An online monitoring method and system based on the operation health of solar photovoltaic panels

Abstract

The present invention discloses an online monitoring method and system based on the operation health of solar photovoltaic panels, which relates to the field of photovoltaic panels. The system collects the operation data of photovoltaic panels in real time through the installed integrated sensor group, and the data preprocessing unit preprocesses the collected data. Combined with the health status index calculation module and the operation health assessment module, the system can analyze the health status of photovoltaic panels. The health status index calculation module calculates the instantaneous efficiency and fill factor FF of the photovoltaic panel based on the preprocessed operation feature set, and then calculates these parameters in correlation to obtain the health index HI. Then, the first health threshold J is set by the operation health assessment module to conduct a preliminary comparative evaluation with the obtained health index HI, analyze the preliminary health status of the photovoltaic panel, and generate a corresponding early warning mechanism. This real-time monitoring and early warning mechanism helps to quickly respond to and handle photovoltaic panel failures.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic panels, and in particular to an online monitoring method and system based on the health of solar photovoltaic panels. Background Art

[0002] Photovoltaic panels, also known as solar PV panels, are devices that convert sunlight directly into electrical energy. Photovoltaic panels consist of multiple photovoltaic cells, which are usually made of silicon. When sunlight shines on a photovoltaic cell, the energy of the photons excites electrons, which generates an electric current. This process of converting light energy into electrical energy is called the photovoltaic effect. Photovoltaic panel health monitoring refers to the real-time monitoring of the performance, status, and working environment of photovoltaic panels to ensure their normal operation and efficient power generation.

[0003] In the current traditional photovoltaic panel health monitoring system, the single variable evaluation method often leads to misjudgments and missed judgments, especially in extreme weather conditions, when the performance of photovoltaic panels may change significantly. Due to the lack of comprehensive consideration of environmental factors such as temperature, the existing system may issue false alarms when the photovoltaic panels are actually operating well, or fail to detect potential problems in the photovoltaic panels in a timely manner. These misjudgments and missed judgments will lead to increased maintenance costs and even affect the overall efficiency and stability of the photovoltaic power generation system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an online monitoring method and system based on the health of solar photovoltaic panels, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: including a data acquisition module, a feature extraction module, a health status index calculation module, an operation health assessment module and a multivariate health index calculation module;

[0006] The data acquisition module is used to install and deploy several detection points on the solar photovoltaic panel, and install relevant sensor groups to collect the operation data of the solar panel in real time, and then pre-process the collected operation data;

[0007] The feature extraction module is used to extract instantaneous features from the preprocessed operation data, and summarize the extracted instantaneous features to obtain an instantaneous operation feature set;

[0008] The health index calculation module is used to perform preliminary calculations to obtain the instantaneous efficiency S(t) and the fill factor FF of the photovoltaic panel based on the acquired operation feature set, respectively calculate the energy conversion efficiency of the photovoltaic panel at the current working point and the electrical performance of the photovoltaic panel, and then correlate the obtained instantaneous efficiency S(t) and the fill factor FF to obtain the health index HI;

[0009] The operation health assessment module is used to preliminarily compare and assess the preset first health threshold J with the acquired health index HI to analyze the initial health status of the photovoltaic panel;

[0010] The multivariate health index calculation module is used to calculate the temperature influencing factor Tf during the operation of the photovoltaic panel after the preliminary abnormality assessment, obtain the multivariate health index HI2 of the photovoltaic panel, and then perform a secondary comparative evaluation between the preset historical health threshold L and the obtained multivariate health index HI2 to further analyze the health status of the photovoltaic panel.

[0011] Preferably, the data acquisition module includes a data acquisition unit and a data preprocessing unit;

[0012] The data acquisition unit is used to install an integrated sensor group on the photovoltaic panel to detect and collect the operation data of the photovoltaic panel in real time;

[0013] The integrated sensor group includes a current sensor, a voltage sensor, a temperature sensor and a radiation sensor;

[0014] The operating data includes current I, voltage V, temperature T and radiation intensity G;

[0015] The data preprocessing unit is used to preprocess the collected operating data, and the preprocessing method includes removing noise and outliers, and normalizing data of different dimensions to the same range.

[0016] Preferably, the feature extraction module is used to analyze the collected operating data at different times t, and extract the instantaneous features of the current I, voltage V, temperature T and radiation intensity G at different times t to obtain the photovoltaic panel instantaneous current I(t), photovoltaic panel instantaneous voltage V(t), photovoltaic panel instantaneous temperature T(t) and photovoltaic panel instantaneous radiation intensity G(t). At the same time, based on the photovoltaic panel instantaneous current I(t) and the photovoltaic panel instantaneous voltage V(t), power features are extracted to obtain the photovoltaic panel output power Pout(t) and the photovoltaic panel input power Pin(t), and then the extracted instantaneous features and power features are summarized to obtain an operating feature set.

