A real-time monitoring and analysis platform for photovoltaic power station power data

Abstract

The present invention discloses a real-time monitoring and analysis platform for photovoltaic power station power data, which relates to the technical field of photovoltaic power stations and includes a processing layer, a working layer and an environment layer. The processing layer includes a central processing module and a database; the database includes a power influence table, and the power influence table includes the theoretical voltage, theoretical current and theoretical power corresponding to the photovoltaic modules, as well as the correction factors and correction coefficients of the environmental data for the theoretical power of the photovoltaic modules; the working layer includes a photovoltaic module monitoring module and an inverter monitoring module. The present invention constructs a more accurate real-time monitoring method for photovoltaic power stations through multi-environment factor correction, time series analysis, temperature change monitoring and abnormal marking mechanism, and improves the abnormal detection ability of photovoltaic modules and inverters. In terms of environmental correction, the present invention comprehensively considers multiple environmental factors, corrects the theoretical power of photovoltaic modules, and sets correction coefficients in combination with different weather conditions, making the power determination more accurate.

Description

Technical Field

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

[0002] With the continuous development of photovoltaic power generation technology, the scale of photovoltaic power stations has gradually expanded, and their power generation efficiency and operation stability have become the focus of attention. Existing photovoltaic power station monitoring systems mainly collect the power generation data and environmental parameters of photovoltaic modules through means such as power sensors, inverter data records, and environmental sensors, and analyze them to judge the operation 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 retrieval, the patent with the authorized 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 dispatching module. Through the setting of the remote monitoring module, the photovoltaic devices in the photovoltaic power station are monitored, and the faulty photovoltaic devices and photovoltaic power stations are marked. It can not only monitor the power generation anomalies of the entire photovoltaic power station, but also accurately monitor the power generation anomalies of each photovoltaic device in the photovoltaic power station. The power station sorting module can judge the repair priority of the faulty power station according to the actual fault situation, and can arrange operation and maintenance personnel to repair the faulty power station with a higher repair priority first, effectively and orderly arranging operation and maintenance personnel for the faulty power station. The operation and maintenance dispatching module can recommend the best operation and maintenance personnel to the faulty power station for repair, improving the efficiency of operation and maintenance personnel dispatching.

[0004] In the prior art, the influence degree of different weather conditions on the power of photovoltaic modules is different. Existing correction models may have certain errors in specific situations. And when monitoring the temperature of photovoltaic modules, the temperature of photovoltaic modules is affected by various factors, and its change rate and hysteresis effect have an important impact on power output. A more accurate detection method is needed to identify temperature anomalies. And existing systems usually judge component anomalies based on a single power deviation threshold, lacking a mechanism for abnormal mark accumulation and hierarchical management, which may lead to a relatively high false alarm rate. Therefore, the present invention proposes a real-time monitoring and analysis platform for photovoltaic power station power data. Summary of the Invention

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

[0006] The present invention can be realized through the following technical solutions: A real-time monitoring and analysis platform for photovoltaic power station power data, 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 a photovoltaic power station at the same geographical location and historical environmental data at the 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 correction factors of environmental data on the theoretical power of photovoltaic modules and correction coefficients corresponding to each correction factor;

[0011] The environmental data includes solar radiation intensity, environmental temperature, wind conditions (wind speed and wind direction), and meteorological conditions (sunny, cloudy, haze, etc.);

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

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

[0014] And the correction factors include:

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

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

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

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

[0019] The working layer includes multiple photovoltaic module monitoring modules and an inverter monitoring module;

[0020] Each photovoltaic module monitoring module is respectively used to monitor the corresponding photovoltaic module, 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 real-time environmental data of the environment where the photovoltaic module is located through corresponding sensors, including real-time solar radiation intensity, real-time environmental temperature, real-time wind conditions, and real-time meteorological status, 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] Among them, the voltage dynamic curve is composed of real-time voltages at multiple time points and 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 currents at multiple time points and 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 powers at multiple time points, that is:

[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, then calculates the voltage deviation rate and current deviation rate at each time point, compares them with the theoretical voltage and theoretical current respectively, and calculates the average voltage deviation rate and average current deviation rate;

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

[0032] The central processing module separately counts the number of abnormal values of each photovoltaic module. When the number of abnormal values of the corresponding photovoltaic module exceeds the preset abnormal quantity threshold within the preset time period, the central processing module makes an abnormal mark for the corresponding photovoltaic module;

[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 difference data at multiple corresponding time points;

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

[0035] Subsequently, the central processing module 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 quantity threshold, the central processing module makes an abnormal mark on the corresponding photovoltaic module;

