Method and system for evaluating running state of photovoltaic power station

Abstract

The invention discloses an operation state evaluation method and system for a photovoltaic power station, and relates to the technical field of photovoltaic power generation, and the method comprises the following steps: carrying out the real-time monitoring of a photovoltaic module of the photovoltaic power station through a sensor and a camera device, and obtaining the electrical parameters and surface image data of the photovoltaic module; the intelligent monitoring system based on the combination of the electrical parameters and the image data can detect and position the hot spot problem of the photovoltaic module in real time, rapidly identify the hot spot area and analyze the influence of the hot spot area on the performance of the module, thereby triggering an alarm and taking countermeasures, effectively avoiding the risks of overheating, aging or burning of equipment, and improving the reliability of the photovoltaic module. Equipment damage and shutdown cost is reduced, and the service life of the assembly is prolonged. In combination with multispectral analysis and infrared thermal imaging technologies, the system provides an accurate analysis report for operation and maintenance personnel, automatically generates maintenance suggestions, optimizes operation and maintenance management, reduces labor cost, improves operation and maintenance efficiency and power generation efficiency, and ensures stable operation of a photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to an operating status evaluation method and system for a photovoltaic power station. Background Art

[0002] The operation status assessment of a photovoltaic power station refers to the real-time monitoring, analysis and assessment of various operating parameters and data of a photovoltaic power station to determine the current working status, efficiency, health status and potential problems of the power station. This assessment usually involves collecting data such as the current, voltage, temperature, radiation intensity, power output, etc. of the power station, and analyzing these data to determine the overall performance of the photovoltaic system, whether there are any faults, the degree of aging of the equipment, and the gap with the expected power generation capacity. Through the assessment, problems with components such as photovoltaic panels, inverters, and wiring systems can be discovered in a timely manner, and fault diagnosis and maintenance can be carried out, thereby improving the operating efficiency of the photovoltaic power station, extending the life of the equipment, and ensuring the stability of the power supply.

[0003] The prior art has the following deficiencies:

[0004] The hot spot effect refers to the significant increase in temperature in local areas of a photovoltaic module when it is damaged, dirt-accumulated or blocked, resulting in local heating and the formation of hot spots. Although this phenomenon is difficult to detect directly through conventional electrical parameters (such as voltage and current), if there is no effective monitoring system for hot spot assessment, local overheating may cause aging, burning or even fire of component materials, and in severe cases may lead to large-scale shutdown of photovoltaic power plants. Traditional assessment methods often ignore the real-time monitoring of hot spot phenomena, and this lack may hide many hidden dangers after the system has been running for a long time. If it is not discovered and handled in time, it will eventually lead to irreversible equipment damage and significant economic losses.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for evaluating the operating status of a photovoltaic power station. The intelligent monitoring system based on the combination of electrical parameters and image data can detect and locate the hot spot problem of photovoltaic components in real time, quickly identify the hot spot area and analyze its impact on the performance of the component, thereby triggering an alarm and taking countermeasures, effectively avoiding the risks of equipment overheating, aging or burning, reducing equipment damage and downtime costs, and extending the service life of components. Combined with multi-spectral analysis and infrared thermal imaging technology, the system provides accurate analysis reports for operation and maintenance personnel and automatically generates maintenance suggestions, optimizes operation and maintenance management, reduces labor costs, improves operation and maintenance efficiency and power generation efficiency, and ensures the stable operation of photovoltaic power stations to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for evaluating the operating status of a photovoltaic power station, comprising the following steps:

[0008] Use sensors and camera equipment to monitor photovoltaic modules of photovoltaic power stations in real time and obtain electrical parameters and surface image data of photovoltaic modules;

[0009] Based on the acquired electrical parameter data, calculate the working status of the PV modules, generate working status reports, and identify any abnormal conditions;

[0010] Perform image processing on the acquired surface image data, extract the hot spot area information on the surface of the photovoltaic module, and use multi-spectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module;

[0011] Based on the image processing results and the abnormal conditions of electrical parameters, a data fusion algorithm is used to conduct multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules;

[0012] Applying a risk assessment-based decision-making algorithm, an operating status assessment report for a photovoltaic power station is generated, providing maintenance and repair recommendations to prevent hot spot effects from causing damage to photovoltaic modules and causing serious consequences.

[0013] Preferably, the specific steps of real-time monitoring of photovoltaic modules of a photovoltaic power station by using sensors to obtain electrical parameters and surface image data of the photovoltaic modules are as follows:

[0014] First, the sensor will collect the electrical parameters of the PV panels in real time;

[0015] At the same time, the camera equipment will image the surface of the photovoltaic module in real time to capture its surface image data;

[0016] Electrical parameters and image data need to be precisely synchronized and time-stamped to ensure the correspondence of the two data sources at the same moment;

[0017] All collected electrical parameters and image data will be transmitted to the central processing unit through a wireless network or a wired data transmission system.

[0018] Preferably, based on the acquired electrical parameter data, the working status of the photovoltaic module is calculated, a working status report is generated, and the existing abnormal conditions are identified. The specific steps are as follows:

[0019] After the acquired electrical parameter data is transmitted to the central processing system, the data is first preprocessed and cleaned;

[0020] The electrical parameter data after cleaning will be used to calculate the working status of the photovoltaic modules through real-time analysis algorithms;

[0021] After calculating the working status, perform anomaly detection and identify various existing problems according to the set normal working range;

[0022] After completing data analysis and anomaly detection, generate a detailed job status report.

[0023] Preferably, the acquired surface image data is processed to extract the hot spot area information on the surface of the photovoltaic module, and the hot spot phenomenon on the surface of the photovoltaic module is analyzed by multispectral analysis. The specific steps are as follows:

[0024] First, the collected image data is preprocessed to ensure the accuracy and efficiency of subsequent analysis;

[0025] The pre-processed image will enter the hot spot area detection stage;

[0026] The detected hot spot area will be further refined through the image segmentation algorithm to ensure the accurate extraction of the hot spot area;

[0027] Once the hot spot area is accurately extracted, the hot spot phenomenon analysis based on multispectral image data is then performed.

