoring a photovoltaic power generation system, such as... Figure 1 The schematic diagram shows a method for monitoring a photovoltaic power generation system according to Embodiment 1 of the present invention. This method mainly includes the following steps:

[0104] Step 1: Obtain the longitude, latitude, altitude, and characteristic meteorological data of the region where the photovoltaic power generation system is located as the corresponding first longitude, first latitude, first altitude, and first meteorological data set; and obtain the current month as the corresponding first month;

[0105] The first meteorological data set is a set of characteristic meteorological data for the region where the photovoltaic power generation system is located for at least the past twenty years; the first meteorological data set includes multiple first-year meteorological data; the first-year meteorological data includes 365*24 first-hour temperature data, 365 first-day sunshine duration data, 1 first-year astronomical radiation statistics, 1 first-year summer astronomical radiation statistics, 1 first-year winter astronomical radiation statistics, and 1 first-year sunshine percentage.

[0106] Here, the photovoltaic power generation system of Embodiment 1 of the present invention periodically acquires the longitude, latitude, altitude, and characteristic meteorological data of its location and uses this data as the first longitude, first latitude, first altitude, and first meteorological data set required for the current prediction. Simultaneously, it acquires the current month information (January-December) as the first month required for the current prediction. It should be noted that the characteristic meteorological data of the photovoltaic power generation system's location, i.e., the first meteorological data set, is a set of characteristic meteorological data for the region over the past twenty years. This set of meteorological data is divided by year, with each year corresponding to a first-year meteorological data set. The first-year meteorological data for each year specifically consists of temperature data, sunshine duration data, astronomical radiation statistics, and annual sunshine percentage statistics. The temperature data is collected hourly, so the temperature data consists of 365*24 first-hour temperature data points, with the unit of the first-hour temperature data being degrees Celsius (°C). The sunshine duration data... The data collection frequency is once a day, so the sunshine duration data consists of 365 first sunshine duration data points, with the unit for the first sunshine duration data being hours / day. The astronomical radiation statistics are further divided into three categories: annual, summer, and winter astronomical radiation statistics, namely, the first annual astronomical radiation statistics, the first annual summer astronomical radiation statistics, and the first annual winter astronomical radiation statistics. By default, the first annual astronomical radiation statistics are approximately equal to the sum of the first annual summer astronomical radiation statistics and the first annual winter astronomical radiation statistics. The unit for these three astronomical radiation statistics is megajoules per square meter (MJ / m²). 2 The annual sunshine percentage, or the sunshine percentage for the first year, is the percentage of the total actual sunshine hours in each year to the total possible sunshine hours (the total sunshine hours that should be available when there are no clouds throughout the day).

[0107] Step 2: Perform meteorological data preprocessing based on the first meteorological data set to generate the corresponding second meteorological data set;

[0108] The second meteorological data set includes the first annual average temperature, the first annual maximum temperature, the first annual minimum temperature, the first annual average astronomical radiation, the first annual average summer astronomical radiation, the first annual average winter astronomical radiation, the first annual average sunshine percentage, and the sunshine duration L for the 12 first months. i ; i is the month index for the next twelve months, 1≤i≤12;

[0109] Specifically, it includes: Step 21, calculating the average of the 365*24 first-hour temperature data collected for each first-year meteorological data to obtain the corresponding first-year average temperature, and calculating the average of all the obtained first-year average temperatures to obtain the corresponding first-year average temperature.

[0110] Step 22: Select the maximum and minimum values ​​from all the temperature data collected in the first hour as the corresponding first highest temperature and first lowest temperature;

[0111] Step 23: Calculate the average annual astronomical radiation, average summer astronomical radiation, and average winter astronomical radiation for the first year by averaging all statistical data of astronomical radiation for the first year, all statistical data of summer astronomical radiation for the first year, and all statistical data of winter astronomical radiation for the first year.

[0112] Step 24: Calculate the average sunshine percentage for all first-year sunshine percentages to obtain the corresponding first-year average sunshine percentage.

[0113] Step 25: Divide the 365 first-day sunshine duration data points of each first-year meteorological data into 12 corresponding first-day sunshine duration data sets by month; sum the multiple first-day sunshine duration data points of each first-day sunshine duration data set to obtain the corresponding first-month sunshine duration data; divide all the obtained first-month sunshine duration data into 12 corresponding first-month sunshine duration data sets by month; and calculate the average of the multiple first-month sunshine duration data points of each first-month sunshine duration data set to obtain the corresponding first-month sunshine duration L. z z is the natural month marker, which consists of January to December; the natural month index z is matched with the first natural month after the first month to determine the sunshine duration L of the first natural month. z As the corresponding first month's sunshine duration L i＝1 Match the natural month index z with the daylight hours L of the first natural month following the first month. z As the corresponding first month's sunshine duration L i＝2 This process continues until the natural month index z is matched with the first natural month's sunshine duration L, which is the twelfth month following the first month. z As the corresponding first month's sunshine duration L i＝12 until;

[0114] Step 26, using the obtained first annual average temperature, first maximum temperature, first minimum temperature, first annual average astronomical radiation, first annual average summer astronomical radiation, first annual average winter astronomical radiation, first annual average sunshine percentage, and sunshine duration L for the 12 first months. i This forms the corresponding second meteorological data set.

[0115] For example, the first meteorological data set is a set of characteristic meteorological data from the past twenty years, consisting of 20 first-year meteorological data sets. Each first-year meteorological data set consists of 365*24 first-hour temperature data sets, 365 first-day sunshine duration data sets, 1 first-year annual astronomical radiation data set, 1 first-year summer astronomical radiation data set, 1 first-year winter astronomical radiation data set, and 1 first-year sunshine percentage.

[0116] Therefore, by averaging the 365*24 first-hour temperature data collected in step 21 for each first-year meteorological data, we can obtain 20 first-year average temperatures. Then, by averaging the 20 first-year average temperatures, we can obtain the first-year average temperature.

[0117] By selecting the maximum and minimum values ​​from the 20*365*24 first-hour temperature data points in step 22, the first highest temperature and the first lowest temperature can be obtained.

[0118] Step 23 involves averaging the 20 first-year astronomical radiation statistics to obtain the first-year average astronomical radiation, averaging the 20 first-year summer astronomical radiation statistics to obtain the first-year average summer astronomical radiation, and averaging the 20 first-year winter astronomical radiation statistics to obtain the first-year average winter astronomical radiation.

[0119] Step 24 is used to calculate the average sunshine percentage of the first year by averaging the 20 sunshine percentages for the first year.

[0120] In step 25, the 365 first-day sunshine duration data points for each first-year meteorological data set are clustered by month to obtain 12 sets of first-day sunshine duration data. Then, the total sunshine duration of each set of first-day sunshine duration data is calculated to obtain the corresponding first-month sunshine duration data. Next, the 20*12 first-month sunshine duration data points from the 20 first-year meteorological data sets are clustered by the same month to obtain 12 sets of first-month sunshine duration data. Each first-month sunshine duration data set includes the first-month sunshine duration data from 20 of the same months. Finally, the average of each set of first-month sunshine duration data is calculated to obtain the sunshine duration L for the 12 first natural months. z Here, the natural month index z represents January, February, March, April, May, June, July, August, September, October, November, and December in sequence. Let the current month, i.e., the first month, be February. Then, in Embodiment 1 of this invention, the natural month index z will be matched with the first natural month after February, i.e., March, to determine the first natural month's sunshine duration L. z＝3 As the corresponding first month's sunshine duration L i＝1And match the natural month index z with the first natural month sunshine duration L of the second month after February, i.e., April. z＝4 As the corresponding first month's sunshine duration L i＝2 And so on, thus determining the duration of sunshine in the first natural month, L. z＝5 L z＝6 L z＝7 L z＝8 L z＝9 L z＝10 L z＝11 L z＝12 L z＝1 L z＝2 As the corresponding first month's sunshine duration L i＝3 L i＝4 L i＝5 L i＝6 L i＝7 L i＝8 L i＝9 L i＝10 L i＝11 L i＝12 ;

[0121] Step 26 provides the following data: first annual average temperature, first highest temperature, first lowest temperature, first annual average astronomical radiation, first annual average summer astronomical radiation, first annual average winter astronomical radiation, first annual average sunshine percentage, and sunshine duration L for the 12 first months. i This forms the corresponding second meteorological data set.

[0122] Step 3: Based on the first month, first longitude, first latitude, first altitude, and second meteorological data set, conduct temperature forecast monitoring for the next twelve months to generate the corresponding first temperature forecast table;

[0123] The first temperature forecast table includes 12 first temperature forecast records; each first temperature forecast record includes one first month field and 12 first time period temperature fields; the first time period temperature fields include first time period data and first temperature data; the first time period data includes 12 two-hour periods: 0-2, 2-4, 4-6, 6-8, 8-10, 10-12, 12-14, 14-16, 16-18, 18-20, 20-22, and 22-24.

