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Work item:
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F.ADSLMV
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Subject/title:
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Requirements and framework for anomaly detection service leveraging machine vision in photovoltaic farms
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Status:
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Under study
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Approval process:
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AAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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2026 (Medium priority)
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Liaison:
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IEC/TC129, ITU-T SG20, ISO/TC83, IEEE CS, ITU-T SG5
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Supporting members:
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SGCC (China), China Telecom, Chongqing University, Huawei Technology Co (China), Electronic and Telecommunication Research Institute (Korea), ZTE Communications Ltd, Zhejiang Laboratory.
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Summary:
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With the growth of the photovoltaic industry and market demand, anomaly detection servers for photovoltaic farms have become increasingly important. Traditional surveillance systems usually discover anomalies through manual inspection, which requires a lot of manpower and is prone to human negligence. With the development of machine learning algorithms, machine vision can achieve automatic intelligent processing such as image feature extraction, object detection, and anomaly detection. Therefore, an intelligent system based on machine vision is very necessary to be deployed on photovoltaic farms for automatic anomaly detection.
The anomaly detection service leveraging machine vision (ADSLMV) can automatically and accurately detect anomalies in photovoltaic farms and modules based on visible light videos and infrared images. The module anomalies include micro-cracks, hot spots, etc. The farm anomalies include human behaviors and external interferences. The application of this service can improve the safety and reliability of photovoltaic farms.
This Recommendation addresses the essential requirements and the framework for ADSLMV in photovoltaic farms. In the reference framework, an anomaly event pre-processing and emergency (AEPE) response layer is defined to deal with emergency abnormal events in photovoltaic farms. In the Appendix of this Recommendation, a test case for detecting a hot spot anomaly is provided.
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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First registration in the WP:
2024-06-21 17:32:22
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Last update:
2024-06-21 17:36:40
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