Photovoltaic inverter total efficiency evaluation system and method thereof
By combining multiple sensors and intelligent analysis units, the problems of incompleteness and real-time performance evaluation of photovoltaic inverters are solved, enabling efficient and intelligent performance assessment and prediction, and improving the operation and maintenance efficiency and reliability of the equipment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU YUNBANG ELECTRONIC TECH CO LTD
- Filing Date
- 2025-12-04
- Publication Date
- 2026-04-28
AI Technical Summary
Existing photovoltaic inverter evaluation methods rely on data from a single sensor, neglecting environmental factors, resulting in incomplete evaluations and an inability to reflect equipment performance in real time, lacking intelligent analysis and prediction capabilities.
By employing multi-sensor collaborative operation, combined with data processing modules and intelligent analysis units, including machine learning and deep learning algorithms, a comprehensive and efficient evaluation of photovoltaic inverters can be achieved, providing real-time performance assessment and future trend prediction.
It enables comprehensive performance evaluation of photovoltaic inverters, improves response speed and prediction accuracy, provides automated maintenance recommendations, reduces maintenance costs, and extends equipment life.
Smart Images
Figure CN121933802A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of performance evaluation technology for photovoltaic inverters, and in particular to a system and method for evaluating the full efficiency of photovoltaic inverters. Background Technology
[0002] Photovoltaic inverters are key components in solar photovoltaic systems, and their performance directly affects the power generation efficiency and stability of the entire system. Traditional evaluation methods mainly rely on a single metric: inverter conversion efficiency. While this method provides an assessment of the initial performance of the equipment, it has several limitations:
[0003] Existing technologies often rely on single sensors to collect limited data types, such as voltage or current, neglecting the impact of environmental factors like temperature on equipment performance. This limits the comprehensiveness and accuracy of evaluations. Existing systems typically suffer from delays in data processing and analysis, failing to provide an accurate reflection of the inverter's real-time operating status and thus hindering timely responses to sudden changes in system performance. Most traditional systems lack integrated intelligent algorithms to analyze historical data, thus failing to predict future performance trends or automatically propose optimization suggestions, limiting their application in preventative maintenance and performance optimization. Summary of the Invention
[0004] To address the above shortcomings, this invention proposes a photovoltaic inverter full-efficiency evaluation system and method. By introducing multiple evaluation indicators, multi-sensor collaborative operation, efficient data processing, and intelligent analysis technology, it achieves a comprehensive, efficient, and intelligent evaluation of photovoltaic inverters.
[0005] To achieve the above objectives, the present invention employs the following technical solution: A photovoltaic inverter full efficiency evaluation system, the system comprising: Multiple sensors are used to collect voltage, current, and temperature data during the operation of the photovoltaic inverter; A data processing module, connected to the plurality of sensors, is used to process and analyze the collected data. The data processing module includes a data acquisition unit, a data storage unit, a data analysis unit, and an intelligent analysis unit. The display module, connected to the data processing module, is used to display the comprehensive efficiency evaluation results of the photovoltaic inverter; A communication module, connected to the data processing module, is used to transmit the processed data to a remote server; The intelligent analysis unit is used to perform intelligent analysis and prediction of the operating status of the photovoltaic inverter based on historical data and current status. As a preferred embodiment of the present invention, the plurality of sensors include: Voltage sensors are used to collect the input and output voltages of photovoltaic inverters. Current sensor, used to collect input and output current of photovoltaic inverter; Temperature sensor used to collect the operating temperature of photovoltaic inverter.
[0006] As a preferred embodiment of the present invention, the data processing module further includes: The data acquisition unit is used to acquire data from the multiple sensors in real time; A data storage unit is used to store the data collected by the data acquisition unit. The data analysis unit is used to perform preliminary analysis and processing on the data stored in the data storage unit.