[0124] Specifically, this includes: Step 31, estimating the diurnal variation coefficient based on the first latitude and the first altitude to generate the corresponding first diurnal variation coefficient D;

[0125] The formula for estimating the diurnal variation coefficient is shown below:

[0126]

[0127] The estimation constant factor a of the daily variation coefficient d,1 The default value is 6100, and the constant factor a is used for estimation. d,2 The default value is 90;

[0128] Here, the above formula for estimating the diurnal variation coefficient is a pre-given empirical formula, and the estimation constant factor a in the formula is... d,1 a d,2 The default values ​​given above are also used under normal circumstances;

[0129] Step 32: Estimate the daily temperature difference based on the first longitude and the first latitude to generate the corresponding first daily temperature difference W;

[0130] The formula for estimating the daily temperature difference is shown below:

[0131] W = a w,1 +[a w,2 (First Latitude - a) w,3 )+a w,4 (a w,5 -First longitude)],

[0132] Daily temperature difference estimation constant factor a w,1 The default value is 8, and the constant factor a is estimated. w,2 The default value is 0.25, and the constant factor a is used for estimation. w,3 The default value is 20, and the constant factor a is estimated. w,4 The default value is 0.0075, and the estimation constant factor a is... w,5 The default value is 130;

[0133] Here, the above formula for estimating daily temperature range is a pre-given empirical formula, and the estimation constant factor 'a' in the formula... w,1 a w,2 a w,3 a w,4 a w,5 The default values ​​given above are also used under normal circumstances;

[0134] Step 33: Substitute the first month, the first diurnal temperature variation coefficient D, the first diurnal temperature variation W, the first longitude, the first highest temperature, the first lowest temperature, and the first annual average temperature into the preset empirical formula for predicted temperature to calculate 12*12 first predicted temperature data T. i,j j is the index of the two time periods, 1≤j≤12;

[0135] The empirical formula for predicting temperature is shown below:

[0136]

[0137] T m,nTo predict temperature, m represents the month, n represents the hour, and T a T represents the average annual temperature. max T min Here, W represents the highest and lowest temperatures, and D represents the daily temperature range. Longitude;

[0138] Here, the above-mentioned empirical formula for predicting temperature is a pre-given empirical formula. The current step predicts the temperature at 0:00, 2:00, 4:00, 6:00, 8:00, 10:00, 12:00, 14:00, 16:00, 18:00, 20:00, and 22:00 for each month in the next twelve months based on the known first month, first diurnal temperature coefficient D, first diurnal temperature difference W, first longitude, first highest temperature, first lowest temperature, first annual average temperature, and the above-mentioned empirical formula for predicting temperature.

[0139] Specifically, this includes: step 331, setting the first month as the current month; initializing the first index to 1; and setting the first longitude as the longitude of the empirical formula for predicting temperature. The first highest temperature and the first lowest temperature are used as the highest temperature T in the empirical formula for predicting temperature. max and lowest temperature T min The average annual temperature of the first year is used as the annual average temperature T in the empirical formula for predicting temperature. a The first daily difference coefficient D and the first daily temperature difference W are used as the daily difference coefficient D and daily temperature difference W in the empirical formula for predicting temperature; and the range of the hour n in the empirical formula for predicting temperature is set to [0,2,4,6,8,10,12,14,16,18,20,22].

[0140] Here, the range of hour n [0,2,4,6,8,10,12,14,16,18,20,22] represents 0:00, 2:00, 4:00, 6:00, 8:00, 10:00, 12:00, 14:00, 16:00, 18:00, 20:00 and 22:00 in a day;

[0141] Step 332: Take the next month after the current month as the month m in the empirical formula for predicting temperature, and substitute the 1st to 12th values ​​of the hour n into the empirical formula for predicting temperature to obtain the corresponding 12 first predicted temperature data T. i＝第一索引,j ;

[0142] For example, given the first diurnal variation coefficient D, the first daily temperature range W, the first longitude, the first maximum temperature, the first minimum temperature, and the first annual average temperature, the diurnal variation coefficient D, daily temperature range W, and longitude in the empirical formula for predicting temperature are... highest temperature T max Minimum temperature T min Average annual temperature T aThese are all fixed constants; the only variables in the formula are the month m and the hour n.

[0143] Let the first month be February in the calendar month, then:

[0144] When the first index is 1, the current month is February, and the next month is March, meaning the month m = 3 in the empirical formula for predicting temperature. Then, substituting the 1st to 12th values ​​of n (from 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22) into the empirical formula for predicting temperature at this point, we can calculate the 12 predicted temperatures (T) when m = 3. m＝3,n＝0 T m＝3,n＝2 T m＝3,n＝4 T m＝3,n＝6 T m＝3,n＝8 T m＝3,n＝10 T m＝3,n＝12 T m＝3,n＝14 T m＝3,n＝16 T m＝3,n＝18 T m＝3,n＝20 T m＝3,n＝22 Then, these 12 predicted temperatures T m,n Transform into T sequentially i＝第一索引,j The expression method can yield 12 first predicted temperature data T. i＝第一索引＝1,j :T i＝1,j＝1 =T m＝3,n＝0 T i＝1,j＝2 =T m＝3,n＝2 T i＝1,j＝3 =T m＝3,n＝4 T i＝1,j＝4 =T m＝3,n＝6 T i＝1,j＝5 =T m＝3,n＝8 T i＝1,j＝6 =T m＝3,n＝10 T i＝1,j＝7 =T m＝3,n＝12 T i＝1,j＝8 =T m＝3,n＝14 T i＝1,j＝9 =T m＝3,n＝16 T i＝1,j＝10 =T m＝3,n＝18 T i＝1,j＝11 =T m＝3,n＝20 T i＝1,j＝12 =T m＝3,n＝22 ;

[0145] When the first index is 2, the current month is March, and the next month is April, which corresponds to month m=4 in the empirical formula for predicting temperature. At this point, the 12 predicted temperatures (T) when m=4 can be calculated. m＝4,n＝0 T m＝4,n＝2 T m＝4,n＝4 T m＝4,n＝6 Tm＝4,n＝8 T m＝4,n＝10 T m＝4,n＝12 T m＝4,n＝14 T m＝4,n＝16 T m＝4,n＝18 T m＝4,n＝20 T m＝4,n＝22 Then, these 12 predicted temperatures T m,n Transform into T sequentially i＝第一索引＝2,j The expression method can yield 12 first predicted temperature data T. i＝2,j ;

[0146] And so on,

[0147] When the first index is 10, the current month is November, and the next month is December, meaning the month m = 12 in the empirical formula for predicting temperature. At this point, the 12 predicted temperatures (T) when m = 12 can be calculated. m＝12,n＝0 T m＝12,n＝2 T m＝12,n＝4 T m＝12,n＝6 T m＝12,n＝8 T m＝12,n＝10 T m＝12,n＝12 T m＝12,n＝14 T m＝12,n＝16 T m＝12,n＝18 T m＝12,n＝20 T m＝12,n＝22 Then, these 12 predicted temperatures T m,n Transform into T sequentially i＝第一索引＝10,j The expression method can yield 12 first predicted temperature data T. i＝10,j ;

[0148] When the first index is 11, the current month is December and the next month is January, meaning the month m = 1 in the empirical formula for predicting temperature. At this point, the 12 predicted temperatures (T) when m = 1 can be calculated. m＝1,n＝0 T m＝1,n＝2 T m＝1,n＝4 T m＝1,n＝6 T m＝1,n＝8 T m＝1,n＝10 T m＝1,n＝12 T m＝1,n＝14 T m＝1,n＝16 T m＝1,n＝18 T m＝1,n＝20 T m＝1,n＝22 Then, these 12 predicted temperatures T m,n Transform into T sequentially i＝第一索引＝11,j The expression method can yield 12 first predicted temperature data T. i＝11,j ;

[0149] When the first index is 12, the current month is January, and the next month is February, meaning the month m = 2 in the empirical formula for predicting temperature. At this point, the 12 predicted temperatures (T) when m = 2 can be calculated. m＝2,n＝0 T m＝2,n＝2 T m＝2,n＝4 T m＝2,n＝6 T m＝2,n＝8 T m＝2,n＝10 T m＝2,n＝12 T m＝2,n＝14 T m＝2,n＝16 T m＝2,n＝18 T m＝2,n＝20 T m＝2,n＝22 Then, these 12 predicted temperatures T m,n Transform into T sequentially i＝第一索引＝12,j The expression method can yield 12 first predicted temperature data T. i＝12,j ;

[0150] Ultimately, we can obtain 12*12 first predicted temperature data T. i,j ;

[0151] Step 333: Increment the first index by 1; take the next month of the current month as the new current month; and determine whether the first index is greater than 12; if yes, proceed to step 334, otherwise proceed to step 332.

[0152] Step 334, obtain the 12*12 first predicted temperature data T i,j Output;

[0153] Step 34: Create the corresponding first temperature forecast table; set 12 first temperature forecast records in the first temperature forecast table; create a first month field and 12 first time period temperature fields in each first temperature forecast record, and create corresponding first time period data and first temperature data in each first time period temperature field; initialize the first month field of the 12 first temperature forecast records to the first month, second month, third month, and so on until the twelfth month; set the first time period data of the 12 first time period temperature fields in each first temperature forecast record to the 0-2 period, 2-4 period, 4-6 period, 6-8 period, 8-10 period, 10-12 period, 12-14 period, 14-16 period, 16-18 period, 18-20 period, 20-22 period, and 22-24 period respectively; and based on the 12 first predicted temperature data T with month index i being 1. i＝1,j The settings are configured for the 12 first temperature data points of the first first temperature prediction record; and based on the 12 first predicted temperature data points T with month index i = 2. i＝2,jThe settings are applied to the 12 first temperature data points of the second first temperature forecast record; this process is repeated until the 12 first predicted temperature data points T based on month index i of 12 are obtained. i＝12,j The setup is completed for the 12th first temperature prediction record.

