Operation fault monitoring method and system for new energy distributed photovoltaic power station

Abstract

The invention discloses an operation fault monitoring method and system for a new energy distributed photovoltaic power station, and relates to the related technical field of photovoltaic power station monitoring, and the method comprises the steps: obtaining the photovoltaic array information of a target distributed photovoltaic power station, and laying a plurality of monitoring units based on the photovoltaic array information; a photovoltaic image sequence and an infrared image sequence of the photovoltaic panel are collected, and abnormal temperature distribution is obtained; performing area detection on the photovoltaic panel difference image to obtain an abnormal area of the photovoltaic panel; analyzing and checking the abnormal detection result of the photovoltaic panel to generate abnormal distribution of the photovoltaic panel; and transmitting the abnormal distribution of the photovoltaic panel to a central monitoring system through wireless communication, and carrying out operation and maintenance planning. The technical problem that the accuracy and the reliability of fault identification are insufficient due to the fact that the surface damage abnormity of the photovoltaic panel is difficult to accurately identify and locate in the operation fault monitoring of the existing new energy distributed photovoltaic power station is solved, and the technical effect of improving the accuracy and the reliability of the operation fault monitoring of the photovoltaic power station is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to photovoltaic power station monitoring, and specifically to a method and system for monitoring operation faults of a new energy distributed photovoltaic power station. Background Art

[0002] Under the background of the transformation of the global energy structure, the development and utilization of new energy has become a key force in promoting sustainable development. As an important part of the new energy field, distributed photovoltaic power stations, with their clean and renewable characteristics, have shown great potential in alleviating energy crises and reducing environmental pollution. However, with the widespread deployment and expansion of distributed photovoltaic power stations, the problems of fault monitoring and maintenance in their operation process have become increasingly prominent. Distributed photovoltaic power stations are usually composed of a large number of photovoltaic panels, which are directly exposed to the outdoor environment and are affected by various natural factors such as light, temperature, humidity, wind and sand for a long time. Various faults are prone to occur, such as photovoltaic panel aging, cracks, hot spot effects, junction box failures, etc. These faults will not only lead to a decrease in power generation efficiency, but also may cause safety hazards such as fires, posing a serious threat to the safe and stable operation of power stations. Traditional photovoltaic power station fault monitoring methods mostly rely on manual inspections, which have the disadvantages of low efficiency, high cost, slow response, etc., and it is difficult to meet the operation and maintenance needs of large-scale distributed photovoltaic power stations.

[0003] Therefore, in the current distributed photovoltaic power station operation fault monitoring related technologies, there is a technical problem that it is difficult to accurately identify and locate abnormal surface damage of photovoltaic panels, which leads to insufficient accuracy and reliability of fault identification. Summary of the invention

[0004] The present application provides a method and system for monitoring the operation faults of a new energy distributed photovoltaic power station. By adopting technical means such as an integrated high-resolution camera and an infrared thermal imager, image differential processing, and analysis and verification of detection results, the present application solves the technical problem that it is difficult to accurately identify and locate abnormal surface damage of photovoltaic panels in the existing new energy distributed photovoltaic power station operation fault monitoring, which leads to insufficient accuracy and reliability of fault identification. The application achieves the technical effect of improving the accuracy and reliability of photovoltaic power station operation fault monitoring.

[0005] The present application provides an operation fault monitoring method for a new energy distributed photovoltaic power station, the method comprising: obtaining photovoltaic array information of a target distributed photovoltaic power station, deploying multiple monitoring units based on the photovoltaic array information, each monitoring unit comprising a high-resolution camera and an infrared thermal imager; collecting a photovoltaic image sequence and an infrared image sequence of a photovoltaic panel through the high-resolution camera and the infrared thermal imager, identifying and marking abnormal temperature areas of multiple infrared images of the infrared image sequence, and obtaining an abnormal temperature distribution; performing image difference on the photovoltaic image sequence to generate a photovoltaic panel differential image, performing region detection on the photovoltaic panel differential image, and obtaining an abnormal region of the photovoltaic panel; classifying and locating the abnormal region of the photovoltaic panel to obtain an abnormal detection result of the photovoltaic panel, analyzing and verifying the abnormal detection result of the photovoltaic panel using the abnormal temperature distribution, and generating an abnormal distribution of the photovoltaic panel; transmitting the abnormal distribution of the photovoltaic panel to a central monitoring system through wireless communication and performing operation and maintenance planning.

[0006] In a possible implementation, the abnormal temperature areas of multiple infrared images of the infrared image sequence are identified and marked to obtain the abnormal temperature distribution, and the following processing is performed: temperature detection is performed on the first infrared image, the temperature value of each pixel is extracted, and a temperature matrix is ​​generated; region segmentation is performed on the temperature matrix according to the temperature threshold interval, and identification is performed based on the region segmentation result to generate an initial abnormal region identification; connectivity analysis is performed on the initial abnormal region identification to extract the shape features of each abnormal region, and the abnormal temperature distribution is obtained by marking.