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

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

[0038] A further technical improvement of the present invention lies in: the method by which the central processing module obtains the corresponding power determination range includes the following steps:

[0039] S1. Calculate the 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. Obtain the correction coefficients of solar radiation intensity, ambient temperature, wind conditions, and meteorological status from the acquired environmental data, and correct the theoretical power through the formula to obtain the corrected power , and the formula adopted is:

[0042] ;

[0043] In the formula, G is the current solar radiation intensity;

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

[0045] is the temperature power coefficient;

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

[0047] is the correction coefficient corresponding to the meteorology;

[0048] S3. Set the allowable error range , and generate a power determination range, that is:

[0049] ≤ Power determination range ≤ .

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

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

[0052] A2. Based on the service time of the photovoltaic module, through the formula Calculate the theoretical power of the photovoltaic module after aging attenuation ; where r is the attenuation rate of the photovoltaic module; t is the service time of the photovoltaic module;

[0053] A3. Based on the theoretical power in A2 , the calculation formula for obtaining the corrected power is corrected to: .

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

[0055] And 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 module is abnormal, an abnormal mark is made for the photovoltaic module.

[0057] A further technical improvement of the present invention lies 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:

[0058] Q1. The central processing module confirms the temperature and temperature change rate of the environment where the substation is located, as well as the real-time temperature data and temperature change rate of the photovoltaic module at time t;

[0059] Among them, ; ;

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

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

[0062] If the temperature change rate < the normal temperature change rate R, or the temperature change rate > the normal temperature change rate R, then the central processing module determines that the temperature change rate is abnormal;

[0063] Q4. The central processing module calculates the time lag between the temperature change rate and the temperature change rate ; ;

[0064] ;

[0065] Q5. The central processing module compares the time lag with a preset delay determination threshold;

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

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

[0068] wherein, ; N is the number of photovoltaic modules; is the theoretical power of the i-th photovoltaic module under standard test conditions.

[0069] A further technical improvement of the present invention is that: an abnormal marking mapping table corresponding to different numerical loss ratios is set in the database;

[0070] For different numerical loss ratios in the abnormal marking mapping table , an assignment corresponding to the number of abnormal marks is set;

[0071] Based on the matching result between the inverter power loss ratio and the abnormal marking mapping table, the central processing module accumulates the number of matching abnormal marks into the number of abnormal marks in the corresponding area.

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

[0073] Through multi-environment factor correction, time series analysis, temperature change monitoring and anomaly marking mechanism, the present invention constructs a more accurate real-time monitoring method for photovoltaic power stations, improving the anomaly detection ability 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, and meteorological conditions, corrects the theoretical power of photovoltaic modules, and sets correction coefficients in combination with different weather conditions, making power determination more accurate and reducing errors caused by short-term light changes or weather factors; 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 component degradation problems in low-temperature environments, improving the reliability of component fault detection;

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

[0076] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

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

[0078] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, with reference to the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and effects of the present invention.

[0079] Embodiment 1

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

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

[0082] The database is used to collect historical power data of a photovoltaic power station at the same geographical location and historical environmental data corresponding to the 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 corresponding correction coefficients for each correction factor;

[0085] The environmental data includes solar radiation intensity, environmental temperature, wind conditions (wind speed and wind direction), and meteorological conditions (sunny, cloudy, haze, etc.);

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

[0087] The theoretical power of the photovoltaic module is the rated power of the photovoltaic 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. Environmental 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 the direct radiation intensity);

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

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

[0095] Each photovoltaic module monitoring module is respectively 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 environment layer obtains real-time environmental data of the environment where the photovoltaic module is located through corresponding sensors, including real-time solar radiation intensity, real-time environmental temperature, real-time wind conditions, and real-time meteorological status, and the environment 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] Among them, the voltage dynamic curve is composed of real-time voltages at multiple time points and 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 currents at multiple time points and 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 powers at multiple time points, that is:

[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, then calculates the voltage deviation rate and current deviation rate at each time point, compares them with the theoretical voltage and theoretical current respectively, and calculates the average voltage deviation rate and average current deviation rate;

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

[0107] The central processing module respectively counts the number of abnormal values of each photovoltaic module. When the number of abnormal values of the corresponding photovoltaic module exceeds the preset abnormal quantity threshold within the preset time period, the central processing module makes an abnormal mark for the corresponding photovoltaic module;

[0108] Meanwhile, after receiving the power dynamic curves of each photovoltaic module, the central processing module compares them with the theoretical power of the photovoltaic module to obtain the difference data at multiple corresponding time points. And the central processing module retrieves the corresponding correction factors from the established power influence table based on the real-time environmental data. Then the central processing module combines the theoretical power and the correction factors for calculation to obtain the corresponding power determination range.