[0017] Preferably, the health status index calculation module includes a photovoltaic instantaneous efficiency calculation unit, an electrical performance calculation unit and a comprehensive calculation unit;

[0018] The instantaneous efficiency calculation unit is used to calculate and obtain the instantaneous efficiency S(t) of photovoltaic according to the extracted operation feature set, and analyze the energy conversion efficiency of the photovoltaic panel at the current working point;

[0019] The instantaneous efficiency S(t) is obtained by the following algorithm formula:

[0020]

[0021] Where A represents the area of ​​the photovoltaic panel.

[0022] Preferably, the electrical performance calculation unit is used to calculate and analyze the electrical performance of the photovoltaic panel by analyzing the voltage V of the photovoltaic panel when it is open circuit and the current I when it is short circuit, so as to obtain the fill factor FF;

[0023] The fill factor FF is obtained by the following algorithm formula;

[0024]

[0025] Where Pm represents the maximum power output of the photovoltaic panel, Voc represents the open circuit voltage of the photovoltaic panel, and Isc represents the short circuit current of the photovoltaic panel.

[0026] Preferably, the comprehensive calculation unit is used to perform dimensionless processing on the instantaneous efficiency S(t) and the fill factor FF, and then perform correlation calculation to obtain the health index HI, so as to conduct a comprehensive analysis on the health of the photovoltaic panel;

[0027] The health index HI is obtained by the following algorithm formula;

[0028] HI=w1·S(t)+w2·FF;

[0029] Wherein, w1 and w2 represent the weight values ​​of instantaneous efficiency S(t) and fill factor FF respectively, and the specific parameters are set by the user according to actual conditions.

[0030] Preferably, the operation health assessment module is used to perform a preliminary comparative assessment between a preset first health threshold J and an acquired health index HI based on the health data of the photovoltaic panel, and analyze the preliminary health status of the photovoltaic panel. The specific assessment scheme is as follows;

[0031] When the health index HI> the first health threshold J, it means that the current photovoltaic panel is healthy;

[0032] When the health index HI ≤ the first health threshold J, it indicates that the current health status of the photovoltaic panel is abnormal, and further detailed evaluation is performed.

[0033] Preferably, the multivariate health index calculation module includes a multivariate health calculation unit and a comprehensive evaluation unit;

[0034] The multivariable health calculation unit includes a temperature variable calculation unit and a multivariable calculation unit;

[0035] The temperature variable calculation unit is used to further analyze the impact of temperature on the performance of the photovoltaic panel when an abnormality is evaluated for the current photovoltaic panel health index HI, and to obtain the temperature impact factor Tf by performing associated calculations by constructing a temperature impact algorithm formula and combining the temperature parameter T detected in real time by the temperature sensor deployed on the photovoltaic panel;

[0036] The temperature influence factor Tf is obtained by the following algorithm formula;

[0037]

[0038] In the formula, T(t) represents the temperature parameter T detected by the sensor at time t, α represents the temperature coefficient, which reflects the influence of temperature on efficiency, and Topt represents the optimal operating temperature;

[0039] The multivariate calculation unit is used to perform correlation calculation based on the acquired health index HI and the temperature influence factor Tf analyzed during the operation of the photovoltaic panel to obtain the multivariate health index HI2;

[0040] The multivariate health index HI2 is obtained by the following algorithm formula;

[0041] HI2=[(a1·HI)+(a2·Tf)]+A;

[0042] In the formula, a1 and a2 represent the preset weight values ​​of the health index HI and the temperature influence factor Tf respectively, and a1+a2=1. The specific value is set by the user, and A represents the correction constant.

[0043] Preferably, the comprehensive evaluation unit is used to construct a historical health threshold L based on the mean of the historical health data of the photovoltaic panel and conduct a secondary comparative evaluation with the obtained multivariate health index HI2, perform a multivariate result analysis on the health of the photovoltaic panel, and generate a corresponding early warning strategy according to the evaluation results. The specific evaluation scheme is as follows;

[0044] When the multivariate health index HI2> the historical health threshold L, it means that the photovoltaic panel is in a healthy state. At this time, the first warning information is generated to prompt the relevant staff to perform regular maintenance on the current photovoltaic panel;

[0045] When the multivariate health index HI2 ≤ the historical health threshold L, it indicates that the photovoltaic panel is in an unhealthy operating state. At this time, a second warning message is generated to prompt relevant staff to repair the current photovoltaic panel immediately.

