Photovoltaic power station electric power data real-time monitoring and analysis platform

Abstract

The invention discloses a photovoltaic power station electric power data real-time monitoring and analysis platform, and relates to the technical field of photovoltaic power stations, the photovoltaic power station electric power data real-time monitoring and analysis platform comprises a processing layer, a working layer and an environment layer, the processing layer comprises a central processing module and a database; the database comprises an electric power influence table, and the electric power influence table comprises theoretical voltage, theoretical current and theoretical power of the corresponding photovoltaic module and correction factors and correction coefficients of the environmental data on the theoretical power of the photovoltaic module; the working layer comprises a photovoltaic assembly monitoring module and an inverter monitoring module. According to the invention, a more accurate photovoltaic power station real-time monitoring method is constructed through multi-environmental factor correction, time sequence analysis, temperature change monitoring and an anomaly marking mechanism, and the anomaly detection capability of the photovoltaic module and the inverter is improved. In the aspect of environment correction, multiple environment factors are comprehensively considered, the theoretical power of the photovoltaic module is corrected, and correction coefficients are set in combination with different weather conditions, so that power judgment is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power stations, and in particular to a real-time monitoring and analysis platform for power data of photovoltaic power stations. Background Art

[0002] With the continuous development of photovoltaic power generation technology, the scale of photovoltaic power stations has gradually expanded, and its power generation efficiency and operation stability have become the focus of attention. The existing photovoltaic power station monitoring system mainly collects the power generation data and environmental parameters of photovoltaic components through power sensors, inverter data recording, environmental sensors, etc., and analyzes them to determine the operating status of the photovoltaic system. These technologies provide important support for the daily operation and maintenance of photovoltaic power stations, helping operation and maintenance personnel detect system anomalies and improve the overall performance of the power station.

[0003] After searching, the patent with authorization announcement number CN115459709B discloses a remote monitoring system for photovoltaic power stations. The patent includes a data acquisition module, a remote monitoring module, a power station sorting module, and an operation and maintenance deployment module. Through the setting of the remote monitoring module, the photovoltaic equipment in the photovoltaic power station is monitored, and the faulty photovoltaic equipment and the photovoltaic power station are marked. Not only can the power generation abnormality of the entire photovoltaic power station be monitored, but also the power generation abnormality of each photovoltaic equipment in the photovoltaic power station can be accurately monitored. The power station sorting module determines the maintenance priority of the faulty power station according to the actual fault situation, and the operation and maintenance personnel can be arranged to the faulty power station with a high maintenance priority for maintenance. The operation and maintenance personnel are arranged to the faulty power station effectively and orderly. The operation and maintenance deployment module can recommend the best operation and maintenance personnel to the faulty power station for maintenance, thereby improving the efficiency of the deployment of operation and maintenance personnel.

[0004] In the prior art, different weather conditions have different degrees of influence on the power of photovoltaic modules. The existing correction model may have certain errors under certain circumstances. In addition, when monitoring the temperature of photovoltaic modules, the temperature of photovoltaic modules is affected by multiple factors, and its rate of change and hysteresis effect have important influence on power output. More accurate detection methods are needed to identify temperature anomalies. In addition, the existing system usually judges component anomalies based on a single power deviation threshold, lacks a mechanism for accumulation and hierarchical management of abnormal markers, which may result in a high false alarm rate. Therefore, the present invention proposes a real-time monitoring and analysis platform for power data of photovoltaic power stations. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time monitoring and analysis platform for power data of a photovoltaic power station to solve the problems mentioned in the above background technology.

[0006] The present invention can be implemented by the following technical solutions: a photovoltaic power station power data real-time monitoring and analysis platform, including a processing layer, a working layer and an environmental layer;

[0007] The processing layer includes a central processing module and a database;

[0008] The database is used to collect historical power data of photovoltaic power stations at the same geographical location and historical environmental data of corresponding time, and the database establishes a power impact table based on the historical power data and the corresponding environmental data;

[0009] At the same time, the database includes the theoretical voltage, theoretical current and theoretical power of the corresponding photovoltaic modules;

[0010] The power impact table includes the correction factors of environmental data on the theoretical power of photovoltaic modules and the correction coefficients corresponding to each correction factor;

[0011] Environmental data include solar radiation intensity, ambient temperature, wind conditions (wind speed and direction) and meteorological conditions (sunny, cloudy, haze, etc.);

[0012] Power data includes the output information of each photovoltaic module (including current, voltage, power), inverter output power, temperature of each photovoltaic module, and inverter temperature;

