Photovoltaic module attenuation online evaluation system and method

Abstract

The invention provides a photovoltaic module attenuation online evaluation system and method, the system comprises a plurality of photovoltaic module attenuation monitoring modules connected in series on a photovoltaic line, the photovoltaic module attenuation monitoring modules are connected with a grid-connected inverter, and the photovoltaic module attenuation monitoring modules exchange data with a cloud server. The photovoltaic module attenuation monitoring module acquires state information of a photovoltaic module and transmits the state information to the cloud server, the cloud server is provided with a comprehensive attenuation rate prediction model based on an equivalent circuit model and a power generation model, and the comprehensive attenuation rate prediction model performs online evaluation on the state information; according to the invention, a system for acquiring, uploading, processing and evaluating the attenuation condition of the photovoltaic module at the module end is supported, so that remote online evaluation of the attenuation condition of the photovoltaic module is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic modules, and in particular to an online evaluation system and method for photovoltaic module attenuation. Background Art

[0002] With the depletion of traditional oil resources and the growing global demand for renewable energy, photovoltaic energy has attracted widespread attention due to its clean and sustainable characteristics. As a key component for converting solar energy into electrical energy, the performance and life of photovoltaic modules are directly related to the cost-effectiveness and environmental benefits of the entire photovoltaic system. However, due to multiple factors such as material aging, environmental factors and manufacturing processes, photovoltaic modules will inevitably experience performance degradation during long-term operation. The degradation of photovoltaic modules is mainly manifested in a year-on-year decrease in power output, which not only shortens the service life of the modules, but also increases the maintenance cost of the system.

[0003] At present, the common method for evaluating the attenuation of photovoltaic modules is to use special equipment, instruments and devices to perform performance tests on photovoltaic modules under specific test conditions, evaluate the ratio of output power to initial power, and estimate the attenuation rate. This method can accurately derive the attenuation rate of photovoltaic modules to a certain extent and reflect the health status of photovoltaic modules, but the test cost is high, the implementation process is complex, and it is difficult to update and evaluate the attenuation of photovoltaic modules in real time online. Summary of the invention

[0004] The present invention solves the problem that it is difficult to update and evaluate the attenuation status of photovoltaic modules in real time online in the prior art, and proposes a photovoltaic module attenuation online evaluation system and method, which supports module-side data collection, uploading, processing and evaluation of the attenuation status of photovoltaic modules, so as to realize remote online evaluation of the attenuation status of photovoltaic modules.

[0005] In order to achieve the above objectives, the following technical solutions are proposed: A photovoltaic module attenuation online evaluation system includes a plurality of photovoltaic module attenuation monitoring modules connected in series on a photovoltaic line, wherein the photovoltaic module attenuation monitoring modules are connected to a grid-connected inverter, wherein the photovoltaic module attenuation monitoring modules exchange data with a cloud server, wherein the photovoltaic module attenuation monitoring modules obtain status information of the photovoltaic module and transmit the information to the cloud server, wherein the cloud server is provided with a comprehensive attenuation rate prediction model based on an equivalent circuit model and a power generation model, and wherein the comprehensive attenuation rate prediction model performs online evaluation on the status information.

[0006] The present invention is based on a photovoltaic module attenuation multi-model online evaluation method based on a photovoltaic power generation model that considers characteristic parameters and a photovoltaic equivalent circuit model that considers multiple environmental factors. The method takes into account the influence of internal aging factors of photovoltaic modules and external environmental factors, and further improves the accuracy of photovoltaic module attenuation evaluation through real-time data monitoring and analysis technology. The present invention is based on a photovoltaic module attenuation evaluation system that includes module-side data collection and cloud computing. The data collected by the module-side monitoring module is calculated and coordinated between the cloud processing module and the cloud server storage module, which is convenient for real-time online updating and remote evaluation of the attenuation status of the entire photovoltaic array and local single photovoltaic modules.

