Mountain photovoltaic array power generation abnormity monitoring method and system based on subarray benchmarking

Abstract

The invention provides a mountain photovoltaic array power generation abnormity monitoring method and system based on subarray benchmarking, and the method comprises the steps: dividing a photovoltaic array into a plurality of secondary arrays according to a preset rule, and each secondary array comprises a plurality of photovoltaic subarrays; selecting a standard sub-array from the plurality of photovoltaic sub-arrays of each secondary square array, and taking other sub-arrays as non-standard sub-arrays; collecting working data of all photovoltaic sub-arrays in each secondary square matrix, and calculating power generation capacity deviation of the standard sub-array and the non-standard sub-array according to the working data; and judging whether the mountain photovoltaic array generates power abnormally based on the power generation capacity deviation. The subarray power generation capacity is managed and detected in a fine manner, and the pertinence and accuracy of operation and maintenance work are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic array power generation anomaly monitoring, and in particular relates to a mountain photovoltaic array power generation anomaly monitoring method and system based on sub-array benchmarking. Background Art

[0002] With the continuous development of photovoltaic technology, large-scale photovoltaic arrays have become the main form of solar power generation. However, due to the large scale and numerous components of photovoltaic arrays, local anomalies often occur during the power generation process, such as damage to some components, obstruction, wiring failure, etc. These problems seriously affect the overall power generation efficiency and operational stability of the photovoltaic system. At present, although the photovoltaic power generation anomaly detection method has made certain progress, there are still many defects and deficiencies, which are mainly manifested in the following aspects: 1. Limited detection accuracy: Infrared image detection can directly display the temperature distribution of photovoltaic modules, which helps to find abnormal phenomena such as hot spots, but its detection accuracy is greatly affected by the environment, and the equipment cost is high, which is not suitable for comprehensive monitoring of large-scale photovoltaic arrays. Although electrical signal detection methods such as time domain reflection method and voltage scanning method are based on electrical signals for detection, they usually require the system to stop working or be carried out under specific conditions, making it difficult to achieve online monitoring, and have high requirements for equipment and limited diagnostic accuracy.

[0003] 2. High cost and low efficiency: The traditional manual handheld infrared thermal imager detection method is inefficient, especially in large-scale photovoltaic power plants, where it is difficult for inspectors to fully cover all components, and the workload is large and the conditions are difficult. Although detection methods based on complex algorithms (such as neural networks, machine learning, etc.) can theoretically improve detection accuracy, they usually require a large amount of labeled data and computing resources, are costly, and may have problems such as overfitting and noise sensitivity in practical applications.

[0004] 3. Reliance on human intervention: Many detection methods still require manual data analysis, judgment, and processing, which not only increases labor costs, but may also lead to misjudgments or missed judgments due to human factors.

[0005] 4. Complex terrain affects detection accuracy: The mountainous terrain is complex, and affected by natural factors such as slope, slope direction, and gullies. The lighting conditions of photovoltaic sub-arrays in different areas may vary greatly. Secondly, there are differences in component type, installation angle, tree obstruction, etc. If compared with a standard array, the deviation is large and it is impossible to accurately determine whether there is any power generation abnormality.

[0006] In summary, photovoltaic power generation anomaly detection methods still have many shortcomings and challenges in terms of detection accuracy, cost efficiency, and manual dependence. Therefore, there is an urgent need for an efficient, accurate, and comprehensive method to achieve comprehensive and real-time monitoring of photovoltaic arrays and avoid false alarms and missed alarms, so as to improve the operating efficiency and reliability of photovoltaic power generation systems. Summary of the invention

[0007] The object of the present invention is to provide a method and system for monitoring power generation anomalies of a mountain photovoltaic array based on sub-array benchmarking to solve the above-mentioned problems.

[0008] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for monitoring power generation anomalies of a mountain photovoltaic array based on subarray benchmarking, comprising: The photovoltaic array is divided into several secondary arrays according to preset rules, and each secondary array contains multiple photovoltaic sub-arrays; A standard sub-array is selected from multiple photovoltaic sub-arrays of each secondary array, and the others are used as non-standard sub-arrays; Collect the working data of all photovoltaic sub-arrays in each secondary array, and calculate the power generation capacity deviation between the standard sub-array and the non-standard sub-array based on the working data; Determine whether the mountain photovoltaic array has abnormal power generation based on the power generation capacity deviation.