[0046] An online monitoring method based on the operation health of a solar photovoltaic panel comprises the following steps:

[0047] S1. First, an integrated sensor group is installed on the photovoltaic panel to detect and collect the operation data of the photovoltaic panel in real time, and the collected operation data is pre-processed;

[0048] S2. After preprocessing the operation data, extract the features of the operation data, extract the instantaneous features and power features of the operation data, and summarize them into an operation feature set;

[0049] S3. Calculate the instantaneous efficiency S(t) and fill factor FF of the photovoltaic panel according to the operation feature set, then perform dimensionless processing on the instantaneous efficiency S(t) and fill factor FF and perform correlation calculation to obtain the health index HI, and preset the first health threshold J for comparative evaluation;

[0050] S4, when an abnormality is detected, the influence of temperature on the performance of the photovoltaic panel is analyzed, and the temperature influence factor Tf is calculated. The health index HI and the temperature influence factor Tf are combined to calculate the multivariate health index HI2;

[0051] S5. Finally, based on the mean of the historical health data of the photovoltaic panels, a secondary comparative evaluation is conducted between the historical health threshold L and the obtained multivariate health index HI2, a multivariate result analysis of the health of the photovoltaic panels is performed, and a corresponding early warning strategy is generated based on the evaluation results.

[0052] The present invention provides an online monitoring method and system based on the operation health of solar photovoltaic panels. It has the following beneficial effects:

[0053] (1) The system monitors the operation data of the photovoltaic panel in real time through the data acquisition module and the sensor group. Specifically, the data acquisition module includes a data acquisition unit and a data preprocessing unit. The data acquisition module collects the operation data of the photovoltaic panel in real time through the installed integrated sensor group, and the data preprocessing unit preprocesses the collected data. Combined with the health status index calculation module and the operation health assessment module, the system can analyze the health status of the photovoltaic panel. The health status index calculation module calculates the instantaneous efficiency S(t) and fill factor FF of the photovoltaic panel based on the preprocessed operation feature set, and then performs dimensionless processing and correlation calculation on these parameters to obtain the health index HI. The operation health assessment module analyzes the preliminary health status of the photovoltaic panel by setting the first health threshold J and performing preliminary comparative evaluation with the obtained health index HI, and generates a corresponding early warning mechanism. This real-time monitoring and early warning mechanism helps to quickly respond to and handle photovoltaic panel failures, reduce downtime, and ensure the stable operation of the photovoltaic system. Through the intelligent early warning strategy, the system can generate corresponding early warning information based on the evaluation results, prompting relevant staff to take appropriate maintenance measures.

[0054] (2) The system is responsible for analyzing the operating data at different times t through the instantaneous feature extraction module. By extracting features from the operating data of the photovoltaic panel, the instantaneous features and power features are extracted, and the operating feature set is summarized. The health status index calculation module uses these operating feature sets to first calculate the instantaneous efficiency S(t) and fill factor FF of the photovoltaic panel. The instantaneous efficiency S(t) and fill factor FF are dimensionlessly processed and correlated to obtain the health index HI, and the preset first health threshold J is compared and evaluated. After the multivariate health index calculation module preliminarily evaluates the abnormality, it further combines the temperature influence factor Tf to comprehensively evaluate the health status of the photovoltaic panel. By constructing these algorithms and formulas, the system can more accurately reflect the health status of the photovoltaic panel under different operating environments and provide more accurate and comprehensive analysis results.

[0055] (3) The system uses a comprehensive evaluation unit to set a historical health threshold L based on the historical health data of the photovoltaic panels, conduct a secondary comparative evaluation of the multivariate health index, and generate an intelligent early warning strategy. The system can generate different levels of early warning information based on the evaluation results and guide staff to take corresponding maintenance measures. This intelligent early warning and maintenance optimization mechanism can improve the operating efficiency of photovoltaic panels, extend their service life, reduce maintenance costs, and improve overall economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the process of an online monitoring system based on the health of solar photovoltaic panels according to the present invention;

[0057] Figure 2 The present invention is a schematic diagram of the steps of an online monitoring method based on the health of solar photovoltaic panels. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Example 1

[0060] See also Figure 1 , the present invention provides an online monitoring system based on the operation health of solar photovoltaic panels. To achieve the above purpose, the present invention is implemented through the following technical solutions: including a data acquisition module, a feature extraction module, a health status index calculation module, an operation health assessment module and a multivariate health index calculation module;

[0061] The data acquisition module is used to install and deploy several detection points on the solar photovoltaic panels, and install relevant sensor groups to collect the operation data of the solar panels in real time, and then pre-process the collected operation data;

[0062] The feature extraction module is used to extract instantaneous features from the preprocessed operation data, and summarize the extracted instantaneous features to obtain an instantaneous operation feature set;

[0063] The health index calculation module is used to perform preliminary calculations to obtain the instantaneous efficiency S(t) and fill factor FF of the photovoltaic system based on the acquired operating characteristic set, respectively calculate the energy conversion efficiency of the photovoltaic panel at the current working point and the electrical performance of the photovoltaic panel, and then correlate the acquired instantaneous efficiency S(t) and fill factor FF to obtain the health index HI;

[0064] The operation health assessment module is used to perform a preliminary comparative assessment of the preset first health threshold J and the acquired health index HI to analyze the preliminary health status of the photovoltaic panel;

[0065] The multivariate health index calculation module is used to calculate the temperature influencing factor Tf during the operation of the photovoltaic panel after the preliminary assessment of the abnormality, obtain the multivariate health index HI2 of the photovoltaic panel, and then perform a secondary comparative evaluation between the preset historical health threshold L and the obtained multivariate health index HI2 to further analyze the health status of the photovoltaic panel.