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

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

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

[0112] S2. Obtain the correction coefficients of solar radiation intensity, ambient temperature, wind conditions, and meteorological status from the acquired environmental data, and correct the theoretical power through the formula to obtain the corrected power , and the formula used is:

[0113] ;

[0114] In the formula, G is the current solar radiation intensity;

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

[0116] is the temperature power coefficient;

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

[0118] is the correction coefficient corresponding to the meteorology, including:

[0119] Sunny day: = 1;

[0120] Cloudy day: = 0.7;

[0121] Haze: = 0.5;

[0122] Rainy day: = 0.3;

[0123] S3. Set the allowable error range , generate a power determination range, i.e.:

[0124] ≤ power determination range ≤ .

[0125] Subsequently, the central processing module 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 quantity threshold, the central processing module makes an abnormal mark on the corresponding photovoltaic module once;

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

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

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

[0129] And 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 module is abnormal, an abnormal mark is made on the photovoltaic module once.

[0131] The method by which the central processing module calculates 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 and temperature change rate of the environment where the substation is located, as well as the real-time temperature data of the photovoltaic module at time t and temperature change rate ;

[0133] Among them, ; ;

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

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

[0136]

[0137] Q3. The central processing module determines the temperature change rate and checks if it matches the normal temperature change rate R;

[0138] If the temperature change rate < the normal temperature change rate R, or the temperature change rate > the normal temperature change rate R, then the central processing module determines that the temperature change rate is abnormal;

[0139] Q4. The central processing module calculates the time lag between the temperature change rate and the temperature change rate ;

[0140] ;

[0141] Q5. The central processing module compares the time lag with a preset delay determination threshold;

[0142] If the time lag is greater than the delay determination threshold, then the central processing module determines that the temperature change delay of the corresponding photovoltaic module is abnormal;

[0143] In this real - time situation, when ≤ 0°C, the delay determination threshold is set to be greater than 30 min;

[0144] When 0°C < ≤ 30°C, the delay determination threshold is set to be greater than 15 min;

[0145] When > 30°C, the delay determination threshold is set to be greater than 30 min.

[0146] The central processing module compares the input power of the inverter with the total theoretical power of each photovoltaic module to determine the inverter power loss ratio ;

[0147] Among them, ; N is the number of photovoltaic modules; is the theoretical power of the i - th photovoltaic module under standard test conditions.

[0148] There is an abnormal marker mapping table with different numerical loss ratios set in the database;

[0149] Abnormal Marking Mapping Table for Loss Ratios of Different Values , an assignment corresponding to the number of abnormal markings is set;

[0150] The central processing module is based on the inverter power loss ratio Based on the matching result with the abnormal marking mapping table, the number of matching abnormal markings is accumulated into the number of abnormal markings in the corresponding area;

[0151] And the inverter calculates the power loss ratio of each photovoltaic module. When the power loss ratio of the corresponding photovoltaic module is greater than the preset loss ratio threshold, the central processing module marks it;

[0152] Subsequently, the central processing module sorts the marked photovoltaic modules according to the power loss ratio from large to small. When the number of marked photovoltaic modules is not less than the assignment of the number of abnormal markings, each photovoltaic module in the corresponding sequence is selected according to the assigned value, and each is marked abnormally once;

[0153] When the number of marked photovoltaic modules is less than the assignment of the number of abnormal markings, the assignment of the number of abnormal markings is evenly divided and rounded to perform abnormal markings of the corresponding values.

[0154] Embodiment 2

[0155] A real-time monitoring and analysis platform for photovoltaic power station power data includes a processing layer, a working layer, and an environment layer;

[0156] The processing layer includes a central processing module and a database;

[0157] The database is used to collect the historical power data of the photovoltaic power station at the same geographical location and the historical environment data at the corresponding time, and the database establishes a power impact table based on the historical power data and the corresponding environment data;

[0158] At the same time, the database includes the theoretical voltage, theoretical current, and theoretical power of the corresponding photovoltaic modules;

[0159] The power impact table includes the correction factors of the environment data on the theoretical power of the photovoltaic modules and the corresponding correction coefficients for each correction factor. Among them, the theoretical power of the photovoltaic module is the rated power of the photovoltaic module calculated under standard test conditions (STC: 1000 W / m², 25°C);