[0009] Furthermore, the photovoltaic array is divided into several secondary arrays according to preset rules, and each secondary array contains multiple photovoltaic sub-arrays, including: According to the geographical location and layout of the photovoltaic power station, the photovoltaic array is divided into several secondary arrays; Preset rules: PV sub-arrays with the same lighting conditions are grouped into the same secondary array; PV modules with the same model and performance parameters are grouped into the same secondary array.

[0010] Furthermore, a standard subarray is selected from the multiple photovoltaic subarrays of each secondary array, including: The photovoltaic modules in the standard sub-array have no hot spots or obstructions, and the module cleanliness, azimuth and inclination meet the design requirements; the current level of each photovoltaic module in the standard sub-array remains consistent; The standard sub-array shielding condition should meet the requirement that there is no shielding between the east-west and north-south directions during the period from 9:00 to 15:00 local true solar time on the winter solstice.

[0011] Furthermore, the working data of all photovoltaic sub-arrays in each secondary array is collected, including: Sensors are installed to collect the voltage, current, temperature and power of the photovoltaic sub-array, and all collected data are cleaned and outliers are eliminated.

[0012] Furthermore, the power generation capacity deviation between the standard subarray and the non-standard subarray is calculated based on the working data, including: 1) Array power generation capacity = array power generation / DC side array installed capacity; 2) Calculate the power generation capacity of all photovoltaic sub-arrays in each secondary array; 3) Calculate the power generation capacity deviation between each non-standard sub-array and the standard sub-array in each secondary array, Power generation capacity deviation = (standard sub-array power generation capacity - sub-array power generation capacity) / standard sub-array power generation capacity.

[0013] Furthermore, the target value of power generation capacity deviation is within 3%, which is used as an indicator to measure the quality of power generation of PV sub-arrays.

[0014] Furthermore, whether the power generation of the mountain photovoltaic array is abnormal is judged based on the power generation capacity deviation, including: if the power generation capacity deviation is less than or equal to 3%, the power generation of the photovoltaic sub-array in the area is normal; if the power generation capacity deviation is greater than 3%, the power generation of the photovoltaic sub-array in the area is abnormal.

[0015] In a second aspect, the present invention provides a mountain photovoltaic array power generation anomaly monitoring system based on sub-array benchmarking, comprising: An array division module is used to divide the photovoltaic array into several secondary arrays according to preset rules, each secondary array containing multiple photovoltaic sub-arrays; A standard selection module is used to select a standard sub-array from multiple photovoltaic sub-arrays of each secondary array, and the others are non-standard sub-arrays; A data processing module is used to collect the working data of all photovoltaic sub-arrays in each secondary array, and calculate the power generation capacity deviation between the standard sub-array and the non-standard sub-array based on the working data; The abnormality judgment module is used to judge whether the mountain photovoltaic array has abnormal power generation based on the power generation capacity deviation.

[0016] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for abnormal power generation monitoring of mountain photovoltaic arrays based on sub-array benchmarking when executing the computer program.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for abnormal power generation monitoring of mountain photovoltaic arrays based on sub-array benchmarking.

[0018] Compared with the prior art, the present invention has the following technical effects: This technical solution classifies each sub-array in the photovoltaic plant area in a refined manner based on the terrain, environment, equipment conditions and other conditions of each sub-array. This classification process classifies sub-arrays with similar actual conditions into a secondary array, thereby ensuring to a greater extent that sub-arrays within the same secondary array have similar environmental conditions and equipment conditions. Subsequently, a standard sub-array is established within the secondary array, and sub-array benchmarking work is carried out. This approach not only achieves refined management and testing of the sub-array power generation capacity, but also greatly improves the pertinence and accuracy of operation and maintenance work.

[0019] Specifically, when a photovoltaic sub-array with similar environmental conditions and equipment conditions has a large deviation in power generation capacity, the operation and maintenance personnel can more confidently judge that the sub-array may have equipment defects on site. This judgment process is based on refined classification and benchmarking work, so it has a higher degree of credibility. On this basis, the operation and maintenance personnel can go to the site to carry out targeted inspections, so as to promptly discover and solve potential equipment problems and ensure the overall power generation efficiency and stable operation of the photovoltaic site.

