CN113780063A - Photovoltaic operation and maintenance control method based on video intelligent analysis - Google Patents
Photovoltaic operation and maintenance control method based on video intelligent analysis Download PDFInfo
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Abstract
The invention relates to the technical field of photovoltaic power station operation and maintenance control, in particular to a photovoltaic operation and maintenance control method based on video intelligent analysis, which mainly comprises the steps of collecting video samples of bird groups in photovoltaic components in a photovoltaic power generation area, bird droppings on the photovoltaic components, positions of the photovoltaic components, which are shielded by plastic films and covered by dust; converting the collected video data into pictures; carrying out manual sample labeling on the picture data in the step two; training a target detection algorithm for the samples marked in the step three according to the specified service scene; deploying the trained algorithm model to an operation and maintenance control system; the operation and maintenance control system monitors and analyzes the power generation area of the photovoltaic power station in real time, and if the operation and maintenance control system finds that: bird crowd gathering, bird feces, plastic film shielding and dust covering can give an alarm in time. The invention effectively improves the efficiency of remote operation and maintenance cooperation, and the operation and maintenance flow is greatly improved.
Description
Technical Field
The invention relates to the technical field of operation and maintenance control of photovoltaic power stations, in particular to a photovoltaic operation and maintenance control method based on video intelligent analysis.
Background
The photovoltaic power station is a photovoltaic power generation system which is connected with a power grid and transmits power to the power grid, is a green power development energy project with the greatest national encouragement, and can be divided into an independent power generation system with a storage battery and a grid-connected power generation system without the storage battery. Solar power generation is divided into photo-thermal power generation and photovoltaic power generation, and the solar power energy entering commercialization at the present time refers to solar photovoltaic power generation.
The current photovoltaic power station operation and maintenance service mainly improves the photovoltaic power station operation and maintenance service level by constantly summarizing maintenance management experience and formulating detailed inspection maintenance content. However, the operation and maintenance service of the photovoltaic power station still depends on manual maintenance, and higher requirements on comprehensive quality and responsibility of people are required; the failure of the photovoltaic module is not found timely, the service life of the photovoltaic module is influenced, and meanwhile, the generating efficiency of the photovoltaic power station is also adversely affected.
Disclosure of Invention
The invention aims to provide a photovoltaic operation and maintenance control method based on video intelligent analysis, so as to solve the problems in the background technology.
A photovoltaic operation and maintenance control method based on video intelligent analysis comprises the following steps:
the method comprises the following steps: collecting video samples of the positions of bird group aggregation in the photovoltaic module, bird droppings on the photovoltaic module, blocking of the photovoltaic module by a plastic film and covering of dust in the photovoltaic power generation area;
step two: converting the collected video data into pictures;
step three: carrying out manual sample labeling on the picture data in the step two;
step four: training a target detection algorithm for the samples marked in the step three according to the specified service scene;
step five: deploying the trained algorithm model to an operation and maintenance control system;
step six: the operation and maintenance control system monitors and analyzes the power generation area of the photovoltaic power station in real time, and if the operation and maintenance control system finds that: bird crowd gathering, bird droppings, plastic film shielding and dust covering are carried out to give an alarm in time;
step seven: triggering a bird repelling system to improve the photovoltaic power generation capacity when the bird group aggregation occurs in the sixth step; and C, triggering a cleaning instruction of the system to improve the photovoltaic power generation capacity when bird droppings, plastic film shielding, dust covering and snow covering happen in the sixth step.
Preferably, the system also comprises a weed extractor which is used for identifying the sheepskin on the photovoltaic assembly and weeds growing in gaps of the photovoltaic assembly, triggering the alarm system to drive the sheepskin in real time and triggering the weed extractor to extract weeds.
Preferably, the target detection algorithm in the fourth step is specifically based on a convolutional neural network algorithm YOLOv 4.
Preferably, the training process of the target detection algorithm in the fourth step includes unfreezing training and unfreezing training, the unfreezing training selects a DarkNet-53 network as a pre-training model, and configures a learning rate, a learning rate attenuation strategy and an algorithm optimizer strategy for training to obtain a training model of a first stage of YOLOv4, which is recorded as YOLOv4_ Ori _ M, the learning rate attenuation strategy is ReduceLRonPlateau, the algorithm optimizer strategy is Adam optimizer, the unfreezing training selects an unfreezing training model YOLOv4_ Ori _ M as a pre-training model, the unfreezing training configures the learning rate, the learning rate attenuation strategy and the algorithm optimizer strategy, and the learning rate, the learning rate attenuation strategy and the algorithm optimizer strategy of the unfreezing training are the same as those of the unfreezing training.