[0160] And the correction factors include:

[0161] 1. Illumination intensity correction (radiation intensity is proportional to power);

[0162] 2. Ambient temperature correction (high temperature reduces power);

[0163] 3. Wind speed correction (affects the heat dissipation and temperature of the photovoltaic module);

[0164] 4. Meteorological correction (affecting direct radiation intensity);

[0165] Among them, the environmental data includes solar radiation intensity, ambient temperature, wind conditions (wind speed and direction), and meteorological states (sunny, cloudy, haze, etc.);

[0166] The power data includes the output information of each photovoltaic module (including current, voltage, and power), the output power of the inverter, the temperature of each photovoltaic module, and the temperature of the inverter;

[0167] The working layer includes multiple photovoltaic module monitoring modules and an inverter monitoring module;

[0168] Each photovoltaic module monitoring module is respectively 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;

[0169] 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;

[0170] The environment layer obtains the real-time environmental data of the environment where the photovoltaic module is located through the corresponding sensors, including real-time solar radiation intensity, real-time ambient temperature, real-time wind conditions, and real-time meteorological states, and the environment layer uploads the obtained real-time environmental data to the central processing module;

[0171] 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;

[0172] 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 respectively, then calculates the voltage deviation rate and current deviation rate at each time point, and compares them with the theoretical voltage and theoretical current respectively, and calculates the average voltage deviation rate and average current deviation rate;

[0173] And when the voltage deviation rate or current deviation rate of the photovoltaic module exceeds the corresponding average voltage deviation rate or average current deviation rate, the central processing module marks it as an abnormal value;

[0174] The central processing module respectively counts the number of abnormal values of each photovoltaic module. When the number of abnormal values of the corresponding photovoltaic module exceeds the preset abnormal number threshold within the preset time period, the central processing module makes an abnormal mark on the corresponding photovoltaic module;

[0175] Meanwhile, after receiving the power dynamic curves of each photovoltaic module, the central processing module compares them with the theoretical power of the photovoltaic module to obtain difference data at multiple corresponding time points. And the central processing module retrieves the corresponding correction factors from the established power influence table based on the real-time environmental data, and the central processing module combines and calculates the theoretical power and the correction factors to obtain the corresponding power determination range;

[0176] The method for the central processing module to obtain the corresponding power determination range includes the following steps:

[0177] S1. Calculate the theoretical power based on standard test conditions ;

[0178] ; Wherein, is the maximum power point voltage; is the maximum power point current;

[0179] S2. Obtain the correction coefficients of solar radiation intensity, ambient temperature, wind conditions and meteorological status from the acquired environmental data, and correct the theoretical power through the formula to obtain the corrected power , and the formula adopted is:

[0180] ;

[0181] The central processing module corrects the theoretical power based on the service life of the corresponding photovoltaic module, and its steps include:

[0182] A1. Calculate the theoretical power of the photovoltaic module;

[0183] A2. Based on the usage time of the photovoltaic module, calculate the theoretical power of the photovoltaic module after aging attenuation through the formula ; Wherein, r is the attenuation rate of the photovoltaic module; t is the usage time of the photovoltaic module; ;

[0184] A3. Based on the theoretical power in A2 , the formula for obtaining the corrected power is corrected to: ;

[0185] S3. Set the allowable error range , and generate the power determination range, that is:

[0186] ≤ Power determination range ≤ .

[0187] Subsequently, the central processing module 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 quantity threshold, the central processing module makes an abnormal mark for the corresponding photovoltaic module.

[0188] Finally, the central processing module counts the number of abnormal marks of the corresponding photovoltaic module. When the number of abnormal marks of the corresponding photovoltaic module reaches the warning quantity within the preset time duration, the corresponding photovoltaic module triggers a warning message.

[0189] Among them, the warning quantity is set based on different environments, photovoltaic module models, photovoltaic systems, and corresponding historical data.

[0190] The central processing module calculates the temperature and temperature change of the environment where the photovoltaic power station is located based on real-time environmental data.

[0191] And 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.

[0192] If the temperature change rate or temperature delay of the corresponding photovoltaic module is abnormal, an abnormal mark is made for the photovoltaic module.