[0020] In summary, this technical solution not only improves the operation and maintenance efficiency and accuracy of photovoltaic sites through the refined construction and management of standard sub-arrays, but also provides strong technical support and guarantee for the sustainable development of the photovoltaic industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the present invention.

[0022] Figure 2 It is a schematic block diagram of the principle of the present invention.

[0023] Figure 3 A schematic block diagram is provided for establishing the matrix division and standard sub-array of the present invention.

[0024] Figure 4 This is the flow chart of the PV sub-array benchmarking analysis. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings: Example 1, please refer to Figure 1 , a method for monitoring abnormal power generation of mountain photovoltaic arrays based on sub-array benchmarking, including: The photovoltaic array is divided into several secondary arrays according to preset rules, and each secondary array contains multiple photovoltaic sub-arrays; A standard sub-array is selected from multiple photovoltaic sub-arrays of each secondary array, and the others are used as non-standard sub-arrays; Collect the working data of all photovoltaic sub-arrays in each secondary array, and calculate the power generation capacity deviation between the standard sub-array and the non-standard sub-array based on the working data; Determine whether the mountain photovoltaic array has abnormal power generation based on the power generation capacity deviation.

[0026] By dividing the photovoltaic array into several secondary arrays according to preset rules, each secondary array contains multiple photovoltaic sub-arrays. This method can more carefully reflect the power generation of photovoltaic sub-arrays in different regions and under different conditions.

[0027] By selecting a standard subarray in each secondary array as a benchmark and comparing it with other non-standard subarrays, subarrays with abnormal power generation can be identified more accurately, thereby improving the accuracy of monitoring.

[0028] The working data of each photovoltaic sub-array is collected and calculated. By comparing the power generation capacity deviations of standard sub-arrays and non-standard sub-arrays, the sub-arrays with abnormal power generation can be quickly located, thereby improving the efficiency of monitoring.

[0029] This method classifies photovoltaic sub-arrays in a refined manner and groups sub-arrays with similar actual conditions into a secondary array to facilitate unified management and operation and maintenance.

[0030] Establishing standard sub-arrays within the secondary array provides a clear benchmarking basis for operation and maintenance personnel, making the operation and maintenance work more orderly and efficient.

[0031] When there is a large deviation in the power generation capacity of a photovoltaic sub-array with similar environmental conditions and equipment conditions, the operation and maintenance personnel can quickly determine whether there is an equipment defect in the sub-array and take timely measures to deal with it.

[0032] By accurately monitoring and locating sub-arrays with abnormal power generation, operation and maintenance personnel can go to the site to conduct targeted inspections, avoiding blind inspections and ineffective work, and reducing operation and maintenance costs.

[0033] Timely detection and resolution of potential equipment problems can avoid power generation losses and safety accidents caused by equipment failures, and reduce operation and maintenance risks.

[0034] This method can continuously monitor the power generation of photovoltaic sub-arrays, promptly detect and resolve abnormal power generation problems, and help maintain stable operation and efficient power generation of photovoltaic power stations.

[0035] By continuously optimizing and adjusting the layout of photovoltaic sub-arrays, equipment selection, and operation and maintenance strategies, the overall performance and economic benefits of photovoltaic power stations can be further improved.

[0036] In summary, the abnormal power generation monitoring method of mountain photovoltaic arrays based on sub-array benchmarking has significant technical effects, which can improve monitoring accuracy and efficiency, realize refined management and operation and maintenance, reduce operation and maintenance costs and risks, and improve the overall performance of photovoltaic power stations.

[0037] Embodiment 2, a method for monitoring abnormal power generation of a mountain photovoltaic array based on sub-array benchmarking, specifically includes: Step 1: Matrix division The photovoltaic array is divided into multiple secondary arrays according to certain rules, and each array contains a certain number of photovoltaic sub-arrays. The division of the secondary array should take into account the layout of the photovoltaic array, component type, environmental factors, etc. 1) Regional division: According to the geographical location and layout of the photovoltaic power station, the photovoltaic array is divided into several areas (i.e., secondary arrays) for easy monitoring.