[0066] In this embodiment, the system collects the operation data of the photovoltaic panel in real time through multiple detection points and sensor groups of the data acquisition module, and preprocesses to ensure the accuracy and integrity of the data. This step provides the necessary basis for subsequent feature extraction and health status assessment, and at the same time greatly improves the timeliness and comprehensiveness of data acquisition compared with traditional means. Secondly, the feature extraction module extracts instantaneous features and power features from the preprocessed data for aggregation to form an instantaneous operation feature set. This refined data analysis method can not only accurately capture the dynamic changes of various key indicators of the photovoltaic panel, but also effectively identify potential operational anomalies, which helps to detect problems early and respond quickly. The health index calculation module uses parameters such as instantaneous efficiency S(t) and fill factor FF to calculate the health index HI of the photovoltaic panel. By comparing and evaluating with the preset first health threshold J, the system can quickly judge the health status of the photovoltaic panel and realize the preliminary automated health monitoring and early warning function. This real-time and accuracy far exceeds the traditional regular inspection method, significantly reduces maintenance costs and risks, and improves the stability and reliability of the photovoltaic system. Finally, the multivariate health index calculation module further comprehensively considers the temperature influencing factor Tf to conduct an in-depth assessment of the multi-dimensional health of the photovoltaic panel. By comparing with the historical health threshold L, the system can more comprehensively analyze the health status of photovoltaic panels under different environmental conditions and provide more operational maintenance suggestions and strategies for operation and maintenance personnel. This comprehensive evaluation not only improves monitoring accuracy and fault diagnosis capabilities, but also significantly improves the operating efficiency and long-term stability of the photovoltaic system, bringing users more reliable and efficient energy supply services.

[0067] Example 2

[0068] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes a data acquisition unit and a data preprocessing unit;

[0069] The data acquisition unit is used to install an integrated sensor group on the photovoltaic panel to detect and collect the operation data of the photovoltaic panel in real time;

[0070] The integrated sensor group includes a current sensor, a voltage sensor, a temperature sensor, and a radiation sensor;

[0071] The operating data include current I, voltage V, temperature T and radiation intensity G;

[0072] The data preprocessing unit is used to preprocess the collected operating data. The preprocessing method includes removing noise and outliers and normalizing data of different dimensions to the same range.

[0073] The feature extraction module is used to analyze the collected operation data at different times t, and extract the instantaneous features of the current I, voltage V, temperature T and radiation intensity G at different times t to obtain the photovoltaic panel instantaneous current I(t), photovoltaic panel instantaneous voltage V(t), photovoltaic panel instantaneous temperature T(t) and photovoltaic panel instantaneous radiation intensity G(t). At the same time, based on the photovoltaic panel instantaneous current I(t) and photovoltaic panel instantaneous voltage V(t), power features are extracted to obtain the photovoltaic panel output power Pout(t) and photovoltaic panel input power Pin(t). The extracted instantaneous features and power features are then summarized to obtain an operation feature set.

[0074] In this embodiment, the system uses an integrated sensor group through a data acquisition module to monitor and collect the operation data of the photovoltaic panel in real time. This real-time acquisition capability enables the system to quickly respond to changes in the operating state of the photovoltaic panel, effectively avoiding energy loss and system efficiency reduction caused by failure to detect faults in time. Secondly, the data preprocessing unit preprocesses the collected operation data, including removing noise and outliers, and normalizing data of different dimensions to the same range. This step ensures the quality and consistency of the data, and provides a reliable data basis for subsequent feature extraction and health status assessment. The feature extraction module further analyzes the preprocessed operation data, extracts instantaneous features at different times t, and thus obtains the instantaneous operation state of the photovoltaic panel. Through power feature extraction, the system can also calculate the output power and input power of the photovoltaic panel. This refined feature extraction enables the system to accurately capture the dynamic changes in the operating state of the photovoltaic panel and identify possible performance problems and potential failure risks. The integration of these modules enables the photovoltaic panel operation health monitoring system to achieve intelligent management of the entire process from real-time data acquisition to refined feature extraction. Compared with traditional periodic inspection methods, this system significantly improves the efficiency and accuracy of fault detection, reduces maintenance costs and risks, and improves the stability and long-term reliability of photovoltaic systems, bringing users a more reliable and efficient energy production and utilization experience.