Preferably, a pixel perturbation strategy and a geometric perturbation strategy are adopted in the YOLOv4 training process.
Preferably, the pixel perturbation strategy comprises histogram equalization, contrast variation and brightness variation, and the geometric perturbation strategy comprises image translation, object translation in image only, image rotation, object rotation in image only, image miscut and object miscut in image only.
Compared with the prior art, the invention has the beneficial effects that: through the video intelligent analysis system, can be all-round, carry out real time monitoring and video analysis to the electricity generation region of photovoltaic power plant in all weather, can improve the photovoltaic generated energy and promote the level of management that becomes more meticulous, reach the purpose of cost reduction increase, effectual reduction human cost simultaneously, accomplish unmanned on duty few people's service, and utilize artificial intelligence analysis algorithm, through gathering target video data, train the target detection ware, the intelligent reasoning of target detection ware, in time report to the police and trigger corresponding instruction, improve photovoltaic module's generating efficiency, realize the target of photovoltaic power plant cost reduction increase.
Detailed Description
The invention discloses a photovoltaic operation and maintenance control method based on video intelligent analysis, which is further detailed by specific embodiments.
The invention provides a photovoltaic operation and maintenance control method based on video intelligent analysis, which comprises the following steps:
the method comprises the following steps: collecting video samples of the positions of bird group aggregation in the photovoltaic module, bird droppings on the photovoltaic module, blocking of the photovoltaic module by a plastic film and covering of dust in the photovoltaic power generation area;
step two: converting the collected video data into pictures;
step three: carrying out manual sample labeling on the picture data in the step two;
step four: training a target detection algorithm for the samples marked in the step three according to the specified service scene;
step five: deploying the trained algorithm model to an operation and maintenance control system;
step six: the operation and maintenance control system monitors and analyzes the power generation area of the photovoltaic power station in real time, and if the operation and maintenance control system finds that: bird crowd gathering, bird droppings, plastic film shielding and dust covering are carried out to give an alarm in time;
step seven: triggering a bird repelling system to improve the photovoltaic power generation capacity when the bird group aggregation occurs in the sixth step; and C, triggering a cleaning instruction of the system to improve the photovoltaic power generation capacity when bird droppings, plastic film shielding, dust covering and snow covering happen in the sixth step.
Still including being used for discerning the sheep crowd on the photovoltaic module and the weeds that photovoltaic module gap is grown to trigger alarm system in real time and drive the sheep crowd and trigger the system of weeding and pull out the grass.
The target detection algorithm in the fourth step is specifically based on a convolutional neural network algorithm YOLOv4, the training process of the target detection algorithm in the fourth step comprises unfrozen training and unfreezing training, a DarkNet-53 network is selected as a pre-training model in the unfrozen training, a learning rate attenuation strategy and an algorithm optimizer strategy are configured for training, a training model in the first stage of YOLOv4 is obtained and recorded as YOLOv4_ Ori _ M, and the learning rate attenuation strategy specifically comprises the following steps: the ReduceLRonPlateau algorithm optimizer strategy is specifically an Adam optimizer, an unfreezing training model YOLOv4_ Ori _ M is selected as a pre-training model for unfreezing training, the unfreezing training is configured with a learning rate, a learning rate attenuation strategy and an algorithm optimizer strategy, and the learning rate, the learning rate attenuation strategy and the algorithm optimizer strategy of the unfreezing training are the same as those of unfreezing training.
A pixel disturbance strategy and a geometric disturbance strategy are adopted in the YOLOv4 training process, the pixel disturbance is to increase pixel interference to a training sample picture to change the overall effect of the training sample picture, so that a detection network can adapt to the influence of detection precision caused by scene change, and the pixel disturbance strategy comprises histogram equalization, contrast change and brightness change.