[0154] For example, given that the first month is February, and 12*12 first predicted temperature data T are obtained. i,j Therefore, the resulting first temperature prediction table should consist of 12 first temperature prediction records in sequence:

[0155] In the first temperature forecast record: the first month field is March, and the first time period temperature field is: [First time period data (0-2 periods), First temperature data (T)] i＝1,j＝1 The second field, the temperature field for the first time period, is: [First time period data (time periods 2-4), first temperature data (T)]. i＝1,j＝2 Following this pattern, the 12th field for the first time period temperature is: [First time period data (22-24 time period), First temperature data (T)] i＝1,j＝12 )];

[0156] In the second first temperature forecast record: the first month field is April, and the first first time period temperature field is: [first time period data (0-2 time period), first temperature data (T)] i＝2,j＝1 The second field, the temperature field for the first time period, is: [First time period data (time periods 2-4), first temperature data (T)]. i＝2,j＝2 Following this pattern, the 12th field for the first time period temperature is: [First time period data (22-24 time period), First temperature data (T)] i＝2,j＝12 )];

[0157] And so on,

[0158] In the 12th first temperature forecast record: the first month field is February (February of the following year), and the first first time period temperature field is: [first time period data (0-2 time period), first temperature data (T)] i＝12,j＝1 The second field, the temperature field for the first time period, is: [First time period data (time periods 2-4), first temperature data (T)]. i＝12,j＝2 Following this pattern, the 12th field for the first time period temperature is: [First time period data (22-24 time period), First temperature data (T)] i＝12,j＝12 )];

[0159] Here, given that the first month is February, the first temperature forecast table is actually the result of pre-monitoring the temperature in the area where the photovoltaic power generation system is located for the next twelve months, from the next natural month, March, to February of the following year.

[0160] Step 4: Based on the first month and the second meteorological data set, conduct solar radiation intensity forecasting for the next twelve months to generate the corresponding first solar radiation intensity forecast table;

[0161] The first sunshine intensity forecast table includes 12 first sunshine intensity forecast records; the first temperature forecast record includes one second month field and 12 first time period sunshine intensity fields; the first time period sunshine intensity field includes second time period data and first sunshine intensity data; the second time period data includes 12 single-hour periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19;

[0162] Specifically, this includes: Step 41, estimating the monthly astronomical radiation for the next twelve months based on the first month, the first year's average astronomical radiation, the first year's average summer astronomical radiation, and the first year's average winter astronomical radiation, generating corresponding 12 first-month astronomical radiation values ​​S. 1,i ;

[0163] Specifically, this includes using the first annual average astronomical radiation as the parameter S. y And the average summer astronomical radiation of the first year is used as the parameter S. s And the average winter astronomical radiation of the first year is used as the parameter S. w And denote the month numbers from the first month to the twelfth month after the first month as the corresponding month m. i ; and parameter S y Parameter S s Parameter S w and each month m i Substituting the values ​​into the empirical formula for lunar astronomical radiation, we can calculate the corresponding 12 lunar astronomical radiation values ​​S for the first month. 1,i ;

[0164] The empirical formula for lunar astronomical radiation is:

[0165]

[0166] Here, the above empirical formula for monthly astronomical radiation is a pre-defined empirical formula; given the first annual average astronomical radiation, the first annual average summer astronomical radiation, and the first annual average winter astronomical radiation, the parameter S in the empirical formula for monthly astronomical radiation... y S s S w It is a fixed constant; given the first month, the number of months from the first month to the twelfth month is 12 m. iIt is also known that at this point, 12 m... i Substituting these values ​​into the empirical formula for monthly astronomical radiation, we can obtain the 12 monthly astronomical radiation values ​​(S). 1,i ;

[0167] For example, if we know that the first month is February, then the number of months from the first month to the twelfth month is 12 m. i They are respectively: m i＝1 =3,m i＝2 =4, m i＝3 =5, m i＝4 =6, m i＝5 =7, m i＝6 =8, m i＝7 =9, m i＝8 =10, m i＝9 =11, m i＝10 =12, m i＝11 =1,m i＝12 =2; m i＝1 to m i＝12 Substituting these values ​​sequentially into the empirical formula for lunar astronomical radiation yields the corresponding 12 lunar astronomical radiation values ​​S for the first month. 1,i＝1 S 1,i＝2 S 1,i＝3 S 1,i＝4 S 1,i＝5 S 1,i＝6 S 1,i＝7 S 1,i＝ 8S 1,i＝9 S 1,i＝10 S 1,i＝11 S 1,i＝12 ;

[0168] Step 42, based on the 12 first-month astronomical radiation values ​​S 1,i The local radiation for the next twelve months is estimated based on the first year's average annual sunshine percentage, generating 12 corresponding local radiation values ​​S for the first month. 2,i ;

[0169] Specifically, this includes using the average annual sunshine percentage in the first year as parameter p. y ; and the parameter p y and the astronomical radiation S of each first month 1,i Substituting the values ​​into the empirical formula for local radiation in a given month, we can calculate the corresponding local radiation values ​​S for the first month of each of the 12 months. 2,i ;

[0170] The empirical formula for monthly local radiation is:

[0171]

[0172] a, b, c, and d are the preset Bahel model parameters;

[0173] Here, the above empirical formula for monthly local radiation is a pre-defined empirical formula, where a, b, c, and d are pre-set parameters of the Bahel model; given the first annual average sunshine percentage, the parameter p in the empirical formula for monthly local radiation... y It is a fixed constant; at this time, the astronomical radiation S of the first month is 12. 1,i Substituting these values ​​into the empirical formula for local monthly radiation, we can obtain the 12 local monthly radiation values ​​(S) for the first month. 2,i ;

[0174] Step 43, based on the local radiation S of the first month and the 12 first months. 2,i And 12 first month sunshine hours L i The solar intensity for each solar term during the next twelve months is estimated to generate the corresponding first predicted solar intensity S. i,k k is the index for a single time period, 1≤k≤12;

[0175] Here, the current step is based on the known local radiation S for the first month and 12 first months. 2,i And 12 first month sunshine hours L i Predict the solar radiation intensity at 7:00, 8:00, 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00, 16:00, 17:00 and 18:00 for each month of the next twelve months;

[0176] Specifically, this includes: Step 431, initializing the second index to 1; and setting the range of the hour n in the empirical formula for solar radiation intensity to [7,8,9,10,11,12,13,14,15,16,17,18];

[0177] Here, the range of hour n [7,8,9,10,11,12,13,14,15,16,17,18] represents 7 o'clock, 8 o'clock, 9 o'clock, 10 o'clock, 11 o'clock, 12 o'clock, 13 o'clock, 14 o'clock, 15 o'clock, 16 o'clock, 17 o'clock and 18 o'clock in a day;

[0178] Step 432, calculate the local radiation level S for the first month. 2,i＝第二索引 And the first month's sunshine duration L i＝第二索引 Substitute the values ​​into the empirical formula for solar radiation intensity, and take the first 12 values ​​of n within the range of hours. k Substituting these values ​​sequentially into the empirical formula for solar radiation intensity yields the corresponding 12 first predicted solar radiation intensities S. i＝第二索引,k ;

[0179] The empirical formula for solar radiation intensity is:

[0180]

[0181] Here, the above empirical formula for solar radiation intensity is a pre-given empirical formula; the local radiation S in the first month of the 12 months is... 2,i And 12 first month sunshine hours L i Given the given information, we can obtain 12 pairs of parameter combinations (S) where the month index i is the same. 2,i L i ); in each given combination of parameters (S) 2,i L i Below, the first to 12th values ​​of n within the range of n are selected. k By substituting these values ​​into the empirical formula for solar radiation intensity, we can obtain the corresponding 12 first predicted solar radiation intensities S. i,k ;

[0182] For example, given 12 pairs of parameter combinations (S) 2,i L i ),So,

[0183] When the second index is 1, determine the parameter combination (S) 2,i＝1 L i＝1 ), 12 n values ​​within the range [7,8,9,10,11,12,13,14,15,16,17,18]. k By substituting these values ​​into the empirical formula for solar radiation intensity, we can obtain 12 first-predicted solar radiation intensities (S). i＝第二索引＝1,k ;

[0184] When the second index is 2, determine the parameter combination (S) 2,i＝2 L i＝2 ), 12 n values ​​within the range [7,8,9,10,11,12,13,14,15,16,17,18]. k By substituting these values ​​into the empirical formula for solar radiation intensity, we can obtain 12 first-predicted solar radiation intensities (S). i＝第二索引＝2,k ;

[0185] And so on,

[0186] When the second index is 12, determine the parameter combination (S) 2,i＝12 L i＝12 ), 12 n values ​​within the range [7,8,9,10,11,12,13,14,15,16,17,18]. k By substituting these values ​​into the empirical formula for solar radiation intensity, we can obtain 12 first-predicted solar radiation intensities (S). i＝第二索引＝12,k ;

[0187] Ultimately, we can obtain 12*12 first predicted solar irradiance S values. i,k;

[0188] Step 433: Increment the second index by 1; take the next month of the current month as the new current month; and determine whether the second index is greater than 12; if yes, proceed to step 434, otherwise proceed to step 432.

