Photovoltaic power station defect detection method, system and equipment and storage medium
By obtaining the operation data of photovoltaic power station strings in real time, automatically determining the location of abnormal strings, and using drones to conduct cruise detection, the problem of linkage and automatic intelligent analysis of test methods in the existing technology is solved, and automatic and intelligent detection of photovoltaic power station faults is realized.
Patent Information
- Application Number
- CN202311684388.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-08
- Publication Date
- 2025-06-10
AI Technical Summary
The existing photovoltaic power plant fault detection technology cannot achieve the linkage of various test methods, and cannot automatically and intelligently analyze and locate the causes and impacts of the faults.
By obtaining the operation data of the photovoltaic power station strings in real time, automatically judge the location of the abnormal strings, and using drones to perform cruise detection, generate infrared detection results and EL fault detection results, and finally compare them with the operation data to analyze the defects of the photovoltaic power station.
It realizes automated and intelligent detection of photovoltaic power station faults, can conduct infrared and EL tests in a coordinated manner, deeply analyze the causes and impacts of the faults, and improves detection efficiency and accuracy.
Smart Images
Figure CN120128085A_ABST
Abstract
Description
Technical Field
[0001] The disclosed embodiments relate to the technical field of photovoltaic power station detection, and specifically to a method, system, device and storage medium for fully automatic drone defect detection of photovoltaic power stations based on real-time data of the photovoltaic power stations. Background Art
[0002] Photovoltaic power stations usually include string inverters and multiple photovoltaic strings. When performing fault detection on photovoltaic power stations, the existing technology uses a single test method, which cannot be linked with each other and cannot automatically and intelligently analyze and locate the cause and impact of the fault. Summary of the invention
[0003] The embodiments of the present disclosure provide a photovoltaic power station defect detection method, system, device and storage medium to solve or alleviate one or more of the above technical problems in the prior art.
[0004] According to one aspect of the present disclosure, a photovoltaic power station defect detection method is provided, comprising:
[0005] Automatically plan the drone test route based on the abnormal string positions in the PV power station;
[0006] The drone is driven to automatically perform cruise detection on abnormal strings according to the drone test route, and generate cruise detection results;
[0007] The cruise detection results are compared with abnormal string operation data to analyze defects in the photovoltaic power station.
[0008] In a possible implementation, before automatically planning a drone test route based on an abnormal string position in a photovoltaic power station, the following steps are included:
[0009] Obtain real-time operation data of photovoltaic power station strings;
[0010] Determine whether the current string is abnormal according to the string operation data;
[0011] Get the location of abnormal strings in the PV power station;
[0012] Stores the abnormal string operation data.
[0013] In a possible implementation, obtaining the position of an abnormal string in a photovoltaic power station includes:
[0014] The location of abnormal strings in the photovoltaic power station is obtained through the global satellite positioning system.
[0015] In a possible implementation, the driving drone automatically performs cruise detection on abnormal strings according to the drone test route, and the generated cruise detection results include:
[0016] The driving drone automatically performs infrared detection and EL fault detection on abnormal strings according to the drone test route, and generates infrared detection results and EL test results.
[0017] In a possible implementation, the cruise detection results are compared with the operation data of abnormal strings to analyze the defects existing in the photovoltaic power station, including:
[0018] The infrared detection results and EL test results are compared with the operation data of abnormal strings to analyze the defects existing in the photovoltaic power station.
[0019] In a possible implementation, the operation data of the strings in the photovoltaic power station includes current data, voltage data, and power data.
[0020] According to one aspect of the present disclosure, a photovoltaic power station defect detection system is provided, including:
[0021] A planning unit for automatically planning a drone test route according to the positions of abnormal strings in the photovoltaic power station;
[0022] A detection unit for driving a drone to automatically perform cruise detection on abnormal strings according to the drone test route and generate cruise detection results;
[0023] A comparison unit for comparing the cruise detection results with the operation data of abnormal strings to analyze the defects existing in the photovoltaic power station.
[0024] In a possible implementation, it further includes:
[0025] A first acquisition unit for real-time acquisition of the operation data of the strings in the photovoltaic power station;
[0026] A judgment unit for judging whether the current string is abnormal according to the string operation data;
[0027] A second acquisition unit for acquiring the positions of abnormal strings in the photovoltaic power station;
[0028] A storage unit for storing the operation data of abnormal strings.