[0038] 2) Consistency of lighting conditions: Try to group PV sub-arrays with similar lighting conditions into the same secondary array to reduce differences in power generation efficiency caused by uneven lighting. 3) Component performance matching: Considering factors such as the model and performance parameters of the photovoltaic components, components with similar performance are divided into the same secondary array to ensure that the power generation efficiency of the components in the secondary array is relatively consistent.

[0039] Step 2: Establishment of standard subarray Select standard sub-arrays from each divided secondary array. In principle, standard sub-arrays should be selected with stable operation, low failure rate, and optimal power generation capacity (historical power generation data since commissioning or power generation data of the previous year). The azimuth, inclination, component power, and current gear of the standard sub-array should be representative.

[0040] 1) The PV modules in the standard sub-array are of the mainstream specifications of the site, and there is no problem of mixed installation of models and powers. The PV modules have no hot spots or obstructions, and the cleanliness of the modules meets the requirements of the regulations. The azimuth and inclination of the modules meet the design requirements. The current gears of the PV modules in the standard sub-array should be consistent.

[0041] 2) The standard sub-array shielding condition should meet the requirement that there is no shielding between the east-west and north-south directions during the period from 9:00 to 15:00 local true solar time on the winter solstice.

[0042] Step 3: Data collection and processing 1) Install sensors (such as voltage sensors, current sensors, temperature sensors, etc.) at key locations of each sub-array to collect power generation data (such as voltage, current, power, temperature, etc.) of each sub-array in real time. 2) Preprocess the collected data, including data cleaning and outlier removal, to ensure the accuracy and reliability of the data.

[0043] Step 4: Benchmarking Analysis A subarray benchmarking model is established to calculate key indicators such as power generation efficiency and power loss of each subarray based on historical data and real-time data, and conduct benchmarking analysis with preset thresholds. If the indicators of a subarray exceed the threshold range, it is determined that the subarray has power generation abnormality. 1) Array power generation capacity = array power generation / DC side array installed capacity; 2) Calculate the power generation capacity of each sub-array in each secondary array; 3) Calculate the power generation capacity deviation between each sub-array and the standard sub-array in each secondary array. Power generation capacity deviation = (standard sub-array power generation capacity - sub-array power generation capacity) / standard sub-array power generation capacity; 4) The target value of power generation capacity deviation is within 3%, which is the main indicator for measuring the quality of power generation of photovoltaic sub-arrays. For sub-arrays with large power generation capacity deviation, a comprehensive analysis should be conducted to find problems; and rectification should be carried out for existing photovoltaic string failures, photovoltaic module shading, module hot spots, and cleanliness.

[0044] Array division is the basis of this monitoring method. Its principle is to reasonably divide the photovoltaic array into multiple secondary arrays by comprehensively considering the layout of the photovoltaic array, component type, environmental factors, etc. The key to this step is to ensure that the photovoltaic sub-arrays in each secondary array have similar lighting conditions, component performance, etc., so as to provide a reliable basis for the subsequent establishment of standard sub-arrays and benchmarking analysis.

[0045] Regional division: According to the geographical location and layout of the photovoltaic power station, the photovoltaic array is divided into several areas for easy monitoring (i.e., secondary arrays). The principle of this step is to divide the photovoltaic power station into multiple relatively independent but easy-to-manage parts through regional division, which is convenient for subsequent monitoring and analysis.

[0046] Consistency of lighting conditions: Try to group PV sub-arrays with similar lighting conditions into the same secondary array. The principle of this step is to reduce the difference in power generation efficiency caused by uneven lighting, so that the PV sub-arrays in the secondary array are comparable in lighting conditions.

[0047] Module performance matching: Considering factors such as the model and performance parameters of PV modules, modules with similar performance are divided into the same secondary array. The principle of this step is to ensure that the power generation efficiency of modules in the secondary array is relatively consistent, which is convenient for the subsequent establishment of standard sub-arrays and benchmarking analysis.

[0048] The establishment of a standard subarray is one of the key steps in the monitoring method. The principle is to select a subarray with stable operation, low failure rate and optimal power generation capacity as the standard subarray to provide a benchmark for other non-standard subarrays.