[0189] Step 434, obtain the 12*12 first predicted solar irradiance S i,k Output;

[0190] Step 44: Create the corresponding first sunshine intensity prediction table; set 12 first sunshine intensity prediction records in the first sunshine intensity prediction table; create a second month field and 12 first time period sunshine intensity fields in each first sunshine intensity prediction record, and create corresponding second time period data and first sunshine intensity data in each first time period sunshine intensity field; initialize the second month field of the 12 first sunshine intensity prediction records to the first month, second month, third month, and so on until the twelfth month; set the second time period data of the 12 first time period sunshine intensity fields in each first sunshine intensity prediction record to the 7-8 time period, 8-9 time period, 9-10 time period, 10-11 time period, 11-12 time period, 12-13 time period, 13-14 time period, 14-15 time period, 15-16 time period, 16-17 time period, 17-18 time period, and 18-19 time period based on the 12 first predicted sunshine intensities S with month index i being 1. i＝1,k The settings are configured for the 12 first-day sunshine intensity data of the first first-day sunshine intensity prediction record; and based on the 12 first-day predicted sunshine intensity S with month index i=2. i＝2,k The settings are applied to the 12 first-day sunshine intensity data of the second first-day sunshine intensity prediction record; and so on, until the 12 first-day sunshine intensity S based on month index i is 12. i＝12,k The process continues until the 12th solar radiation intensity prediction record for the first solar radiation intensity is set up.

[0191] For example, given that the first month is February, and 12*12 first predicted solar irradiance S values ​​are obtained. i,k Therefore, the resulting first solar intensity prediction table should consist of 12 first solar intensity prediction records in sequence:

[0192] In the first record of the first solar intensity prediction: the second month field is March, and the first time period solar intensity field is: [Second time period data (7-8 periods), first solar intensity data (S)] i＝1,k＝1 The second field, the solar radiation intensity for the first time period, is: [Second time period data (9-10 time period), First solar radiation intensity data (S)]. i＝1,k＝2)], and so on, the 12th solar intensity field for the first time period is: [second time period data (18-19 time period), first solar intensity data (S) i＝1,k＝12 )],

[0193] In the second record of the first solar intensity prediction: the second month field is April, and the first time period solar intensity field is: [Second time period data (7-8 periods), first solar intensity data (S)] i＝2,k＝1 The second field, the solar radiation intensity for the first time period, is: [Second time period data (9-10 time period), First solar radiation intensity data (S)]. i＝2,k＝2 )], and so on, the 12th solar intensity field for the first time period is: [second time period data (18-19 time period), first solar intensity data (S) i＝2,k＝12 )],

[0194] And so on,

[0195] In the second record of the first solar intensity prediction: the second month field is February (February of the following year), and the first time period solar intensity field is: [Second time period data (7-8 periods), first solar intensity data (S)] i＝12,k＝1 The second field, the solar radiation intensity for the first time period, is: [Second time period data (9-10 time period), First solar radiation intensity data (S)]. i＝12,k＝2 )], and so on, the 12th solar intensity field for the first time period is: [second time period data (18-19 time period), first solar intensity data (S) i＝12,k＝12 )];

[0196] Here, given that the first month is February, the first solar irradiance prediction table is actually the result of pre-monitoring the solar irradiance in the area where the photovoltaic power generation system is located for the next twelve months, from the next natural month, March, to February of the following year.

[0197] Step 5: Based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table, conduct power generation output pre-monitoring for the next twelve months to generate the corresponding first power generation output forecast table and save it.

[0198] The first power generation output forecast table includes 12 first power generation output forecast records; each first power generation output forecast record includes one third month field and 12 first time period power generation output fields; the first time period power generation output fields include third time period data and first power generation output data; the third time period data includes 12 single time periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18, and 18-19.

[0199] Specifically, this includes: Step 51, creating a corresponding first power generation output forecast table; setting 12 first power generation output forecast records in the first power generation output forecast table; creating a third month field and 12 first time period power generation output fields in each first power generation output forecast record, and creating corresponding third time period data and first power generation output data in each first time period power generation output field; sequentially initializing the third month field of the 12 first power generation output forecast records to the first month after the first month, the second month, the third month, and so on until the twelfth month; sequentially setting the third time period data of the 12 first time period power generation output fields in each first power generation output forecast record to the 7-8 time period, 8-9 time period, 9-10 time period, 10-11 time period, 11-12 time period, 12-13 time period, 13-14 time period, 14-15 time period, 15-16 time period, 16-17 time period, 17-18 time period, and 18-19 time period; and initializing the 12 first power generation output data in each first power generation output forecast record to empty;

[0200] Here, the current step is to initialize the first power generation output prediction table; the initialized first power generation output prediction table consists of 12 first power generation output prediction records in sequence:

[0201] In the first power generation output forecast record: the third month field is March; the first first time period power generation output field is: [Third time period data (7-8 periods), first power generation output data is empty or 0]; the second first time period power generation output field is: [Third time period data (8-9 periods), first power generation output data is empty or 0]; and so on, the twelfth first time period power generation output field is: [Third time period data (18-19 periods), first power generation output data is empty or 0];

[0202] In the second first-period power generation output forecast record: the third month field is April; the first first-period power generation output field is: [Third-period data (periods 7-8), first-period power generation output data is empty or 0]; the second first-period power generation output field is: [Third-period data (periods 8-9), first-period power generation output data is empty or 0]; and so on, the twelfth first-period power generation output field is: [Third-period data (periods 18-19), first-period power generation output data is empty or 0];

[0203] And so on,

[0204] In the 12th first power generation output forecast record: the third month field is February (February of the following year), the first first period power generation output field is: [third period data (segments 7-8), first power generation output data is empty or 0], the second first period power generation output field is: [third period data (segments 8-9), first power generation output data is empty or 0], and so on, the 12th first period power generation output field is: [third period data (segments 18-19), first power generation output data is empty or 0];

[0205] Step 52: Count the number of photovoltaic arrays in the photovoltaic power generation system as the corresponding first quantity G;

[0206] It should be noted here that the photovoltaic power generation system of Embodiment 1 of the present invention includes multiple identical photovoltaic arrays; each photovoltaic array corresponds to a set of first standard data, the first standard data including a first standard temperature T. STC First standard solar radiation intensity S STC and at the first standard temperature T STC and the first standard solar radiation intensity S STC Under the condition that the photovoltaic array can output the first maximum power P max ;

[0207] Step 53: Iterate through each of the first power generation output data in the first power generation output prediction table; during the iteration, the currently iterated first power generation output data is taken as the corresponding current power generation output data, the third time period data corresponding to the current power generation output data is taken as the corresponding current time period, and the record index of the first power generation output prediction record corresponding to the current power generation output data is taken as the corresponding current record index; the first temperature prediction record in the first temperature prediction table that matches the current record index is recorded as the corresponding first matching record, and the first sunshine intensity prediction record in the first sunshine intensity prediction table that matches the current record index is recorded as the corresponding second matching record; the first temperature data of the first time period data in the first matching record that matches the current time period temperature field is taken as the corresponding current temperature T. * The solar intensity data of the solar intensity field of the first time period that matches the second time period data in the second matching record is taken as the corresponding current solar intensity S. * ; and the current temperature T * Subtract the first standard temperature T STC The temperature difference is taken as the corresponding first temperature difference ΔT, and the current solar radiation intensity S is used as the reference. * Subtract the first standard solar radiation intensity S STC The difference in solar radiation intensity is taken as the corresponding first intensity difference ΔS; and based on the first quantity G and the first maximum power P max First temperature difference ΔT, first intensity difference ΔS, current solar radiation intensity S *and the first standard solar radiation intensity S STC Calculate the corresponding first power output P * , α, β, and γ are all preset empirical coefficients; and based on the first power output P * Configure the current power generation data in the first power generation forecast table.

[0208] Here, when iterating through each of the first power generation output data in the first power generation output prediction table...

[0209] If the current power generation output data is the first power generation output data of the first time period field of the first power generation output prediction record, then the current time period is period 7-8, the current record index is 1, the first matching record is the first temperature prediction record in the first temperature prediction table, and the second matching record is the first sunshine intensity prediction record in the first sunshine intensity prediction table. Because the first time period data matching the current time period (7-8) in the first matching record is period 6-8, the corresponding first temperature data of the first time period temperature field is the first temperature data of the fourth first time period temperature field, i.e., T. i＝1,j＝4 Therefore, the current temperature T * =T i＝1,j＝4 Because the second matching record matches the current time period (7-8 time period) data for the second time period, the corresponding first time period sunshine intensity data for the first time period sunshine intensity field is the first sunshine intensity data for the first first time period sunshine intensity field, i.e., S. i＝1,k＝1 Therefore, the current solar radiation intensity S * =S i＝1,k＝1 Then we obtain the first temperature difference ΔT = T * -T STC =T i＝1,j＝4 -T STC The first strength difference ΔS = S * -S STC =S i＝1,k＝1 -S STC Then, the first quantity G and the first maximum power P are... max First temperature difference ΔT, first intensity difference ΔS, current solar radiation intensity S * and the first standard solar radiation intensity S STC Substitute into the pre-set empirical formula for power generation output The first power output P can be obtained by performing calculations. * The power generation formula here is a pre-defined empirical formula, and the empirical coefficients α, β, and γ are also pre-defined empirical constants; after calculating the corresponding first power generation P... *Then, the power generation data of the first power generation prediction record in the first power generation prediction table for the first time period can be updated from the initial empty or 0 to the corresponding first power generation capacity P. * ;

[0210] By following the same process, the 12*12 data points of the first power generation prediction table that are initialized to be empty or 0 can be updated through the above traversal method. The updated first power generation prediction table is actually the result of pre-monitoring the power generation level, i.e., the power generation capacity, of the photovoltaic power generation system during each sunshine period in the next twelve months.