[0007] As a preferred embodiment of the present invention, the intelligent analysis unit includes the following configuration: A machine learning algorithm is used to receive voltage, current, and temperature data collected by the multiple sensors during the operation of the photovoltaic inverter. The data preprocessing module is used to filter and format the received data to adapt it to machine learning algorithms; The performance evaluation model, trained based on historical data, is used to analyze current data and predict the future performance trend of photovoltaic inverters. The optimization module is used to automatically propose maintenance and tuning suggestions based on the output of the performance evaluation model. The optimization algorithm includes the following detailed steps: Initialize the parameters of the policy network and the evaluation network, and set the initial parameters of the target network to be the same as those of the policy network and the evaluation network. Initialize the experience replay pool D; At each time step t, obtain the current state S from the environment. t The input is fed into the policy network to obtain action a. t ; Perform action a t Observe the next state S ｔ+1 and instant rewards t , will (s t ,a t ,r t ,s t+1 Store the samples in the experience replay pool; randomly draw a small batch of samples from the experience replay pool to construct the target value y. i for: ; in, and These are the target evaluation network and the target policy network, respectively. As a discount factor, The action output by the target policy network; The evaluation network parameters are updated using mini-batch samples, and the loss function L is minimized as follows: ; Among them, Q θ To evaluate the network, S i and a i These represent states and actions, respectively, and N is the number of mini-batch samples. Update the policy network parameters to maximize the output of the evaluation network: ; in, The gradient of the policy network, To evaluate the gradient of the network to the action; Soft update target network parameters: ; in, This is the soft update coefficient, which controls the update rate of the target network; Repeat the above steps until the policy converges.
[0008] As a preferred embodiment of the present invention, the intelligent analysis unit further includes: Deep learning technology is used to receive data collected by the multiple sensors, perform in-depth feature extraction, and automatically identify key factors affecting the performance of photovoltaic inverters. A real-time performance update mechanism is used to update the performance evaluation model in real time based on the received data, so as to improve the evaluation accuracy and prediction reliability of the model. The report generation module is used to generate a detailed performance evaluation report based on the performance evaluation model and the output of the optimization module, including efficiency trend prediction, potential risk analysis and operation optimization strategies. The improved algorithm optimization steps are as follows: Determine the state set and action set. The state set includes voltage, current, and temperature data, and the action set includes adjusting inverter parameters. Define and initialize the reward function, taking into account efficiency, stability, and reliability. Initialization and training of the online policy network and evaluation network, including weight initialization and establishment of the experience replay pool; Perform the following operations at each time step: Obtain the optimal action for the current state from the policy network; Perform actions to gain new status and rewards; Store status, actions, rewards, and new statuses in the experience replay pool; A small batch of samples is randomly drawn from the experience replay pool for training; Update the evaluation network to minimize prediction error; Update the policy network to maximize policy value; Perform a soft update on the target network; Repeat the above steps until the system performance indicators converge and a stable optimization strategy is formed.
[0009] As a preferred embodiment of the present invention, the communication module adopts wireless communication technology, including but not limited to Wi-Fi, Bluetooth and cellular networks.
[0010] As a preferred embodiment of the present invention, the display module includes: A display screen is used to display the comprehensive efficiency evaluation results; The user interface allows users to input and query data.
[0011] As a preferred embodiment of the present invention, the intelligent analysis unit uses the following algorithm formula for comprehensive efficiency evaluation: ; Among them, E 综合 For overall efficiency, E 转换 For conversion efficiency, E 能量利用 For energy utilization efficiency, S 稳定性 For system stability, α, β, and γ are weighting coefficients, and α+β+γ=1.
[0012] As a preferred embodiment of the present invention, the intelligent analysis unit evaluates the stability of the photovoltaic inverter based on the following formula: ; Where, S 稳定性 For system stability, n is the number of measurements, and V i Let V be the voltage value measured in the i-th measurement. ref This is the reference voltage value.
[0013] As a preferred embodiment of the present invention, a method for evaluating the full efficiency of a photovoltaic inverter includes: Data acquisition steps: Collect voltage, current, and temperature data of the photovoltaic inverter during operation using the multiple sensors; Data processing steps: The collected data is transmitted to the data processing module for processing and analysis; Data storage step: The processed and analyzed data is stored in the data storage unit; Data analysis steps: The stored data is analyzed by the data analysis unit to generate the comprehensive efficiency evaluation result; Display step: Display the comprehensive efficiency evaluation results through the display module; Communication steps: The processed data is transmitted to the remote server through the communication module.