[0007] In a possible implementation, the photovoltaic image sequence is subjected to image differentiation to generate a photovoltaic panel differential image, and the following processing is performed: the photovoltaic image sequence is subjected to grayscale processing to obtain a photovoltaic grayscale image sequence; the photovoltaic grayscale image sequence is subjected to image registration, and image differentiation is performed based on the image registration result to generate a photovoltaic panel differential image.

[0008] In a possible implementation, image registration is performed on the photovoltaic grayscale image sequence, image differentiation is performed based on the image registration result, and the following processing is performed: key feature points of each photovoltaic grayscale image are extracted, feature points of two adjacent photovoltaic grayscale images are matched, and multiple matching points are obtained; images are aligned according to the matching points, an image transformation matrix is ​​generated, and geometric transformation is performed to complete image registration; image difference calculation is performed based on the registered image sequence to generate a photovoltaic panel differential image.

[0009] In a possible implementation, the photovoltaic panel differential image is subjected to regional detection to obtain abnormal regions of the photovoltaic panel, and the following processing is also performed: the photovoltaic panel differential image is binarized by setting a threshold to generate a binary differential image of the photovoltaic panel; connectivity detection is performed on the binary differential image of the photovoltaic panel to mark connected white pixels as the same connected region to obtain multiple connected regions; feature extraction is performed on the multiple connected regions respectively, and the abnormal regions of the photovoltaic panel are obtained by segmentation.

[0010] In a possible implementation, the abnormal areas of the photovoltaic panels are classified and located to obtain abnormal detection results of the photovoltaic panels, and the following processing is also performed: the abnormal areas of the photovoltaic panels are clustered based on feature similarity, and based on the feature clustering results, they are classified using an abnormal classification model to obtain multiple abnormal types; the boundaries of the abnormal areas of the photovoltaic panels are marked to generate abnormal detection results of the photovoltaic panels, and the abnormal detection results of the photovoltaic panels include the number and location of abnormal areas and the degree of abnormality.

[0011] In a possible implementation, the abnormal temperature distribution is used to analyze and verify the photovoltaic panel abnormality detection results to generate the photovoltaic panel abnormality distribution, and the following processing is also performed: through spatial coordinate matching, the abnormal temperature distribution of the infrared image is matched with the abnormal area in the photovoltaic panel abnormality detection results; a consistency analysis is performed on each matched abnormal area; if the consistency check passes, the photovoltaic panel abnormality distribution is generated.

[0012] The present application also provides an operation fault monitoring system for a new energy distributed photovoltaic power station, including: a monitoring unit deployment module, the monitoring unit deployment module is used to obtain photovoltaic array information of a target distributed photovoltaic power station, and multiple monitoring units are deployed based on the photovoltaic array information, each monitoring unit includes a high-resolution camera and an infrared thermal imager; an image sequence acquisition module, the image sequence acquisition module is used to collect photovoltaic image sequences and infrared image sequences of photovoltaic panels through the high-resolution camera and the infrared thermal imager, identify and mark abnormal temperature areas of multiple infrared images of the infrared image sequence, and obtain abnormal temperature distribution; a photovoltaic panel abnormal area acquisition module, the The photovoltaic panel abnormal area acquisition module is used to perform image differentiation on the photovoltaic image sequence to generate a photovoltaic panel differential image, perform area detection on the photovoltaic panel differential image, and obtain the abnormal area of ​​the photovoltaic panel; the photovoltaic panel abnormal distribution generation module is used to classify and locate the abnormal area of ​​the photovoltaic panel, obtain the photovoltaic panel abnormal detection result, use the abnormal temperature distribution to analyze and verify the photovoltaic panel abnormal detection result, and generate the photovoltaic panel abnormal distribution; the photovoltaic panel abnormal distribution operation and maintenance planning module is used to transmit the photovoltaic panel abnormal distribution to the central monitoring system through wireless communication and perform operation and maintenance planning.

[0013] The proposed method and system for monitoring the operation fault of a new energy distributed photovoltaic power station in this application is intended to obtain the photovoltaic array information of the target distributed photovoltaic power station, and deploy multiple monitoring units based on the photovoltaic array information; collect the photovoltaic image sequence and infrared image sequence of the photovoltaic panel to obtain the abnormal temperature distribution; perform regional detection on the photovoltaic panel differential image to obtain the abnormal area of ​​the photovoltaic panel; analyze and verify the abnormal detection results of the photovoltaic panel to generate the abnormal distribution of the photovoltaic panel; transmit the abnormal distribution of the photovoltaic panel to the central monitoring system through wireless communication and perform operation and maintenance planning. It solves the technical problem that it is difficult to accurately identify and locate the abnormal surface damage of the photovoltaic panel in the existing new energy distributed photovoltaic power station operation fault monitoring, which leads to insufficient accuracy and reliability of fault identification, and achieves the technical effect of improving the accuracy and reliability of photovoltaic power station operation fault monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a method for monitoring operating faults of a new energy distributed photovoltaic power station provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the structure of an operation fault monitoring system for a new energy distributed photovoltaic power station provided in an embodiment of the present application.