[0007] Preferably, the photovoltaic component attenuation monitoring module includes a component-end monitoring main control module, the component-end monitoring main control module has a built-in wireless communication module for exchanging data with a cloud server, the component-end monitoring main control module is electrically connected to a current acquisition module and a voltage acquisition module, and the component-end monitoring main control module is electrically connected to a status information acquisition sensor.

[0008] refer to Figure 2 The photovoltaic module attenuation monitoring module of the present invention includes a module-end monitoring main control module, a wireless communication module, a current acquisition module, a voltage acquisition module and a status information acquisition sensor. The wireless communication module is used to exchange data with a cloud server and is built into the module-end monitoring main control module. The current acquisition module, the voltage acquisition module and the status information acquisition sensor are all electrically connected to the module-end monitoring main control module.

[0009] Preferably, the status information collection sensor includes a wind speed and direction sensor, a solar irradiance sensor, and a temperature and humidity sensor.

[0010] The state information collection sensor of the present invention comprises a solar irradiance sensor, a wind speed and direction sensor, and a temperature and humidity sensor.

[0011] Preferably, the component-end monitoring main control module is electrically connected to the photovoltaic power generation panel of the photovoltaic component through a DC / DC power supply module, and the photovoltaic power generation panel supplies power to the component-end monitoring main control module.

[0012] The component-end monitoring main control module of the present invention is electrically connected to a DC / DC power supply module, and the DC / DC power supply module is electrically connected to the photovoltaic power generation panel of the photovoltaic component. The component-end monitoring main control module is powered by the photovoltaic power generation panel through the DC / DC power supply module.

[0013] Preferably, the output end of the wireless communication module is connected to a power amplifier module.

[0014] The output end of the wireless communication module of the present invention is connected with a power amplification module for signal amplification.

[0015] A photovoltaic module attenuation online evaluation method, using the photovoltaic module attenuation online evaluation system mentioned above, comprises the following steps: S1, building a comprehensive attenuation rate prediction model based on an equivalent circuit model and a power generation model on a cloud server; S2, the component-side monitoring main control module collects monitoring data; S3, the component-side monitoring control module transmits the monitoring data to the cloud server; S4, the comprehensive attenuation rate prediction model calculates and evaluates the attenuation status of the PV module based on the monitoring data; S5, the cloud server saves the calculation results of the comprehensive attenuation rate prediction model and the original data.

[0016] Preferably, the S1 specifically comprises the following steps: S11, establishing a photovoltaic module equivalent circuit model and a photovoltaic module power generation model; S12, testing the photovoltaic module equivalent circuit model to obtain a mapping relationship between the photovoltaic module attenuation rate and the change status of each parameter of the photovoltaic module equivalent circuit model, and then obtaining an estimated attenuation rate based on the photovoltaic module equivalent circuit model; S13, testing the photovoltaic module power generation model to obtain a mapping relationship between the photovoltaic module attenuation rate and the change status of each characteristic parameter of the photovoltaic module power generation model, and then obtaining an estimated attenuation rate based on the photovoltaic module power generation model; S14, presetting weight coefficients of the photovoltaic module equivalent circuit model and the photovoltaic module power generation model, and calculating the comprehensive attenuation amplitude of the photovoltaic module by weighted summation; S15, calculating the error function value of the photovoltaic module equivalent circuit model and the photovoltaic module power generation model, correcting and updating the weight coefficients of the photovoltaic module equivalent circuit model and the photovoltaic module power generation model through the error function value, and obtaining the comprehensive attenuation rate prediction model.

[0017] Preferably, in S11, an equivalent circuit model of the photovoltaic module is established based on various power generation data, historical data and environmental data of the photovoltaic module acquired online.

[0018] The present invention establishes an equivalent circuit model of a photovoltaic module based on various power generation data, environmental data and historical data of the photovoltaic modules obtained online, specifically: obtaining the voltage, current, solar radiation value, wind speed, wind direction, temperature and radiation angle of a single photovoltaic module, based on a single diode model, integrating the nonlinear influence of temperature effect, solar irradiance and attenuation rate on the equivalent circuit model, introducing voltage and current correction factors, and establishing an equivalent circuit model of a photovoltaic module including factors such as temperature, solar irradiance and module aging.