[0013] The theoretical power of a PV module is the rated power of the PV module calculated under standard test conditions (STC: 1000 W / m², 25°C);

[0014] And the correction factors include:

[0015] 1. Light intensity correction (radiation intensity is proportional to power);

[0016] 2. Ambient temperature correction (high temperature reduces power);

[0017] 3. Wind speed correction (affects the heat dissipation and temperature of photovoltaic modules);

[0018] 4. Meteorological correction (affects direct radiation intensity);

[0019] The working layer includes a plurality of photovoltaic component monitoring modules and an inverter monitoring module;

[0020] Each photovoltaic module monitoring module is used to monitor the corresponding photovoltaic module and obtain the real-time output information and real-time temperature data of the corresponding photovoltaic module, and the photovoltaic module monitoring module uploads the real-time output information and real-time temperature data of each photovoltaic module to the central processing module;

[0021] The inverter monitoring module is used to monitor the input power and real-time temperature data of the inverter, and the inverter monitoring module uploads the input power and real-time temperature data of the inverter to the central processing module;

[0022] The environmental layer obtains the real-time environmental data of the environment in which the photovoltaic components are located through corresponding sensors, including real-time solar radiation intensity, real-time ambient temperature, real-time wind conditions and real-time meteorological conditions, and the environmental layer uploads the obtained real-time environmental data to the central processing module;

[0023] After receiving the real-time output information of each photovoltaic module, the central processing module performs time series analysis on it to obtain the voltage dynamic curve, current dynamic curve and power dynamic curve of each photovoltaic module;

[0024] The voltage dynamic curve is composed of real-time voltages at multiple time points, which is time series data. That is, a voltage value is recorded at each time point t. , forming a voltage curve that changes with time:

[0025] ;

[0026] The current dynamic curve is composed of real-time current at multiple time points, which is also time series data, that is, a current value is recorded at each time point t. , forming a current curve that changes with time:

[0027] ;

[0028] The power dynamic curve is composed of real-time power at multiple time points, namely:

[0029] ;

[0030] The central processing module uses the mean standard deviation method to remove abnormal data from the voltage dynamic curve and current dynamic curve of each photovoltaic module, and then calculates the voltage deviation rate and current deviation rate at each time point, and compares them with the theoretical voltage and theoretical current, and calculates the average voltage deviation rate and the average current deviation rate;

[0031] And the central processing module marks the photovoltaic module as an abnormal value when the voltage deviation rate or the current deviation rate exceeds the corresponding average voltage deviation rate or the average current deviation rate;

[0032] The central processing module counts the number of abnormal values ​​of each photovoltaic module respectively. When the number of abnormal values ​​of the corresponding photovoltaic module within a preset time period exceeds a preset abnormal number threshold, the central processing module marks the corresponding photovoltaic module as abnormal.

[0033] At the same time, after receiving the power dynamic curve of each photovoltaic module, the central processing module compares it with the theoretical power of the photovoltaic module to obtain the difference data of multiple corresponding time points;

[0034] And the central processing module retrieves the corresponding correction factor from the established power impact table based on the real-time environmental data, and the central processing module calculates the theoretical power and the correction factor to obtain the corresponding power determination range;

[0035] The central processing module then compares each difference data with the power judgment range. When the number of difference data exceeding the judgment range is greater than the preset judgment number threshold, the central processing module marks the corresponding PV module as abnormal.

[0036] Finally, the central processing module counts the number of abnormal marks of the corresponding photovoltaic components. When the number of abnormal marks of the corresponding photovoltaic components reaches the warning number within a preset time, the corresponding photovoltaic components trigger a warning message;

[0037] Among them, the warning quantity is set based on different environments, PV module models, PV systems and corresponding historical data.

[0038] A further technical improvement of the present invention is that the method for the central processing module to obtain the corresponding power determination range comprises the following steps:

[0039] S1. Calculate theoretical power based on standard test conditions ;

[0040] ; In the formula, is the maximum power point voltage; is the maximum power point current;

[0041] S2. Correction coefficients of solar radiation intensity, ambient temperature, wind conditions and meteorological conditions are obtained from environmental data, and the theoretical power is corrected through the formula , get the corrected power , the formula used is:

[0042] ;

[0043] Where G is the current solar radiation intensity;

[0044] is the current temperature of the photovoltaic module, which is obtain, and is the current ambient temperature, k is the corresponding correction coefficient;

[0045] is the temperature power coefficient;

[0046] W is the current wind speed; is the wind speed correction factor;

[0047] is the correction factor corresponding to the weather;

[0048] S3. Set the allowable error range , generating the power determination range, namely:

[0049] ≤Power determination range≤ .