[0017] Explanation of the reference numerals: monitoring unit deployment module 10 , image sequence acquisition module 20 , photovoltaic panel abnormal area acquisition module 30 , photovoltaic panel abnormal distribution generation module 40 , photovoltaic panel abnormal distribution operation and maintenance planning module 50 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a method for monitoring the operation failure of a new energy distributed photovoltaic power station, such as Figure 1 As shown, the method includes:

[0022] Step S100, obtain the photovoltaic array information of the target distributed photovoltaic power station, and deploy multiple monitoring units based on the photovoltaic array information, each monitoring unit including a high-resolution camera and an infrared thermal imager. Acquiring the photovoltaic array information of the target distributed photovoltaic power station specifically refers to fully understanding the basic information such as the photovoltaic array layout of the target distributed photovoltaic power station, the model, quantity, installation location of the photovoltaic panels, and the power generation capacity of the entire power station, and rationally planning and deploying the monitoring units based on the acquired photovoltaic array information. For example, ensure that each photovoltaic array or key area can be covered by at least one monitoring unit to achieve comprehensive monitoring; for areas prone to failure or low power generation efficiency, the deployment density of the monitoring units should be increased to promptly detect and deal with problems; the deployment location of the monitoring units should be convenient for operation and maintenance personnel to carry out daily maintenance and overhaul; each monitoring unit includes a high-resolution camera and an infrared thermal imager. The high-resolution camera is used to capture clear images of photovoltaic panels. Through image processing techniques such as image difference, it can identify abnormal conditions such as physical damage, stains, and obstructions on the surface of photovoltaic panels. The high-resolution camera can provide sufficient detail information to ensure the accuracy of the monitoring results. The infrared thermal imager is used to obtain the temperature distribution information of photovoltaic panels in real time. The infrared thermal imager can measure the temperature of the surface of photovoltaic panels non-contactly and display different temperature areas through color gradation changes, so as to find abnormal temperature areas caused by hot spot effect, poor contact and other reasons. These abnormal temperature areas are often the precursor or direct manifestation of photovoltaic panel failure.

[0023] Step S200, collecting a photovoltaic image sequence and an infrared image sequence of a photovoltaic panel by using the high-resolution camera and the infrared thermal imager, identifying and marking abnormal temperature areas of a plurality of infrared images of the infrared image sequence, and obtaining abnormal temperature distribution. Visible light images of photovoltaic panels are collected by high-resolution cameras to form photovoltaic image sequences. These images can clearly show the appearance of photovoltaic panels, including physical damage such as surface stains, obstructions, and cracks. Infrared thermal imagers use the principle of infrared radiation to measure the temperature distribution on the surface of photovoltaic panels and generate infrared image sequences. These images represent different temperature areas with different colors or grayscale levels, thereby intuitively showing the thermal state of photovoltaic panels. The collected infrared image sequences are preprocessed, including denoising, contrast enhancement, etc., to improve image quality. The infrared images are then analyzed using image processing algorithms to identify areas with significant differences in temperature from the surrounding environment, namely, abnormal temperature areas. After the abnormal temperature areas are identified, the locations and ranges of these areas are automatically marked by algorithms. The marking method may include marking the abnormal areas with different colors or borders on the image to obtain the abnormal temperature areas. Finally, the abnormal temperature area data in multiple infrared images are integrated to generate the abnormal temperature distribution of photovoltaic panels, intuitively showing the temperature differences and distribution of various areas on the photovoltaic panels.

[0024] In a possible implementation, step S200 further includes step S210, performing temperature detection on the first infrared image, extracting the temperature value of each pixel, and generating a temperature matrix; step S220, performing region segmentation on the temperature matrix according to the temperature threshold interval, marking based on the region segmentation result, and generating an initial abnormal region mark; step S230, performing connectivity analysis on the initial abnormal region mark, extracting the shape features of each abnormal region, and marking to obtain abnormal temperature distribution.

[0025] Preferably, the first infrared image is any frame image in the infrared image sequence, each pixel in the infrared image corresponds to the temperature information of a certain point on the surface of the photovoltaic panel, the temperature value corresponding to each pixel is extracted from the infrared image, and the temperature values ​​of all the extracted pixels are arranged in a certain order (such as from left to right, from top to bottom) into a two-dimensional matrix, i.e., a temperature matrix, the size of the matrix corresponds to the resolution of the infrared image, and each element in the matrix represents the temperature value of a pixel in the infrared image; according to the normal operating temperature of the photovoltaic panel and possible abnormal conditions, one or more temperature threshold intervals are set, each element in the temperature matrix (i.e., the temperature value of each pixel) is traversed, and it is compared with the set temperature threshold interval, and according to the comparison result , divide the pixels in the temperature matrix into different areas, each area corresponds to a specific temperature range, and for the pixels falling within the abnormal temperature threshold interval, the area where they are located is marked as the initial abnormal area; based on the identification of the initial abnormal area, connectivity analysis is performed. Connectivity analysis is an image processing technology used to determine the area composed of interconnected pixels in the image. Through connectivity analysis, the independent sub-areas in the initial abnormal area are merged into a complete abnormal area. For each connected abnormal area, its shape features are extracted, including the size of the area (such as the number of pixels), shape (such as rectangularity, circularity, etc.), direction, etc. Finally, the extracted abnormal area and its shape features are marked on the infrared image to form an abnormal temperature distribution.

