CN113485432A - Photovoltaic power station electroluminescence intelligent diagnosis system and method based on unmanned aerial vehicle - Google Patents
Photovoltaic power station electroluminescence intelligent diagnosis system and method based on unmanned aerial vehicle Download PDFInfo
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- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
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Abstract
The invention discloses an intelligent diagnosis system and method for electroluminescence of a photovoltaic power station based on an unmanned aerial vehicle, wherein the system comprises the photovoltaic power station, an airborne electroluminescence image acquisition end, an image receiving and processing server, a data processing and storing alarm terminal and a mobile alarm terminal, wherein the airborne electroluminescence image acquisition end is arranged on the unmanned aerial vehicle, acquires images of a working space of the photovoltaic power station in real time and transmits the acquired images to the image receiving and processing server; the image receiving and processing server analyzes and processes the received image, judges whether the assembly has a fault or not and transmits a judgment result to the data processing and storing alarm terminal and the mobile alarm terminal; the data processing and storing alarm terminal stores the received picture, extracts the position and the component problem information, performs alarm display on a computer page, simultaneously transmits alarm information displayed by the computer to the mobile alarm terminal, and the mobile alarm terminal timely alarms and prompts the information to operation and maintenance detection personnel.
Description
Technical Field
The invention belongs to the field of photovoltaic power station fault diagnosis, and particularly relates to an intelligent photovoltaic power station electroluminescence diagnosis system and method based on an unmanned aerial vehicle.
Background
Nowadays, the energy is increasingly scarce, the world energy is accelerated to be transformed into diversified, clean and low-carbon energy, and the clean energy accounts for 56% in 2050 year. Solar energy has gained more and more attention as inexhaustible clean energy, and the technical field of photovoltaic power generation as new clean energy application is increasing with the continuous improvement of technical means. The solar photovoltaic energy is continuously reduced, the absorption capacity of a power grid is improved, and a distributed energy system is rapidly developed, while the use cost of the traditional fossil energy is continuously increased due to the restriction of factors such as environmental externality and the like. It is expected that by the end of the 21 st century, renewable energy sources will be no less than 80% in total energy structure and solar power generation will be no less than 60%. These are enough to demonstrate that solar power generation will continue to evolve worldwide.
The photovoltaic power generation system is mainly composed of a photovoltaic module, a controller, an inverter, a storage battery and other accessories, and the storage battery is not needed in grid connection. The method is divided into an off-grid system and a grid-connected system according to whether the public power grid is relied on, wherein the off-grid system operates independently and does not depend on the power grid. The off-grid photovoltaic system is provided with a storage battery with an energy storage function, so that the power stability of the system can be ensured, and the power can be supplied to a load under the conditions that the photovoltaic system does not generate power at night or generates power insufficiently in rainy days and the like. In any form, the working principle is that the photovoltaic module converts light energy into direct current, the direct current is converted into alternating current under the action of the inverter, and finally the functions of power utilization and internet surfing are achieved.
When the photovoltaic power station is built, the requirements of climate environment and land are considered, most of the photovoltaic power station is built in hard areas such as wastelands, partitions and the like which are not suitable for industrial and agricultural development, and the photovoltaic power station is built by connecting a plurality of photovoltaic components in series and in parallel and then by devices such as a power controller and the like.
If a certain piece or several pieces of the equipment break down in the operation process, because the photovoltaic power station has huge scale and bad environment, huge manpower, material resources and time are consumed through traditional manual maintenance, and meanwhile, the operation and maintenance cost is greatly improved.
Disclosure of Invention
The invention aims to provide an intelligent photovoltaic power station electroluminescence diagnosis system based on an unmanned aerial vehicle, and solves the problems of high cost and low efficiency of the existing photovoltaic power station maintenance work.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent photovoltaic power station electroluminescence diagnosis system based on an unmanned aerial vehicle comprises a photovoltaic power station, an airborne electroluminescence image acquisition end, an image receiving and processing server, a data processing and storing alarm terminal and a mobile alarm terminal, wherein the airborne electroluminescence image acquisition end is an electroluminescence detection device provided with a charge coupling detection piece and arranged on the unmanned aerial vehicle; the system comprises a photovoltaic power station, an image receiving and processing server, a photovoltaic power station working space acquisition server and a photovoltaic power station working space acquisition server, wherein the photovoltaic power station working space acquisition server is used for acquiring images of a photovoltaic power station working space in real time and transmitting the acquired images to the image receiving and processing server; the image receiving and processing server is used for analyzing and processing the received image, judging whether the assembly has a fault or not and transmitting the judgment result to the data processing and storing alarm terminal and the mobile alarm terminal. The data processing and storing alarm terminal receives the picture processing data of the image receiving and processing server, stores the received picture, extracts the problem information of the component and the position of the picture, performs alarm display on a computer page, simultaneously transmits alarm information displayed by the computer to the mobile alarm terminal, and the mobile alarm terminal timely alarms and prompts the information to operation and maintenance detection personnel.