[0050] A further technical improvement of the present invention is that the central processing module corrects the theoretical power of the corresponding photovoltaic module based on its service life, and the steps include:

[0051] A1. Calculate the theoretical power of photovoltaic modules;

[0052] A2. Based on the usage time of the photovoltaic modules, the formula Calculate the theoretical power of photovoltaic modules after aging and attenuation ; In the formula, r is the attenuation rate of the photovoltaic module; t is the use time of the photovoltaic module;

[0053] A3, based on the theoretical power in A2 , the corrected power will be obtained The calculation formula is modified to: .

[0054] A further technical improvement of the present invention is that: the central processing module calculates the temperature and temperature change of the environment in which the photovoltaic power station is located based on real-time environmental data;

[0055] The central processing module monitors the temperature change rate and temperature change delay of each photovoltaic module based on the temperature and temperature change of the environment, and the central processing module monitors whether the temperature change rate and temperature change delay of each photovoltaic module are normal;

[0056] If the temperature change rate or temperature delay of the corresponding photovoltaic component is abnormal, the photovoltaic component will be marked as abnormal.

[0057] A further technical improvement of the present invention is that the method in which the central processing module calculates whether the temperature change rate and temperature change delay of each photovoltaic module are normal comprises the following steps:

[0058] Q1. The central processing module confirms the temperature of the substation environment and temperature change rate , and the real-time temperature data of the PV module at time t and temperature change rate ;

[0059] in, ; ;

[0060] Q2, the central processing module is based on temperature The temperature range is set to the normal temperature change rate R and normal temperature change delay of the corresponding photovoltaic module. ;

[0061] Q3. The central processing module determines the temperature change rate Whether it matches the normal temperature change rate R;

[0062] If the temperature change rate <Normal temperature change rate R, or temperature change rate > normal temperature change rate R, the central processing module determines the temperature change rate Abnormal;

[0063] Q4. The central processing module calculates the temperature change rate The rate of change of temperature Time lag ;

[0064] ;

[0065] Q5. The central processing module will delay the time Compare with the preset delay determination threshold;

[0066] If the time lag If the delay is greater than the delay determination threshold, the central processing module determines that the temperature change delay of the corresponding PV module is abnormal.

[0067] A further technical improvement of the present invention is that the central processing module converts the input power of the inverter Compare with the total theoretical power of each photovoltaic module to determine the inverter power loss ratio ;

[0068] in, ; N is the number of photovoltaic modules; is the theoretical power of the ith PV module under standard test conditions.

[0069] A further technical improvement of the present invention is that: the database is provided with different numerical loss ratios The corresponding exception tag mapping table;

[0070] Abnormal label mapping table for different values ​​of loss ratio , set the value corresponding to the number of abnormal markings;

[0071] The central processing module is based on the inverter power loss ratio The matching result with the abnormal mark mapping table accumulates the number of matched abnormal marks to the number of abnormal marks in the corresponding area.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention constructs a more accurate real-time monitoring method for photovoltaic power stations through multi-environmental factor correction, time series analysis, temperature change monitoring and abnormal marking mechanism, and improves the abnormality detection capability of photovoltaic modules and inverters;

[0074] In terms of environmental correction, the present invention comprehensively considers factors such as solar radiation intensity, ambient temperature, wind speed, meteorological conditions, etc., corrects the theoretical power of photovoltaic modules, and sets correction coefficients in combination with different weather conditions, so that power determination is more accurate and errors caused by short-term light changes or weather factors are reduced; in addition, by introducing temperature change rate and hysteresis analysis, it is possible to more accurately identify abnormal heat dissipation of photovoltaic modules, such as hot spot effects in high temperature weather or module degradation problems in low temperature environments, thereby improving the reliability of module fault detection;

[0075] In terms of power monitoring, the present invention introduces time series analysis, adopts dynamic curves of voltage, current and power for monitoring, and combines short-term fluctuation detection with long-term trend analysis to effectively distinguish between short-term power drops caused by cloud obstruction and long-term attenuation caused by component aging. At the same time, the system can establish a power judgment range based on historical data and calculate the time-weighted power loss ratio to reduce the impact of data fluctuations at a single time point on fault judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0077] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0078] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0079] Example 1