[0026] Step S300, performing image difference on the photovoltaic image sequence to generate a photovoltaic panel difference image, performing regional detection on the photovoltaic panel difference image, and obtaining the abnormal area of ​​the photovoltaic panel. Performing image difference on the photovoltaic image sequence to generate a photovoltaic panel difference image, and performing regional detection on the difference image to obtain the abnormal area of ​​the photovoltaic panel, wherein image difference is an image processing technology, which obtains the difference between two images by subtracting images at consecutive time points (or adjacent frames), and the difference usually reflects the movement or change of objects in the image. In the photovoltaic image sequence, difference technology is used to detect small changes that may occur on the surface of the photovoltaic panel, such as shadow movement, increase or decrease of stains, crack expansion, etc. Specifically, two or more consecutive frames of photovoltaic images are selected as input, and these images are subtracted pixel by pixel to obtain a difference image, and each pixel value in the difference image represents the value of the corresponding position between the two images. Brightness or color difference in frame images; region detection identifies and locates regions with specific features in the differential image. Specifically, a suitable threshold is set to distinguish the foreground (i.e., abnormal area) and background in the differential image. The area in the differential image whose pixel value exceeds the threshold is considered to be the foreground area, i.e., the abnormal area of ​​the photovoltaic panel. The differential image is subjected to morphological processing, such as corrosion, expansion, opening operation, closing operation, etc., to remove noise, fill holes, connect adjacent areas, etc. Finally, the processed differential image is subjected to region marking and extraction. Through methods such as connectivity analysis, the foreground area in the differential image is marked as an independent object, and their boundary and position information is extracted to obtain the abnormal area of ​​the photovoltaic panel.

[0027] In a possible implementation, step S300 further includes step S310, performing grayscale processing on the photovoltaic image sequence to obtain a photovoltaic grayscale image sequence; step S320, performing image registration on the photovoltaic grayscale image sequence, performing image difference based on the image registration result, and generating a photovoltaic panel differential image.

[0028] Furthermore, step S320 also includes step S321, extracting key feature points of each photovoltaic grayscale image, matching feature points of two adjacent photovoltaic grayscale images, and obtaining multiple matching points; step S322, aligning images according to matching points, generating image transformation matrix, and performing geometric transformation to complete image registration; step S323, performing image difference calculation based on the registered image sequence to generate a photovoltaic panel difference image

[0029] Preferably, the grayscale processing converts the color image into a grayscale image, that is, removes the color information of the image, retains only the brightness information, simplifies the data volume of the image, and specifically, converts the photovoltaic image sequence from the RGB color space to the grayscale color space, assigns different weights to the three color channels of red, green, and blue, and then calculates the weighted average as the grayscale value. The commonly used weight ratios are R: 0.299, G: 0.587, B: 0.114, and each image in the photovoltaic image sequence is grayscaled to finally obtain a grayscale image sequence. Image registration is to spatially transform two or more images so that they achieve the best matching state in the same coordinate system. Specifically, stable feature points such as corner points and edge points are extracted from the grayscale image, and feature descriptors (such as SIFT, SURF, etc.) are used to match the extracted feature points to find the corresponding feature point pairs in adjacent images. Based on the matched feature point pairs, the spatial transformation model between the images is estimated, such as affine transformation, perspective transformation, etc. The images are transformed using the estimated transformation model so that the adjacent images are in the same coordinate system. The best matching state is achieved in the same coordinate system, and finally image difference processing is performed to detect slight changes that may occur on the surface of the photovoltaic panel, such as shadow movement, increase or decrease of stains, crack extension, etc. Specifically, an image is selected as the reference image in the grayscale image sequence, and the other images are transformed according to the transformation model between the reference image so that they are spatially aligned with the reference image. The transformed image is subtracted pixel by pixel from the reference image to obtain a differential image. Each pixel value in the differential image represents the brightness difference of the corresponding position in the two images. According to the brightness difference in the differential image, abnormal areas on the surface of the photovoltaic panel can be identified and a differential image of the photovoltaic panel can be generated. These abnormal areas may be caused by shadows, stains, cracks, etc.

[0030] In a possible implementation, step S300 further includes step S330, binarizing the photovoltaic panel differential image by setting a threshold to generate a photovoltaic panel binary differential image; step S340, performing connectivity detection on the photovoltaic panel binary differential image, marking connected white pixels as the same connected area, and obtaining multiple connected areas; step S350, performing feature extraction on the multiple connected areas respectively, and segmenting to obtain abnormal areas of the photovoltaic panel.