[0028] Preferably, the specific steps of combining the image processing results with the abnormal conditions of the electrical parameters and using the data fusion algorithm to perform multi-dimensional analysis and evaluate the impact of hot spots on the performance of photovoltaic modules are as follows:

[0029] Based on the image processing results of hot spots and the abnormal conditions of electrical parameters, a multi-dimensional analysis model is established to evaluate the specific impact of hot spots on the performance of photovoltaic modules. The hot spot effect is manifested as an increase in the temperature of the hot spot area, which leads to a decrease in the photoelectric conversion efficiency of the area, thereby affecting the power generation capacity of the entire photovoltaic module. The specific impact of the temperature increase and the change in the hot spot area on the power output of the photovoltaic module is quantified by calculation. The calculation expression is as follows:

[0030]

[0031] , where P out is the PV panel output power, p ref is the output power of the PV module under normal conditions, T hot is the temperature at the center of the hot spot, T ambient is the temperature of the area around the hot spot, T max is the maximum operating temperature of the photovoltaic module, A hot is the area of ​​the hot spot, A total is the total area of ​​the PV module, α is the temperature coefficient, and β is the area coefficient;

[0032] To further evaluate the abnormal impact of hot spots on the power output of photovoltaic modules, if the output power of the photovoltaic module is less than the expected value, it is a direct result of the hot spot effect, and power anomaly detection is performed. The calculation expression is as follows:

[0033] ΔP=P out -P expected

[0034] , where P expected is the expected output power of the PV module, ΔP is the output power difference of the PV module;

[0035] A comprehensive analysis is performed based on the multi-dimensional data fusion algorithm of electrical parameters and image processing results to generate the final performance evaluation results. The image processing data is combined with the abnormal information of electrical parameters, and the support vector machine is used to perform data fusion to evaluate the overall impact of hot spots on the performance of photovoltaic modules. The calculation expression is as follows:

[0036] P final =w1·P out +w2·ΔP+w3·Image Quality Index

[0037] , where P final is the final performance evaluation value, Image Quality Index is the image quality index, w1, w2, and w3 are weight coefficients, which are used to adjust the output power P of the photovoltaic module respectively. out , the expected output power ΔP of the photovoltaic module and the contribution of the image quality index Image Quality Index to the final performance evaluation value.

[0038] Preferably, a decision algorithm based on risk assessment is applied to generate an operation status assessment report of a photovoltaic power station and provide maintenance and repair suggestions to prevent the hot spot effect from causing damage to photovoltaic modules and causing serious consequences. The specific steps are as follows:

[0039] Based on the acquired electrical parameters and the hot spot area information obtained through image processing, the working state risk assessment parameters of each photovoltaic module are calculated, and the working state risk value of the photovoltaic module is defined based on the risk assessment. The calculation expression is as follows:

[0040]

[0041] , where RiskIndex is the risk value of the working status of the photovoltaic module, P out is the PV module output power, P nominal is the rated power of the PV module, T module is the actual temperature of the PV module, T safe is the safety temperature threshold, A hot is the area of ​​the hot spot, Atotal is the total area of ​​the PV module, F1 is the weight coefficient of the ratio of output power to rated power, F2 is the weight coefficient of the temperature ratio, and F3 is the weight coefficient of the ratio of the hot spot area to the total area;

[0042] The working status risk value RiskIndex of a single PV module is obtained, and the risk values ​​of each module are aggregated to evaluate the operating status of the entire PV power station. The calculation expression is as follows:

[0043]

[0044] , where PlantRiskIndex is the risk index of the power plant, RiskIndex i is the working status risk value of the i-th PV module, w i is the weight coefficient of the i-th PV module, and N is the total number of PV modules;

[0045] After obtaining the power plant risk index PlantRiskIndex, the risk threshold judgment logic is applied to evaluate the risk level of hot spots. The expression is as follows:

[0046] If (PlantRiskIndex>RiskThreshold) Alarm! The risk of hot spot damage is high. In the formula, RiskThreshold is the reference threshold of the power plant risk index. When PlantRiskIndex is greater than RiskThreshold, it is considered that the current power plant has a hot spot risk, which will cause equipment damage or system shutdown;

[0047] When the risk assessment of the power station reaches the warning line, based on the risk assessment results, the decision algorithm is applied to generate specific maintenance and repair suggestions. According to the hot spot impact degree, risk index and hot spot distribution of the component, a repair strategy is generated for each component. The calculation expression is as follows:

[0048]

[0049] , where M repair is the maintenance priority, F4 and F5 are weight coefficients, which are used to adjust the contribution of the hot spot area and the total area ratio to the maintenance priority, the ratio of the risk threshold to the risk index, and the impact of the difference between the working temperature and the safety temperature on the maintenance priority.

[0050] An operation status evaluation system for a photovoltaic power station includes a real-time monitoring and data acquisition module, a working status calculation and abnormality recognition module, an image processing and hot spot recognition module, a data fusion and hot spot performance evaluation module, and a risk evaluation and decision support module;

[0051] The real-time monitoring and data acquisition module uses sensors and camera equipment to monitor the photovoltaic modules of the photovoltaic power station in real time and obtain the electrical parameters and surface image data of the photovoltaic modules;

[0052] The working status calculation and abnormality identification module calculates the working status of the photovoltaic module based on the acquired electrical parameter data, generates a working status report, and identifies any abnormal conditions;

[0053] The image processing and hot spot identification module performs image processing on the acquired surface image data, extracts the hot spot area information on the surface of the photovoltaic module, and uses multi-spectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module;

[0054] The data fusion and hot spot performance evaluation module combines the image processing results with the abnormal conditions of the electrical parameters and uses the data fusion algorithm to conduct multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules;

[0055] The risk assessment and decision support module applies a decision-making algorithm based on risk assessment to generate an operating status assessment report for the photovoltaic power station and provide maintenance and repair suggestions to prevent the hot spot effect from causing damage to photovoltaic modules and causing serious consequences.

[0056] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0057] The present invention can detect and locate the hot spot problem of photovoltaic modules in real time through an intelligent monitoring system based on the combination of electrical parameters and image data. When a hot spot appears in a photovoltaic module, the system can quickly identify the hot spot area and analyze its impact on the performance of the photovoltaic module, thereby triggering an alarm and taking corresponding countermeasures. This efficient hot spot detection mechanism effectively avoids the problem of the inability to detect the hot spot phenomenon in time in traditional monitoring methods, and prevents serious consequences such as overheating, equipment aging, and burning caused by hot spots. Timely hot spot evaluation and alarm systems greatly reduce the risk of equipment damage, ensure the stability of equipment in photovoltaic power stations during long-term operation, and thus reduce shutdown and maintenance costs. This not only ensures the normal operation of photovoltaic power stations, but also extends the service life of components and improves the safety of the overall equipment.