[0029] According to one aspect of the present disclosure, a photovoltaic power station defect detection device is provided, including:
[0030] A processor and a memory;
[0031] The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute the photovoltaic power station defect detection method described in any one of the above.
[0032] According to one aspect of the present disclosure, there is provided a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the photovoltaic power station defect detection method described in any one of the above.
[0033] The exemplary embodiments of the present disclosure have the following beneficial effects: The online intelligent detection fusion method of the exemplary embodiments of the present disclosure realizes the linkage of three testing methods. First, the faulty string is determined by the real-time current during the operation of the power station, and then an automated cruise route is generated based on the position of the faulty string in the power station to perform infrared and EL tests on the faulty string. Moreover, it can realize the automated in-depth analysis of the cause and impact of the fault, providing operation and maintenance data support for the power station.
[0034] Details of one or more embodiments of the present application are set forth in the following drawings and description. Other features and advantages of the present application will become apparent from the drawings of the specification. It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.
[0036] Figure 1 is a flowchart of a photovoltaic power station defect detection method according to this exemplary embodiment;
[0037] Figure 2 is a block diagram of a photovoltaic power station defect detection system according to this exemplary embodiment;
[0038] Figure 3 is a structural schematic diagram of a photovoltaic power station defect detection device according to this exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0039] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be used. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring aspects of the present disclosure.
[0040] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or in one or more hardware units or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.
[0041] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.
[0042] The terms "first", "second", etc. in the specification, claims, and above-mentioned accompanying drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data may be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.
[0043] In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or sub-modules does not necessarily have to be limited to those steps or sub-modules clearly listed, but may include other steps or sub-modules not clearly listed or inherent to these processes, methods, products, or devices.
[0044] Figure 1 is a flowchart of a method for detecting defects in a photovoltaic power station in this exemplary embodiment, as Figure 1As shown in the figure, an exemplary embodiment of the present disclosure provides a method for detecting defects in a photovoltaic power station, including:
[0045] S1 Automatically plan a drone test route according to the position of the string with anomalies in the photovoltaic power station;
[0046] S2 Drive the drone to automatically conduct cruise detection on the abnormal string according to the drone test route, and generate a cruise detection result;
[0047] S3 Compare the cruise detection result with the operation data of the abnormal string, and analyze the defects existing in the photovoltaic power station.
[0048] In this embodiment, first, problems existing in the strings of the photovoltaic power station are analyzed through data such as current, voltage, and power. Then, according to the analysis results and the GPS positions of the defective components, the test route is automatically planned, and the route planning is more refined. According to the automated test route of the drone, infrared and EL fault detection of the components are carried out fully automatically along the route. After the detection is completed, the infrared and EL test results are analyzed and compared with the defect data such as current, voltage, and power to analyze the defects existing in the power station, the losses in electricity, power, current, etc. caused by the defects, and output specific defect problems to guide on-site operation and maintenance.
[0049] Specifically, before S1 automatically plans a drone test route according to the position of the string with anomalies in the photovoltaic power station, it includes:
[0050] S11 Real-time obtain the operation data of the strings in the photovoltaic power station;
[0051] S12 Judge whether the current string is abnormal according to the string operation data;
[0052] S13 Obtain the position of the string with anomalies in the photovoltaic power station;
[0053] S14 Store the operation data of the strings with anomalies.
[0054] This embodiment provides an online intelligent fusion detection method for a photovoltaic power station. By real-time obtaining the operation data of the strings in the photovoltaic power station, predicting the strings with anomalies, and obtaining the positions of the strings with anomalies; then automatically generating a drone test route for the abnormal strings, and the drone automatically realizes the test of the abnormal strings according to this route, realizing fault prediction and fault analysis, and further realizing automated and intelligent operation and maintenance analysis of the photovoltaic power station.
[0055] Exemplarily, the current data of the strings in the photovoltaic power station is obtained in real time to predict the strings with lower power generation; for the strings with lower power generation in the power station, an infrared and EL test route for the drone is automatically generated, and the drone automatically realizes the infrared and EL tests of the strings with lower power generation according to this route, realizing fault prediction and fault analysis.