[0019] Preferably, in S11, a photovoltaic module power generation model is established according to a number of power generation characteristic parameters.

[0020] The present invention calculates multiple power generation characteristic parameters and establishes a photovoltaic module power generation model, specifically: according to the obtained solar radiation value, wind speed, wind direction, temperature, radiation angle and other environmental factors, a photovoltaic power generation model is established based on power generation indicators such as the cumulative amount of power generation, power conversion rate, power generation peak value, power generation time series variance, power generation time series sample entropy and other power generation indicators at multiple time scales under different weather environments.

[0021] Preferably, the calculation process of updating the weight coefficient in S15 is as follows: ω' z To update the weight coefficient.

[0022] The beneficial effects of the present invention are: 1. The present invention is based on a photovoltaic module attenuation multi-model online evaluation method based on a photovoltaic power generation model that considers characteristic parameters and a photovoltaic equivalent circuit model that considers multiple environmental factors. It considers the influence of internal aging factors of photovoltaic modules and external environmental factors, and further improves the accuracy of photovoltaic module attenuation evaluation through real-time data monitoring and analysis technology.

[0023] 2. The present invention is based on a photovoltaic module attenuation assessment system that includes module-side data collection and cloud computing. The data collected by the module-side monitoring module is calculated and coordinated between the cloud processing module and the cloud server storage module, which is convenient for real-time online updating and remote assessment of the attenuation status of the entire photovoltaic array and local single photovoltaic modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a framework diagram of the photovoltaic module attenuation multi-model online evaluation system of the present invention.

[0025] Figure 2 It is a schematic diagram of the photovoltaic module attenuation monitoring module of the present invention.

[0026] Figure 3 It is a schematic diagram of the overall process of the photovoltaic module attenuation multi-model online evaluation system of the present invention.

[0027] Figure 4 It is a schematic diagram of the flow of the multi-model online evaluation method for photovoltaic module attenuation of the present invention. DETAILED DESCRIPTION

[0028] Embodiment 1: This embodiment provides a photovoltaic module attenuation online evaluation system. Figure 1, including a photovoltaic component attenuation monitoring module, a grid-connected inverter, a cloud server and a photovoltaic line. The photovoltaic component attenuation monitoring modules are provided in a plurality and are connected in series on the photovoltaic line. The grid-connected inverter is electrically connected to the photovoltaic component attenuation monitoring module, the cloud server exchanges data with the photovoltaic component attenuation monitoring module, the photovoltaic component attenuation monitoring module obtains the status information of the photovoltaic component in real time, and transmits the status information of the photovoltaic component obtained in real time to the cloud server, the cloud server obtains the status information online in real time, the cloud server is provided with a comprehensive attenuation rate prediction model, the comprehensive attenuation rate prediction model is a combined prediction model based on an equivalent circuit model and a power generation model, and the comprehensive attenuation rate prediction model performs online evaluation on the status information obtained online in real time.

[0029] The present invention is based on a photovoltaic module attenuation multi-model online evaluation method based on a photovoltaic power generation model that considers characteristic parameters and a photovoltaic equivalent circuit model that considers multiple environmental factors. The method takes into account the influence of internal aging factors of photovoltaic modules and external environmental factors, and further improves the accuracy of photovoltaic module attenuation evaluation through real-time data monitoring and analysis technology. The present invention is based on a photovoltaic module attenuation evaluation system that includes module-side data collection and cloud computing. The data collected by the module-side monitoring module is calculated and coordinated between the cloud processing module and the cloud server storage module, which is convenient for real-time online updating and remote evaluation of the attenuation status of the entire photovoltaic array and local single photovoltaic modules.

[0030] refer to Figure 2 The photovoltaic module attenuation monitoring module of the present invention includes a module-end monitoring main control module, a wireless communication module, a current acquisition module, a voltage acquisition module and a status information acquisition sensor. The wireless communication module is used to exchange data with a cloud server and is built into the module-end monitoring main control module. The current acquisition module, the voltage acquisition module and the status information acquisition sensor are all electrically connected to the module-end monitoring main control module.