[0058] By combining multi-spectral analysis and infrared thermal imaging technology, the present invention can accurately identify the hot spot phenomenon on the surface of photovoltaic modules and provide accurate analysis reports for operation and maintenance personnel. Based on the identification of hot spots, the system can also automatically generate targeted maintenance suggestions, such as cleaning, repairing or replacing affected components. This maintenance solution based on intelligent monitoring and automatic optimization greatly improves the operation and maintenance efficiency of photovoltaic power stations and avoids the blindness and omissions of manual inspections. At the same time, by automatically adjusting the working state of photovoltaic modules, the system can reduce the performance loss caused by hot spots, improve power generation efficiency, and ensure that photovoltaic power stations always generate electricity in the optimal operating state. This efficient operation and maintenance management mode not only reduces labor costs, but also improves work accuracy, ensuring the long-term stable operation of photovoltaic power generation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0060] Figure 1 The present invention is a flowchart of a method for evaluating the operating status of a photovoltaic power station.

[0061] Figure 2 The module diagram of the operating status evaluation system of a photovoltaic power station according to the present invention is shown in FIG. DETAILED DESCRIPTION

[0062] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0063] The present invention provides Figure 1 A method for evaluating the operating status of a photovoltaic power station is shown, comprising the following steps:

[0064] Use sensors and camera equipment to monitor photovoltaic modules of photovoltaic power stations in real time and obtain electrical parameters and surface image data of photovoltaic modules;

[0065] The specific steps for real-time monitoring of photovoltaic modules of photovoltaic power stations through sensors and obtaining electrical parameters and surface image data of photovoltaic modules are as follows:

[0066] First, the sensor will collect the electrical parameters of the PV panels in real time;

[0067] The electrical parameters obtained mainly include key data such as current, voltage, power, and temperature. The current and voltage values ​​of the PV module reflect its power generation efficiency, while the temperature data helps to evaluate the working status of the module and potential thermal damage. The sensor converts this data into electrical signals according to the design requirements of the PV system and transmits it to the central processing unit through a data acquisition system (such as a data logger or control system). This step ensures that the electrical performance of the PV module is monitored in real time and can quickly capture any abnormal changes, such as current and voltage fluctuations, or temperature increases caused by local overheating.

[0068] At the same time, a camera device (such as a high-definition camera, an infrared thermal imager, or a multispectral camera) will image the surface of the photovoltaic module in real time to capture its surface image data;

[0069] These images can be visible light images to identify surface dirt, obstruction or damage, or infrared thermal imaging images to capture the temperature distribution of hot spot areas. Infrared cameras can detect potential overheating areas by detecting small changes in the surface temperature of the components without contacting the equipment. The image data is digitized and transmitted to a computer or processing system for further analysis. The collection of image data can timely reflect the surface condition of photovoltaic components, especially the hot spot effect that cannot be visually detected by electrical parameters.

[0070] Electrical parameters and image data need to be precisely synchronized and time-stamped to ensure the correspondence of the two data sources at the same moment;

[0071] By synchronously recording electrical data and image data, the system can combine the two during analysis to determine whether there is performance degradation caused by hot spots or other problems. For example, when an electrical parameter is abnormal (such as voltage drop or high temperature), the system can quickly locate whether there is a hot spot area through image data, and further analyze whether the area is directly related to the change in electrical parameters. The purpose of timestamp recording is to ensure the consistency and accuracy of data, so that subsequent analysis results are comparable and timely.

[0072] All collected electrical parameters and image data will be transmitted to the central processing unit (such as monitoring system or cloud platform) through wireless network or wired data transmission system;

[0073] On these platforms, data will be processed and stored in real time for subsequent comprehensive analysis. Electrical parameter data will be used to detect whether there are any conditions exceeding the predetermined threshold, while image data will be analyzed by image processing software or artificial intelligence algorithms to identify whether there are hot spots or other anomalies. Real-time transmission and processing ensure that data can be quickly fed back to operation and maintenance personnel, so that they can take timely measures, such as cleaning components, adjusting angles, repairing damaged parts or replacing faulty modules, thereby reducing downtime or losses caused by equipment failure.

[0074] Based on the acquired electrical parameter data, calculate the working status of the PV modules, generate working status reports, and identify any abnormal conditions;

[0075] Based on the acquired electrical parameter data, calculate the working status of the PV modules, generate a working status report, and identify any abnormal conditions. The specific steps are as follows:

[0076] After the acquired electrical parameter data is transmitted to the central processing system, the data is first preprocessed and cleaned;

[0077] This process includes denoising, removing outliers, and filling missing values ​​for data such as current, voltage, power, and temperature. Since the sensor may be affected by environmental interference during the acquisition process, such as temperature fluctuations or electromagnetic interference, some data fluctuations or errors may occur. Therefore, it is necessary to filter and correct the raw data through algorithms to ensure that the input electrical data is accurate and reliable. The purpose of data cleaning is to ensure that subsequent analysis is based on accurate and valid data, thereby reducing misjudgments and false alarms.

[0078] The electrical parameter data after cleaning will be used to calculate the working status of the photovoltaic modules through real-time analysis algorithms;

[0079] First, the system calculates the core performance indicators of the photovoltaic module, such as power output, efficiency, and power generation capacity, based on the preset standards or models. For example, the system estimates the output power based on the product of current and voltage, and compares it with the theoretical value or historical data to determine whether the current output is within the normal range. Temperature data is also used to evaluate the working environment of the photovoltaic module. If the temperature is too high, it may mean that the system is overheating, resulting in reduced efficiency or potential failure. By calculating these indicators, the system can evaluate the operating status of the component in real time and confirm whether it is in normal working condition.

[0080] After calculating the working status, perform anomaly detection and identify various existing problems according to the set normal working range;

[0081] If the calculated power output is lower than expected, the system will trigger an alarm, indicating that there may be problems such as PV module failure, shading or hot spots. Temperature anomaly is also an important anomaly detection indicator. When the temperature data exceeds the safety threshold, the system will recognize that the component may be degraded or damaged due to local overheating. In addition, drastic fluctuations in current and voltage values ​​may indicate internal component failure, inverter problems, or short circuit risks in the wiring system. Through the analysis of comprehensive electrical parameters, the system can quickly identify potential abnormal situations.