[0056] Furthermore, the real-time current is monitored through the power station operation management system, and the method of discrete rate determination is adopted to perform multiple discrete rate determinations on the full amount of data N, N-1, N-2, N-3... N-n in sequence to screen out abnormal strings. Then, comprehensive management is carried out on the faulty strings at the power station site. According to the calibrated positions of the abnormal strings in the power station, the flight routes are automatically specified in the way from near to far and from east to west to achieve the maximum test route and the highest test efficiency. Finally, the UAV EL and UAV infrared tests are automatically started with one key, and the component failure conditions are automatically determined by using the UAV EL and infrared test picture recognition functions. The current of the faulty strings and the picture determination results are analyzed and compared. By comparing the corresponding relationship between the low-current strings and the faulty components, the analysis of the cause of the fault and the analysis of the current affected by the fault are realized.
[0057] Specifically, S13 obtaining the positions of the strings with anomalies in the photovoltaic power station includes:
[0058] Obtaining the positions of the strings with anomalies in the photovoltaic power station through the Global Positioning System.
[0059] Specifically, S2 driving the UAV to automatically perform cruise detection on the abnormal strings according to the UAV test route, and the generated cruise detection results include:
[0060] S21 driving the UAV to automatically perform infrared detection and EL (Electro Luminescence) fault detection on the abnormal strings according to the UAV test route, and generating infrared detection results and EL test results.
[0061] It should be noted that the EL fault detection is a detection method that utilizes the principle of electroluminescence of crystalline silicon, cooperates with a high-resolution infrared camera to take near-infrared images of crystalline silicon, and analyzes and processes the obtained imaging images through image software to detect whether there are hidden cracks, fragments, virtual soldering, broken grids and abnormal phenomena of single cells with different conversion efficiencies in the solar cell modules.
[0062] Specifically, S3 comparing the cruise detection results with the operation data of the abnormal strings and analyzing the defects existing in the photovoltaic power station includes:
[0063] S31 comparing the infrared detection results and EL test results with the operation data of the abnormal strings and analyzing the defects existing in the photovoltaic power station.
[0064] Specifically, the operation data of the strings in the photovoltaic power station includes current data, voltage data and power data.
[0065] In this embodiment, the faulty strings are determined by current, the test flight routes are automatically generated according to the positions of the faulty strings, and the EL and infrared tests are automatically started with one key; the entire testing process is fully automated, with high testing efficiency; the entire testing process is more refined, directly targeting the problematic strings for testing to avoid ineffective testing; and it can directly perform automated analysis on the causes and impacts of faults to generate an operation and maintenance report.
[0066] Figure 2 is a block diagram of a photovoltaic power station defect detection system according to an exemplary embodiment of the present disclosure, as Figure 2 shown, an exemplary embodiment of the present disclosure provides a photovoltaic power station defect detection system, including:
[0067] A planning unit 10, configured to automatically plan the drone test flight routes according to the positions of the strings with anomalies in the photovoltaic power station;
[0068] A detection unit 20, configured to drive the drone to automatically perform cruise detection on the strings with anomalies according to the drone test flight routes and generate cruise detection results;
[0069] A comparison unit 30, configured to compare the cruise detection results with the operation data of the strings with anomalies and analyze the defects existing in the photovoltaic power station.
[0070] Specifically, the photovoltaic power station defect detection system further includes:
[0071] A first acquisition unit, configured to acquire the operation data of the strings in the photovoltaic power station in real time;
[0072] A judgment unit, configured to judge whether the current string is abnormal according to the string operation data;
[0073] A second acquisition unit, configured to acquire the positions of the strings with anomalies in the photovoltaic power station;
[0074] A storage unit, configured to store the operation data of the strings with anomalies.
[0075] Specifically, the second acquisition unit includes:
[0076] A global positioning system, configured to acquire the positions of the strings with anomalies in the photovoltaic power station.
[0077] Specifically, the detection unit 20 includes:
[0078] A detection module, configured to drive the drone to automatically perform infrared detection and EL fault detection on the strings with anomalies according to the drone test flight routes and generate infrared detection results and EL test results.
[0079] Specifically, the comparison unit includes:
[0080] A comparison module 30 is configured to compare the infrared detection result and the EL test result with abnormal string operation data, and analyze the defects existing in the photovoltaic power station.
[0081] Specifically, the string operation data in the photovoltaic power station includes current data, voltage data, and power data.