The diagnosis method of the photovoltaic power station electroluminescence intelligent diagnosis system based on the unmanned aerial vehicle,
the first step is as follows: the electroluminescence detection equipment is arranged on the unmanned aerial vehicle, so that aerial inspection of the whole photovoltaic power station can be ensured, and an electroluminescence picture is transmitted to the image receiving and processing server through a 5G network;
the second step is that: checking whether the photovoltaic power station is in an operating state, ensuring the normal operation of the photovoltaic power station, carrying out patrol inspection on the whole photovoltaic power station through an airborne electroluminescence image acquisition end to shoot an electroluminescence image, transmitting the electroluminescence image to an image receiving and processing server through a 5G network, analyzing and processing the electroluminescence image by the image receiving and processing server, marking and numbering components on the photovoltaic power station, extracting the characteristics of each numbered component, analyzing, classifying and storing compared component faults;
the third step: the image receiving and processing server analyzes and processes the received image, and comprises two steps: an image deduplication part and a defect identification part, respectively; the image duplication removing part provides a method of firstly segmenting and then removing duplication aiming at the repetition condition of a received image, firstly, an image segmentation algorithm based on a threshold value is utilized to segment the image, a battery and a frame main body in a photovoltaic assembly in the image are segmented, and natural and human backgrounds are reserved for repeated judgment of the image; on the basis, extracting local features of the picture, establishing a visual dictionary and feature expression vectors of the picture, performing first-class image repetition judgment by using the occurrence frequency of the feature expression vectors, performing second-class image repetition judgment by using a feature matching geometric verification method, and removing received repeated images; the defect identification part detects and identifies image defects by using a method of first detection and then identification, and firstly, detects and extracts abnormal parts in a picture by using an image detection method based on gradient direction histogram characteristics and a support vector machine learning algorithm; after the abnormal part is extracted, calculating the area of the abnormal part, then segmenting a defect area in hue, saturation, brightness and space according to a color threshold value and calculating the area of the defect area; judging the defect degree of the component according to the area ratio of the defect part to the abnormal part; finally, comparing the extracted defect area with a defect image prestored in a server to obtain the defect type of the defect area; and extracting a fault numbering component, carrying out fault classification on the fault component, transmitting the fault component to a data processing and storing alarm terminal and a mobile alarm terminal, then carrying out analysis and comparison on an electroluminescence image of the next component, and reading the next transmitted electroluminescence image if no fault exists.
Compared with the prior art, the invention has the following advantages:
compared with the traditional mode that the photovoltaic power station detects and adopts manual field detection and human eyes to distinguish the defects of the photovoltaic modules, the photovoltaic power station electroluminescence intelligent diagnosis system based on the unmanned aerial vehicle not only solves the problem of high cost of manpower and material resources, but also solves the problems of poor timeliness and large workload of personnel. Utilize unmanned aerial vehicle can understand information such as topography and landform, subassembly arrangement of photovoltaic power plant comprehensively, fast, improvement detection efficiency that can be very quick.
Unmanned aerial vehicle carries on electroluminescent image acquisition end to take photo by plane and shoots whole photovoltaic power plant region to give processing terminal with data transfer, not only can be fast accurate in time understand the regional geography of power station subassembly, subassembly data information, can mark the detection to photovoltaic subassembly in addition, it is low with traditional artificial detection selective examination quantity, the poor detection of comparing of representativeness has the universality, the detection of representativeness to whole photovoltaic power plant is more comprehensive concrete, has persuasive power to the performance evaluation of whole power plant.
The whole system has the functions of storage and preservation, can call the component detection conditions of all different stages, can provide reliable basis for component stage property quantity evaluation, is more accurate for economic benefit evaluation of the whole power station, is provided with the alarm terminal, especially the mobile alarm terminal, can reduce manpower and material resources waste in the power station inspection process, and improves inspection efficiency of the whole power station.
Drawings
Fig. 1 is a diagram of a network transmission link according to the present invention.
Fig. 2 is a flow chart of the operation of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the photovoltaic power station electroluminescence intelligent diagnosis system based on the unmanned aerial vehicle provided by the invention comprises a photovoltaic power station 1, an airborne electroluminescence image acquisition end 2, an image receiving and processing server 3, a data processing and storing alarm terminal 4 and a mobile alarm terminal 5, wherein the airborne electroluminescence image acquisition end 2 is a high-definition camera provided with a charge-coupled detection sheet and mounted on the unmanned aerial vehicle; the image acquisition and processing server is used for acquiring images of the working space of the photovoltaic power station in real time and transmitting the acquired images to the image receiving and processing server 3; the image receiving and processing server 3 is used for analyzing and processing the received image, judging whether the component has a fault or not, and transmitting the judgment result to the data processing and storing alarm terminal 4 and the mobile alarm terminal 5. The data processing and storing alarm terminal 4 receives the picture processing data of the image receiving and processing server 3, stores the received picture, and displays the position and the extraction component problem information on the computer page in an alarm manner, and simultaneously transmits alarm information displayed by the computer to the mobile alarm terminal 5, and the mobile alarm terminal 5 timely alarms and prompts the information to operation and maintenance detection personnel.