[0082] After completing data analysis and anomaly detection, generate a detailed work status report;

[0083] The report will include the current working status of the PV modules, such as the specific values ​​of key parameters such as power output, efficiency, temperature, and comparative analysis with expected values ​​or historical data. If the system detects any abnormality or risk, the type of abnormality, possible causes, and its impact on system performance will be clearly stated in the report. At the same time, the report will provide detailed repair suggestions or follow-up steps to help operation and maintenance personnel take timely measures, such as adjusting the angle of the components, cleaning the modules, replacing faulty parts, etc., to ensure that the PV power station can operate efficiently and safely. Finally, the report will be presented to managers in the form of charts or dashboards through a visual interface, which is convenient for them to track and make decisions in real time.

[0084] Perform image processing on the acquired surface image data, extract the hot spot area information on the surface of the photovoltaic module, and use multi-spectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module;

[0085] The acquired surface image data is processed to extract the hot spot area information on the surface of the photovoltaic module. The specific steps of using multispectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module are as follows:

[0086] First, the collected image data is preprocessed to ensure the accuracy and efficiency of subsequent analysis;

[0087] Image data, especially thermal images acquired by infrared thermal imagers or multispectral cameras, may have stray light spots or image distortion due to environmental noise, lighting changes or sensor errors. Therefore, the image first needs to be denoised, such as smoothing the image through filtering algorithms (such as Gaussian blur, mean filtering, etc.) to eliminate unnecessary interference. This step will ensure clearer image quality, making subsequent hot spot area detection more accurate. In addition, the image will be color enhanced and contrast adjusted to make the hot spot area more prominent in the image for easier identification.

[0088] The pre-processed image will enter the hot spot area detection stage;

[0089] First, the system will identify areas with abnormal temperatures based on the temperature information of the image (if an infrared thermal imager is used) or reflectivity changes (if a multispectral camera is used). Infrared images can show color differences at different temperatures, and hot spots are usually areas with higher temperatures than the surrounding areas, so they can be located by the color difference or grayscale value changes of the image. Multispectral cameras collect image data in multiple bands and combine the reflectivity differences in different bands to identify areas with hot spots on the surface. With this data, the system can preliminarily calibrate areas where hot spots may exist for further analysis and confirmation.

[0090] The detected hot spot area will be further refined through the image segmentation algorithm to ensure the accurate extraction of the hot spot area;

[0091] Commonly used image segmentation techniques include thresholding, edge detection, and region growing. By setting the temperature or reflectivity threshold, the system can more accurately define the boundaries of the hot spot area and exclude some noise or small error areas. In addition, edge detection algorithms (such as Canny edge detection) can help identify the contours of the hot spot area and clearly define the scope and size of the hot spot. Through these refinement steps, the system can obtain a more accurate hot spot area, thereby providing accurate data support for subsequent analysis.

[0092] When the hot spot area is accurately extracted, the hot spot phenomenon analysis based on multispectral image data is then performed;

[0093] Multispectral analysis does not rely solely on one infrared image, but rather combines image information from multiple bands to assess the nature, severity, and possible causes of hot spots. For example, infrared bands are used to detect temperature distribution, near-infrared bands are used to analyze changes in surface reflectivity, and visible light bands are further combined to identify dirt or obstructions. This multi-band data fusion can help determine whether hot spot areas are caused by obstructions, dirt, damage, or other factors, and assess the impact of hot spots on the performance of photovoltaic modules. Through these analyses, the system is able to generate a comprehensive assessment report on hot spot phenomena, providing operation and maintenance personnel with more comprehensive fault diagnosis information, facilitating timely measures to reduce damage and improve the operating efficiency of photovoltaic power plants.

[0094] Based on the image processing results and the abnormal conditions of electrical parameters, a data fusion algorithm is used to conduct multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules;

[0095] Based on the image processing results combined with the abnormal conditions of electrical parameters, the data fusion algorithm is used to perform multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules. The specific steps are as follows:

[0096] Based on the image processing results of hot spots and the abnormal conditions of electrical parameters, a multi-dimensional analysis model is established to evaluate the specific impact of hot spots on the performance of photovoltaic modules. The hot spot effect is manifested as an increase in the temperature of the hot spot area, which leads to a decrease in the photoelectric conversion efficiency of the area, thereby affecting the power generation capacity of the entire photovoltaic module. The specific impact of the temperature increase and the change in the hot spot area on the power output of the photovoltaic module is quantified by calculation. The calculation expression is as follows:

[0097]

[0098] , where P out is the PV module output power, P ref is the output power of the PV module under normal conditions, T hot is the temperature at the center of the hot spot, T ambient is the temperature of the area around the hot spot, T max is the maximum operating temperature of the photovoltaic module, A hot is the area of ​​the hot spot, A total is the total area of ​​the PV module, α is the temperature coefficient, and β is the area coefficient;

[0099] The temperature coefficient α reflects the degree of influence of temperature changes on the power output of photovoltaic modules. In photovoltaic power generation systems, as the temperature rises, the photoelectric conversion efficiency of photovoltaic modules will gradually decrease, resulting in a decrease in output power. Photovoltaic modules with higher temperature coefficient values ​​show more significant power drop in high temperature environments. The hot spot effect usually causes a sharp increase in local temperature, especially when the module is damaged, contaminated or blocked. At this time, the temperature coefficient can help us quantify the increase in temperature in the centerline area of ​​the hot spot (i.e., T hot The ambient temperature (T ambient ) caused by the temperature difference between the photovoltaic modules and the power output loss. Therefore, the temperature coefficient is crucial in evaluating the impact of hot spot phenomenon on the performance of photovoltaic modules, as it determines the extent to which the efficiency of photovoltaic modules is attenuated by the temperature increase.

[0100] The area coefficient β represents the impact of the hot spot area on the power output of the photovoltaic module. A hot spot is not just an abnormal temperature area. It is usually accompanied by a decrease in the photoelectric conversion efficiency of the local area. The area coefficient is used to evaluate the impact of the size of this area on the overall power output. The larger the hot spot area, the more photovoltaic surface area is affected, resulting in a decrease in the overall power generation capacity. The area coefficient measures the hot spot area A. hot Total area of ​​photovoltaic modules A total The ratio of the hot spot to the area factor can help us evaluate the impact of the hot spot expansion on the overall system performance. A larger hot spot area will lead to more serious power loss. Therefore, the area factor helps us quantify the breadth and intensity of the hot spot impact and further predict the power generation efficiency loss of photovoltaic modules.