[0082] Figure 3 It is a schematic structural diagram of a photovoltaic power station defect detection device according to an exemplary embodiment. As Figure 3 shown, corresponding to the photovoltaic power station defect detection method provided above, the present invention also provides a photovoltaic power station defect detection device. Since the embodiments of this device are similar to the method embodiments above, the description is relatively simple. For related parts, please refer to the description in the method embodiment part above. The device described below is only illustrative. The device may include: a processor 1, a memory 2, a communication bus (i.e., the above-mentioned device bus), and a search engine. Among them, the processor 1 and the memory 2 complete mutual communication through the communication bus and communicate with the outside through a communication interface. The processor 1 can call the logical instructions in the memory 2 to execute the photovoltaic power station defect detection method.
[0083] In addition, when the logical instructions in the above-mentioned memory 2 are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned storage medium includes: storage chips, USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks, etc., which can store program codes.
[0084] On the other hand, an embodiment of the present invention also provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, it is configured to execute the photovoltaic power station defect detection methods provided in the above-mentioned embodiments.
[0085] A processor-readable storage medium can be any available medium or data storage device accessible to Processor 1, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical discs (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid state drives (SSD)), etc.
[0086] The above are only the preferred embodiments of the present disclosure. The protection scope of the present disclosure is not limited to the above embodiments. All technical solutions falling within the concept of the present disclosure belong to the protection scope of the present disclosure. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present disclosure should be regarded as within the protection scope of the present disclosure.
Claims
1. A method for detecting defects in a photovoltaic power station, characterized in that, it includes: Automatically planning a drone test route according to the position of the string with anomalies in the photovoltaic power station; Driving the drone to automatically conduct cruise detection on the abnormal string according to the drone test route, and generating a cruise detection result; Comparing the cruise detection result with the operation data of the abnormal string to analyze the defects existing in the photovoltaic power station.
2. The method for detecting defects in a photovoltaic power station according to claim 1, characterized in that, Before automatically planning the drone test route according to the position of the string with anomalies in the photovoltaic power station, it includes: Obtaining the operation data of the strings in the photovoltaic power station in real time; Judging whether the current string is abnormal according to the string operation data; Obtaining the position of the string with anomalies in the photovoltaic power station; Storing the operation data of the strings with anomalies.
3. The method for detecting defects in a photovoltaic power station according to claim 2, characterized in that, S13 obtaining the position of the string with anomalies in the photovoltaic power station includes: Obtaining the position of the string with anomalies in the photovoltaic power station through the Global Positioning System.
4. The method for detecting defects in a photovoltaic power station according to claim 2 or 3, characterized in that, S2 driving the drone to automatically conduct cruise detection on the abnormal string according to the drone test route and generating a cruise detection result includes: Driving the drone to automatically conduct infrared detection and EL fault detection on the abnormal string according to the drone test route, and generating an infrared detection result and an EL test result.
5. The method for detecting defects in a photovoltaic power station according to claim 4, characterized in that, S3 comparing the cruise detection result with the operation data of the abnormal string to analyze the defects existing in the photovoltaic power station includes: Comparing the infrared detection result and the EL test result with the operation data of the abnormal string to analyze the defects existing in the photovoltaic power station.
6. The method for detecting defects in a photovoltaic power station according to claim 2, characterized in that, The string operation data in the photovoltaic power station includes current data, voltage data and power data.
7. A system for detecting defects in a photovoltaic power station, characterized in that, it includes: A planning unit for automatically planning a drone test route according to the position of the string with anomalies in the photovoltaic power station; A detection unit for driving the drone to automatically conduct cruise detection on the abnormal string according to the drone test route and generating a cruise detection result; A comparison unit for comparing the cruise detection result with the operation data of the abnormal string to analyze the defects existing in the photovoltaic power station.
8. The system for detecting defects in a photovoltaic power station according to claim 7, characterized in that, it further includes: A first acquisition unit for obtaining the operation data of the strings in the photovoltaic power station in real time; A judgment unit for judging whether the current string is abnormal according to the string operation data; A second acquisition unit for obtaining the position of the string with anomalies in the photovoltaic power station; A storage unit for storing the operation data of the strings with anomalies.
9. A device for detecting defects in a photovoltaic power station, characterized in that, it includes: A processor and a memory; The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute the photovoltaic power station defect detection method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the photovoltaic power station defect detection method according to any one of claims 1 to 6.