[0031] This embodiment also provides a photovoltaic module attenuation online evaluation method, using the photovoltaic module attenuation online evaluation system mentioned above, referring to Figure 3 , including the following steps: S1, on the cloud server, build a comprehensive attenuation rate prediction model based on the equivalent circuit model and the power generation model; S2, the component-side monitoring main control module collects monitoring data in real time; S3, the cloud server online obtains the monitoring data transmitted by the component-side monitoring main control module; S4, the comprehensive attenuation rate prediction model on the cloud server uses the monitoring data to calculate and evaluate the attenuation status of the PV modules; S5, the cloud server saves the original data and calculation results of the comprehensive attenuation rate prediction model.

[0032] Embodiment 2: This embodiment improves the state information acquisition sensor based on embodiment 1 and improves method S1 accordingly, and proposes an online evaluation system for photovoltaic module attenuation. Figure 1 , including a photovoltaic component attenuation monitoring module, a grid-connected inverter, a cloud server and a photovoltaic line. The photovoltaic component attenuation monitoring modules are provided in a plurality and are connected in series on the photovoltaic line. The grid-connected inverter is electrically connected to the photovoltaic component attenuation monitoring module, the cloud server exchanges data with the photovoltaic component attenuation monitoring module, the photovoltaic component attenuation monitoring module obtains the status information of the photovoltaic component in real time, and transmits the status information of the photovoltaic component obtained in real time to the cloud server, the cloud server obtains the status information online in real time, the cloud server is provided with a comprehensive attenuation rate prediction model, the comprehensive attenuation rate prediction model is a combined prediction model based on an equivalent circuit model and a power generation model, and the comprehensive attenuation rate prediction model performs online evaluation on the status information obtained online in real time.

[0033] The present invention is based on a photovoltaic module attenuation multi-model online evaluation method based on a photovoltaic power generation model that considers characteristic parameters and a photovoltaic equivalent circuit model that considers multiple environmental factors. The method takes into account the influence of internal aging factors of photovoltaic modules and external environmental factors, and further improves the accuracy of photovoltaic module attenuation evaluation through real-time data monitoring and analysis technology. The present invention is based on a photovoltaic module attenuation evaluation system that includes module-side data collection and cloud computing. The data collected by the module-side monitoring module is calculated and coordinated between the cloud processing module and the cloud server storage module, which is convenient for real-time online updating and remote evaluation of the attenuation status of the entire photovoltaic array and local single photovoltaic modules.

[0034] refer to Figure 2 The photovoltaic module attenuation monitoring module of the present invention includes a module-side monitoring main control module, a wireless communication module, a current acquisition module, a voltage acquisition module and a state information acquisition sensor. The wireless communication module is used to exchange data with a cloud server and is built into the module-side monitoring main control module. The current acquisition module, the voltage acquisition module and the state information acquisition sensor are all electrically connected to the module-side monitoring main control module. The state information acquisition sensor of the present invention includes a solar irradiance sensor, a wind speed and direction sensor and a temperature and humidity sensor.

[0035] This embodiment also provides a photovoltaic module attenuation online evaluation method, using the photovoltaic module attenuation online evaluation system mentioned above, referring to Figure 3 , including the following steps: S1, on the cloud server, build a comprehensive attenuation rate prediction model based on the equivalent circuit model and the power generation model; refer to Figure 4, the S1 specifically includes the following steps: S11, establishing a photovoltaic module equivalent circuit model based on various power generation data, environmental data and historical data of the photovoltaic module obtained online, and updating the model parameters in real time based on the parameter identification algorithm, establishing a photovoltaic module power generation model based on multiple power generation characteristic parameters obtained by calculation, and updating the model characteristic parameters in real time based on the acquired power generation data; The photovoltaic module equivalent circuit model is established based on various power generation data, environmental data and historical data of the photovoltaic modules obtained online, specifically: the voltage, current, solar radiation value, wind speed, wind direction, temperature and radiation angle of a single photovoltaic module are obtained, based on the single diode model, the nonlinear influence of temperature effect, solar irradiance and attenuation rate on the equivalent circuit model is integrated, voltage and current correction factors are introduced, and an equivalent circuit model of the photovoltaic module including factors such as temperature, solar irradiance and module aging is established.