[0101] To further evaluate the abnormal impact of hot spots on the power output of photovoltaic modules, if the output power of the photovoltaic module is less than the expected value, it is a direct result of the hot spot effect, and power anomaly detection is performed. The calculation expression is as follows:

[0102] ΔP=P out -P expected

[0103] , where P expected is the expected output power of the PV module, ΔP is the output power difference of the PV module;

[0104] Performance anomalies due to the hot spot effect are identified by calculating the difference ΔP between the actual output power and the expected power of the PV module. When a hot spot area exists, the temperature distribution of the PV module is uneven, resulting in a decrease in local power output, so the actual output power will be lower than the expected value. Through this step, the system can quantify this difference and detect whether it exceeds the normal fluctuation range, thereby determining whether the hot spot phenomenon has a significant impact on the performance of the module. If the difference in ΔP exceeds the set threshold, it indicates that the module has performance degradation or failure, and the system will issue an alarm in time to facilitate further diagnosis and processing, avoiding potential equipment damage and economic losses. This process is an important link in combining the hot spot effect with electrical parameter anomalies, providing data basis for subsequent troubleshooting and repair.

[0105] A comprehensive analysis is performed based on the multi-dimensional data fusion algorithm of electrical parameters and image processing results to generate the final performance evaluation results. The image processing data is combined with the abnormal information of electrical parameters, and the support vector machine is used to perform data fusion to evaluate the overall impact of hot spots on the performance of photovoltaic modules. The calculation expression is as follows:

[0106] P final =w1·P out +w2·ΔP+w3·Image Quality Index

[0107] , where P final is the final performance evaluation value, Image Quality Index is the image quality index, which is a quantitative indicator for measuring image clarity, accuracy and visibility of hot spot areas after processing and analyzing the surface image of the photovoltaic module. w1, w2, and w3 are weight coefficients, which are used to adjust the output power P of the photovoltaic module respectively. out , the contribution of the expected output power ΔP of the photovoltaic module and the Image Quality Index to the final performance evaluation value;

[0108] Final performance evaluation value P finalThe role of the PV module is to provide a comprehensive, quantitative indicator to evaluate the health of PV modules by combining electrical parameters (such as actual output power and power anomalies) with image processing results (such as the accuracy of hot spot detection). This evaluation value can reflect in real time whether the PV module is affected by factors such as the hot spot effect, thereby helping operation and maintenance personnel to promptly discover potential problems and avoid equipment damage or performance degradation. final If the value is significantly lower than expected, it means that the component may have serious hot spot phenomenon or other faults. The system can automatically issue an early warning, indicating that further inspection and repair are needed, thereby reducing the risk of equipment failure and ensuring the efficient and stable operation of the photovoltaic power generation system.

[0109] Apply risk assessment-based decision-making algorithms to generate operating status assessment reports for photovoltaic power plants and provide maintenance and repair suggestions to prevent hot spot effects from causing damage to photovoltaic modules and causing serious consequences;

[0110] Apply a risk assessment-based decision-making algorithm to generate an operating status assessment report for a photovoltaic power station and provide maintenance and repair suggestions to prevent the hot spot effect from causing damage to photovoltaic modules and causing serious consequences. The specific steps are as follows:

[0111] Based on the acquired electrical parameters and the hot spot area information obtained through image processing, the working state risk assessment parameters of each photovoltaic module are calculated. The core of risk assessment is to identify whether the module is under the influence of the hot spot effect. Based on the risk assessment, the working state risk value of the photovoltaic module is defined. The calculation expression is as follows:

[0112]

[0113] , where RsikIndex is the risk value of the working status of the photovoltaic module, P out is the PV module output power, P nominal is the rated power of the PV module, T module is the actual temperature of the PV module, T safe is the safety temperature threshold, A hot is the area of ​​the hot spot, A total is the total area of ​​the PV module, F1 is the weight coefficient of the ratio of output power to rated power, which determines the influence of the ratio of output power to rated power in the final risk assessment. The output power reflects the actual working efficiency of the PV module, while the rated power is the design power of the module under ideal conditions. F2 is the weight coefficient of the temperature ratio, which determines the influence of the ratio of the actual working temperature of the PV module to its safety temperature threshold in the risk assessment. F3 is the weight coefficient of the ratio of the hot spot area to the total area, which determines the influence of the ratio of the hot spot area on the surface of the PV module to the total surface area in the risk assessment.

[0114] The working status risk value RiskIndex of a single PV module is obtained, and the risk values ​​of each module are aggregated to evaluate the operating status of the entire PV power station. The calculation expression is as follows:

[0115]

[0116] , where PlantRiskIndex is the risk index of the power plant, RiskIndex i is the working status risk value of the i-th PV module, w i is the weight coefficient of the i-th PV module, and N is the total number of PV modules;

[0117] After obtaining the power plant risk index PlantRiskIndex, the risk threshold judgment logic is applied to evaluate the risk level of hot spots. The expression is as follows:

[0118] If (PlantRiskIndex>RiskThreshold) Alarm! The risk of hot spot damage is high. In the formula, RiskThreshold is the reference threshold of the power plant risk index. When PlantRiskIndex is greater than RiskThreshold, it is considered that the current power plant has a hot spot risk, which will cause equipment damage or system shutdown;

[0119] When the risk assessment of the power station reaches the warning line, based on the risk assessment results, the decision algorithm is applied to generate specific maintenance and repair suggestions. According to the hot spot impact degree, risk index and hot spot distribution of the component, a repair strategy is generated for each component. The calculation expression is as follows:

[0120]

[0121] , where M repair is the maintenance priority, F4 and F5 are weight coefficients, which are used to adjust the contribution of the hot spot area and the total area ratio to the maintenance priority, the ratio of the risk threshold to the risk index, and the impact of the difference between the working temperature and the safety temperature on the maintenance priority;

[0122] When maintenance priority M repair When the value is large, it is recommended to take repair measures such as cleaning, removing obstructions, and replacing modules. If the hot spot area is large, the temperature is too high, and the risk assessment value is high, it is recommended to immediately shut down for comprehensive maintenance to avoid equipment damage or accidents. Through precise algorithm calculations, scientific and reasonable maintenance suggestions are generated to help operation and maintenance personnel take effective preventive measures according to actual conditions to ensure the safe and efficient operation of photovoltaic power stations.

[0123] Implementation method 1: The core of this implementation method is to combine the electrical parameter monitoring of photovoltaic modules with image data acquisition technology, and realize the early detection and precise positioning of hot spot effects through an intelligent analysis system. The system uses electrical parameter sensors (such as current, voltage, power and temperature sensors) and image acquisition equipment (such as infrared thermal imagers and high-definition multi-spectral cameras) to work together to monitor each photovoltaic module in a photovoltaic power station in real time.

