袁嘉玮, 焦在滨. 基于零序信号全波形识别的小电流接地系统故障选线方法[J]. 电网技术, 2024, 48(2): 839-850. DOI: 10.13335/j.1000-3673.pst.2023.0025
引用本文: 袁嘉玮, 焦在滨. 基于零序信号全波形识别的小电流接地系统故障选线方法[J]. 电网技术, 2024, 48(2): 839-850. DOI: 10.13335/j.1000-3673.pst.2023.0025
YUAN Jiawei, JIAO Zaibin. Faulty Feeder Detection in Small Current Grounding System Based on Complete Waveform Recognition of Zero-sequence Signals[J]. Power System Technology, 2024, 48(2): 839-850. DOI: 10.13335/j.1000-3673.pst.2023.0025
Citation: YUAN Jiawei, JIAO Zaibin. Faulty Feeder Detection in Small Current Grounding System Based on Complete Waveform Recognition of Zero-sequence Signals[J]. Power System Technology, 2024, 48(2): 839-850. DOI: 10.13335/j.1000-3673.pst.2023.0025

基于零序信号全波形识别的小电流接地系统故障选线方法

Faulty Feeder Detection in Small Current Grounding System Based on Complete Waveform Recognition of Zero-sequence Signals

  • 摘要: 小电流接地系统发生单相接地故障时,故障电流微弱,暂态特征复杂,导致现有选线方法正确率偏低,可靠性较差。该文提出了一种基于零序信号全波形识别的选线方法。首先,介绍了基于故障信号全波形识别的通用方法,指出从波形的角度认识并识别信号,可以充分利用信号的全部故障信息;其次,选取母线电压和所有馈线电流波形作为待识别信号,并根据电流波形分布特征自适应捕捉信号中特征差异最明显的波形进行故障识别,提出了基于故障信号全波形识别的选线方法;然后,利用改进后的全卷积神经网络来识别不同信号波形,并基于Dice系数对识别出的故障信号波形进行可信赖性评价。最后,PSCAD仿真和真型实验测试结果表明该方法具有很高的选线正确率和可靠性。

     

    Abstract: When single-phase-to-ground (SPG) faults occur in a small current grounding system, the fault transients appear complex due to the weak fault currents, thereby leading to the poor detection accuracy and reliability of the existing faulty feeder detection. This paper proposes a novel detection method based on the complete waveform recognition of zero-sequence signals. Firstly, a general method for identification of the fault signals based on the complete waveform recognition is introduced. It is pointed out that the fault information can be fully exploited when the raw signals are learned and recognized on the waveform scale. Secondly, both zero-sequence voltage (ZSV) on the bus and the zero-sequence currents (ZSC) of all the feeders are used for recognition, where the waveforms with the distinct characteristics are captured based on the distribution features of the ZSCs, and the detection method based on the complete waveform recognition of fault signals is constructed. Thirdly, an improved fully convolutional network is established for waveform recognition, and the credibility of the recognized fault waveforms is estimated based on the Dice coefficient. The PSCAD simulation and the field test show that the proposed method has a better detection accuracy and strong reliability.

     

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