[0031] Preferably, the binarization process is to divide the pixels in the image into two categories (usually black and white) so as to more clearly represent the information in the image. In the photovoltaic panel differential image, by setting a suitable threshold, the brightness change caused by anomalies (such as shadows, stains, cracks, etc.) is distinguished from the background noise. Specifically, according to the actual situation of the photovoltaic panel differential image, a suitable threshold is set, and the brightness value of each pixel in the photovoltaic panel differential image is compared with the threshold. If the brightness value of the pixel is greater than the threshold, the pixel is set to white (or foreground color); otherwise, it is set to black (or background color). The image after binarization is the photovoltaic panel binary differential image, in which the white area represents the possible abnormal area and the black area represents the background. Connectivity detection is used to identify the area composed of interconnected pixels in the image (called connected area). In the photovoltaic panel binary differential image, multiple white pixels caused by the same abnormality can be marked as the same connected area through connectivity detection. Specifically, each pixel in the photovoltaic panel binary differential image is traversed, and when a white pixel is encountered, the detection is performed. Check its adjacent pixels (usually in the four directions of up, down, left and right, and can also include the diagonal direction). If the adjacent pixel is also white and is not marked as part of other connected areas, add it to the current connected area, and continue to recursively check the adjacent pixels of the pixel. Through the traversal and marking process, multiple connected areas are finally generated, and each connected area represents a possible abnormal area; feature extraction is to extract information that can describe its properties (such as size, shape, position, etc.) from the connected area. Through feature extraction, it can be further determined whether the connected area does represent an abnormal area. Segmentation is to separate the connected area confirmed as abnormal after feature extraction from the original image. Specifically, feature extraction is performed on each connected area, including calculating its area, perimeter, shape factor (such as circularity, rectangularity, etc.), centroid position, etc. According to the extracted features and preset judgment criteria (such as area threshold, shape factor threshold, etc.), it is determined whether each connected area represents an abnormal area, and the connected area judged to be abnormal is segmented from the original image or the binary difference image, and finally the abnormal area of ​​the photovoltaic panel is obtained.

[0032] Step S400, classify and locate the abnormal areas of the photovoltaic panel, obtain the abnormal detection results of the photovoltaic panel, use the abnormal temperature distribution to analyze and verify the abnormal detection results of the photovoltaic panel, and generate the abnormal distribution of the photovoltaic panel. According to the characteristics of the abnormal area (such as shape, size, color, temperature, etc.), it is divided into different categories. For example, the abnormal area caused by shadow occlusion can be marked as a "shadow area", the abnormal area caused by surface stains can be marked as a "stain area", and the abnormal area caused by cracks can be marked as a "crack area", etc. At the same time, a unique identifier is assigned to each abnormal area. By calculating the coordinates of the abnormal area in the image and converting them into the actual position on the photovoltaic panel, the specific position of each abnormal area on the photovoltaic panel is determined. After completing the classification and positioning of the abnormal area, this information is integrated to form the abnormal detection result of the photovoltaic panel, which usually includes the type, quantity, location and other information of the abnormal area, and is used. The infrared thermal imager obtains the temperature distribution information of the photovoltaic panel, and performs temperature detection on the abnormal area. For example, the abnormal temperature area is usually associated with the fault state of the photovoltaic panel. For example, the hot spot effect will cause the temperature of the local area to rise. The abnormal temperature distribution is compared and analyzed with the abnormal detection results of the photovoltaic panel. If an abnormal area also appears as an abnormal temperature area on the temperature distribution map, the authenticity and accuracy of the abnormal area are verified. If there is a difference between the two, further inspection and confirmation are required. After the analysis and verification are completed, the abnormal areas of the photovoltaic panel and related information that have passed the verification are integrated to generate the abnormal distribution of the photovoltaic panel, which clearly shows the location, type, quantity and other information of all abnormal areas on the photovoltaic panel.

[0033] In a possible implementation, step S400 further includes step S410, performing feature clustering on the abnormal areas of the photovoltaic panel based on feature similarity, and classifying them using an abnormal classification model based on the feature clustering results to obtain multiple abnormal types; step S420, marking the boundaries of the abnormal areas of the photovoltaic panel, and generating photovoltaic panel abnormality detection results, wherein the photovoltaic panel abnormality detection results include the number and location of abnormal areas and the degree of abnormality.

[0034] Preferably, feature clustering divides data points into multiple groups or clusters according to their similarities. In photovoltaic panel anomaly detection, feature clustering can be used to cluster abnormal areas with similar features (such as size, shape, texture, etc.). Specifically, features for clustering are extracted from each abnormal area of ​​photovoltaic panels, including geometric properties of the area (such as area, perimeter, shape factor, etc.), texture features, color features, etc. The features of each abnormal area are combined into a feature vector. Clustering algorithms such as K-means, hierarchical clustering, DBSCAN, etc. are used to cluster the feature vectors and group similar abnormal areas into the same cluster. The clustered abnormal areas can be further subdivided into different abnormal types (such as shadows, stains, cracks, etc.) through an abnormal classification model. Specifically, photovoltaic panels of known categories are collected. Abnormal area images are obtained and their abnormal types are marked. A classification model is built based on a support vector machine. The training data is used for training to obtain an abnormal classification model. The clustered abnormal areas are input into the classification model to obtain the abnormal type of each area. The boundary of the abnormal area is marked to clearly mark the specific location and range of the abnormality on the photovoltaic panel image. Specifically, image processing technology (such as edge detection algorithm) is used to detect and mark the boundary of each abnormal area, and the number, location (such as coordinates or relative position), abnormal type and other information of the abnormal area are counted. According to the characteristics of the abnormal area (such as size, shape, color depth, etc.) and the abnormal type, the abnormal degree of each abnormal area is evaluated, which can be expressed by quantitative indicators (such as abnormal score), and finally integrated to generate a comprehensive and accurate photovoltaic panel abnormality detection result.