[0014] Compared with existing technologies, the advantages of this invention are as follows: By combining data acquisition from multiple sensors, this invention can provide comprehensive information on various performance aspects of the photovoltaic inverter, such as voltage, current, and temperature. This comprehensive evaluation considers not only conversion efficiency but also energy utilization and system stability, thus providing a more comprehensive performance assessment. Utilizing an intelligent analysis unit and advanced machine learning algorithms, this invention can process the acquired data in real time. This allows the system to not only reflect the current operating status but also predict future performance changes based on historical data and current data trends. The intelligent analysis unit can automatically provide maintenance and optimization suggestions based on the output of the performance evaluation model. This intelligent function makes inverter maintenance more accurate and timely, effectively extending the equipment's lifespan and reducing maintenance costs. This system includes a user-friendly display module and interface, allowing users to intuitively view evaluation results and performance reports. Through these intuitive data expressions, operators can more easily understand the system status and make corresponding operational decisions. By using advanced wireless communication technologies such as Wi-Fi, Bluetooth, and cellular networks, this system supports remote data transmission and monitoring functions. This allows system maintenance personnel to receive and analyze data in real time even when not on-site, optimizing the management and operational efficiency of the photovoltaic power generation system. Attached Figure Description
[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a system structure diagram of an embodiment of the present invention; Figure 2 This is a functional block diagram of the intelligent analysis unit according to an embodiment of the present invention; Figure 3 This is a flowchart of the method in an embodiment of the present invention. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.
[0017] Example 1 like Figure 1As shown, this is an embodiment of the present invention, which provides a photovoltaic inverter full efficiency evaluation system, the system comprising: Multiple sensors are used to collect voltage, current, and temperature data during the operation of the photovoltaic inverter; The plurality of sensors include: Voltage sensors are used to collect the input and output voltages of photovoltaic inverters. Current sensor, used to collect input and output current of photovoltaic inverter; Temperature sensor used to collect the operating temperature of photovoltaic inverter.
[0018] In one embodiment, a data processing module is connected to the plurality of sensors and is used to process and analyze the collected data. The data processing module includes a data acquisition unit, a data storage unit, a data analysis unit, and an intelligent analysis unit. The data processing module further includes: The data acquisition unit is used to acquire data from the multiple sensors in real time; A data storage unit is used to store the data collected by the data acquisition unit. The data analysis unit is used to perform preliminary analysis and processing on the data stored in the data storage unit.
[0019] Furthermore, such as Figure 2 As shown, the intelligent analysis unit includes the following configuration: A machine learning algorithm is used to receive voltage, current, and temperature data collected by the multiple sensors during the operation of the photovoltaic inverter. The data preprocessing module is used to filter and format the received data to adapt it to machine learning algorithms; The performance evaluation model, trained based on historical data, is used to analyze current data and predict the future performance trend of photovoltaic inverters. The optimization module is used to automatically propose maintenance and tuning suggestions based on the output of the performance evaluation model. The optimization algorithm includes the following detailed steps: Initialize the parameters of the policy network and the evaluation network, and set the initial parameters of the target network to be the same as those of the policy network and the evaluation network. Initialize the experience replay pool D; At each time step t, obtain the current state S from the environment. t The input is fed into the policy network to obtain action a. t ; Perform action a t Observe the next state S ｔ+1 and instant rewards t , will (s t ,a t ,rt ,s t+1 Store the samples in the experience replay pool; randomly draw a small batch of samples from the experience replay pool to construct the target value y. i for: ; in, and These are the target evaluation network and the target policy network, respectively. As a discount factor, The action output by the target policy network; The evaluation network parameters are updated using mini-batch samples, and the loss function L is minimized as follows: ; Among them, Q θ To evaluate the network, S i and a i These represent states and actions, respectively, and N is the number of mini-batch samples. Update the policy network parameters to maximize the output of the evaluation network: ; in, The gradient of the policy network, To evaluate the gradient of the network to the action; Soft update target network parameters: ; in, This is the soft update coefficient, which controls the update rate of the target network; Repeat the above steps until the policy converges.
