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Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining

Author

Listed:
  • Yunhai Song

    (Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Sen He

    (Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Liwei Wang

    (Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Zhenzhen Zhou

    (Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Yuhao He

    (Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Yaohui Xiao

    (Overhaul and Test Center of UHV Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Yi Zheng

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yunfeng Yan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems.

Suggested Citation

  • Yunhai Song & Sen He & Liwei Wang & Zhenzhen Zhou & Yuhao He & Yaohui Xiao & Yi Zheng & Yunfeng Yan, 2023. "Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining," Sustainability, MDPI, vol. 15(18), pages 1-13, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13721-:d:1239889
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    References listed on IDEAS

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    1. Yan, Xiaoan & Liu, Ying & Xu, Yadong & Jia, Minping, 2021. "Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity," Renewable Energy, Elsevier, vol. 170(C), pages 724-748.
    2. Leijiao Ge & Yuanliang Li & Yuanliang Li & Jun Yan & Yonghui Sun, 2022. "Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review," Energies, MDPI, vol. 15(3), pages 1-24, January.
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