[0035] In a possible implementation, step S400 further includes step S440, matching the abnormal temperature distribution of the infrared image with the abnormal area in the photovoltaic panel abnormal detection result through spatial coordinate matching; step S450, performing consistency analysis on each matched abnormal area; step S460, if the consistency check passes, generating the photovoltaic panel abnormal distribution.

[0036] Preferably, the spatial coordinate matching aims to match the abnormal temperature distribution detected in the infrared image with the abnormal area in the photovoltaic panel abnormality detection result to confirm the specific abnormal position on the photovoltaic panel. Specifically, it is ensured that the coordinate systems in the infrared image and the photovoltaic panel abnormality detection result are consistent, or at least can be converted to each other, and common feature points (such as edges, corners, etc.) are found on the infrared image and the photovoltaic panel image. The correspondence between the two images is established through these feature points. According to the matching results of the feature points, the abnormal temperature distribution in the infrared image is mapped to the photovoltaic panel abnormality detection result to achieve corresponding matching of the abnormal area; consistency analysis is used to verify the results obtained through spatial coordinate matching. Whether the abnormal area found is consistent with the actual situation, ensure the accuracy and reliability of the detection results. Specifically, the abnormal temperature data in the infrared image is compared with the abnormal area data in the photovoltaic panel abnormal detection results to check whether they are consistent in position, shape, size, etc., and a certain threshold range is set to judge the matching degree of the abnormal data. If the difference exceeds the threshold, it is considered that the consistency check has failed; if the consistency check passes, it means that the abnormal temperature distribution in the infrared image corresponds to the abnormal area in the photovoltaic panel abnormal detection results. The abnormal temperature data in the infrared image and the abnormal area data in the photovoltaic panel abnormal detection results are integrated to form the photovoltaic panel abnormal distribution.

[0037] Step S500, transmit the abnormal distribution of the photovoltaic panels to the central monitoring system through wireless communication and perform operation and maintenance planning. The abnormal distribution of photovoltaic panels including the location, type, quantity and other information of the abnormal area is transmitted to the central monitoring system using wireless communication technology (such as Wi-Fi, GPRS, LoRa, NB-IoT, etc.). After receiving the wirelessly transmitted data, the central monitoring system evaluates and classifies the faults according to the analysis results of the abnormal distribution of photovoltaic panels, determines the severity of the faults, the scope of impact and the possible causes, and formulates targeted operation and maintenance plans according to the results of the fault assessment. The planning content should include fault handling solutions, personnel arrangements, material preparation, time plans, etc. According to the requirements of the operation and maintenance plan, specific fault handling tasks are performed, including going to the site for troubleshooting, repairing or replacing faulty equipment, etc.

[0038] In the above, refer to Figure 1 A method for monitoring operating faults of a distributed photovoltaic power station of new energy according to an embodiment of the present invention is described in detail. Figure 2 An operation fault monitoring system for a new energy distributed photovoltaic power station according to an embodiment of the present invention is described.

[0039] According to an embodiment of the present invention, a new energy distributed photovoltaic power station operation fault monitoring system is used to solve the technical problem that it is difficult to accurately identify and locate abnormal surface damage of photovoltaic panels in existing new energy distributed photovoltaic power station operation fault monitoring, which leads to insufficient accuracy and reliability of fault identification, and achieves the technical effect of improving the accuracy and reliability of photovoltaic power station operation fault monitoring. A new energy distributed photovoltaic power station operation fault monitoring system includes: a monitoring unit layout module 10, an image sequence acquisition module 20, a photovoltaic panel abnormal area acquisition module 30, a photovoltaic panel abnormal distribution generation module 40, and a photovoltaic panel abnormal distribution operation and maintenance planning module 50.