[0211] In addition to the method provided in Embodiment 1 of the present invention for pre-monitoring the power generation output level (i.e., power generation capacity) of the photovoltaic power generation system during each solar irradiance period in the next twelve months, the photovoltaic power generation system of Embodiment 2 of the present invention can also perform real-time monitoring of the system based on the first power generation output prediction table obtained from the pre-monitoring using a processing method for monitoring the photovoltaic power generation system provided in Embodiment 2 of the present invention. Figure 2 This is a schematic diagram of a method for monitoring a photovoltaic power generation system according to Embodiment 2 of the present invention, as shown below. Figure 2 As shown, this method mainly includes the following steps:

[0212] Step 101: Starting from the preset start time each day, acquire the current month information, time information, and real-time power generation information every hour as the corresponding second month, first time, and first real-time power; and query the preset first power generation output prediction table based on the second month and first time to obtain the corresponding first predicted power; and set the photovoltaic power generation system monitoring status based on the first real-time power and the first predicted power to generate the corresponding first monitoring status.

[0213] The preset start time is no earlier than 7:00 AM each day; the preset end time is no later than 7:00 PM each day; the first power generation output prediction table includes 12 first power generation output prediction records; each first power generation output prediction record includes one third month field and 12 first time period power generation output fields; the first time period power generation output fields include third time period data and first power generation output data; the third time period data includes 12 single-hour periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18, and 18-19; here, the first power generation output prediction table of Embodiment 2 of the present invention is the same as the first power generation output prediction table of Embodiment 1 of the present invention;

[0214] Specifically, it includes: Step 1011, starting from the preset start time each day, acquiring the current month information, time information and real-time power generation information every hour as the corresponding second month, first time and first real-time power;

[0215] Here, the photovoltaic power generation system of Embodiment 2 of the present invention is the same as the photovoltaic power generation system of Embodiment 1 of the present invention. The photovoltaic power generation system can obtain the current month information, time information and real-time power generation information at any time. The photovoltaic power generation system acquires the current month information, time information and real-time power generation information every hour from a time point no earlier than 7:00 AM, i.e., a preset start time, to generate a set of second month, first time and first real-time power corresponding to the current time.

[0216] Step 1012, and obtain the corresponding first predicted power by querying the preset first power generation output prediction table according to the second month and the first time;

[0217] Specifically, this includes: recording the first power generation forecast record in the first power generation forecast table that matches the third month field with the second month as the corresponding matching record; and extracting the first power generation data in the matching record that matches the third time period data with the first time period power generation output field as the corresponding first predicted power.

[0218] Here, the photovoltaic power generation system queries the pre-monitoring results obtained from Embodiment 1 of the present invention, namely the first power generation output prediction table, takes the first power generation output prediction record in the first power generation output prediction table that matches the current second month as the matching record, and extracts the first power generation output data of the first time period power generation output field that matches the first time period in the matching record as the corresponding first predicted power.

[0219] Step 1013, and generate the corresponding first monitoring state by setting the monitoring state of the photovoltaic power generation system according to the first real-time power and the first predicted power;

[0220] Specifically, this includes: using the ratio of the first real-time power to the first predicted power as the corresponding first ratio; when the first ratio is lower than a preset first power ratio threshold, setting the corresponding first monitoring state as a Class I state; when the first ratio is not lower than the first power ratio threshold but lower than a preset second power ratio threshold, setting the corresponding first monitoring state as a Class II state; when the first ratio is not lower than the second power ratio threshold, setting the corresponding first monitoring state as a Class III state; and the first power ratio threshold being less than the second power ratio threshold.

[0221] Here, the first ratio reflects the approximation relationship between the actual power generation output and the pre-monitoring results. The larger the first ratio, the higher the similarity between the actual power generation output and the pre-monitoring results; conversely, the smaller the first ratio, the greater the deviation between the actual power generation output and the pre-monitoring results. The first and second power ratio thresholds are two pre-set empirical thresholds, with the first power ratio threshold < the second power ratio threshold. When the first monitoring state is Class I, it means that the first ratio is low, and the deviation between the actual power generation output and the pre-monitoring results is large, with low similarity. When the first monitoring state is Class III, it means that the first ratio is high, and the deviation between the actual power generation output and the pre-monitoring results is small, with high similarity. When the first monitoring state is Class II, it means that the first ratio is at an intermediate level, and the deviation or similarity between the actual power generation output and the pre-monitoring results is at an intermediate level.

[0222] Step 102, and at the preset end time of each day, perform a power generation output status analysis of the photovoltaic power generation system based on all the first monitoring statuses obtained that day to generate the corresponding first analysis report;

[0223] Specifically, this includes: step 1021, generating a first total number by counting the number of first monitoring states; and generating a second total number by counting the number of first monitoring states during the preset peak power generation period;

[0224] Here, the peak power generation period is a preset time parameter, such as setting the period from 10:00 AM to 3:00 PM as the peak power generation period;

[0225] Step 1022: Statistically generate the first and second quantities of the number of the first monitoring states, which are respectively Class I and Class III states; and statistically generate the third and fourth quantities of the number of the first monitoring states, which are respectively Class I and Class III states, during the peak power generation period.

[0226] Step 1023, and take the ratio of the first and second quantities to the first total as the corresponding first and second ratios; and take the ratio of the third and fourth quantities to the second total as the corresponding third and fourth ratios;

[0227] Here, the first and second ratios reflect the operating condition of the photovoltaic power generation system throughout the entire power generation period of the day. The smaller the first ratio and the larger the second ratio, the better the operating condition of the photovoltaic power generation system throughout the entire power generation period of the day. Conversely, the larger the first ratio and the smaller the second ratio, the worse the operating condition of the photovoltaic power generation system throughout the entire power generation period of the day. The third and fourth ratios reflect the operating condition of the photovoltaic power generation system during the peak power generation period of the day. The smaller the third ratio and the larger the fourth ratio, the better the operating condition of the photovoltaic power generation system during the peak power generation period of the day. Conversely, the larger the third ratio and the smaller the fourth ratio, the worse the operating condition of the photovoltaic power generation system during the peak power generation period of the day.

[0228] Step 1024: When both the second ratio and the fourth ratio are greater than the preset first ratio threshold, the corresponding first analysis report is set to "Excellent power generation output throughout the day and excellent power generation output during peak hours"; when the second ratio is greater than the first ratio threshold but the fourth ratio is lower than the first ratio threshold, the corresponding first system state is set to "Good power generation output throughout the day but decreased power generation output during peak hours"; when the second ratio is lower than the first ratio threshold but the fourth ratio is greater than the first ratio threshold, the corresponding first system state is set to "Decreased power generation output throughout the day but good power generation output during peak hours"; when both the second ratio and the fourth ratio are lower than the first ratio threshold, the corresponding first system state is set to "Decreased power generation output throughout the day and decreased power generation output during peak hours".

[0229] Figure 3 This is a module structure diagram of a processing device for monitoring a photovoltaic power generation system provided in Embodiment 3 of the present invention. This device is capable of implementing the processing method for monitoring a photovoltaic power generation system provided in Embodiment 1 of the present invention. Figure 3 As shown, the device includes: a first acquisition module 201, a first preprocessing module 202, a first temperature pre-monitoring module 203, a first solar radiation intensity pre-monitoring module 204, and a first power generation output pre-monitoring module 205.

[0230] The first acquisition module 201 is used to acquire the longitude, latitude, altitude and characteristic meteorological data of the area where the photovoltaic power generation system is located as the corresponding first longitude, first latitude, first altitude and first meteorological data set; and to acquire the current month as the corresponding first month.

[0231] The first preprocessing module 202 is used to perform meteorological data preprocessing based on the first meteorological data set to generate a corresponding second meteorological data set.

[0232] The first temperature forecast module 203 is used to generate a corresponding first temperature forecast table by performing temperature forecasting for the next twelve months based on the first month, first longitude, first latitude, first altitude, and second meteorological data set.

[0233] The first sunshine intensity pre-monitoring module 204 is used to generate a corresponding first sunshine intensity forecast table by performing sunshine intensity pre-monitoring for the next twelve months based on the first month and the second meteorological data set.

[0234] The first power generation output pre-monitoring module 205 is used to perform power generation output pre-monitoring for the next twelve months based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table, generate the corresponding first power generation output forecast table, and save it.

[0235] The third embodiment of the present invention provides a processing device for monitoring a photovoltaic power generation system, which is used to execute the steps of the method provided in the first embodiment of the present invention. Its implementation principle and technical effect are similar, and will not be described again here.

[0236] Figure 4 This is a module structure diagram of a processing device for monitoring a photovoltaic power generation system provided in Embodiment 4 of the present invention. This device is capable of implementing the processing method for monitoring a photovoltaic power generation system provided in Embodiment 2 of the present invention. Figure 4 As shown, the device includes: a first time period monitoring module 301 and a first daily analysis module 302.

[0237] The first time period monitoring module 301 is used to acquire the current monthly information, time information, and real-time power generation information every hour starting from the preset start time each day as the corresponding second month, first time, and first real-time power; and to query the preset first power generation output prediction table according to the second month and first time to obtain the corresponding first predicted power; and to set the photovoltaic power generation system monitoring status according to the first real-time power and the first predicted power to generate the corresponding first monitoring status.

