基于人工智能的配电网状态监测与故障诊断
Condition Monitoring and Fault Diagnosis of Distribution Network Based on Artificial Intelligence
柴瑞1
作者信息
- 1. 国网山西省电力公司大同供电公司,山西大同 037000
- 折叠
摘要
文章以提高配电网的运行效率与可靠性为目标研究配电网状态监测与故障诊断问题.以人工智能为基础,结合深度学习技术,对配电网状态进行实时监测和故障诊断,并对配电网的运行状态进行时序分析,以识别潜在的故障模式.在研究过程中,依托大数据处理与分析技术,以卷积神经网络(Convolutional Neural Networks,CNN)为基础进行特征提取与分类,以循环神经网络(Recurrent Neural Network,RNN)为基础进行时序分析,有效监测与诊断配电网运行状态.经过实验验证证实,所提方法在配电网状态监测与故障诊断方面取得显著成效,其诊断准确率达到90%,为配电网的运维工作提供有效的技术支撑,能够保障配电网运行稳定.
Abstract
In order to improve the operation efficiency and reliability of distribution network,this paper studies the condition monitoring and fault diagnosis of distribution network.Based on artificial intelligence,combined with deep learning technology,real-time monitoring and fault diagnosis of distribution network state are carried out,and the running state of distribution network is analyzed in order to identify potential fault modes.In the research process,relying on the big data processing and analysis technology,feature extraction and classification are carried out on the basis of Convolutional Neural Networks(CNN),and time series analysis is carried out on the basis of Recurrent Neural Network(RNN),so as to effectively monitor and diagnose the operation state of the distribution network.The experimental results show that the proposed method has achieved remarkable results in the condition monitoring and fault diagnosis of distribution network,and its diagnostic accuracy reaches 90%,which provides effective technical support for the operation and maintenance of distribution network and can ensure the stable operation of distribution network.
关键词
配电网/状态监测/故障诊断/人工智能/深度学习Key words
distribution network/condition monitoring/fault diagnosis/artificial intelligence/deep learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据