[0040] A monitoring unit deployment module 10, the monitoring unit deployment module 10 is used to obtain photovoltaic array information of a target distributed photovoltaic power station, and deploy multiple monitoring units based on the photovoltaic array information, each monitoring unit including a high-resolution camera and an infrared thermal imager;

[0041] An image sequence acquisition module 20, the image sequence acquisition module 20 is used to acquire a photovoltaic image sequence and an infrared image sequence of the photovoltaic panel through the high-resolution camera and the infrared thermal imager, identify and mark abnormal temperature areas of multiple infrared images of the infrared image sequence, and obtain abnormal temperature distribution;

[0042] A photovoltaic panel abnormal region acquisition module 30, the photovoltaic panel abnormal region acquisition module 30 is used to perform image difference on the photovoltaic image sequence to generate a photovoltaic panel differential image, perform region detection on the photovoltaic panel differential image, and acquire the photovoltaic panel abnormal region;

[0043] A photovoltaic panel abnormal distribution generation module 40, the photovoltaic panel abnormal distribution generation module 40 is used to classify and locate the abnormal area of ​​the photovoltaic panel, obtain the photovoltaic panel abnormality detection result, analyze and verify the photovoltaic panel abnormality detection result using the abnormal temperature distribution, and generate the photovoltaic panel abnormal distribution;

[0044] The photovoltaic panel abnormal distribution operation and maintenance planning module 50 is used to transmit the photovoltaic panel abnormal distribution to the central monitoring system through wireless communication and perform operation and maintenance planning.

[0045] The specific configuration of the image sequence acquisition module 20 will be described in detail below. The image sequence acquisition module 20 further includes: performing temperature detection on the first infrared image, extracting the temperature value of each pixel, and generating a temperature matrix; performing region segmentation on the temperature matrix according to the temperature threshold interval, marking based on the region segmentation result, and generating an initial abnormal region mark; performing connectivity analysis on the initial abnormal region mark, extracting the shape features of each abnormal region, and marking to obtain abnormal temperature distribution.

[0046] The specific configuration of the photovoltaic panel abnormal area acquisition module 30 will be described in detail below. The photovoltaic panel abnormal area acquisition module 30 may further include: graying the photovoltaic image sequence to obtain a photovoltaic grayscale image sequence; performing image registration on the photovoltaic grayscale image sequence, performing image difference based on the image registration result, and generating a photovoltaic panel differential image.

[0047] The specific configuration of the photovoltaic panel abnormal area acquisition module 30 will be described in detail below. The photovoltaic panel abnormal area acquisition module 30 may further include: extracting key feature points of each photovoltaic grayscale image, matching feature points of two adjacent photovoltaic grayscale images, and acquiring multiple matching points; aligning images according to matching points, generating an image transformation matrix, and performing geometric transformation to complete image registration; performing image difference calculation based on the registered image sequence to generate a photovoltaic panel differential image.

[0048] The specific configuration of the photovoltaic panel abnormal region acquisition module 30 will be described in detail below. The photovoltaic panel abnormal region acquisition module 30 may further include: performing binarization processing on the photovoltaic panel differential image by setting a threshold to generate a photovoltaic panel binary differential image; performing connectivity detection on the photovoltaic panel binary differential image, marking connected white pixels as the same connected region, and obtaining multiple connected regions; performing feature extraction on the multiple connected regions respectively, and segmenting to obtain the photovoltaic panel abnormal region.

[0049] The specific configuration of the photovoltaic panel abnormal distribution generation module 40 will be described in detail below. The photovoltaic panel abnormal distribution generation module 40 further includes: clustering the abnormal areas of the photovoltaic panel based on feature similarity, and classifying them using an abnormal classification model based on the feature clustering results to obtain multiple abnormal types; marking the boundaries of the abnormal areas of the photovoltaic panel, and generating photovoltaic panel abnormality detection results, wherein the abnormality detection results of the photovoltaic panel include the number and location of the abnormal areas and the degree of abnormality.

[0050] The specific configuration of the photovoltaic panel abnormal distribution generation module 40 will be described in detail below. The photovoltaic panel abnormal distribution generation module 40 may further include: matching the abnormal temperature distribution of the infrared image with the abnormal area in the photovoltaic panel abnormal detection result by spatial coordinate matching; performing consistency analysis on each matched abnormal area; and generating the photovoltaic panel abnormal distribution if the consistency check passes.

[0051] An operation fault monitoring system for a new energy distributed photovoltaic power station provided by an embodiment of the present invention can execute an operation fault monitoring method for a new energy distributed photovoltaic power station provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0052] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0053] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for monitoring operation failures of a new energy distributed photovoltaic power station, characterized in that: The method comprises: Acquire photovoltaic array information of a target distributed photovoltaic power station, and deploy multiple monitoring units based on the photovoltaic array information, each monitoring unit including a high-resolution camera and an infrared thermal imager; By using the high-resolution camera and the infrared thermal imager, a photovoltaic image sequence and an infrared image sequence of the photovoltaic panel are collected, and abnormal temperature areas of multiple infrared images of the infrared image sequence are identified and marked to obtain abnormal temperature distribution; Performing image difference on the photovoltaic image sequence to generate a photovoltaic panel difference image, performing region detection on the photovoltaic panel difference image to obtain abnormal regions of the photovoltaic panel; Classifying and locating the abnormal area of ​​the photovoltaic panel to obtain the abnormal detection result of the photovoltaic panel, analyzing and verifying the abnormal detection result of the photovoltaic panel using the abnormal temperature distribution to generate the abnormal distribution of the photovoltaic panel; The abnormal distribution of the photovoltaic panels is transmitted to a central monitoring system through wireless communication and operation and maintenance planning is carried out.