[0075] Example 3

[0076] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the health status index calculation module includes a photovoltaic instantaneous efficiency calculation unit, an ,electrical performance calculation unit and a comprehensive calculation unit;

[0077] The instantaneous efficiency calculation unit is used to calculate and obtain the instantaneous efficiency S(t) of photovoltaics based on the extracted operating feature set, and to analyze the energy conversion efficiency of the photovoltaic panel at the current working point;

[0078] The instantaneous efficiency S(t) is obtained by the following algorithm formula;

[0079]

[0080] Where A represents the area of ​​the photovoltaic panel.

[0081] The electrical performance calculation unit is used to calculate and analyze the electrical performance of the photovoltaic panel by analyzing the voltage V of the photovoltaic panel when it is open circuit and the current I when it is short circuit, and obtain the fill factor FF;

[0082] The filling factor FF is obtained by the following algorithm formula;

[0083]

[0084] Where Pm represents the maximum power output of the photovoltaic panel, Voc represents the open circuit voltage of the photovoltaic panel, and Isc represents the short circuit current of the photovoltaic panel.

[0085] In this embodiment, the system calculates the instantaneous efficiency S(t) of the photovoltaic panel based on the operating feature set provided by the feature extraction module through the instantaneous efficiency calculation unit. This indicator directly reflects the energy conversion efficiency of the photovoltaic panel at the current working point and is an important basis for evaluating the performance of the photovoltaic system. Through real-time monitoring and analysis of instantaneous efficiency, the system can quickly capture the changing trend of the working efficiency of the photovoltaic panel, promptly discover potential performance problems, and provide accurate operating status evaluation and optimization suggestions for operation and maintenance personnel. Secondly, the electrical performance calculation unit calculates the fill factor FF by analyzing the performance of the photovoltaic panel at the open circuit voltage Voc and the short circuit current Isc. The fill factor FF is a key indicator for measuring the electrical performance of the photovoltaic panel, which directly affects the output power and efficiency of the photovoltaic system. By accurately calculating and analyzing the fill factor, the system can comprehensively evaluate the electrical performance status of the photovoltaic panel, help the operation and maintenance personnel understand the actual working efficiency of the photovoltaic panel, and guide the optimization and adjustment of the system operating parameters, thereby improving the energy utilization efficiency and the long-term stability of the system. The introduction of the health status index calculation module not only enables the photovoltaic panel operation health monitoring system to monitor and evaluate the energy conversion efficiency and electrical performance of photovoltaic panels in real time, but also provides accurate data support and analysis tools to help operation and maintenance personnel to promptly discover and solve potential problems, thereby maximizing the operating efficiency and reliability of the photovoltaic system. This comprehensive and accurate monitoring and analysis capability far exceeds the traditional regular inspection method, and provides strong technical support for the optimized operation and long-term maintenance of photovoltaic systems.

[0086] Example 4

[0087] This embodiment is explained in Example 3, please refer to Figure 1 Specifically: the comprehensive calculation unit is used to perform dimensionless processing on the instantaneous efficiency S(t) and the fill factor FF, and then calculate and obtain the health index HI to comprehensively analyze the health status of the photovoltaic panel;

[0088] The health index HI is obtained through the following algorithm formula;

[0089] HI=w1·S(t)+w2·FF;

[0090] Wherein, w1 and w2 represent the weight values ​​of instantaneous efficiency S(t) and fill factor FF respectively, and the specific parameters are set by the user according to actual conditions.

[0091] The operation health assessment module is used to perform a preliminary comparative assessment between the preset first health threshold J and the acquired health index HI based on the health data of the photovoltaic panel, and analyze the preliminary health status of the photovoltaic panel. The specific assessment scheme is as follows;

[0092] When the health index HI> the first health threshold J, it means that the current photovoltaic panel is healthy;

[0093] When the health index HI ≤ the first health threshold J, it indicates that the current health status of the photovoltaic panel is abnormal, and further detailed evaluation is performed.

[0094] In this embodiment, the system obtains the health index HI by comprehensive calculation through the comprehensive calculation unit after dimensionless processing of the instantaneous efficiency S(t) and the filling factor FF. This index comprehensively considers the performance of the photovoltaic panel in two key aspects, energy conversion efficiency and electrical performance, and provides an intuitive and comprehensive evaluation index for system operation and maintenance personnel. Through real-time monitoring and analysis of HI, the health status of the photovoltaic panel can be quickly discovered, and decision makers can be assisted to formulate optimization strategies in a timely manner to ensure the continuous and efficient operation of the photovoltaic system. Secondly, the operation health assessment module performs a preliminary comparative evaluation of the obtained health index HI according to the preset first health threshold J. When the health index HI exceeds the threshold J, the system prompts that the photovoltaic panel is in normal condition; otherwise, it prompts that there may be an abnormality. This real-time health assessment mechanism can timely alert the operation and maintenance personnel and guide them to conduct further detailed evaluation and investigation to prevent further deterioration of potential faults and minimize downtime and maintenance costs. In summary, the introduction of the comprehensive calculation unit and the operation health assessment module not only improves the real-time monitoring and evaluation capabilities of the operation status of the photovoltaic system, but also effectively reduces the complexity and risk of operation and maintenance by providing an accurate health index HI. Compared with traditional regular inspections, this advanced monitoring and evaluation method greatly improves the efficiency of system fault prediction and response, and provides reliable technical support for the long-term stable operation and performance optimization of photovoltaic panels.