[0080] See also Figure 1 As shown, the present invention provides a photovoltaic power station power data real-time monitoring and analysis platform, including a processing layer, a working layer and an environment layer;

[0081] The processing layer includes the central processing module and the database;

[0082] The database is used to collect historical power data of photovoltaic power stations at the same geographical location and historical environmental data of the corresponding time, and the database establishes a power impact table based on the historical power data and the corresponding environmental data;

[0083] At the same time, the database includes the theoretical voltage, theoretical current and theoretical power of the corresponding photovoltaic modules;

[0084] The power impact table includes the correction factors of environmental data on the theoretical power of photovoltaic modules and the correction coefficients corresponding to each correction factor;

[0085] Environmental data include solar radiation intensity, ambient temperature, wind conditions (wind speed and direction) and meteorological conditions (sunny, cloudy, haze, etc.);

[0086] Power data includes the output information of each photovoltaic module (including current, voltage, power), inverter output power, temperature of each photovoltaic module, and inverter temperature;

[0087] The theoretical power of a PV module is the rated power of the PV module calculated under standard test conditions (STC: 1000 W / m², 25°C);

[0088] And the correction factors include:

[0089] 1. Light intensity correction (radiation intensity is proportional to power);

[0090] 2. Ambient temperature correction (high temperature reduces power);

[0091] 3. Wind speed correction (affects the heat dissipation and temperature of photovoltaic modules);

[0092] 4. Meteorological correction (affects direct radiation intensity);

[0093] In addition, when the database collects historical power data and environmental data of the corresponding time, it performs data cleaning, interpolation, and outlier detection on them, which will reduce the noise, missing values, and error effects in the historical power data and environmental data of the corresponding time, and avoid the decrease in the accuracy of the power impact table;

[0094] The working layer includes multiple photovoltaic module monitoring modules and inverter monitoring modules;

[0095] Each photovoltaic module monitoring module is used to monitor the corresponding photovoltaic module and obtain the real-time output information and real-time temperature data of the corresponding photovoltaic module, and the photovoltaic module monitoring module uploads the real-time output information and real-time temperature data of each photovoltaic module to the central processing module;

[0096] The inverter monitoring module is used to monitor the input power and real-time temperature data of the inverter, and the inverter monitoring module uploads the input power and real-time temperature data of the inverter to the central processing module;

[0097] The environmental layer obtains the real-time environmental data of the environment in which the photovoltaic modules are located through the corresponding sensors, including the real-time solar radiation intensity, real-time ambient temperature, real-time wind conditions and real-time meteorological conditions, and the environmental layer uploads the obtained real-time environmental data to the central processing module;

[0098] After receiving the real-time output information of each photovoltaic module, the central processing module performs time series analysis on it to obtain the voltage dynamic curve, current dynamic curve and power dynamic curve of each photovoltaic module;

[0099] The voltage dynamic curve is composed of real-time voltages at multiple time points, which is time series data. That is, a voltage value is recorded at each time point t. , forming a voltage curve that changes with time:

[0100] ;

[0101] The current dynamic curve is composed of real-time current at multiple time points, which is also time series data, that is, a current value is recorded at each time point t. , forming a current curve that changes with time:

[0102] ;

[0103] The power dynamic curve is composed of real-time power at multiple time points, namely:

[0104] ;

[0105] The central processing module uses the mean standard deviation method to remove abnormal data from the voltage dynamic curve and current dynamic curve of each photovoltaic module, and then calculates the voltage deviation rate and current deviation rate at each time point, and compares them with the theoretical voltage and theoretical current, and calculates the average voltage deviation rate and average current deviation rate;

[0106] And the central processing module marks the photovoltaic module as an abnormal value when the voltage deviation rate or the current deviation rate exceeds the corresponding average voltage deviation rate or the average current deviation rate;

[0107] The central processing module counts the number of abnormal values ​​of each photovoltaic module respectively. When the number of abnormal values ​​of the corresponding photovoltaic module within a preset time period exceeds a preset abnormal number threshold, the central processing module marks the corresponding photovoltaic module as abnormal.

