Distributed Photovoltaic Power Outlier Detection Based on Quantile Regression Neural Network
The distributed photovoltaic power generation system is widely dispersed and lacks a scientific and standardized operation and maintenance management system. Due to the limited availability of data, it is difficult to accurately detect abnormal conditions in photovoltaic devices caused by fluctuatio...
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| Main Author: | |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Editorial Office of Journal of Shanghai Jiao Tong University
2025-06-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
| Subjects: | |
| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-6-836.shtml |
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| Summary: | The distributed photovoltaic power generation system is widely dispersed and lacks a scientific and standardized operation and maintenance management system. Due to the limited availability of data, it is difficult to accurately detect abnormal conditions in photovoltaic devices caused by fluctuations in weather. In this paper, according to the operation and maintenance status and data characteristics of distributed photovoltaic, a quantile regression neural network (QRNN)-based method is proposed for detecting photovoltaic power outliers. First, the solar irradiance characteristics of sunny days are analyzed, and the influence of rainy weather is excluded by using a sunny day screening method. Then, the power output correlation of different power stations is analyzed to identify the photovoltaic stations with high power output correlation, which is used as a horizontal reference. Subsequently, the curves of the power output of the stations tested on different sunny days are compared vertically to eliminate the interfering factors such as weather and environmental conditions. The measured active power data is fed into the QRNN model to establish the normal active power range for the photovoltaic system, whose threshold is used to detect photovoltaic power outliers. The simulation results of actual photovoltaic system data show that the method proposed can eliminate the meteorological influence, accurately identify the faulty photovoltaic system, and promote the fine operation and maintenance of distributed photovoltaic. |
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| ISSN: | 1006-2467 |