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张恒超, 沈秋英, 沈杰, 李苏芙, 范彪, 王琨, 蔡嘉辉, 吕自贵. 基于深度学习的线损异常诊断系统研究[J]. 农村电气化, 2023, (11): 58-64. DOI: 10.13882/j.cnki.ncdqh.2023.11.019
引用本文: 张恒超, 沈秋英, 沈杰, 李苏芙, 范彪, 王琨, 蔡嘉辉, 吕自贵. 基于深度学习的线损异常诊断系统研究[J]. 农村电气化, 2023, (11): 58-64. DOI: 10.13882/j.cnki.ncdqh.2023.11.019
ZHANG Hengchao, SHEN Qiuying, SHEN Jie, LI Sufu, FAN Biao, WANG Kun, CAI Jiahui, LYU Zigui. Research on Line Loss Abnormal Diagnosis System Based on Deep Learning[J]. RURAL ELECTRIFICATION, 2023, (11): 58-64. DOI: 10.13882/j.cnki.ncdqh.2023.11.019
Citation: ZHANG Hengchao, SHEN Qiuying, SHEN Jie, LI Sufu, FAN Biao, WANG Kun, CAI Jiahui, LYU Zigui. Research on Line Loss Abnormal Diagnosis System Based on Deep Learning[J]. RURAL ELECTRIFICATION, 2023, (11): 58-64. DOI: 10.13882/j.cnki.ncdqh.2023.11.019

基于深度学习的线损异常诊断系统研究

Research on Line Loss Abnormal Diagnosis System Based on Deep Learning

  • 摘要: 为了降低台区线损、加快台区线损治理的数字化、智能化转型,应当对供电过程中可能出现的线损异常进行及时监测并诊断。本文研究了基于深度学习的线损异常诊断技术,从用电采集系统中获取海量的电力运行数据,通过算法构建窃电分析模型。基于该模型,对台区关口计量装置故障、窃电、用户计量装置异常、户变关系异常等问题进行诊断。该系统可以减少台区线损异常分析的时间,提高台区线损分析的准确率及效率,是实现低压台区线损管理智能化的重要举措。

     

    Abstract: In order to cope with the possible problem of line loss, this paper studies the diagnosis system of line loss abnormality based on deep learning and applies it to line loss management. Obtain massive line loss-related power consumption data from the power consumption collection system of the State Grid Power Supply Company. Conduct in-depth mining of the data through deep learning methods, and build a reliable power theft detection model. Based on the model, Diagnose the main transformer failure in the station area, suspected meter, suspected household transformer problem, etc. This system can reduce the time cost of line loss abnormal situation analysis, improve the efficiency of power management and management. It is an important measure to realize the intelligent management of power grid enterprises.

     

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