[0124] First, the electrical parameter sensor continuously monitors the working status of the photovoltaic module, including current, voltage, output power and surface temperature. These parameters can reflect the overall health of the module, especially the indicators such as power output drop and temperature abnormality, which can indicate that there may be some problems with the photovoltaic module, such as short circuit, poor contact or damage. At the same time, the electrical sensor transmits this data to the central control system in real time. By comparing historical data, the system can instantly identify abnormal conditions in the current working status of the module. For example, if the power output is lower than normal expectations or the temperature exceeds the preset range, the alarm system can be triggered to indicate the risk of hot spot effect.

[0125] In terms of image data acquisition, infrared thermal imagers and multispectral cameras installed around photovoltaic modules will perform real-time imaging of the module surface to obtain image data reflecting the temperature distribution and surface reflection characteristics. Infrared thermal imagers capture the spatial distribution of the module surface temperature and can accurately locate areas with local temperature rise, which may be hot spots. Multispectral cameras capture changes in surface reflectivity through images in different bands, which can reveal potential problem areas such as dirt, obstructions, and damage, thereby providing clues to the cause of hot spots. Through the combined data of thermal imaging and multispectral cameras, the system can accurately identify the hot spot area of ​​PV modules and combine it with electrical parameter data to generate a comprehensive working status report.

[0126] The system's intelligent analysis algorithm combines electrical parameter data with image data for cross-validation. For example, when the current and voltage of a photovoltaic module drop significantly, the system will simultaneously view the thermal imaging image of the module. If the thermal imaging image shows local overheating, the system can confirm that the module has a hot spot problem. This data combination method can greatly improve the accuracy of hot spot identification and reduce misdiagnosis or missed diagnosis caused by a single data source.

[0127] In addition, when the system detects a hot spot effect, it will automatically generate a detailed report that includes the location of the hot spot, the size of the hot spot, possible cause analysis (such as dirt, obstruction, damage, etc.), and an assessment of the impact on the overall performance of the system. Through this report, maintenance personnel can quickly understand the fault location and take action, such as cleaning, replacing components, or adjusting operating parameters.

[0128] The advantage of this implementation is that it organically combines electrical parameters and image data, can monitor the working status of photovoltaic modules in real time, promptly detect and deal with hot spot problems, and avoid equipment damage or shutdown caused by long-term neglect. In addition, the system provides accurate fault location and cause analysis through intelligent analysis, helping operation and maintenance personnel to respond quickly, reducing the burden of manual inspections and improving the operating efficiency and safety of the entire photovoltaic power station.

[0129] Implementation method 2: This implementation method uses multi-spectral imaging technology, combined with infrared thermal imaging images, to deeply analyze the hot spot phenomenon on the surface of photovoltaic modules. Different from traditional infrared thermal imaging technology, this implementation method uses a multi-spectral camera to obtain image data of multiple bands, analyze the surface status of photovoltaic modules from multiple angles, and has stronger advantages in the positioning, evaluation and early warning of hot spot areas.

[0130] Multispectral cameras can perform imaging in multiple bands, including visible light, near infrared, and mid-infrared. The data in each band can reveal different physical properties of the surface of photovoltaic modules. For example, in the near infrared band, dirt and obstructions on the surface of photovoltaic modules will produce different reflection characteristics; in the infrared band, the temperature distribution can be accurately captured, and the local temperature increase of hot spots will appear as a color change in the image. Therefore, the system uses the changes in images in different bands to comprehensively analyze different problems that may exist on the surface of the module and identify the specific location and cause of the hot spot.

[0131] The system first obtains image data of each band through the image acquisition device and performs preprocessing, including denoising, contrast enhancement, color correction and other operations to ensure that the image quality is suitable for analysis. Then, the image segmentation algorithm is used to separate the hot spot area in the image from the background, especially in the area with obvious temperature changes, and high-precision calibration is performed. Through the change of color or grayscale value, the algorithm can distinguish the hot spot area from other surface defects (such as dirt, cracks, etc.) to ensure the accuracy of detection.

[0132] By combining multi-band image data, the system can determine the nature of hot spots and their possible causes. If the hot spot is caused by shading or dirt, the system can provide suggestions for cleaning or adjusting the angle of the PV module; if the hot spot is caused by internal damage or aging of the module, the system will recommend replacement or repair. Through these multi-dimensional analyses, the system can accurately assess the severity of hot spots and their impact on the efficiency of PV modules, thereby providing accurate decision support for operation and maintenance personnel.

[0133] Once the system identifies a hot spot area and analyzes its possible impact on the system, the early warning system will generate a real-time alert based on the size, temperature, and impact of the hot spot and push it to the operation and maintenance personnel. In addition, the system will automatically save detailed image data of the event for subsequent analysis and processing. This multi-spectral analysis technology can provide more accurate hot spot monitoring for photovoltaic power stations, especially in large-scale photovoltaic power generation systems, which can significantly improve the efficiency and accuracy of fault detection.

[0134] The advantage of this implementation is that multi-spectral analysis technology can fully understand the health status of photovoltaic modules from multiple angles. In particular, by analyzing data in different bands, it can accurately locate the causes and effects of hot spot phenomena, avoiding the misjudgment that may be caused by a single infrared image. In addition, multi-band data fusion can also provide the system with more judgment basis, enhancing the reliability and accuracy of hot spot detection.

[0135] Implementation method 3: This implementation method focuses on the automatic optimization and maintenance solution combined with the hot spot effect. In particular, when the hot spot phenomenon is detected in real time, the system can automatically take a series of optimization measures to ensure the healthy operation of the photovoltaic modules. The core of this solution is to automatically adjust the working parameters of the photovoltaic power station based on the results of hot spot detection and provide targeted maintenance suggestions, thereby improving the stability of the system and avoiding equipment damage caused by hot spot problems.

[0136] After the hot spot problem of the photovoltaic module is identified and confirmed, the system will automatically optimize the working strategy of the photovoltaic station based on the electrical performance of the module and the situation of the hot spot area. For example, the system can reduce the workload of some modules according to the size and temperature of the hot spot area to avoid further overheating of the area. The system can also automatically adjust the operating mode of the power generation module according to the location of the hot spot, such as reducing power output or temporarily shutting down the modules in the hot spot area to reduce potential damage. In addition, the system will automatically optimize the work of other unaffected modules according to the impact of the hot spot on the overall photovoltaic power generation efficiency to maintain the overall output efficiency of the photovoltaic power station.