[0036] The calculation obtains multiple power generation characteristic parameters and establishes a photovoltaic module power generation model, specifically: based on the obtained solar radiation value, wind speed, wind direction, temperature, radiation angle and other environmental factors, a photovoltaic power generation model is established based on power generation indicators such as the cumulative amount of power generation, power conversion rate, power generation peak value, power generation time series variance, power generation time series sample entropy and other power generation indicators at multiple time scales under different weather environments.

[0037] S12, through pre-testing, obtain the mapping relationship between the attenuation rate of the photovoltaic module and the change status of each parameter of the model, and obtain the estimated attenuation rate based on the equivalent circuit model of the photovoltaic module according to the result of multi-parameter comparison between the ideal circuit model and the real-time updated circuit model, as well as the mapping relationship between the attenuation rate of the photovoltaic module and the change status of each parameter of the model; the multi-parameter comparison between the ideal circuit model and the real-time updated circuit model is specifically: the deviation of the equivalent parallel resistance value and the equivalent series resistance value in the zero attenuation ideal circuit model and the real-time circuit model.

[0038] S13, obtaining a mapping relationship between the attenuation rate of the photovoltaic module and the change status of each characteristic parameter of the power generation model through preliminary testing, obtaining power generation characteristic parameters such as predicted power generation according to the acquired environmental data and the photovoltaic power generation prediction algorithm, and obtaining an estimated attenuation rate based on the photovoltaic module power generation model according to a comparison between the obtained predicted power generation characteristic parameters and the actual power generation model characteristic parameters, as well as a mapping relationship between the attenuation rate of the photovoltaic module and the change status of each characteristic parameter of the power generation model; The comparison between the obtained predicted power generation characteristic parameters and the actual power generation model characteristic parameters is specifically: the deviation of the cumulative power generation, power conversion rate, power generation peak, power generation time series variance, and power generation time series sample entropy in multiple time scales under different weather environments in the zero attenuation prediction power generation model and the real-time power generation model.

[0039] S14, according to the estimated attenuation rate of the photovoltaic components obtained by the two models and the assigned weight coefficients, a weighted sum is calculated to obtain the comprehensive attenuation amplitude of the photovoltaic components; the assigned weight coefficients are initially set to give the same weight to all models.

[0040] S15, according to the obtained comprehensive attenuation amplitude, and the mapping relationship between the attenuation rate of the photovoltaic module and the change status of each characteristic parameter of the model, in the photovoltaic module circuit model and the power generation model, the error function value of each model parameter is calculated, and according to the size of the error function value, the calculation weight coefficients of the two models are corrected and updated, which specifically includes the following steps: S151, according to the comprehensive attenuation amplitude and the attenuation rate estimated by the real-time circuit model, obtain the difference between the equivalent parallel resistance value and the equivalent series resistance value in the corresponding equivalent circuit model, calculate the simple weighted sum of the absolute value of the difference, and obtain the error function value of the equivalent circuit model; S152, according to the comprehensive attenuation amplitude and the attenuation rate estimated by the real-time power generation model, obtain the cumulative power generation, power conversion rate, power generation peak value, power generation time series variance, and power generation time series sample entropy of multiple time scales under different weather environments in the corresponding power generation model, calculate the simple weighted sum of the absolute value of the difference, and obtain the error function value of the equivalent circuit model; S153, normalize the error function value sequence and calculate the model update weight coefficient ω' z

[0041] S2, the component-side monitoring main control module collects monitoring data in real time; S3, the cloud server online obtains the monitoring data transmitted by the component-side monitoring main control module; S4, the comprehensive attenuation rate prediction model on the cloud server uses the monitoring data to calculate and evaluate the attenuation status of the PV modules; S5, the cloud server saves the original data and calculation results of the comprehensive attenuation rate prediction model.