2. The method for monitoring operation failure of a new energy distributed photovoltaic power station according to claim 1, characterized in that: The step of identifying and labeling abnormal temperature areas of a plurality of infrared images in the infrared image sequence to obtain abnormal temperature distribution includes: Performing temperature detection on the first infrared image, extracting the temperature value of each pixel, and generating a temperature matrix; Performing regional segmentation on the temperature matrix according to the temperature threshold interval, marking based on the regional segmentation result, and generating an initial abnormal region mark; Connectivity analysis is performed on the initial abnormal area identification, shape features of each abnormal area are extracted, and abnormal temperature distribution is obtained by annotation.

3. The method for monitoring operation failure of a new energy distributed photovoltaic power station according to claim 1, characterized in that: The performing image difference on the photovoltaic image sequence to generate a photovoltaic panel difference image comprises: Performing grayscale processing on the photovoltaic image sequence to obtain a photovoltaic grayscale image sequence; Image registration is performed on the photovoltaic grayscale image sequence, and image difference is performed based on the image registration result to generate a photovoltaic panel difference image.

4. The method for monitoring operation failure of a new energy distributed photovoltaic power station according to claim 3, characterized in that: Performing image registration on the photovoltaic grayscale image sequence, and performing image difference based on the image registration result, including: Extract key feature points of each photovoltaic grayscale image, perform feature point matching on two adjacent photovoltaic grayscale images, and obtain multiple matching points; Align the images according to the matching points, generate the image transformation matrix, and perform geometric transformation to complete the image registration; Image difference calculation is performed based on the registered image sequence to generate a photovoltaic panel difference image.

5. The method for monitoring operation failure of a new energy distributed photovoltaic power station according to claim 4, characterized in that: Performing region detection on the photovoltaic panel differential image to obtain abnormal regions of the photovoltaic panel includes: The photovoltaic panel differential image is binarized by setting a threshold value to generate a photovoltaic panel binary differential image; Performing connectivity detection on the binary difference image of the photovoltaic panel, marking connected white pixels as the same connected area, and obtaining multiple connected areas; Feature extraction is performed on the multiple connected areas respectively, and the abnormal areas of the photovoltaic panels are obtained by segmentation.

6. A method for monitoring operation failures of a new energy distributed photovoltaic power station as claimed in claim 5, characterized in that: Classifying and locating the abnormal area of ​​the photovoltaic panel to obtain abnormal photovoltaic panel detection results, including: Based on feature similarity, the abnormal area of ​​the photovoltaic panel is clustered, and based on the feature clustering result, the abnormal classification model is used to classify the abnormal area to obtain multiple abnormal types; The boundaries of the abnormal areas of the photovoltaic panels are marked to generate abnormal detection results of the photovoltaic panels, wherein the abnormal detection results of the photovoltaic panels include the number, location and degree of abnormality of the abnormal areas.

7. The method for monitoring operation failure of a new energy distributed photovoltaic power station according to claim 1, characterized in that: Analyzing and verifying the photovoltaic panel abnormality detection result by using the abnormal temperature distribution to generate the photovoltaic panel abnormality distribution includes: Through spatial coordinate matching, the abnormal temperature distribution of the infrared image is matched with the abnormal area in the abnormal detection result of the photovoltaic panel; Perform consistency analysis on each matched abnormal region; If the consistency check passes, the abnormal distribution of photovoltaic panels is generated.

8. An operation fault monitoring system for a new energy distributed photovoltaic power station, characterized in that: The system is used to implement the operation fault monitoring method of a new energy distributed photovoltaic power station according to any one of claims 1 to 7, and the system comprises: A monitoring unit deployment module, the monitoring unit deployment module is used to obtain photovoltaic array information of a target distributed photovoltaic power station, and deploy multiple monitoring units based on the photovoltaic array information, each monitoring unit including a high-resolution camera and an infrared thermal imager; An image sequence acquisition module, the image sequence acquisition module is used to acquire a photovoltaic image sequence and an infrared image sequence of the photovoltaic panel through the high-resolution camera and the infrared thermal imager, identify and mark abnormal temperature areas of multiple infrared images of the infrared image sequence, and obtain abnormal temperature distribution; A photovoltaic panel abnormal area acquisition module, the photovoltaic panel abnormal area acquisition module is used to perform image difference on the photovoltaic image sequence to generate a photovoltaic panel differential image, perform area detection on the photovoltaic panel differential image, and acquire the photovoltaic panel abnormal area; A photovoltaic panel abnormal distribution generation module, the photovoltaic panel abnormal distribution generation module is used to classify and locate the abnormal area of ​​the photovoltaic panel, obtain the photovoltaic panel abnormality detection result, analyze and verify the photovoltaic panel abnormality detection result using the abnormal temperature distribution, and generate the photovoltaic panel abnormal distribution; A photovoltaic panel abnormal distribution operation and maintenance planning module is used to transmit the abnormal distribution of photovoltaic panels to a central monitoring system through wireless communication and perform operation and maintenance planning.

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