[0095] Example 5

[0096] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the multivariate health index calculation module includes a multivariate health ,calculation unit and a comprehensive evaluation unit;

[0097] The multivariable health calculation unit includes a temperature variable calculation unit and a multivariable calculation unit;

[0098] The temperature variable calculation unit is used to further analyze the impact of temperature on the performance of the photovoltaic panel when the current photovoltaic panel health index HI is evaluated as abnormal. By constructing a temperature impact algorithm formula and combining it with the temperature parameter T detected in real time by the temperature sensor deployed on the photovoltaic panel, a correlation calculation is performed to obtain the temperature impact factor Tf;

[0099] The temperature influence factor Tf is obtained by the following algorithm formula;

[0100]

[0101] In the formula, T(t) represents the temperature parameter T detected by the sensor at time t, α represents the temperature coefficient, which reflects the influence of temperature on efficiency, and Topt represents the optimal operating temperature;

[0102] The multivariable calculation unit is used to perform correlation calculation based on the obtained health index HI and the temperature influence factor Tf analyzed during the operation of the photovoltaic panel to obtain the multivariable health index HI2;

[0103] The multivariate health index HI2 is obtained by the following algorithm formula;

[0104] HI2=[(a1·HI)+(a2·Tf)]+A;

[0105] In the formula, a1 and a2 represent the preset weight values ​​of the health index HI and the temperature influence factor Tf respectively, and a1+a2=1. The specific value is set by the user, and A represents the correction constant.

[0106] The comprehensive evaluation unit is used to construct the historical health threshold L based on the mean of the historical health data of the photovoltaic panel and conduct a secondary comparative evaluation with the obtained multivariate health index HI2, perform multivariate result analysis on the health of the photovoltaic panel, and generate corresponding early warning strategies according to the evaluation results. The specific evaluation scheme is as follows;

[0107] When the multivariate health index HI2> the historical health threshold L, it means that the photovoltaic panel is in a healthy state. At this time, the first warning information is generated to prompt the relevant staff to perform regular maintenance on the current photovoltaic panel;

[0108] When the multivariate health index HI2 ≤ the historical health threshold L, it indicates that the photovoltaic panel is in an unhealthy operating state. At this time, a second warning message is generated to prompt relevant staff to repair the current photovoltaic panel immediately.

[0109] In this embodiment, the system effectively enhances the in-depth analysis capability of the health status of the photovoltaic panel through the collaboration of the temperature variable calculation unit and the multivariable calculation unit through the multivariable health calculation unit. When the health index HI evaluates an abnormality, the temperature variable calculation unit uses the temperature parameter T detected in real time to calculate the temperature influence factor Tf through the temperature influence algorithm. This factor reflects the actual impact of temperature changes on the performance of the photovoltaic panel, enabling the system to more accurately evaluate the health status of the photovoltaic panel under different working environments. Secondly, the multivariable calculation unit combines the health index HI and the temperature influence factor Tf to calculate the multivariable health index HI2. The multivariable health index HI2 comprehensively considers the comprehensive influence of multiple variable factors such as the instantaneous efficiency S(t) and fill factor FF of the photovoltaic panel and the temperature influence, providing more comprehensive and accurate health assessment data for operation and maintenance personnel. This comprehensive assessment not only helps to identify potential problems in a timely manner, but also guides the formulation of system optimization and maintenance strategies, thereby minimizing the risk of failure and maintenance costs. The comprehensive assessment unit further uses the mean of the historical health data of the photovoltaic panel to construct the historical health threshold L. Through a secondary comparative evaluation with the multivariable health index HI2, the system generates a corresponding early warning strategy. When the multivariable health index HI2 exceeds the historical health threshold L, the system issues a first warning message to prompt the operation and maintenance personnel to perform regular maintenance; and when the multivariable health index HI2 is lower than the historical health threshold L, the system issues a second warning message to require immediate repair. This warning mechanism based on multivariable results effectively improves the accuracy and response speed of system fault prediction, helping to ensure the long-term stable operation and performance optimization of photovoltaic panels.