As shown in fig. 2, the diagnosis method of the photovoltaic power station electroluminescence intelligent diagnosis system based on the unmanned aerial vehicle of the invention comprises the following steps: the first step is as follows: the electroluminescence detection equipment is arranged on the unmanned aerial vehicle, so that aerial inspection can be carried out on the whole photovoltaic power station, and an electroluminescence picture is transmitted to the image receiving and processing server 3 through a 5G network; the second step is that: checking whether the photovoltaic power station is in an operating state, ensuring the normal operation of the photovoltaic power station, polling and shooting an electroluminescence image of the whole photovoltaic power station through an airborne electroluminescence image acquisition terminal 2, transmitting the image to an image receiving and processing server 3 through a 5G network, analyzing and processing the image by the image receiving and processing server 3, marking and numbering components on the photovoltaic power station, extracting the characteristics of each numbered component, analyzing, classifying and processing faults of the compared components, and storing in a warehouse; the third step: the unmanned aerial vehicle periodically inspects and shoots electroluminescent images of the photovoltaic power station, and the image receiving and processing server 3 analyzes and processes the received images and comprises two steps: respectively an image deduplication portion and a defect identification portion. The image duplication removing part provides a method of firstly segmenting and then removing duplication aiming at the repetition condition of a received image, firstly, an image is segmented by utilizing an image segmentation algorithm based on a threshold value, a battery and a frame main body in a photovoltaic assembly in the image are segmented, and a natural background and a human background are reserved for repeated judgment of the image. On the basis, local features of the picture are extracted, a visual dictionary and feature expression vectors of the picture are established, and the first type of image repeated judgment is carried out by using the frequency of the feature expression vectors. And the second type of image repeated judgment is carried out by using a feature matching geometric verification method. And eliminating the received repeated images. The defect identification part detects and identifies the image defects by using a method of detection before identification. Firstly, detecting abnormal parts in a picture and extracting the abnormal parts by adopting an image detection method based on gradient direction histogram characteristics and a support vector machine learning algorithm. After the abnormal part is extracted, the area of the abnormal part is calculated, and then the defect area is divided according to the color threshold value in the hue, the saturation, the brightness and the space, and the area of the defect area is calculated. And judging the defect degree of the component according to the area ratio of the defect part to the abnormal part. And finally, comparing the extracted defect area with a defect image prestored in the server to obtain the defect type of the defect area. And extracting a fault numbering component, carrying out fault classification on the fault component, transmitting the fault component to a data processing and storing alarm terminal 4 and a mobile alarm terminal 5, then carrying out analysis and comparison on an electroluminescence image of the next component, and reading the electroluminescence image transmitted back by the next component if no fault exists.
The photovoltaic power station electroluminescence intelligent diagnosis system based on the unmanned aerial vehicle is characterized in that electroluminescence detection equipment is carried on the unmanned aerial vehicle, and can patrol and examine the whole photovoltaic power station assembly and shoot electroluminescence images of the photovoltaic assembly.
Photovoltaic power plant electroluminescence intelligence diagnostic system based on unmanned aerial vehicle installs wireless network card transceiver on its unmanned aerial vehicle and can real-timely utilize the 5G network to transmit, can transmit the electroluminescence picture of shooing for image reception and processing server 3.
According to the photovoltaic power station electroluminescence intelligent diagnosis system based on the unmanned aerial vehicle, the image receiving and processing server 3 is used for comparing and registering the transmitted electroluminescence images by applying digital technology matching, description and identification, the symbolized description of the images is obtained by respectively extracting the characteristics and the mutual relation of the images, and then the symbolized description of the images is compared with the characteristics of the prestored assemblies to determine the fault type of the images and determine alarm according to the fault type.
The invention has the advantages that the electroluminescence camera carries the unmanned aerial vehicle to shoot the electroluminescence image of the photovoltaic power station, and the 5G network system compares the electroluminescence images of all the components to carry out intelligent diagnosis and alarm, thereby not only monitoring the power station in real time, but also greatly improving the efficiency.