[0020] Furthermore, the intelligent analysis unit further includes: Deep learning technology is used to receive data collected by the multiple sensors, perform in-depth feature extraction, and automatically identify key factors affecting the performance of photovoltaic inverters. A real-time performance update mechanism is used to update the performance evaluation model in real time based on the received data, so as to improve the evaluation accuracy and prediction reliability of the model. The report generation module is used to generate a detailed performance evaluation report based on the performance evaluation model and the output of the optimization module, including efficiency trend prediction, potential risk analysis and operation optimization strategies. The improved algorithm optimization steps are as follows: Determine the state set and action set. The state set includes voltage, current, and temperature data, and the action set includes adjusting inverter parameters. Define and initialize the reward function, taking into account efficiency, stability, and reliability. Initialization and training of the online policy network and evaluation network, including weight initialization and establishment of the experience replay pool; Perform the following operations at each time step: Obtain the optimal action for the current state from the policy network; Perform actions to gain new status and rewards; Store status, actions, rewards, and new statuses in the experience replay pool; A small batch of samples is randomly drawn from the experience replay pool for training; Update the evaluation network to minimize prediction error; Update the policy network to maximize policy value; Perform a soft update on the target network; Repeat the above steps until the system performance indicators converge and a stable optimization strategy is formed.
[0021] In one embodiment, a display module, connected to the data processing module, is used to display the comprehensive efficiency evaluation results of the photovoltaic inverter. The display module includes: A display screen is used to display the comprehensive efficiency evaluation results; The user interface allows users to input and query data.
[0022] Furthermore, the intelligent analysis unit uses the following algorithm formula for comprehensive efficiency evaluation: ; Among them, E 综合 For overall efficiency, E 转换 For conversion efficiency, E 能量利用 For energy utilization efficiency, S 稳定性 For system stability, α, β, and γ are weighting coefficients, and α+β+γ=1.
[0023] Furthermore, the intelligent analysis unit evaluates the stability of the photovoltaic inverter based on the following formula: ; Where, S 稳定性 For system stability, n is the number of measurements, and V i Let V be the voltage value measured in the i-th measurement. ref This is the reference voltage value.
[0024] In one embodiment, a communication module is connected to the data processing module and is used to transmit the processed data to a remote server. The communication module employs wireless communication technologies, including but not limited to Wi-Fi, Bluetooth, and cellular networks.
[0025] Example 2 like Figure 3 As shown, another embodiment of the present invention provides a method for evaluating the full efficiency of a photovoltaic inverter, the method comprising: S1. Data acquisition steps: Collect voltage, current, and temperature data of the photovoltaic inverter during operation through the multiple sensors; S2, Data Processing Step: This step involves transmitting the collected data to the data processing module for processing and analysis. S3. Data storage step: Store the processed and analyzed data in the data storage unit; S4. Data analysis step: The stored data is analyzed by the data analysis unit to generate the comprehensive efficiency evaluation result; S5. Display Step: Display the comprehensive efficiency evaluation results through the display module; S6. Communication steps: The processed data is transmitted to the remote server through the communication module.
[0026] Example 3 Another embodiment of the present invention provides specific experimental data as follows:
[0027] Data Analysis Description Response time: The response time of the system of the present invention is greatly shortened from 15 seconds to 3 seconds, which significantly improves the operating efficiency of the system and enables the inverter to adapt to environmental changes more quickly.
[0028] Prediction accuracy: By introducing advanced machine learning algorithms, the prediction accuracy of this system has been improved from 70% to 95%, enhancing its ability to predict equipment failures and analyze performance trends.
[0029] System stability score: The improved stability score reflects the advantages of the system of the present invention in monitoring and maintaining the stability of photovoltaic inverters, which helps to extend equipment life and reduce unexpected downtime.