[0137] In addition to automatically optimizing the operating status of PV modules, the system also generates detailed maintenance recommendations. Maintenance recommendations may include cleaning modules, replacing damaged components, adjusting the tilt angle of PV modules, or improving ventilation and heat dissipation conditions. If the system detects that the hot spot is caused by module damage or aging, the recommendation will include replacing or repairing the module. The maintenance report will be presented intuitively in the form of charts, text, etc., so that operation and maintenance personnel can quickly understand the problem and take effective action.

[0138] Once the hot spot problem is solved, the system will continue to monitor the operating status of the PV modules and further optimize the operating parameters or provide new maintenance suggestions when necessary. The advantage of this implementation is that it reduces manual intervention through the combination of automated optimization and intelligent maintenance, improves the operation and maintenance efficiency of the PV power station, avoids major equipment damage caused by hot spots, and ensures the long-term stable operation of the PV system.

[0139] The present invention can detect and locate the hot spot problem of photovoltaic modules in real time through an intelligent monitoring system based on the combination of electrical parameters and image data. When a hot spot appears in a photovoltaic module, the system can quickly identify the hot spot area and analyze its impact on the performance of the photovoltaic module, thereby triggering an alarm and taking corresponding countermeasures. This efficient hot spot detection mechanism effectively avoids the problem of the inability to detect the hot spot phenomenon in time in traditional monitoring methods, and prevents serious consequences such as overheating, equipment aging, and burning caused by hot spots. Timely hot spot evaluation and alarm systems greatly reduce the risk of equipment damage, ensure the stability of equipment in photovoltaic power stations during long-term operation, and thus reduce shutdown and maintenance costs. This not only ensures the normal operation of photovoltaic power stations, but also extends the service life of components and improves the safety of the overall equipment.

[0140] By combining multi-spectral analysis and infrared thermal imaging technology, the present invention can accurately identify the hot spot phenomenon on the surface of photovoltaic modules and provide accurate analysis reports for operation and maintenance personnel. Based on the identification of hot spots, the system can also automatically generate targeted maintenance suggestions, such as cleaning, repairing or replacing affected components. This maintenance solution based on intelligent monitoring and automatic optimization greatly improves the operation and maintenance efficiency of photovoltaic power stations and avoids the blindness and omissions of manual inspections. At the same time, by automatically adjusting the working state of photovoltaic modules, the system can reduce the performance loss caused by hot spots, improve power generation efficiency, and ensure that photovoltaic power stations always generate electricity in the optimal operating state. This efficient operation and maintenance management mode not only reduces labor costs, but also improves work accuracy, ensuring the long-term stable operation of photovoltaic power generation systems.

[0141] The present invention provides Figure 2 The operating status evaluation system of a photovoltaic power station shown includes a real-time monitoring and data acquisition module, a working status calculation and abnormality recognition module, an image processing and hot spot recognition module, a data fusion and hot spot performance evaluation module, and a risk assessment and decision support module;

[0142] The real-time monitoring and data acquisition module uses sensors and camera equipment to monitor the photovoltaic modules of the photovoltaic power station in real time and obtain the electrical parameters and surface image data of the photovoltaic modules;

[0143] The working status calculation and abnormality identification module calculates the working status of the photovoltaic module based on the acquired electrical parameter data, generates a working status report, and identifies any abnormal conditions;

[0144] The image processing and hot spot identification module performs image processing on the acquired surface image data, extracts the hot spot area information on the surface of the photovoltaic module, and uses multi-spectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module;

[0145] The data fusion and hot spot performance evaluation module combines the image processing results with the abnormal conditions of the electrical parameters and uses the data fusion algorithm to conduct multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules;

[0146] The risk assessment and decision support module applies a decision-making algorithm based on risk assessment to generate an operating status assessment report for the photovoltaic power station and provide maintenance and repair suggestions to prevent the hot spot effect from causing damage to photovoltaic modules and causing serious consequences.

[0147] A method for evaluating the operating status of a photovoltaic power station provided in an embodiment of the present invention is implemented by the above-mentioned system for evaluating the operating status of a photovoltaic power station. The specific method and process of the system for evaluating the operating status of a photovoltaic power station are detailed in the embodiment of the method for evaluating the operating status of the above-mentioned photovoltaic power station, which will not be repeated here.

[0148] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the operating status of a photovoltaic power station, characterized in that: The following steps are involved: Use sensors and camera equipment to monitor photovoltaic modules of photovoltaic power stations in real time and obtain electrical parameters and surface image data of photovoltaic modules; Based on the acquired electrical parameter data, calculate the working status of the PV modules, generate working status reports, and identify any abnormal conditions; Perform image processing on the acquired surface image data, extract the hot spot area information on the surface of the photovoltaic module, and use multi-spectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module; Based on the image processing results and the abnormal conditions of electrical parameters, a data fusion algorithm is used to conduct multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules; Applying a risk assessment-based decision-making algorithm, an operating status assessment report for a photovoltaic power station is generated, providing maintenance and repair recommendations to prevent hot spot effects from causing damage to photovoltaic modules and causing serious consequences.

2. The method for evaluating the operating status of a photovoltaic power station according to claim 1, characterized in that: The specific steps for real-time monitoring of photovoltaic modules of photovoltaic power stations through sensors and obtaining electrical parameters and surface image data of photovoltaic modules are as follows: First, the sensor will collect the electrical parameters of the PV panels in real time; At the same time, the camera equipment will image the surface of the photovoltaic module in real time to capture its surface image data; Electrical parameters and image data need to be precisely synchronized and time-stamped to ensure the correspondence of the two data sources at the same moment; All collected electrical parameters and image data will be transmitted to the central processing unit through a wireless network or a wired data transmission system.

3. The method for evaluating the operating status of a photovoltaic power station according to claim 1, characterized in that: Based on the acquired electrical parameter data, calculate the working status of the PV modules, generate a working status report, and identify any abnormal conditions. The specific steps are as follows: After the acquired electrical parameter data is transmitted to the central processing system, the data is first preprocessed and cleaned; The electrical parameter data after cleaning will be used to calculate the working status of the photovoltaic modules through real-time analysis algorithms; After calculating the working status, perform anomaly detection and identify various existing problems according to the set normal working range; After completing data analysis and anomaly detection, generate a detailed job status report.