[0108] At the same time, after receiving the power dynamic curve of each photovoltaic module, the central processing module compares it with the theoretical power of the photovoltaic module to obtain the difference data of multiple corresponding time points, and the central processing module retrieves the corresponding correction factor from the established power impact table based on the real-time environmental data, and the central processing module combines the theoretical power with the correction factor to calculate and obtain the corresponding power judgment range;

[0109] The method for the central processing module to obtain the corresponding power determination range includes the following steps:

[0110] S1. Calculate theoretical power based on standard test conditions ;

[0111] ; In the formula, is the maximum power point voltage; is the maximum power point current;

[0112] S2. Correction coefficients of solar radiation intensity, ambient temperature, wind conditions and meteorological conditions are obtained from environmental data, and the theoretical power is corrected through the formula , get the corrected power , the formula used is:

[0113] ;

[0114] Where G is the current solar radiation intensity;

[0115] is the current temperature of the photovoltaic module, which is obtain, and is the current ambient temperature, k is the corresponding correction coefficient;

[0116] is the temperature power coefficient;

[0117] W is the current wind speed; is the wind speed correction factor;

[0118] is the correction factor corresponding to the weather, including:

[0119] sunny: =1;

[0120] cloudy day: =0.7;

[0121] Haze: =0.5;

[0122] rain: =0.3;

[0123] S3. Set the allowable error range , generating the power determination range, namely:

[0124] ≤Power determination range≤ .

[0125] The central processing module then compares each difference data with the power judgment range. When the number of difference data exceeding the judgment range is greater than the preset judgment number threshold, the central processing module marks the corresponding PV module as abnormal.

[0126] Finally, the central processing module counts the number of abnormal marks of the corresponding photovoltaic components. When the number of abnormal marks of the corresponding photovoltaic components reaches the warning number within a preset time, the corresponding photovoltaic components trigger a warning message;

[0127] Among them, the warning quantity is set based on different environments, PV module models, PV systems and corresponding historical data.

[0128] The central processing module calculates the temperature and temperature changes of the environment in which the photovoltaic power station is located based on real-time environmental data;

[0129] The central processing module monitors the temperature change rate and temperature change delay of each photovoltaic module based on the temperature and temperature change of the environment, and the central processing module monitors whether the temperature change rate and temperature change delay of each photovoltaic module are normal;

[0130] If the temperature change rate or temperature delay of the corresponding photovoltaic component is abnormal, the photovoltaic component will be marked as abnormal.

[0131] The method for the central processing module to calculate whether the temperature change rate and temperature change delay of each photovoltaic module are normal includes the following steps:

[0132] Q1. The central processing module confirms the temperature of the substation environment and temperature change rate , and the real-time temperature data of the PV module at time t and temperature change rate ;

[0133] in, ; ;

[0134] Q2, the central processing module is based on temperature The temperature range is set to the normal temperature change rate R and normal temperature change delay of the corresponding photovoltaic module. ;

[0135] In this embodiment, the normal temperature change rate R and the normal temperature change delay The corresponding relationship is as follows:

[0136] Q3. The central processing module determines the temperature change rate Whether it matches the normal temperature change rate R;

[0137] If the temperature change rate <Normal temperature change rate R, or temperature change rate > normal temperature change rate R, the central processing module determines the temperature change rate Abnormal;

[0138] Q4. The central processing module calculates the temperature change rate The rate of change of temperature Time lag ;

[0139] ;

[0140] Q5. The central processing module will delay the time Compare with the preset delay determination threshold;

[0141] If the time lag If it is greater than the delay determination threshold, the central processing module determines that the temperature change delay of the corresponding photovoltaic module is abnormal;

[0142] In this real time, when When the temperature is ≤0℃, the delay judgment threshold is set to be greater than 30min;

[0143] When 0℃< When the temperature is ≤30℃, the delay judgment threshold is set to be greater than 15min;

[0144] when When the temperature is greater than 30°C, the delay judgment threshold is set to be greater than 30 minutes.

[0145] The central processing module converts the input power of the inverter Compare with the total theoretical power of each photovoltaic module to determine the inverter power loss ratio ;

[0146] in, ; N is the number of photovoltaic modules; is the theoretical power of the ith PV module under standard test conditions.

[0147] Different numerical loss ratios are set in the database The corresponding exception tag mapping table;

[0148] Abnormal label mapping table for different values ​​of loss ratio , set the value corresponding to the number of abnormal markings;

[0149] The central processing module is based on the inverter power loss ratio The matching result with the abnormal mark mapping table is added to the number of abnormal marks in the corresponding area;

[0150] And the inverter calculates the power loss ratio of each photovoltaic module, and when the power loss ratio of the corresponding photovoltaic module is greater than a preset loss ratio threshold, the central processing module marks it;

[0151] Then the central processing module sorts the marked photovoltaic modules according to the power loss ratio. When the marked photovoltaic modules are not less than the assigned value of the abnormal marking times, the photovoltaic modules in the corresponding sequence are selected according to the assigned value and are marked as abnormal once respectively.