[0110] Example 6

[0111] See also Figure 1 and Figure 2 , an online monitoring method based on the operation health of solar photovoltaic panels, comprising the following steps:

[0112] S1. First, an integrated sensor group is installed on the photovoltaic panel to detect and collect the operation data of the photovoltaic panel in real time, and the collected operation data is pre-processed;

[0113] S2. After preprocessing the operation data, extract the features of the operation data, extract the instantaneous features and power features of the operation data, and summarize them into an operation feature set;

[0114] S3. Calculate the instantaneous efficiency S(t) and fill factor FF of the photovoltaic panel according to the operation feature set, then perform dimensionless processing on the instantaneous efficiency S(t) and fill factor FF and perform correlation calculation to obtain the health index HI, and preset the first health threshold J for comparative evaluation;

[0115] S4, when an abnormality is detected, the influence of temperature on the performance of the photovoltaic panel is analyzed, and the temperature influence factor Tf is calculated. The health index HI and the temperature influence factor Tf are combined to calculate the multivariate health index HI2;

[0116] S5. Finally, based on the mean of the historical health data of the photovoltaic panels, a secondary comparative evaluation is conducted between the historical health threshold L and the obtained multivariate health index HI2, a multivariate result analysis of the health of the photovoltaic panels is performed, and a corresponding early warning strategy is generated based on the evaluation results.

[0117] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. An online monitoring system based on the health of solar photovoltaic panels, characterized by: It includes a data acquisition module, a feature extraction module, a health status index calculation module, an operation health assessment module and a multivariate health index calculation module; The data acquisition module is used to install and deploy several detection points on the solar photovoltaic panel, and install relevant sensor groups to collect the operation data of the solar panel in real time, and then pre-process the collected operation data; The feature extraction module is used to extract instantaneous features from the preprocessed operation data, and summarize the extracted instantaneous features to obtain an instantaneous operation feature set; The health index calculation module is used to perform preliminary calculations to obtain the instantaneous efficiency S(t) and the fill factor FF of the photovoltaic according to the acquired operation feature set, respectively calculate the energy conversion efficiency of the photovoltaic panel at the current working point and the electrical performance of the photovoltaic panel, and then correlate the obtained instantaneous efficiency S(t) and the fill factor FF to obtain the health index HI; The health status index calculation module includes a photovoltaic instantaneous efficiency calculation unit, an electrical performance calculation unit and a comprehensive calculation unit; The comprehensive calculation unit is used to perform dimensionless processing on the instantaneous efficiency S(t) and the fill factor FF, and then calculate and obtain the health index HI to comprehensively analyze the health of the photovoltaic panel. The health index HI is obtained by the following algorithm formula; ； In the formula, w1 and w2 represent the weight values ​​of instantaneous efficiency S(t) and fill factor FF respectively. The specific parameters are set by the user according to the actual situation. The operation health assessment module is used to perform a preliminary comparative assessment between the preset first health threshold J and the acquired health index HI, and analyze the preliminary health status of the photovoltaic panel; The multivariate health index calculation module is used to calculate the multivariate health index HI2 of the photovoltaic panel after the initial abnormality assessment, and then perform a secondary comparative evaluation on the preset historical health threshold L and the obtained multivariate health index HI2 to further analyze the health of the photovoltaic panel; The multivariate health index calculation module includes a multivariate health calculation unit and a comprehensive evaluation unit; The multivariable health calculation unit includes a temperature variable calculation unit and a multivariable calculation unit; The temperature variable calculation unit is used to further analyze the impact of temperature on the performance of the photovoltaic panel when an abnormality is evaluated for the current photovoltaic panel health index HI, and to obtain the temperature impact factor Tf by performing associated calculations by constructing a temperature impact algorithm formula and combining the temperature parameter T detected in real time by the temperature sensor deployed on the photovoltaic panel; The temperature influence factor Tf is obtained by the following algorithm formula; ； Where T(t) represents the temperature parameter T detected by the sensor at time t. It represents the temperature coefficient, which reflects the influence of temperature on efficiency. Topt represents the optimal operating temperature. The multivariate calculation unit is used to perform correlation calculation based on the acquired health index HI and the temperature influence factor Tf analyzed during the operation of the photovoltaic panel to obtain the multivariate health index HI2; The multivariate health index HI2 is obtained by the following algorithm formula; ； Wherein, a1 and a2 represent the preset weight values ​​of the health index HI and the temperature influence factor Tf respectively, and a1+a2=1. The specific value is set by the user, and A represents the correction constant.

2. The online monitoring system based on the operation health of solar photovoltaic panels according to claim 1 is characterized by: The data acquisition module includes a data acquisition unit and a data preprocessing unit; The data acquisition unit is used to install an integrated sensor group on the photovoltaic panel to detect and collect the operation data of the photovoltaic panel in real time; The integrated sensor group includes a current sensor, a voltage sensor, a temperature sensor and a radiation sensor; The operating data includes current I, voltage V, temperature T and radiation intensity G; The data preprocessing unit is used to preprocess the collected operating data, and the preprocessing method includes removing noise and outliers, and normalizing data of different dimensions to the same range.