[0193] The method by which the central processing module calculates whether the temperature change rate and temperature change delay of each photovoltaic module are normal includes the following steps:

[0194] Q1. The central processing module confirms the temperature and temperature change rate of the environment where the substation is located, as well as the real-time temperature data of the photovoltaic module at time t and temperature change rate ;

[0195] Among them, ; ;

[0196] Q2. The central processing module sets the normal temperature change rate R and normal temperature change delay of the corresponding photovoltaic module based on the temperature range of the temperature ;

[0197] Q3. The central processing module determines whether the temperature change rate matches the normal temperature change rate R;

[0198] If the temperature change rate < normal temperature change rate R, or the temperature change rate > normal temperature change rate R, then the central processing module determines that the temperature change rate Abnormal;

[0199] Q4. The central processing module calculates the temperature change rate and the time lag with the temperature change rate ;

[0200] ;

[0201] Q5. The central processing module compares the time lag with a preset delay determination threshold;

[0202] If the time lag is greater than the delay determination threshold, the central processing module determines that the temperature change delay of the corresponding photovoltaic module is abnormal;

[0203] The central processing module compares the input power of the inverter with the total theoretical power of each photovoltaic module to judge the inverter power loss ratio ;

[0204] Among them, ; N is the number of photovoltaic modules; is the theoretical power of the i-th photovoltaic module under standard test conditions;

[0205] In this real time, since the central processing module corrects its theoretical power based on the service life of the corresponding photovoltaic module, the theoretical power after aging attenuation of the photovoltaic module is calculated through the formula , so the formula for the loss ratio is corrected to ;

[0206] There is an abnormal mark mapping table with different numerical loss ratios set in the database;

[0207] The abnormal mark mapping table sets assignments for the corresponding number of abnormal marks for different numerical loss ratios;

[0208] The central processing module accumulates the matched number of abnormal marks into the number of abnormal marks in the corresponding area based on the matching result of the inverter power loss ratio and the abnormal mark mapping table.

[0209] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the above-disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence 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 deviation rate of current or voltage 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 determination range in combination with the environmental data, including the following steps: S1. Calculate theoretical power P based on standard test conditions STC ; S2, obtain the correction coefficient of solar radiation intensity, ambient temperature, wind conditions and meteorological conditions from the environmental data, and correct the theoretical power P through the formula STC , get the corrected power P q , the formula used is: Where G is the current solar radiation intensity; T z is the current temperature of the photovoltaic module, which is expressed by T z =t 环境 + k × G is obtained, and t 环境 is the current ambient temperature, k is the corresponding correction coefficient; γ is the temperature power coefficient; W is the current wind speed; β is the wind speed correction coefficient; F q is the correction factor corresponding to the weather; S3. Set the allowable error range δ to generate the power determination range: P q ×(1-δ)≤Power determination range≤P q ×(1+δ); 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 photovoltaic modules is the power of photovoltaic modules under standard test conditions (STC: 1000W / m 2 , 25°C) calculated at the rated power of the PV module; 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 1, 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 P of the photovoltaic module STC ; A2. Based on the usage time of the photovoltaic modules, the formula P x =P STC ×(1-r×t) to calculate the theoretical power P of the photovoltaic module after aging and attenuation x ; 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 P in A2 x , the corrected power P q The calculation formula is modified to:

4. 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.

5. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 4, 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 T of the substation environment 环境 and the temperature change rate ΔT 环境 (t), and the real-time temperature data T of the PV module at time t z (t) and the temperature change rate ΔT z (t); Q2, the central processing module is based on temperature T 环境 The temperature range is set to the normal temperature change rate R and normal temperature change delay τ of the corresponding photovoltaic module; Q3. The central processing module determines the temperature change rate ΔT z (t) Whether it matches the normal temperature change rate R; If the temperature change rate ΔT z (t)<normal temperature change rate R, or temperature change rate ΔT z (t)>normal temperature change rate R, then the central processing module determines the temperature change rate R z (t) abnormal; Q4. The central processing module calculates the temperature change rate R z (t) and temperature change rate ΔT z (t) time lag τ i ; Q5, the central processing module delays the time by τ i Compare with a preset delay determination threshold; If the time lag τ i 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.

6. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 1, characterized in that: The central processing module converts the inverter input power P s Compare with the total theoretical power of each photovoltaic module to determine the inverter power loss ratio η; in, N is the number of photovoltaic modules; P STC,i is the theoretical power of the ith PV module under standard test conditions.

7. A photovoltaic power station power data real-time monitoring and analysis platform according to claim 6, characterized in that: The database is provided with an abnormality mark mapping table corresponding to different numerical loss ratios η; The abnormal marking mapping table is set with the corresponding abnormal marking times for different values ​​of loss ratio η; The central processing module accumulates the number of matched abnormal marks to the number of abnormal marks of the corresponding photovoltaic component based on the matching result between the inverter power loss ratio η and the abnormal mark mapping table.

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