[0152] When the number of marked photovoltaic modules is less than the assigned number of abnormal marking times, the assigned number of abnormal marking times is evenly divided and rounded to perform abnormal marking of the corresponding value.

[0153] Example 2

[0154] A photovoltaic power station power data real-time monitoring and analysis platform, including a processing layer, a working layer and an environmental layer;

[0155] The processing layer includes the central processing module and the database;

[0156] The database is used to collect historical power data of photovoltaic power stations at the same geographical location and historical environmental data of the corresponding time, and the database establishes a power impact table based on the historical power data and the corresponding environmental data;

[0157] At the same time, the database includes the theoretical voltage, theoretical current and theoretical power of the corresponding photovoltaic modules;

[0158] The power impact table includes the correction factors of environmental data on the theoretical power of photovoltaic modules and the correction coefficients corresponding to each correction factor, where the theoretical power of photovoltaic modules is the rated power of photovoltaic modules calculated under standard test conditions (STC: 1000 W / m², 25°C);

[0159] And the correction factors include:

[0160] 1. Light intensity correction (radiation intensity is proportional to power);

[0161] 2. Ambient temperature correction (high temperature reduces power);

[0162] 3. Wind speed correction (affects the heat dissipation and temperature of photovoltaic modules);

[0163] 4. Meteorological correction (affects direct radiation intensity);

[0164] Among them, environmental data include solar radiation intensity, ambient temperature, wind conditions (wind speed and direction) and meteorological conditions (sunny, cloudy, haze, etc.);

[0165] Power data includes the output information of each photovoltaic module (including current, voltage, power), inverter output power, temperature of each photovoltaic module, and inverter temperature;

[0166] The working layer includes multiple photovoltaic module monitoring modules and inverter monitoring modules;

[0167] Each photovoltaic module monitoring module is used to monitor the corresponding photovoltaic module and obtain the real-time output information and real-time temperature data of the corresponding photovoltaic module, and the photovoltaic module monitoring module uploads the real-time output information and real-time temperature data of each photovoltaic module to the central processing module;

[0168] The inverter monitoring module is used to monitor the input power and real-time temperature data of the inverter, and the inverter monitoring module uploads the input power and real-time temperature data of the inverter to the central processing module;

[0169] The environmental layer obtains the real-time environmental data of the environment in which the photovoltaic modules are located through the corresponding sensors, including the real-time solar radiation intensity, real-time ambient temperature, real-time wind conditions and real-time meteorological conditions, and the environmental layer uploads the obtained real-time environmental data to the central processing module;

[0170] After receiving the real-time output information of each photovoltaic module, the central processing module performs time series analysis on it to obtain the voltage dynamic curve, current dynamic curve and power dynamic curve of each photovoltaic module;

[0171] The central processing module uses the mean standard deviation method to remove abnormal data from the voltage dynamic curve and current dynamic curve of each photovoltaic module, and then calculates the voltage deviation rate and current deviation rate at each time point, and compares them with the theoretical voltage and theoretical current, and calculates the average voltage deviation rate and average current deviation rate;

[0172] And the central processing module marks the photovoltaic module as an abnormal value when the voltage deviation rate or the current deviation rate exceeds the corresponding average voltage deviation rate or the average current deviation rate;

[0173] The central processing module counts the number of abnormal values ​​of each photovoltaic module respectively. When the number of abnormal values ​​of the corresponding photovoltaic module within a preset time period exceeds a preset abnormal number threshold, the central processing module marks the corresponding photovoltaic module as abnormal.

[0174] At the same time, after receiving the power dynamic curve of each photovoltaic module, the central processing module compares it with the theoretical power of the photovoltaic module to obtain the difference data of multiple corresponding time points, and the central processing module retrieves the corresponding correction factor from the established power impact table based on the real-time environmental data, and the central processing module combines the theoretical power with the correction factor to calculate and obtain the corresponding power judgment range;

[0175] The method for the central processing module to obtain the corresponding power determination range includes the following steps:

[0176] S1. Calculate theoretical power based on standard test conditions ;

[0177] ; In the formula, is the maximum power point voltage; is the maximum power point current;

[0178] S2. Correction coefficients of solar radiation intensity, ambient temperature, wind conditions and meteorological conditions are obtained from environmental data, and the theoretical power is corrected through the formula , get the corrected power , the formula used is:

[0179] ;

[0180] The central processing module corrects the theoretical power of the corresponding photovoltaic module based on its service life, and the steps include:

[0181] A1. Calculate the theoretical power of photovoltaic modules;

[0182] A2. Based on the usage time of the photovoltaic modules, the formula Calculate the theoretical power of photovoltaic modules after aging and attenuation ; In the formula, r is the attenuation rate of the photovoltaic module; t is the use time of the photovoltaic module;

[0183] A3, based on the theoretical power in A2 , the corrected power will be obtained The calculation formula is modified to: ;

[0184] S3. Set the allowable error range , generating the power determination range, namely:

[0185] ≤Power determination range≤ .