[0049] Principles for selecting standard subarrays: Standard subarrays should be selected with stable operation, low failure rate and optimal power generation capacity. The principle of this step is to provide a high standard benchmark for other subarrays by selecting the best subarray as the standard subarray.

[0050] Specific requirements for standard sub-arrays: The model, power, azimuth, inclination, etc. of the photovoltaic modules in the standard sub-array should be representative; the modules should have no hot spots or obstructions; the cleanliness of the modules should meet the requirements of the regulations, etc. The principle of this step is to ensure that the standard sub-array reaches the optimal state in all aspects, thereby improving the accuracy and reliability of the benchmark analysis.

[0051] Data collection and processing are important parts of the monitoring method. The principle is to collect the power generation data of each sub-array in real time by installing sensors, and pre-process the data to ensure the accuracy and reliability of the data.

[0052] Sensor installation: Install sensors (such as voltage sensors, current sensors, temperature sensors, etc.) at key locations of each sub-array. The principle of this step is to collect power generation data of each sub-array in real time through sensors to provide data support for subsequent benchmarking analysis.

[0053] Data preprocessing: preprocess the collected data, including data cleaning, outlier removal, etc. The principle of this step is to improve the accuracy and reliability of the data through preprocessing, and provide a reliable data basis for subsequent benchmarking analysis.

[0054] Benchmarking analysis is the core link of the monitoring method. Its principle is to establish a sub-array benchmarking model, calculate key indicators such as the power generation efficiency and power loss of each sub-array, and perform benchmarking analysis with preset thresholds to determine whether there is power generation abnormality in each sub-array.

[0055] Calculate power generation capacity: Calculate the power generation capacity of each sub-array using the formula "array power generation capacity = array power generation / DC side array installed capacity". The principle of this step is to evaluate the power generation efficiency of each sub-array by calculating the power generation capacity.

[0056] Calculate the power generation capacity deviation: Calculate the power generation capacity deviation of each subarray and the standard subarray through the formula "power generation capacity deviation = (standard subarray power generation capacity - subarray power generation capacity) / standard subarray power generation capacity". The principle of this step is to evaluate the gap between each subarray and the standard subarray by calculating the power generation capacity deviation.

[0057] Set thresholds and judge abnormalities: Set the threshold of power generation capacity deviation (such as within 3%). If the power generation capacity deviation of a subarray exceeds the threshold range, it is determined that the subarray has power generation abnormality. The principle of this step is to distinguish normal subarrays from abnormal subarrays by setting thresholds, providing a basis for subsequent rectification measures.

[0058] In summary, the principle of the abnormal power generation monitoring method of mountain photovoltaic arrays based on sub-array benchmarking is to timely discover and solve the abnormal power generation of mountain photovoltaic arrays through four core steps: array division, standard sub-array establishment, data collection and processing, and benchmarking analysis. This monitoring method improves the operation and maintenance efficiency and power generation efficiency of photovoltaic power stations through refined management and data analysis, and provides strong support for the sustainable development of the photovoltaic industry.

[0059] In yet another embodiment of the present invention, a system for monitoring power generation anomalies of a mountain photovoltaic array based on subarray benchmarking is provided, which can be used to implement the above-mentioned method for monitoring power generation anomalies of a mountain photovoltaic array based on subarray benchmarking. Specifically, the system includes: An array division module is used to divide the photovoltaic array into several secondary arrays according to preset rules, each secondary array containing multiple photovoltaic sub-arrays; A standard selection module is used to select a standard sub-array from multiple photovoltaic sub-arrays of each secondary array, and the others are non-standard sub-arrays; A data processing module is used to collect the working data of all photovoltaic sub-arrays in each secondary array, and calculate the power generation capacity deviation between the standard sub-array and the non-standard sub-array based on the working data; The abnormality judgment module is used to judge whether the mountain photovoltaic array has abnormal power generation based on the power generation capacity deviation.

[0060] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0061] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the abnormal monitoring method for power generation of mountain photovoltaic arrays based on sub-array benchmarking.

[0062] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment of the method for monitoring abnormal power generation of a mountain photovoltaic array based on sub-array benchmarking.