[0238] The first daily analysis module 302 is used to analyze the power output status of the photovoltaic power generation system based on all the first monitoring statuses obtained on that day at the preset end time of each day, and generate a corresponding first analysis report.

[0239] The fourth embodiment of the present invention provides a processing device for monitoring a photovoltaic power generation system, which is used to execute the steps of the method provided in the second embodiment of the present invention. Its implementation principle and technical effect are similar, and will not be described again here.

[0240] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the acquisition module can be a separate processing element, or it can be integrated into a chip in the above device. Alternatively, it can be stored as program code in the memory of the above device, and its function can be called and executed by a processing element of the above device. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, the steps of the method provided in the embodiments of the present invention or the various modules of the device provided in the embodiments of the present invention can be completed by integrated logic circuits in the hardware of the processor element or by instructions in software form.

[0241] For example, the modules of the apparatus provided in the embodiments of the present invention may be one or more integrated circuits configured as the methods provided in the embodiments of the present invention, such as: one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), etc. As another example, when a module of the apparatus provided in the embodiments of the present invention is implemented in the form of processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling program code. Furthermore, these modules of the apparatus provided in the embodiments of the present invention may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0242] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the methods provided according to the embodiments of the present invention are generated. The computer described above can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The aforementioned computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the aforementioned computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, Bluetooth, microwave, etc.) means. The aforementioned computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The aforementioned available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0243] Figure 5 This is a module structure diagram of a processing system for monitoring a photovoltaic power generation system provided in Embodiment 5 of the present invention, as shown below. Figure 5 As shown, the system of Embodiment 5 of the present invention may specifically include: a first device 401 and a second device 402; the first device 401 is as follows: Figure 3 The diagram shows a processing device for monitoring a photovoltaic power generation system, wherein the second device 402 is as follows: Figure 4 The diagram shows a processing device for monitoring a photovoltaic power generation system.

[0244] Figure 6 This is a module structure diagram of a processing component for monitoring a photovoltaic power generation system provided in Embodiment Six of the present invention. This component is an electronic component, electronic device, or server that implements the method provided in Embodiment One or Embodiment Two of the present invention. Figure 6As shown, the component may include: a processor 601 (e.g., a CPU) and a memory 602; the memory 602 stores instructions executable by at least one processor 601, which, when executed by at least one processor 601, enable at least one processor 601 to perform the method provided in Embodiment 1 or Embodiment 2 of the present invention. Preferably, the component involved in Embodiment 6 of the present invention may further include: a transceiver 603, a power supply 604, a system bus 605, and a communication port 606. The transceiver 603 is coupled to the processor 601, the system bus 605 is used to realize communication connections between components, and the communication port 606 is used for communication between the component and other peripherals.

[0245] exist Figure 6 The system bus mentioned can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). Memory may include Random Access Memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0246] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0247] This invention provides a method, apparatus, system, and components for monitoring photovoltaic (PV) power generation systems. First, based on the PV system's latitude, longitude, altitude, and meteorological data from the region over the past twenty years, the system's power output (i.e., power generation capacity) for the next twelve months is periodically pre-monitored to obtain a corresponding power output forecast table. Then, during system operation, the actual power output level for each time period of the day is monitored in real-time, referring to the pre-monitored power output forecast table, to obtain the corresponding monitoring status. Based on the monitoring status of multiple time periods, the daily power output status of the system is analyzed in real-time to obtain a corresponding daily analysis report. Through this invention, adaptive pre-monitoring processing can be performed based on the personalized data of the PV power generation system, and real-time monitoring of the system can be performed with reference to the pre-monitoring results, improving the personalized and refined management level of the PV power generation system.

[0248] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0249] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0250] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring a photovoltaic power generation system, characterized in that, The method includes: The system acquires the longitude, latitude, altitude, and characteristic meteorological data of the region where the photovoltaic power generation system is located as the corresponding first longitude, first latitude, first altitude, and first meteorological data set; and acquires the current month as the corresponding first month. The first meteorological data set is a set of characteristic meteorological data for the region where the photovoltaic power generation system is located over the past twenty years; The first meteorological data set includes multiple first-year meteorological data; the first-year meteorological data includes 365*24 first-hour temperature collection data, 365 first-day sunshine duration collection data, 1 first-year astronomical radiation statistics data, 1 first-year summer astronomical radiation statistics data, 1 first-year winter astronomical radiation statistics data, and 1 first-year sunshine percentage; The first meteorological data set is used to perform meteorological data preprocessing to generate a corresponding second meteorological data set; The second meteorological data set includes the first annual average temperature, the first highest temperature, the first lowest temperature, the first annual average astronomical radiation, the first annual average summer astronomical radiation, the first annual average winter astronomical radiation, the first annual average sunshine percentage, and the sunshine duration L for the 12 first months. i ; i is the month index for the next twelve months, 1≤i≤12; Based on the first month, the first longitude, the first latitude, the first altitude, and the second meteorological data set, a first temperature forecast table is generated for the next twelve months through temperature forecasting; specifically including: The first diurnal variation coefficient D is generated by estimating the diurnal variation coefficient based on the first latitude and the first altitude. The constant factor a for estimating the diurnal variation coefficient d,1 The default value is 6100, and the constant factor a is used for estimation. d,2 The default value is 90; The first daily temperature difference W is generated by estimating the daily temperature difference based on the first longitude and the first latitude. Daily temperature difference estimation constant factor a w,1 The default value is 8, and the constant factor a is estimated. w,2 The default value is 0.25, and the constant factor a is used for estimation. w,3 The default value is 20, and the constant factor a is estimated. w,4 The default value is 0.0075, and the estimation constant factor a is... w,5 The default value is 130; Substituting the first month, the first diurnal variation coefficient D, the first daily temperature difference W, the first longitude, the first highest temperature, the first lowest temperature, and the first annual average temperature into the preset empirical formula for predicted temperature, 12*12 first predicted temperature data T are obtained. i,j j is the index of the two time periods, 1≤j≤12; Based on the first month and the second meteorological data set, the first solar intensity prediction table is generated by monitoring the solar intensity for the next twelve months. Based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table, the power generation output is pre-monitored for the next twelve months to generate a corresponding first power generation output forecast table, which is then saved.

2. The processing method for monitoring a photovoltaic power generation system according to claim 1, characterized in that, The first temperature forecast table includes 12 first temperature forecast records; each first temperature forecast record includes one first month field and 12 first time period temperature fields; each first time period temperature field includes first time period data and first temperature data; the first time period data includes 12 two-hour periods, namely 0-2, 2-4, 4-6, 6-8, 8-10, 10-12, 12-14, 14-16, 16-18, 18-20, 20-22, and 22-24. The first sunshine intensity prediction table includes 12 first sunshine intensity prediction records; the first temperature prediction record includes one second month field and 12 first time period sunshine intensity fields; the first time period sunshine intensity fields include second time period data and first sunshine intensity data; the second time period data includes 12 single-hour periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19; The first power generation forecast table includes 12 first power generation forecast records; the first power generation forecast record includes one third month field and 12 first time period power generation fields; the first time period power generation fields include third time period data and first power generation data; the third time period data includes 12 single time periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19.

3. The method for monitoring a photovoltaic power generation system according to claim 2, characterized in that, The step of generating a corresponding second meteorological data set by performing meteorological data preprocessing based on the first meteorological data set specifically includes: The average temperature of the first year is calculated by averaging the 365*24 first hour temperature data of each first year meteorological data, and the average temperature of the first year is calculated by averaging all the first year average temperatures. From all the temperature data collected in the first hour, select the maximum and minimum values ​​as the corresponding first highest temperature and first lowest temperature; The average annual astronomical radiation, the average summer astronomical radiation, and the average winter astronomical radiation of the first year are calculated by averaging all the first annual astronomical radiation statistics, all the first annual summer astronomical radiation statistics, and all the first annual winter astronomical radiation statistics, respectively. The average sunshine percentage for the first year is calculated by averaging all the sunshine percentages for the first year. The 365 sunshine duration data points collected for each of the first year's meteorological data were divided into 12 sets of first sunshine duration data by month. The sunshine duration data for each set was then summed to obtain the corresponding first month's sunshine duration data. All the obtained first month's sunshine duration data were then divided into 12 sets of first month's sunshine duration data by month. Finally, the average of the sunshine duration data for each set was calculated to obtain the corresponding first natural month's sunshine duration L. z z is a natural month marker, which consists of January to December; the natural month index z is matched with the first month following the first month to determine the sunshine duration L of the first natural month. z As the corresponding first month's sunshine duration L i=1 The natural month index z is matched with the sunshine duration L of the first natural month after the first month. z As the corresponding first month's sunshine duration L i=2 This process continues until the natural month index z is matched with the twelfth month following the first month, and the sunshine duration L of the first natural month is obtained. z As the corresponding first month's sunshine duration L i=12 until; The first annual average temperature, the first maximum temperature, the first minimum temperature, the first annual average astronomical radiation, the first annual average summer astronomical radiation, the first annual average winter astronomical radiation, the first annual average sunshine percentage, and the sunshine duration L of the 12 first months are obtained. i This forms the corresponding second meteorological data set.