[0042] Embodiment 3: This embodiment improves the power supply mode and signal transmission of the component-side monitoring main control module on the basis of embodiment 2, and proposes an online evaluation system for photovoltaic component attenuation. Figure 1, including a photovoltaic component attenuation monitoring module, a grid-connected inverter, a cloud server and a photovoltaic line. The photovoltaic component attenuation monitoring modules are provided in a plurality and are connected in series on the photovoltaic line. The grid-connected inverter is electrically connected to the photovoltaic component attenuation monitoring module, the cloud server exchanges data with the photovoltaic component attenuation monitoring module, the photovoltaic component attenuation monitoring module obtains the status information of the photovoltaic component in real time, and transmits the status information of the photovoltaic component obtained in real time to the cloud server, the cloud server obtains the status information online in real time, the cloud server is provided with a comprehensive attenuation rate prediction model, the comprehensive attenuation rate prediction model is a combined prediction model based on an equivalent circuit model and a power generation model, and the comprehensive attenuation rate prediction model performs online evaluation on the status information obtained online in real time.

[0043] The present invention is based on a photovoltaic module attenuation multi-model online evaluation method based on a photovoltaic power generation model that considers characteristic parameters and a photovoltaic equivalent circuit model that considers multiple environmental factors. The method takes into account the influence of internal aging factors of photovoltaic modules and external environmental factors, and further improves the accuracy of photovoltaic module attenuation evaluation through real-time data monitoring and analysis technology. The present invention is based on a photovoltaic module attenuation evaluation system that includes module-side data collection and cloud computing. The data collected by the module-side monitoring module is calculated and coordinated between the cloud processing module and the cloud server storage module, which is convenient for real-time online updating and remote evaluation of the attenuation status of the entire photovoltaic array and local single photovoltaic modules.

[0044] refer to Figure 2 The photovoltaic module attenuation monitoring module of the present invention includes a module-side monitoring main control module, a wireless communication module, a current acquisition module, a voltage acquisition module and a state information acquisition sensor. The wireless communication module is used to exchange data with a cloud server and is built into the module-side monitoring main control module. The current acquisition module, the voltage acquisition module and the state information acquisition sensor are all electrically connected to the module-side monitoring main control module. The state information acquisition sensor of the present invention includes a solar irradiance sensor, a wind speed and direction sensor and a temperature and humidity sensor.

[0045] The component-side monitoring main control module of the present invention is electrically connected to a DC / DC power supply module, and the DC / DC power supply module is electrically connected to the photovoltaic power generation panel of the photovoltaic component, and the component-side monitoring main control module is powered by the photovoltaic power generation panel through the DC / DC power supply module. The output end of the wireless communication module of the present invention is connected to a power amplifier module for signal amplification.

[0046] This embodiment also provides a photovoltaic module attenuation online evaluation method, using the photovoltaic module attenuation online evaluation system mentioned above, referring to Figure 3 , including the following steps: S1, on the cloud server, build a comprehensive attenuation rate prediction model based on the equivalent circuit model and the power generation model; refer to Figure 4 , the S1 specifically includes the following steps: S11, establishing a photovoltaic module equivalent circuit model based on various power generation data, environmental data and historical data of the photovoltaic module obtained online, and updating the model parameters in real time based on the parameter identification algorithm, establishing a photovoltaic module power generation model based on multiple power generation characteristic parameters obtained by calculation, and updating the model characteristic parameters in real time based on the acquired power generation data; The photovoltaic module equivalent circuit model is established based on various power generation data, environmental data and historical data of the photovoltaic modules obtained online, specifically: the voltage, current, solar radiation value, wind speed, wind direction, temperature and radiation angle of a single photovoltaic module are obtained, based on the single diode model, the nonlinear influence of temperature effect, solar irradiance and attenuation rate on the equivalent circuit model is integrated, voltage and current correction factors are introduced, and an equivalent circuit model of the photovoltaic module including factors such as temperature, solar irradiance and module aging is established.