[0063] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring abnormal power generation of mountain photovoltaic arrays based on sub-array benchmarking, characterized in that: include: The photovoltaic array is divided into several secondary arrays according to preset rules, and each secondary array contains multiple photovoltaic sub-arrays; A standard sub-array is selected from multiple photovoltaic sub-arrays of each secondary array, and the others are used as non-standard sub-arrays; Collect the working data of all photovoltaic sub-arrays in each secondary array, and calculate the power generation capacity deviation between the standard sub-array and the non-standard sub-array based on the working data; Determine whether the mountain photovoltaic array has abnormal power generation based on the power generation capacity deviation.

2. The method for monitoring abnormal power generation of a mountain photovoltaic array based on sub-array benchmarking according to claim 1 is characterized in that: The photovoltaic array is divided into several secondary arrays according to preset rules. Each secondary array contains multiple photovoltaic sub-arrays, including: According to the geographical location and layout of the photovoltaic power station, the photovoltaic array is divided into several secondary arrays; Preset rules: PV sub-arrays with the same lighting conditions are grouped into the same secondary array; PV modules with the same model and performance parameters are grouped into the same secondary array.

3. The method for monitoring abnormal power generation of mountain photovoltaic arrays based on sub-array benchmarking according to claim 1 is characterized in that: A standard subarray is selected from multiple photovoltaic subarrays of each secondary array, including: The photovoltaic modules in the standard sub-array have no hot spots or obstructions, and the module cleanliness, azimuth and inclination meet the design requirements; the current level of each photovoltaic module in the standard sub-array remains consistent; The standard sub-array shielding condition should meet the requirement that there is no shielding between the east-west and north-south directions during the period from 9:00 to 15:00 local true solar time on the winter solstice.

4. The method for monitoring abnormal power generation of a mountain photovoltaic array based on sub-array benchmarking according to claim 1 is characterized in that: Collect the working data of all photovoltaic sub-arrays in each secondary array, including: Sensors are installed to collect the voltage, current, temperature and power of the photovoltaic sub-array, and all collected data are cleaned and outliers are eliminated.

5. The method for monitoring abnormal power generation of mountain photovoltaic arrays based on sub-array benchmarking according to claim 1 is characterized in that: The power generation capacity deviation of the standard subarray and the non-standard subarray is calculated based on the working data, including: 1) Array power generation capacity = array power generation / DC side array installed capacity; 2) Calculate the power generation capacity of all photovoltaic sub-arrays in each secondary array; 3) Calculate the power generation capacity deviation between each non-standard sub-array and the standard sub-array in each secondary array, Power generation capacity deviation = (standard sub-array power generation capacity - sub-array power generation capacity) / standard sub-array power generation capacity.

6. The method for monitoring abnormal power generation of a mountain photovoltaic array based on sub-array benchmarking according to claim 5 is characterized in that: The target value of power generation capacity deviation is within 3%, which is used as an indicator to measure the quality of power generation of photovoltaic sub-arrays.

7. The method for monitoring abnormal power generation of a mountain photovoltaic array based on sub-array benchmarking according to claim 6 is characterized in that: Whether the power generation of the mountain photovoltaic array is abnormal is judged based on the power generation capacity deviation, including: if the power generation capacity deviation is less than or equal to 3%, the power generation of the photovoltaic sub-array in the area is normal; if the power generation capacity deviation is greater than 3%, the power generation of the photovoltaic sub-array in the area is abnormal.

8. The abnormal power generation monitoring system for mountain photovoltaic arrays based on sub-array benchmarking is characterized by: include: An array division module is used to divide the photovoltaic array into several secondary arrays according to preset rules, each secondary array containing multiple photovoltaic sub-arrays; A standard selection module is used to select a standard sub-array from multiple photovoltaic sub-arrays of each secondary array, and the others are non-standard sub-arrays; A data processing module is used to collect the working data of all photovoltaic sub-arrays in each secondary array, and calculate the power generation capacity deviation between the standard sub-array and the non-standard sub-array based on the working data; The abnormality judgment module is used to judge whether the mountain photovoltaic array has abnormal power generation based on the power generation capacity deviation.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for monitoring power generation anomalies of a mountain photovoltaic array based on sub-array benchmarking as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring power generation anomalies of a mountain photovoltaic array based on sub-array benchmarking as claimed in any one of claims 1 to 7 are implemented.

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