[0186] The central processing module then compares each difference data with the power judgment range. When the number of difference data exceeding the judgment range is greater than the preset judgment number threshold, the central processing module marks the corresponding PV module as abnormal.

[0187] Finally, the central processing module counts the number of abnormal marks of the corresponding photovoltaic components. When the number of abnormal marks of the corresponding photovoltaic components reaches the warning number within a preset time, the corresponding photovoltaic components trigger a warning message;

[0188] Among them, the warning quantity is set based on different environments, PV module models, PV systems and corresponding historical data.

[0189] The central processing module calculates the temperature and temperature changes of the environment in which the photovoltaic power station is located based on real-time environmental data;

[0190] The central processing module monitors the temperature change rate and temperature change delay of each photovoltaic module based on the temperature and temperature change of the environment, and the central processing module monitors whether the temperature change rate and temperature change delay of each photovoltaic module are normal;

[0191] If the temperature change rate or temperature delay of the corresponding photovoltaic component is abnormal, the photovoltaic component will be marked as abnormal.

[0192] The method for the central processing module to calculate whether the temperature change rate and temperature change delay of each photovoltaic module are normal includes the following steps:

[0193] Q1. The central processing module confirms the temperature of the substation environment and temperature change rate , and the real-time temperature data of the PV module at time t and temperature change rate ;

[0194] in, ; ;

[0195] Q2, the central processing module is based on temperature The temperature range is set to the normal temperature change rate R and normal temperature change delay of the corresponding photovoltaic module. ;

[0196] Q3. The central processing module determines the temperature change rate Whether it matches the normal temperature change rate R;

[0197] If the temperature change rate <Normal temperature change rate R, or temperature change rate > normal temperature change rate R, the central processing module determines the temperature change rate Abnormal;

[0198] Q4. The central processing module calculates the temperature change rate The rate of change of temperature Time lag ;

[0199] ;

[0200] Q5. The central processing module will delay the time Compare with the preset delay determination threshold;

[0201] If the time lag If it is greater than the delay determination threshold, the central processing module determines that the temperature change delay of the corresponding photovoltaic module is abnormal;

[0202] The central processing module converts the input power of the inverter Compare with the total theoretical power of each photovoltaic module to determine the inverter power loss ratio ;

[0203] in, ; N is the number of photovoltaic modules; is the theoretical power of the ith PV module under standard test conditions;

[0204] In this real-time, the central processing module corrects the theoretical power based on the service life of the corresponding photovoltaic module, through the formula Calculate the theoretical power of photovoltaic modules after aging and attenuation , so the loss ratio The formula is modified to ;

[0205] Different numerical loss ratios are set in the database The corresponding exception tag mapping table;

[0206] Abnormal label mapping table for different values ​​of loss ratio , set the value corresponding to the number of abnormal markings;

[0207] The central processing module is based on the inverter power loss ratio The matching result with the abnormal mark mapping table accumulates the number of matched abnormal marks to the number of abnormal marks in the corresponding area.