[0030] Overall efficiency score: The improvement in the overall efficiency score demonstrates the high efficiency of the system of the present invention when integrating multiple performance indicators (including voltage, current and temperature, etc.) for evaluation.
[0031] Through the experimental data and comparative analysis above, the data in the table clearly demonstrates the significant advantages of this invention in improving the response speed, accuracy, stability, and overall efficiency of photovoltaic inverter evaluation systems. These improvements not only enhance system performance but also provide substantial technical support for the management and maintenance of photovoltaic inverters, meeting market demands for efficient and reliable photovoltaic equipment. These results strengthen the market application potential and practical value of this invention, making it an innovative breakthrough in the field of photovoltaic inverter technology.
[0032] This invention employs a multi-sensor system, extending beyond traditional voltage and current measurements to include environmental parameters such as temperature, enabling comprehensive monitoring of the photovoltaic inverter's operating status. By integrating this multi-dimensional data, the system provides a more comprehensive and accurate performance assessment, surpassing the limitations of existing technologies that rely primarily on a single data source. Utilizing advanced data processing modules and intelligent analysis units, the system achieves real-time data processing and analysis. The introduction of machine learning and deep learning algorithms allows the system to not only process real-time data but also predict performance trends and potential problems based on historical data, a feature uncommon in existing technologies. The intelligent analysis unit of this invention can automatically generate maintenance and tuning recommendations based on a comprehensive efficiency model. This automated function significantly improves operational efficiency, assists maintenance personnel in decision support, and reduces human error and equipment failure rates. The system features a user-friendly interface, allowing operators to easily access and interpret efficiency assessment results. Through remote monitoring and communication modules, users can obtain equipment status and performance reports in real time without geographical limitations, improving system accessibility and convenience.
[0033] Experimental data demonstrates that this invention significantly outperforms existing technologies in terms of response time, prediction accuracy, system stability, and overall efficiency. These performance improvements not only enhance the operating efficiency of photovoltaic inverters but also improve energy conversion efficiency and the overall reliability of the system.
[0034] In summary, this invention, through multiple technological innovations, achieves a comprehensive, efficient, and intelligent evaluation of photovoltaic inverters, overcoming the shortcomings of existing technologies in areas such as data acquisition, real-time performance, intelligent analysis, and user interaction. These innovations give this invention significant creative and practical value in the field of photovoltaic inverter evaluation.
[0035] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.
[0036] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.
[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A photovoltaic inverter full efficiency evaluation system, characterized in that, The system includes: Multiple sensors are used to collect voltage, current, and temperature data during the operation of the photovoltaic inverter; A data processing module, connected to the plurality of sensors, is used to process and analyze the collected data. The data processing module includes a data acquisition unit, a data storage unit, a data analysis unit, and an intelligent analysis unit. The display module, connected to the data processing module, is used to display the comprehensive efficiency evaluation results of the photovoltaic inverter; A communication module, connected to the data processing module, is used to transmit the processed data to a remote server; The intelligent analysis unit is used to intelligently analyze and predict the operating status of the photovoltaic inverter based on historical data and current status.
2. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The plurality of sensors include: Voltage sensors are used to collect the input and output voltages of photovoltaic inverters. Current sensors are used to collect the input and output current of photovoltaic inverters; Temperature sensor used to collect the operating temperature of photovoltaic inverter.
3. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The data processing module further includes: The data acquisition unit is used to acquire data from the multiple sensors in real time; A data storage unit is used to store the data collected by the data acquisition unit. The data analysis unit is used to perform preliminary analysis and processing on the data stored in the data storage unit.
4. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The intelligent analysis unit includes the following configuration: A machine learning algorithm is used to receive voltage, current, and temperature data collected by the multiple sensors during the operation of the photovoltaic inverter. The data preprocessing module is used to filter and format the received data to adapt it to machine learning algorithms; The performance evaluation model, trained based on historical data, is used to analyze current data and predict the future performance trend of photovoltaic inverters. The optimization module is used to automatically propose maintenance and tuning suggestions based on the output of the performance evaluation model. The optimization algorithm includes the following detailed steps: Initialize the parameters of the policy network and the evaluation network, and set the initial parameters of the target network to be the same as those of the policy network and the evaluation network. Initialize the experience replay pool D; At each time step t, obtain the current state S from the environment. t The input is fed into the policy network to obtain action a. t ; Perform action a t Observe the next state S ｔ+1 and instant rewards t , will (s t ,a t ,r t ,s t+1 Store the samples in the experience replay pool; randomly draw a small batch of samples from the experience replay pool to construct the target value y. i for: in, and These are the target evaluation network and the target policy network, respectively. As a discount factor, The action output by the target policy network; The evaluation network parameters are updated using mini-batch samples, and the loss function L is minimized as follows: Among them, Q θ To evaluate the network, S i and a i These represent states and actions, respectively, and N is the number of mini-batch samples. Update the policy network parameters to maximize the output of the evaluation network: in, The gradient of the policy network, To evaluate the gradient of the network to the action; Soft update target network parameters: ； in, This is the soft update coefficient, which controls the update rate of the target network; Repeat the above steps until the policy converges.
5. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The intelligent analysis unit further includes: Deep learning technology is used to receive data collected by the multiple sensors, perform in-depth feature extraction, and automatically identify key factors affecting the performance of photovoltaic inverters. A real-time performance update mechanism is used to update the performance evaluation model in real time based on the received data, so as to improve the evaluation accuracy and prediction reliability of the model. The report generation module is used to generate a detailed performance evaluation report based on the performance evaluation model and the output of the optimization module, including efficiency trend prediction, potential risk analysis, and operational optimization strategies. The improved algorithm optimization steps are as follows: Determine the state set and action set. The state set includes voltage, current, and temperature data, and the action set includes adjusting inverter parameters. Define and initialize the reward function, taking into account efficiency, stability, and reliability. Initialization and training of the online policy network and evaluation network, including weight initialization and establishment of the experience replay pool; Perform the following operations at each time step: Obtain the optimal action for the current state from the policy network; Perform actions to gain new status and rewards; Store status, actions, rewards, and new statuses in the experience replay pool; A small batch of samples is randomly drawn from the experience replay pool for training; Update the evaluation network to minimize prediction error; Update the policy network to maximize policy value; Perform a soft update on the target network; Repeat the above steps until the system performance indicators converge and a stable optimization strategy is formed.
6. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The communication module employs wireless communication technologies, including but not limited to Wi-Fi, Bluetooth, and cellular networks.
7. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The display module includes: A display screen is used to display the comprehensive efficiency evaluation results; The user interface allows users to input and query data.
8. The photovoltaic inverter full efficiency evaluation system according to claim 1, characterized in that, The intelligent analysis unit uses the following algorithm formula for comprehensive efficiency evaluation: Among them, E 综合 For overall efficiency, E 转换 For conversion efficiency, E 能量利用 For energy utilization efficiency, S 稳定性 For system stability, α, β, and γ are weighting coefficients, and α+β+γ=1.
9. The photovoltaic inverter full efficiency evaluation system according to claim 1, wherein the intelligent analysis unit evaluates the stability of the photovoltaic inverter based on the following formula: Where, S 稳定性 For system stability, n is the number of measurements, and V i Let V be the voltage value measured in the i-th measurement. ref This is the reference voltage value.
10. An evaluation method based on the photovoltaic inverter full efficiency evaluation system according to any one of claims 1-9, characterized in that, The method includes: Data acquisition steps: Collect voltage, current, and temperature data of the photovoltaic inverter during operation using the multiple sensors; Data processing steps: The collected data is transmitted to the data processing module for processing and analysis; Data storage step: The processed and analyzed data is stored in the data storage unit; Data analysis steps: The stored data is analyzed by the data analysis unit to generate the comprehensive efficiency evaluation result; Display step: Display the comprehensive efficiency evaluation results through the display module; Communication steps: The processed data is transmitted to the remote server through the communication module.