4. The processing method for monitoring a photovoltaic power generation system according to claim 2, characterized in that, The step of generating a corresponding first temperature forecast table by performing temperature forecasting for the next twelve months based on the first month, the first longitude, the first latitude, the first altitude, and the second meteorological data set also includes: Create a corresponding first temperature forecast table; set 12 first temperature forecast records in the first temperature forecast table; create a first month field and 12 first time period temperature fields in each first temperature forecast record, and create corresponding first time period data and first temperature data in each first time period temperature field; initialize the first month field of the 12 first temperature forecast records sequentially to the first month, second month, third month, and so on until the twelfth month; set the first time period data of the 12 first time period temperature fields in each first temperature forecast record sequentially to the 0-2 time period, 2-4 time period, 4-6 time period, 6-8 time period, 8-10 time period, 10-12 time period, 12-14 time period, 14-16 time period, 16-18 time period, 18-20 time period, 20-22 time period, and 22-24 time period; and based on the 12 first predicted temperature data T with month index i being 1. i=1,j The first 12 first temperature data points of the first first temperature prediction record are set; and based on the 12 first predicted temperature data points T with month index i being 2. i=2,j The settings are applied to the 12 first temperature data points of the second first temperature prediction record; and so on, until the 12 first predicted temperature data points T based on the month index i being 12 are obtained. i=12,j The setup is completed for the 12 first temperature data records of the 12th first temperature prediction record.

5. The processing method for monitoring a photovoltaic power generation system according to claim 4, characterized in that, The empirical formula for predicting temperature is: T m,n To predict temperature, m represents the month and n represents the hour. T a The average annual temperature T max T min The highest and lowest temperatures, W represents the daily temperature difference. D is the diurnal variation coefficient. Longitude.

6. The processing method for monitoring a photovoltaic power generation system according to claim 5, characterized in that, The process involves substituting the first month, the first diurnal variation coefficient D, the first diurnal temperature variation W, the first longitude, the first highest temperature, the first lowest temperature, and the first annual average temperature into a preset empirical formula for predicting temperature to calculate 12*12 first predicted temperature data T. i,j Specifically, it includes: Step 61: Set the first month as the current month; initialize the first index to 1; and set the first longitude as the longitude of the predicted temperature empirical formula. The first highest temperature and the first lowest temperature are used as the highest temperature T in the empirical formula for predicting temperature. max and lowest temperature T min The first annual average temperature is used as the annual average temperature T in the empirical formula for predicting temperature. a The first daily difference coefficient D and the first daily temperature difference W are used as the daily difference coefficient D and daily temperature difference W in the empirical formula for predicting temperature; and the range of the hour n in the empirical formula for predicting temperature is set to [0,2,4,6,8,10,12,14,16,18,20,22]. Step 62: Take the next month after the current month as the month m in the empirical formula for predicted temperature, and substitute the 1st to 12th values ​​of the hour n into the empirical formula for predicted temperature to calculate the corresponding 12 first predicted temperature data T. i=第一索引,j ; Step 63: Increment the first index by 1; and take the next month of the current month as the new current month; and determine whether the first index is greater than 12; if yes, proceed to step 64, otherwise proceed to step 62. Step 64, the obtained 12*12 first predicted temperature data T i,j Output.

7. The processing method for monitoring a photovoltaic power generation system according to claim 2, characterized in that, The step of generating a corresponding first sunshine intensity forecast table by performing sunshine intensity prediction monitoring for the next twelve months based on the first month and the second meteorological data set specifically includes: Based on the first month, the first year's average astronomical radiation, the first year's average summer astronomical radiation, and the first year's average winter astronomical radiation, the monthly astronomical radiation for the next twelve months is estimated to generate 12 corresponding first-month astronomical radiation values ​​S. 1,i ; Based on the first month's astronomical radiation S (12) 1,i The first annual average sunshine percentage is used to estimate the monthly local radiation for the next twelve months, generating 12 corresponding first-month local radiation values ​​S. 2,i ; Based on the local radiation S of the first month and 12 months of the first month 2,i And 12 of the first month's sunshine duration L i The solar intensity for each solar term during the next twelve months is estimated to generate the corresponding first predicted solar intensity S. i,k k is the index for a single time period, 1≤k≤12; Create a corresponding first sunshine intensity prediction table; set 12 first sunshine intensity prediction records in the first sunshine intensity prediction table; create a second month field and 12 first time period sunshine intensity fields in each first sunshine intensity prediction record, and create corresponding second time period data and first sunshine intensity data in each first time period sunshine intensity field; initialize the second month field of the 12 first sunshine intensity prediction records to the first month, second month, third month, and so on until the twelfth month; set the second time period data of the 12 first time period sunshine intensity fields in each first sunshine intensity prediction record to the 7-8 time period, 8-9 time period, 9-10 time period, 10-11 time period, 11-12 time period, 12-13 time period, 13-14 time period, 14-15 time period, 15-16 time period, 16-17 time period, 17-18 time period, and 18-19 time period based on the 12 first predicted sunshine intensities S with month index i being 1. i=1,k The first 12 solar intensity data points of the first solar intensity prediction record are set; and the 12 predicted solar intensity S points based on the month index i being 2 are configured. i=2,k The settings are applied to the 12 first sunshine intensity data points of the second first sunshine intensity prediction record; and so on, until the 12 first predicted sunshine intensities S based on the month index i being 12 are obtained. i=12,k The process continues until the 12th first solar intensity prediction record is set up.

8. The processing method for monitoring a photovoltaic power generation system according to claim 7, characterized in that, The method involves estimating the monthly astronomical radiation for the next twelve months based on the first month, the first annual average astronomical radiation, the first annual average summer astronomical radiation, and the first annual average winter astronomical radiation, generating corresponding 12 monthly astronomical radiation values ​​S. 1,i Specifically, it includes: The first annual average astronomical radiation is used as parameter S. y And the first annual average summer astronomical radiation is used as parameter S. s And the first annual average winter astronomical radiation is used as parameter S. w And the month numbers from the first month to the twelfth month after the first month are respectively denoted as the corresponding month m. i ; and the parameter S y The parameter S s The parameter S w and each of the months m i Substituting the values ​​into the empirical formula for lunar astronomical radiation, we obtain the corresponding 12 values ​​of the first lunar astronomical radiation S. 1,i ; The empirical formula for lunar astronomical radiation is: .

9. The processing method for monitoring a photovoltaic power generation system according to claim 7, characterized in that, The above is based on 12 of the first month's astronomical radiation S 1,i The first annual average sunshine percentage is used to estimate the monthly local radiation for the next twelve months, generating 12 corresponding first-month local radiation values ​​S. 2,i Specifically, it includes: The first annual average daily sunshine percentage is used as parameter p. y ; and the parameter p y and the astronomical radiation S of each of the first months 1,i Substituting the values ​​into the empirical formula for local radiation in the first month, we can calculate the corresponding 12 local radiation values ​​S for the first month. 2,i ; The empirical formula for the monthly local radiation is: a, b, c, and d are the preset Bahel model parameters, respectively.

10. The processing method for monitoring a photovoltaic power generation system according to claim 7, characterized in that, The local radiation S based on the first month and 12 of the first month. 2,i And 12 of the first month's sunshine duration L i The solar intensity for each solar term during the next twelve months is estimated to generate the corresponding first predicted solar intensity S. i,k Specifically, it includes: Step 101: Initialize the second index to 1; and set the range of values ​​for hour n in the empirical formula for solar radiation intensity to [7,8,9,10,11,12,13,14,15,16,17,18]. Step 102, calculate the local radiation level S for the first month. 2,i=第二索引 And the first month's sunshine duration L i=第二索引 Substitute the values ​​into the empirical formula for solar radiation intensity, and take the first to 12th values ​​of n within the range of hours. k Substituting these values ​​sequentially into the empirical formula for solar radiation intensity yields the corresponding 12 first predicted solar radiation intensities S. i=第二索引,k ; The empirical formula for solar radiation intensity is: ; Step 103: Increment the second index by 1; and take the next month of the current month as the new current month; and determine whether the second index is greater than 12; if yes, proceed to step 104, otherwise proceed to step 102. Step 104, the obtained 12*12 first predicted solar irradiance S i,k Output.

11. The processing method for monitoring a photovoltaic power generation system according to claim 2, characterized in that, The photovoltaic power generation system includes multiple identical photovoltaic arrays; The photovoltaic array corresponds to a set of first standard data, which includes a first standard temperature T. STC First standard solar radiation intensity S STC And at the first standard temperature T STC and the first standard solar radiation intensity S STC Under the condition that the photovoltaic array can output the first maximum power P max .