[0208] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A photovoltaic power station power data real-time monitoring and analysis platform, including a processing layer, a working layer and an environment layer, characterized in that: The processing layer includes a central processing module and a database; the database includes a power impact table, which includes the theoretical voltage, theoretical current and theoretical power of the corresponding photovoltaic module and the correction factor and correction coefficient of the environmental data on the theoretical power; The working layer includes photovoltaic module monitoring module and inverter monitoring module; Each photovoltaic module monitoring module obtains the current, voltage, power and real-time temperature data of the corresponding photovoltaic module and uploads them to the central processing module; the inverter monitoring module obtains the input power and real-time temperature data of the inverter and uploads them to the central processing module; the environmental layer obtains real-time environmental data and uploads it to the central processing module; The central processing module analyzes the time series and constructs the corresponding current dynamic curve, voltage dynamic curve and power dynamic curve; and the central processing module calculates the corresponding deviation rate after removing the abnormal data of current and voltage, and then compares it with the theoretical voltage and theoretical current respectively to calculate the average deviation rate; When the voltage or voltage deviation rate exceeds the corresponding average deviation rate, the central processing module marks it as an abnormal value; when the number of abnormal values ​​accumulated by the photovoltaic module within a preset time exceeds the preset abnormal number threshold, the central processing module marks the corresponding photovoltaic module as abnormal; At the same time, the central processing module compares the power dynamic curve with the theoretical power to obtain the power difference, and calculates the power judgment range in combination with the environmental data; If the power difference at multiple time points exceeds the power determination range, and the number is greater than the preset determination number threshold, the central processing module will perform an abnormal marking; The central processing module counts the number of abnormal marks. When the number of abnormal marks of the corresponding photovoltaic components reaches the warning number within a preset time period, the corresponding photovoltaic components trigger a warning message.

2. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 1, characterized in that: The theoretical power of a PV module is the rated power of the PV module calculated under standard test conditions (STC: 1000 W / m², 25°C); And the correction factors include: light intensity correction, ambient temperature correction, wind speed correction and weather correction.

3. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 2, characterized in that: The method for the central processing module to obtain the corresponding power determination range includes the following steps: S1. Calculate theoretical power based on standard test conditions ; S2. Correction coefficients of solar radiation intensity, ambient temperature, wind conditions and meteorological conditions are obtained from environmental data, and the theoretical power is corrected through the formula , get the corrected power , the formula used is: ; Where G is the current solar radiation intensity; is the current temperature of the photovoltaic module, which is obtain, and is the current ambient temperature, k is the corresponding correction coefficient; is the temperature power coefficient; W is the current wind speed; is the wind speed correction factor; is the correction factor corresponding to the weather; S3. Set the allowable error range , generating power determination range: ≤Power determination range≤ .

4. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 3, characterized in that: The central processing module corrects the theoretical power of the corresponding photovoltaic module based on its service life, and the steps include: A1. Calculate the theoretical power of photovoltaic modules ; A2. Based on the usage time of the photovoltaic modules, the formula Calculate the theoretical power of photovoltaic modules after aging and attenuation ; In the formula, r is the attenuation rate of the photovoltaic module; t is the use time of the photovoltaic module; A3, based on the theoretical power in A2 , the corrected power The calculation formula is modified to: 。 5. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 1, characterized in that: The central processing module calculates the temperature and temperature change of the environment in which the photovoltaic power station is located based on real-time environmental data; The central processing module monitors the temperature change rate and temperature change delay of each photovoltaic module based on the temperature and temperature change of the environment, and the central processing module monitors whether the temperature change rate and temperature change delay of each photovoltaic module are normal; If the temperature change rate or temperature delay of the corresponding photovoltaic component is abnormal, the photovoltaic component will be marked as abnormal.

6. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 5, characterized in that: The method for the central processing module to calculate whether the temperature change rate and temperature change delay of each photovoltaic module are normal includes the following steps: Q1. The central processing module confirms the temperature of the substation environment and temperature change rate , and the real-time temperature data of the PV module at time t and temperature change rate ; Q2, the central processing module is based on temperature The temperature range is set to the normal temperature change rate R and normal temperature change delay of the corresponding photovoltaic module. ; Q3. The central processing module determines the temperature change rate Whether it matches the normal temperature change rate R; If the temperature change rate <Normal temperature change rate R, or temperature change rate > normal temperature change rate R, the central processing module determines the temperature change rate Abnormal; Q4. The central processing module calculates the temperature change rate The rate of change of temperature Time lag ; ； Q5. The central processing module will delay the time Compare with the preset delay determination threshold; If the time lag If the delay is greater than the delay determination threshold, the central processing module determines that the temperature change delay of the corresponding PV module is abnormal.

7. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 3, characterized in that: The central processing module converts the input power of the inverter Compare with the total theoretical power of each photovoltaic module to determine the inverter power loss ratio ; in, ; N is the number of photovoltaic modules; is the theoretical power of the ith PV module under standard test conditions.

8. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 7, characterized in that: The database is provided with different numerical loss ratios. The corresponding exception tag mapping table; Abnormal label mapping table for different values ​​of loss ratio , set the value corresponding to the number of abnormal markings; The central processing module is based on the inverter power loss ratio The matching result with the abnormal mark mapping table accumulates the number of matched abnormal marks to the number of abnormal marks of the corresponding PV module.

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