[0047] The calculation obtains multiple power generation characteristic parameters and establishes a photovoltaic module power generation model, specifically: based on the obtained solar radiation value, wind speed, wind direction, temperature, radiation angle and other environmental factors, a photovoltaic power generation model is established based on power generation indicators such as the cumulative amount of power generation, power conversion rate, power generation peak value, power generation time series variance, power generation time series sample entropy and other power generation indicators at multiple time scales under different weather environments.

[0048] S12, through pre-testing, obtain the mapping relationship between the attenuation rate of the photovoltaic module and the change status of each parameter of the model, and obtain the estimated attenuation rate based on the equivalent circuit model of the photovoltaic module according to the result of multi-parameter comparison between the ideal circuit model and the real-time updated circuit model, as well as the mapping relationship between the attenuation rate of the photovoltaic module and the change status of each parameter of the model; the multi-parameter comparison between the ideal circuit model and the real-time updated circuit model is specifically: the deviation of the equivalent parallel resistance value and the equivalent series resistance value in the zero attenuation ideal circuit model and the real-time circuit model.

[0049] S13, obtaining a mapping relationship between the attenuation rate of the photovoltaic module and the change status of each characteristic parameter of the power generation model through preliminary testing, obtaining power generation characteristic parameters such as predicted power generation according to the acquired environmental data and the photovoltaic power generation prediction algorithm, and obtaining an estimated attenuation rate based on the photovoltaic module power generation model according to a comparison between the obtained predicted power generation characteristic parameters and the actual power generation model characteristic parameters, as well as a mapping relationship between the attenuation rate of the photovoltaic module and the change status of each characteristic parameter of the power generation model; The comparison between the obtained predicted power generation characteristic parameters and the actual power generation model characteristic parameters is specifically: the deviation of the cumulative power generation, power conversion rate, power generation peak, power generation time series variance, and power generation time series sample entropy in multiple time scales under different weather environments in the zero attenuation prediction power generation model and the real-time power generation model.

[0050] S14, according to the estimated attenuation rate of the photovoltaic components obtained by the two models and the assigned weight coefficients, a weighted sum is calculated to obtain the comprehensive attenuation amplitude of the photovoltaic components; the assigned weight coefficients are initially set to give the same weight to all models.

[0051] S15, according to the obtained comprehensive attenuation amplitude, and the mapping relationship between the attenuation rate of the photovoltaic module and the change status of each characteristic parameter of the model, in the photovoltaic module circuit model and the power generation model, the error function value of each model parameter is calculated, and according to the size of the error function value, the calculation weight coefficients of the two models are corrected and updated, which specifically includes the following steps: S151, according to the comprehensive attenuation amplitude and the attenuation rate estimated by the real-time circuit model, obtain the difference between the equivalent parallel resistance value and the equivalent series resistance value in the corresponding equivalent circuit model, calculate the simple weighted sum of the absolute value of the difference, and obtain the error function value of the equivalent circuit model; S152, according to the comprehensive attenuation amplitude and the attenuation rate estimated by the real-time power generation model, obtain the cumulative power generation, power conversion rate, power generation peak value, power generation time series variance, and power generation time series sample entropy of multiple time scales under different weather environments in the corresponding power generation model, calculate the simple weighted sum of the absolute value of the difference, and obtain the error function value of the equivalent circuit model; S153, normalize the error function value sequence and calculate the model update weight coefficient ω' z

[0052] S2, the component-side monitoring main control module collects monitoring data in real time; S3, the cloud server online obtains the monitoring data transmitted by the component-side monitoring main control module; S4, the comprehensive attenuation rate prediction model on the cloud server uses the monitoring data to calculate and evaluate the attenuation status of the PV modules; S5, the cloud server saves the original data and calculation results of the comprehensive attenuation rate prediction model.