12. The processing method for monitoring a photovoltaic power generation system according to claim 11, characterized in that, The step of generating and saving a corresponding first power generation output forecast table based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table for the next twelve months specifically includes: Create a corresponding first power generation output forecast table; set 12 first power generation output forecast records in the first power generation output forecast table; create a third month field and 12 first time period power generation output fields in each first power generation output forecast record, and create corresponding third time period data and first power generation output data in each first time period power generation output field; initialize the third month field of the 12 first power generation output forecast records sequentially to the first month, second month, third month after the first month, and so on until the twelfth month; set the third time period data of the 12 first time period power generation output fields in each first power generation output forecast record sequentially to the 7-8 time period, 8-9 time period, 9-10 time period, 10-11 time period, 11-12 time period, 12-13 time period, 13-14 time period, 14-15 time period, 15-16 time period, 16-17 time period, 17-18 time period, and 18-19 time period; initialize the 12 first power generation output data in each first power generation output forecast record to empty; The number of photovoltaic arrays in the photovoltaic power generation system is counted as the corresponding first quantity G; The system iterates through each of the first power generation output data in the first power generation output prediction table. During the iteration, the currently iterated first power generation output data is taken as the corresponding current power generation output data, and the third time period data corresponding to the current power generation output data is taken as the corresponding current time period. The record index of the first power generation output prediction record corresponding to the current power generation output data is taken as the corresponding current record index. The first temperature prediction record in the first temperature prediction table that corresponds to the current record index is recorded as the corresponding first matching record, and the first sunshine intensity prediction record in the first sunshine intensity prediction table that corresponds to the current record index is recorded as the corresponding second matching record. The first temperature data in the first time period temperature field of the first matching record that matches the current time period data is taken as the corresponding current temperature T. * And take the first solar intensity data of the solar intensity field of the first time period that matches the second time period data in the second matching record with the current time period as the corresponding current solar intensity S. * ; and the current temperature T * Subtract the first standard temperature T STC The temperature difference is taken as the corresponding first temperature difference ΔT, and the current solar radiation intensity S is used as the reference. * Subtract the first standard solar radiation intensity S STC The difference in solar radiation intensity is taken as the corresponding first intensity difference ΔS; and based on the first quantity G and the first maximum power P max The first temperature difference ΔT, the first intensity difference ΔS, and the current solar radiation intensity S * and the first standard solar radiation intensity S STC Calculate the corresponding first power output P * , α, β, and γ are all preset empirical coefficients; and based on the first power generation P * The current power generation output data in the first power generation output prediction table is set.

13. A method for monitoring a photovoltaic power generation system, characterized in that, The method includes: generating and saving a first power generation output prediction table using the method described in any one of claims 1-12; and, Every day, starting from a preset start time, the current month information, time information, and real-time power generation information are obtained every hour as the corresponding second month, first time, and first real-time power; and the corresponding first predicted power is obtained by querying a preset first power generation prediction table based on the second month and the first time; and the corresponding first monitoring status is generated by setting the monitoring status of the photovoltaic power generation system based on the first real-time power and the first predicted power. At the preset end time of each day, the power output status of the photovoltaic power generation system is analyzed based on all the first monitoring statuses obtained that day, and a corresponding first analysis report is generated.

14. The processing method for monitoring a photovoltaic power generation system according to claim 13, characterized in that, The preset start time shall not exceed 7:00 AM each day; The preset end time shall not be later than 7 p.m. each day; The first power generation forecast table includes 12 first power generation forecast records; the first power generation forecast record includes one third month field and 12 first time period power generation fields; the first time period power generation fields include third time period data and first power generation data; the third time period data includes 12 single time periods, namely 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-17, 17-18 and 18-19.

15. The processing method for monitoring a photovoltaic power generation system according to claim 14, characterized in that, The step of obtaining the corresponding first predicted power by querying a preset first power generation output prediction table based on the second month and the first time specifically includes: Record the first power generation forecast record in the first power generation forecast table that matches the third month field with the second month as the corresponding matching record; and extract the first power generation data in the first time period field that matches the third time period data with the first time period data in the matching record as the corresponding first predicted power.

16. The processing method for monitoring a photovoltaic power generation system according to claim 13, characterized in that, The step of generating a corresponding first monitoring state based on the first real-time power and the first predicted power for setting the monitoring state of the photovoltaic power generation system specifically includes: The ratio of the first real-time power to the first predicted power is taken as the corresponding first ratio. When the first ratio is lower than a preset first power ratio threshold, the corresponding first monitoring state is set to a Class I state; when the first ratio is not lower than the first power ratio threshold but lower than a preset second power ratio threshold, the corresponding first monitoring state is set to a Class II state; when the first ratio is not lower than the second power ratio threshold, the corresponding first monitoring state is set to a Class III state; the first power ratio threshold is less than the second power ratio threshold.

17. The processing method for monitoring a photovoltaic power generation system according to claim 13, characterized in that, The step of generating a corresponding first analysis report based on the power output status analysis of the photovoltaic power generation system obtained on the same day, specifically includes: The number of the first monitoring states is counted to generate a corresponding first total; and the number of the first monitoring states during the preset peak power generation period is counted to generate a corresponding second total. The number of the first monitoring states, which are respectively Class I and Class III, is statistically analyzed to generate corresponding first and second quantities; and the number of the first monitoring states, which are respectively Class I and Class III, is statistically analyzed during the peak power generation period to generate corresponding third and fourth quantities. The ratios of the first and second quantities to the first total quantity are taken as the corresponding first and second ratios; and the ratios of the third and fourth quantities to the second total quantity are taken as the corresponding third and fourth ratios. When both the second ratio and the fourth ratio are greater than the preset first ratio threshold, the corresponding first analysis report is set to "excellent power generation output throughout the entire time period and excellent power generation output during the peak period". When the second ratio is greater than the first ratio threshold but the fourth ratio is lower than the first ratio threshold, the corresponding first analysis report is set to "Power generation output is good throughout the day but power generation output decreases during peak hours". When the second ratio is lower than the first ratio threshold but the fourth ratio is greater than the first ratio threshold, the corresponding first analysis report is set to "Power generation output decreases throughout the day but power generation output is good during peak hours". When both the second ratio and the fourth ratio are lower than the first ratio threshold, the corresponding first analysis report is set to a decrease in power generation output during the entire period and a decrease in power generation output during the peak period.

18. An apparatus for implementing the processing method for monitoring a photovoltaic power generation system according to any one of claims 1-12, characterized in that, The device includes: a first acquisition module, a first preprocessing module, a first temperature pre-monitoring module, a first solar radiation intensity pre-monitoring module, and a first power generation output pre-monitoring module; The first acquisition module is used to acquire the longitude, latitude, altitude, and characteristic meteorological data of the area where the photovoltaic power generation system is located as the corresponding first longitude, first latitude, first altitude, and first meteorological data set; and to acquire the current month as the corresponding first month; The first meteorological data set is a set of characteristic meteorological data for the region where the photovoltaic power generation system is located over the past twenty years; The first meteorological data set includes multiple first-year meteorological data; the first-year meteorological data includes 365*24 first-hour temperature collection data, 365 first-day sunshine duration collection data, 1 first-year astronomical radiation statistics data, 1 first-year summer astronomical radiation statistics data, 1 first-year winter astronomical radiation statistics data, and 1 first-year sunshine percentage; The second meteorological data set includes the first annual average temperature, the first highest temperature, the first lowest temperature, the first annual average astronomical radiation, the first annual average summer astronomical radiation, the first annual average winter astronomical radiation, the first annual average sunshine percentage, and the sunshine duration L for the 12 first months. i ; i is the month index for the next twelve months, 1≤i≤12; The first preprocessing module is used to perform meteorological data preprocessing based on the first meteorological data set to generate a corresponding second meteorological data set; The first temperature forecast module is used to generate a corresponding first temperature forecast table by performing temperature forecasting for the next twelve months based on the first month, the first longitude, the first latitude, the first altitude, and the second meteorological data set; specifically, it includes: The first diurnal variation coefficient D is generated by estimating the diurnal variation coefficient based on the first latitude and the first altitude. The constant factor a for estimating the diurnal variation coefficient d,1 The default value is 6100, and the constant factor a is used for estimation. d,2 The default value is 90; The first daily temperature difference W is generated by estimating the daily temperature difference based on the first longitude and the first latitude. Daily temperature difference estimation constant factor a w,1 The default value is 8, and the constant factor a is estimated. w,2 The default value is 0.25, and the constant factor a is used for estimation. w,3 The default value is 20, and the constant factor a is estimated. w,4 The default value is 0.0075, and the estimation constant factor a is... w,5 The default value is 130; Substituting the first month, the first diurnal variation coefficient D, the first daily temperature difference W, the first longitude, the first highest temperature, the first lowest temperature, and the first annual average temperature into the preset empirical formula for predicted temperature, 12*12 first predicted temperature data T are obtained. i,j j is the index of the two time periods, 1≤j≤12; The first solar intensity pre-monitoring module is used to perform solar intensity pre-monitoring for the next twelve months based on the first month and the second meteorological data set to generate a corresponding first solar intensity forecast table; The first power generation output pre-monitoring module is used to perform power generation output pre-monitoring for the next twelve months based on the first month, the first temperature forecast table, and the first solar radiation intensity forecast table, generate a corresponding first power generation output forecast table, and save it.

19. An apparatus for implementing the processing method for monitoring a photovoltaic power generation system according to any one of claims 13-17, characterized in that, The device includes: a first time period monitoring module and a first single-day analysis module; The first time period monitoring module is used to acquire the current monthly information, time information and real-time power generation information every hour starting from the preset start time each day as the corresponding second month, first time and first real-time power; and to query the preset first power generation prediction table according to the second month and the first time to obtain the corresponding first predicted power; and to set the photovoltaic power generation system monitoring status according to the first real-time power and the first predicted power to generate the corresponding first monitoring status. The first daily analysis module is used to perform photovoltaic power generation output status analysis based on all the first monitoring statuses obtained on that day at the preset end time of each day, and generate a corresponding first analysis report.

20. A processing system for monitoring a photovoltaic power generation system, characterized in that, The system includes the apparatus of claim 18 and the apparatus of claim 19.

21. A processing component for monitoring a photovoltaic power generation system, characterized in that, The component includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a processing method for monitoring a photovoltaic power generation system as described in any one of claims 1-12, or to perform a processing method for monitoring a photovoltaic power generation system as described in any one of claims 13-17.

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