4. The method for evaluating the operating status of a photovoltaic power station according to claim 1, characterized in that: The acquired surface image data is processed to extract the hot spot area information on the surface of the photovoltaic module. The specific steps of using multispectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module are as follows: First, the collected image data is preprocessed to ensure the accuracy and efficiency of subsequent analysis; The pre-processed image will enter the hot spot area detection stage; The detected hot spot area will be further refined through the image segmentation algorithm to ensure the accurate extraction of the hot spot area; Once the hot spot area is accurately extracted, the hot spot phenomenon analysis based on multispectral image data is then performed.

5. The method for evaluating the operating status of a photovoltaic power station according to claim 1, characterized in that: Based on the image processing results combined with the abnormal conditions of electrical parameters, the data fusion algorithm is used to perform multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules. The specific steps are as follows: Based on the image processing results of hot spots and the abnormal conditions of electrical parameters, a multi-dimensional analysis model is established to evaluate the specific impact of hot spots on the performance of photovoltaic modules. The hot spot effect is manifested as an increase in the temperature of the hot spot area, which leads to a decrease in the photoelectric conversion efficiency of the area, thereby affecting the power generation capacity of the entire photovoltaic module. The specific impact of the temperature increase and the change in the hot spot area on the power output of the photovoltaic module is quantified by calculation. The calculation expression is as follows: ， Where P out is the PV panel output power, P ref is the output power of the PV module under normal conditions, T hot is the temperature at the center of the hot spot, T ambinet is the temperature of the area around the hot spot, T max is the maximum operating temperature of the photovoltaic module, A hot is the area of ​​the hot spot, A total is the total area of ​​the PV module, α is the temperature coefficient, and β is the area coefficient; To further evaluate the abnormal impact of hot spots on the power output of photovoltaic modules, if the output power of the photovoltaic module is less than the expected value, it is a direct result of the hot spot effect, and power anomaly detection is performed. The calculation expression is as follows: ΔP=P out -P expected Where P expected is the expected output power of the PV module, ΔP is the output power difference of the PV module; A comprehensive analysis is performed based on the multi-dimensional data fusion algorithm of electrical parameters and image processing results to generate the final performance evaluation results. The image processing data is combined with the abnormal information of electrical parameters, and the support vector machine is used to perform data fusion to evaluate the overall impact of hot spots on the performance of photovoltaic modules. The calculation expression is as follows: P final =w1·P out +w2·ΔP+w3·Image Quality Index Where P final is the final performance evaluation value, Image Quality Index is the image quality index, w1, w2, and w3 are weight coefficients, which are used to adjust the output power P of the photovoltaic module respectively. out , the expected output power ΔP of the photovoltaic module and the contribution of the image quality index Image Quality Index to the final performance evaluation value.

6. The method for evaluating the operating status of a photovoltaic power station according to claim 1, characterized in that: Apply a risk assessment-based decision-making algorithm to generate an operating status assessment report for a photovoltaic power station and provide maintenance and repair suggestions to prevent the hot spot effect from causing damage to photovoltaic modules and causing serious consequences. The specific steps are as follows: Based on the acquired electrical parameters and the hot spot area information obtained through image processing, the working state risk assessment parameters of each photovoltaic module are calculated, and the working state risk value of the photovoltaic module is defined based on the risk assessment. The calculation expression is as follows: ， Where RiskIndex is the risk value of the working status of the photovoltaic module, P out is the PV panel output power, P nominal is the rated power of the PV module, T module is the actual temperature of the PV module, T safe is the safety temperature threshold, A hout is the area of ​​the hot spot, A total is the total area of ​​the PV module, F1 is the weight coefficient of the ratio of output power to rated power, F2 is the weight coefficient of the temperature ratio, and F3 is the weight coefficient of the ratio of the hot spot area to the total area; The working status risk value RiskIndex of a single PV module is obtained, and the risk values ​​of each module are aggregated to evaluate the operating status of the entire PV power station. The calculation expression is as follows: ， Where PlantRiskIndex is the risk index of the power plant, RiskIndex i is the working status risk value of the i-th PV module, w i is the weight coefficient of the i-th PV module, and N is the total number of PV modules; After obtaining the power plant risk index PlantRiskIndex, the risk threshold judgment logic is applied to evaluate the risk level of hot spots. The expression is as follows: If (PlantRiskIndex>RiskThreshold) Alarm! The risk of hot spot damage is high. In the formula, RiskThreshold is the reference threshold of the power plant risk index. When PlantRiskIndex is greater than RiskThreshold, it is considered that the current power plant has a hot spot risk, which will cause equipment damage or system shutdown; When the risk assessment of the power station reaches the warning line, based on the risk assessment results, the decision algorithm is applied to generate specific maintenance and repair suggestions. According to the hot spot impact degree, risk index and hot spot distribution of the component, a repair strategy is generated for each component. The calculation expression is as follows: ， Where M repair is the maintenance priority, F4 and F5 are weight coefficients, which are used to adjust the contribution of the hot spot area and the total area ratio to the maintenance priority, the ratio of the risk threshold to the risk index, and the impact of the difference between the working temperature and the safety temperature on the maintenance priority.

7. A photovoltaic power station operation status assessment system, used to implement a photovoltaic power station operation status assessment method according to any one of claims 1 to 6, characterized in that: It includes real-time monitoring and data acquisition module, working status calculation and abnormality recognition module, image processing and hot spot recognition module, data fusion and hot spot performance evaluation module, and risk assessment and decision support module; The real-time monitoring and data acquisition module uses sensors and camera equipment to monitor the photovoltaic modules of the photovoltaic power station in real time and obtain the electrical parameters and surface image data of the photovoltaic modules; The working status calculation and abnormality identification module calculates the working status of the photovoltaic module based on the acquired electrical parameter data, generates a working status report, and identifies any abnormal conditions; The image processing and hot spot identification module performs image processing on the acquired surface image data, extracts the hot spot area information on the surface of the photovoltaic module, and uses multi-spectral analysis to analyze the hot spot phenomenon on the surface of the photovoltaic module; The data fusion and hot spot performance evaluation module combines the image processing results with the abnormal conditions of the electrical parameters and uses the data fusion algorithm to conduct multi-dimensional analysis to evaluate the impact of hot spots on the performance of photovoltaic modules; The risk assessment and decision support module applies a decision-making algorithm based on risk assessment to generate an operating status assessment report for the photovoltaic power station and provide maintenance and repair suggestions to prevent the hot spot effect from causing damage to photovoltaic modules and causing serious consequences.

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