3. The online monitoring system based on the operation health of solar photovoltaic panels according to claim 2 is characterized by: The feature extraction module is used to analyze the collected operation data at different times t, and extract the instantaneous features of the current I, voltage V, temperature T and radiation intensity G at different times t to obtain the photovoltaic panel instantaneous current I(t), photovoltaic panel instantaneous voltage V(t), photovoltaic panel instantaneous temperature T(t) and photovoltaic panel instantaneous radiation intensity G(t). At the same time, based on the photovoltaic panel instantaneous current I(t) and the photovoltaic panel instantaneous voltage V(t), power features are extracted to obtain the photovoltaic panel output power Pout(t) and the photovoltaic panel input power Pin(t), and then the extracted instantaneous features and power features are summarized to obtain an operation feature set.

4. The online monitoring system based on the operation health of solar photovoltaic panels according to claim 1 is characterized by: The instantaneous efficiency calculation unit is used to calculate and obtain the instantaneous efficiency S(t) of photovoltaic according to the extracted operation feature set, and analyze the energy conversion efficiency of the photovoltaic panel at the current working point; The instantaneous efficiency S(t) is obtained by the following algorithm formula; ； Where A represents the area of ​​the photovoltaic panel.

5. The online monitoring system based on the operation health of solar photovoltaic panels according to claim 1 is characterized by: The electrical performance calculation unit is used to calculate and analyze the electrical performance of the photovoltaic panel by analyzing the voltage V of the photovoltaic panel when it is open circuit and the current I when it is short circuit, so as to obtain the fill factor FF; The fill factor FF is obtained by the following algorithm formula; ； Where Pm represents the maximum power output of the photovoltaic panel, Voc represents the open circuit voltage of the photovoltaic panel, and Isc represents the short circuit current of the photovoltaic panel.

6. The online monitoring system based on the operation health of solar photovoltaic panels according to claim 1 is characterized by: The operation health assessment module is used to perform a preliminary comparative assessment between the preset first health threshold J and the acquired health index HI based on the health data of the photovoltaic panel, and analyze the preliminary health status of the photovoltaic panel. The specific assessment scheme is as follows; When the health index HI> the first health threshold J, it means that the current photovoltaic panel is healthy; When the health index HI ≤ the first health threshold J, it indicates that the current health status of the photovoltaic panel is abnormal, and further detailed evaluation is performed.

7. The online monitoring system based on the operation health of solar photovoltaic panels according to claim 1 is characterized by: The comprehensive evaluation unit is used to construct a historical health threshold L based on the mean of the historical health data of the photovoltaic panel and conduct a secondary comparative evaluation with the obtained multivariate health index HI2, perform a multivariate result analysis on the health of the photovoltaic panel, and generate a corresponding early warning strategy according to the evaluation results. The specific evaluation scheme is as follows; When the multivariate health index HI2> the historical health threshold L, it means that the photovoltaic panel is in a healthy state. At this time, the first warning information is generated to prompt the relevant staff to perform regular maintenance on the current photovoltaic panel; When the multivariate health index HI2 ≤ the historical health threshold L, it indicates that the photovoltaic panel is in an unhealthy operating state. At this time, a second warning message is generated to prompt relevant staff to repair the current photovoltaic panel immediately.

8. An online monitoring method based on the operation health of a solar photovoltaic panel, comprising an online monitoring system based on the operation health of a solar photovoltaic panel according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. First, an integrated sensor group is installed on the photovoltaic panel to detect and collect the operation data of the photovoltaic panel in real time, and the collected operation data is pre-processed; S2. After preprocessing the operation data, extract the features of the operation data, extract the instantaneous features and power features of the operation data, and summarize them into an operation feature set; S3. Calculate the instantaneous efficiency S(t) and fill factor FF of the photovoltaic panel according to the operation feature set, then perform dimensionless processing on the instantaneous efficiency S(t) and fill factor FF and perform correlation calculation to obtain the health index HI, and preset the first health threshold J for comparative evaluation; S4, when an abnormality is detected, the influence of temperature on the performance of the photovoltaic panel is analyzed, and the temperature influence factor Tf is calculated. The health index HI and the temperature influence factor Tf are combined to calculate the multivariate health index HI2; S5. Finally, based on the mean of the historical health data of the photovoltaic panels, a secondary comparative evaluation is conducted between the historical health threshold L and the obtained multivariate health index HI2, a multivariate result analysis of the health of the photovoltaic panels is performed, and a corresponding early warning strategy is generated based on the evaluation results.

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