The geometric disturbance strategy is to carry out spatial geometric disturbance on a training sample picture so that a detected target is changed at a spatial position to increase the diversity of the training sample, so that the detection of the target at different positions of the detection network is more accurate, and the stability of the detection network is improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A photovoltaic operation and maintenance control method based on video intelligent analysis is characterized by comprising the following steps: comprises the following steps:
the method comprises the following steps: collecting video samples of the positions of bird group aggregation in the photovoltaic module, bird droppings on the photovoltaic module, blocking of the photovoltaic module by a plastic film and covering of dust in the photovoltaic power generation area;
step two: converting the collected video data into pictures;
step three: carrying out manual sample labeling on the picture data in the step two;
step four: training a target detection algorithm for the samples marked in the step three according to the specified service scene;
step five: deploying the trained algorithm model to an operation and maintenance control system;
step six: the operation and maintenance control system monitors and analyzes the power generation area of the photovoltaic power station in real time, and if the operation and maintenance control system finds that: bird crowd gathering, bird droppings, plastic film shielding and dust covering are carried out to give an alarm in time;
step seven: triggering a bird repelling system to improve the photovoltaic power generation capacity when the bird group aggregation occurs in the sixth step; and C, triggering a cleaning instruction of the system to improve the photovoltaic power generation capacity when bird droppings, plastic film shielding, dust covering and snow covering happen in the sixth step.
2. The photovoltaic operation and maintenance control method based on video intelligent analysis according to claim 1, characterized in that: still including being used for discerning the sheep crowd on the photovoltaic module and the weeds that photovoltaic module gap is grown to trigger alarm system in real time and drive the sheep crowd and trigger the system of weeding and pull out the grass.
3. The photovoltaic operation and maintenance control method based on video intelligent analysis according to claim 1, characterized in that: the target detection algorithm in the fourth step is specifically based on a convolutional neural network algorithm YOLOv 4.
4. The photovoltaic operation and maintenance control method based on video intelligent analysis according to claim 1, characterized in that: the training process of the target detection algorithm in the fourth step comprises unfreezing training and unfreezing training, wherein the unfreezing training selects a DarkNet-53 network as a pre-training model, and configures a learning rate, a learning rate attenuation strategy and an algorithm optimizer strategy for training to obtain a training model of a first stage of YOLOv4, which is recorded as YOLOv4_ Ori _ M, the learning rate attenuation strategy is ReduceLRonPlateau, the algorithm optimizer strategy is an Adam optimizer, the unfreezing training selects an unfrozen training model YOLOv4_ Ori _ M as the pre-training model, the unfreezing training is configured with the learning rate, the learning rate attenuation strategy and the algorithm optimizer strategy, and the learning rate, the learning rate attenuation strategy and the algorithm optimizer strategy of the unfreezing training are the same as those of the unfreezing training.
5. The photovoltaic operation and maintenance control method based on video intelligent analysis according to claim 4, characterized in that: a pixel perturbation strategy and a geometric perturbation strategy are adopted in the YOLOv4 training process.
6. The photovoltaic operation and maintenance control method based on video intelligent analysis according to claim 5, characterized in that: the pixel disturbance strategies comprise histogram equalization, contrast change and brightness change, and the geometric disturbance strategies comprise image translation, only image target translation, image rotation, only image target rotation, image miscut and only image target miscut.
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| CN110889841A (en) * | 2019-11-28 | 2020-03-17 | 江苏电力信息技术有限公司 | YOLOv 3-based bird detection algorithm for power transmission line |
| WO2020206862A1 (en) * | 2019-04-08 | 2020-10-15 | 江西理工大学 | Automatic sorting system |
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| CN112633176A (en) * | 2020-12-24 | 2021-04-09 | 广西大学 | Rail transit obstacle detection method based on deep learning |
| CN113011319A (en) * | 2021-03-16 | 2021-06-22 | 上海应用技术大学 | Multi-scale fire target identification method and system |
| CN113076860A (en) * | 2021-03-30 | 2021-07-06 | 南京大学环境规划设计研究院集团股份公司 | Bird detection system under field scene |
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- 2021-07-27 CN CN202110848733.0A patent/CN113780063A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020206862A1 (en) * | 2019-04-08 | 2020-10-15 | 江西理工大学 | Automatic sorting system |
| CN110889841A (en) * | 2019-11-28 | 2020-03-17 | 江苏电力信息技术有限公司 | YOLOv 3-based bird detection algorithm for power transmission line |
| CN111985455A (en) * | 2020-09-08 | 2020-11-24 | 国网江西省电力有限公司电力科学研究院 | Training and identifying method and device for photovoltaic module visible light fault model |
| CN112633176A (en) * | 2020-12-24 | 2021-04-09 | 广西大学 | Rail transit obstacle detection method based on deep learning |
| CN113011319A (en) * | 2021-03-16 | 2021-06-22 | 上海应用技术大学 | Multi-scale fire target identification method and system |
| CN113076860A (en) * | 2021-03-30 | 2021-07-06 | 南京大学环境规划设计研究院集团股份公司 | Bird detection system under field scene |
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