Claims

1. A photovoltaic module attenuation online assessment system, characterized in that: It includes several photovoltaic component attenuation monitoring modules connected in series on the photovoltaic line, the photovoltaic component attenuation monitoring modules are connected to the grid-connected inverter, the photovoltaic component attenuation monitoring modules exchange data with the cloud server, the photovoltaic component attenuation monitoring modules obtain the status information of the photovoltaic components and transmit it to the cloud server, the cloud server is provided with a comprehensive attenuation rate prediction model based on the equivalent circuit model and the power generation model, and the comprehensive attenuation rate prediction model performs online evaluation on the status information.

2. A photovoltaic module attenuation online assessment system according to claim 1, characterized in that: The photovoltaic component attenuation monitoring module includes a component-side monitoring main control module, which has a built-in wireless communication module for exchanging data with a cloud server, the component-side monitoring main control module is electrically connected to a current acquisition module and a voltage acquisition module, and the component-side monitoring main control module is electrically connected to a status information acquisition sensor.

3. A photovoltaic module attenuation online assessment system according to claim 2, characterized in that: The state information collection sensor includes a wind speed and direction sensor, a solar irradiance sensor, and a temperature and humidity sensor.

4. The photovoltaic module attenuation online assessment system according to claim 2, characterized in that: The component-side monitoring main control module is electrically connected to the photovoltaic power generation panel of the photovoltaic component through a DC / DC power supply module, and the photovoltaic power generation panel supplies power to the component-side monitoring main control module.

5. The photovoltaic module attenuation online assessment system according to claim 3, characterized in that: The output end of the wireless communication module is connected to a power amplification module.

6. A photovoltaic module attenuation online evaluation method, using a photovoltaic module attenuation online evaluation system according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1, building a comprehensive attenuation rate prediction model based on an equivalent circuit model and a power generation model on a cloud server; S2, the component-side monitoring main control module collects monitoring data; S3, the component-side monitoring control module transmits the monitoring data to the cloud server; S4, the comprehensive attenuation rate prediction model calculates and evaluates the attenuation status of the PV module based on the monitoring data; S5, the cloud server saves the calculation results of the comprehensive attenuation rate prediction model and the original data.

7. A photovoltaic module attenuation online evaluation method according to claim 6, characterized in that: The S1 specifically includes the following steps: S11, establishing a photovoltaic module equivalent circuit model and a photovoltaic module power generation model; S12, testing the photovoltaic module equivalent circuit model to obtain a mapping relationship between the photovoltaic module attenuation rate and the change status of each parameter of the photovoltaic module equivalent circuit model, and then obtaining an estimated attenuation rate based on the photovoltaic module equivalent circuit model; S13, testing the photovoltaic module power generation model to obtain a mapping relationship between the photovoltaic module attenuation rate and the change status of each characteristic parameter of the photovoltaic module power generation model, and then obtaining an estimated attenuation rate based on the photovoltaic module power generation model; S14, presetting weight coefficients of the photovoltaic module equivalent circuit model and the photovoltaic module power generation model, and calculating the comprehensive attenuation amplitude of the photovoltaic module by weighted summation; S15, calculating the error function value of the photovoltaic module equivalent circuit model and the photovoltaic module power generation model, correcting and updating the weight coefficients of the photovoltaic module equivalent circuit model and the photovoltaic module power generation model through the error function value, and obtaining the comprehensive attenuation rate prediction model.

8. A photovoltaic module attenuation online evaluation method according to claim 7, characterized in that: In the S11, an equivalent circuit model of the photovoltaic module is established according to various power generation data, historical data and environmental data of the photovoltaic module acquired online.

9. The photovoltaic module attenuation online evaluation method according to claim 7, characterized in that: In the S11, a photovoltaic module power generation model is established according to a number of power generation characteristic parameters.

10. The photovoltaic module attenuation online evaluation method according to claim 7, characterized in that: The calculation process of updating the weight coefficient in S15 is as follows: ω′ z To update the weight coefficient.

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