Claims (6)
1. The utility model provides a photovoltaic power plant electroluminescence intelligent diagnosis system based on unmanned aerial vehicle which characterized in that: the system comprises a photovoltaic power station (1), an airborne electroluminescence image acquisition end (2), an image receiving and processing server (3), a data processing and storing alarm terminal (4) and a mobile alarm terminal (5), wherein the airborne electroluminescence image acquisition end (2) is an electroluminescence detection device provided with a charge coupling detection piece and arranged on an unmanned aerial vehicle; the system comprises a data acquisition and processing server (3) and a data processing and processing server, wherein the data acquisition and processing server is used for acquiring images of a working space of a photovoltaic power station in real time and transmitting the acquired images to the image receiving and processing server; the image receiving and processing server (3) is used for analyzing and processing the received image, judging whether the assembly has a fault or not and transmitting the judgment result to the data processing and storing alarm terminal (4) and the mobile alarm terminal (5); the data processing and storing alarm terminal (4) receives the picture processing data of the image receiving and processing server (3), stores the received picture, extracts the problem information of the component and the position of the picture, performs alarm display on a computer page, simultaneously transmits the alarm information displayed by the computer to the mobile alarm terminal (5), and the mobile alarm terminal (5) timely alarms and prompts the information to operation and maintenance detection personnel.
2. The photovoltaic power plant electroluminescence intelligent diagnosis system based on unmanned aerial vehicle of claim 1, characterized in that: electroluminescent check out test set carries on unmanned aerial vehicle, can patrol and examine and shoot photovoltaic module's electroluminescent image whole photovoltaic power plant subassembly.
3. The photovoltaic power plant electroluminescence intelligent diagnosis system based on unmanned aerial vehicle of claim 1, characterized in that: the unmanned aerial vehicle is provided with the wireless network card receiving and transmitting device, the wireless network card receiving and transmitting device can transmit the shot electroluminescent pictures to the image receiving and processing server (3) in real time by using a 5G network.
4. The photovoltaic power plant electroluminescence intelligent diagnosis system based on unmanned aerial vehicle of claim 1, characterized in that: the image receiving and processing server (3) compares and registers the transmitted electroluminescent images by applying digital technology matching, description and identification, obtains symbolic description of the images by respectively extracting the characteristics and the mutual relation of the images, compares the symbolic description with the prestored component characteristics to determine the fault type of the image, and determines alarm according to the fault type.
5. The photovoltaic power plant electroluminescence intelligent diagnosis system based on unmanned aerial vehicle of claim 1, characterized in that: the electroluminescent detection device is a high-definition camera.
6. The method for diagnosing an intelligent diagnosis system for photovoltaic power plant electroluminescence based on unmanned aerial vehicle of any one of claims 1 to 5, wherein:
the first step is as follows: the electroluminescence detection equipment is arranged on the unmanned aerial vehicle, so that aerial inspection of the whole photovoltaic power station can be guaranteed, and electroluminescence pictures are transmitted to the image receiving and processing server (3) through a 5G network;
the second step is that: whether the photovoltaic power station is in an operating state or not is checked, normal operation of the photovoltaic power station is ensured, an airborne electroluminescence image acquisition end (2) is used for carrying out inspection shooting on the whole photovoltaic power station to obtain an electroluminescence image, the electroluminescence image is transmitted to an image receiving and processing server (3) through a 5G network, the image receiving and processing server (3) is used for analyzing and processing the electroluminescence image, components on the photovoltaic power station are marked and numbered, the characteristics of each numbered component are extracted, analysis is carried out, and classification processing and storage of component faults are compared;
the third step: the image receiving and processing server (3) analyzes and processes the received image and comprises two steps: an image deduplication part and a defect identification part, respectively; the image duplication removing part provides a method of firstly segmenting and then removing duplication aiming at the repetition condition of a received image, firstly, an image segmentation algorithm based on a threshold value is utilized to segment the image, a battery and a frame main body in a photovoltaic assembly in the image are segmented, and natural and human backgrounds are reserved for repeated judgment of the image; on the basis, extracting local features of the picture, establishing a visual dictionary and feature expression vectors of the picture, performing first-class image repetition judgment by using the occurrence frequency of the feature expression vectors, performing second-class image repetition judgment by using a feature matching geometric verification method, and removing received repeated images; the defect identification part detects and identifies image defects by using a method of first detection and then identification, and firstly, detects and extracts abnormal parts in a picture by using an image detection method based on gradient direction histogram characteristics and a support vector machine learning algorithm; after the abnormal part is extracted, calculating the area of the abnormal part, then segmenting a defect area in hue, saturation, brightness and space according to a color threshold value and calculating the area of the defect area; judging the defect degree of the component according to the area ratio of the defect part to the abnormal part; finally, comparing the extracted defect area with a defect image prestored in a server to obtain the defect type of the defect area; and extracting a fault numbering component, carrying out fault classification on the fault component, transmitting the fault component to a data processing and storing alarm terminal (4) and a mobile alarm terminal (5), then carrying out analysis and comparison on an electroluminescence image of the next component, and reading the next electroluminescence image transmitted back if no fault exists.
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