基于大数据分析的智能配电网运行状态识别

Min Fan, Bo Zhang, Q. Yao, Jianliang Zhang, Darong Huang, Qi Han
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引用次数: 2

摘要

为了保证电网的电能质量和高效运行,需要一个可靠的配电网运行状态识别系统。为解决多工况识别问题,提出了一种基于决策树工作流的工况识别系统。在线记录系统采集的波形大数据通过时域、频域和小波变换转化为特征,对波形数据的特征进行训练,自动建立ANN(Artificial Neural Networks)模型。实验结果表明,该识别系统能够准确识别配电网运行工况,提高配电网的自动运行能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart distribution network operating condition recognition based on big data analysis
In order to guarantee the power quality and the highly efficient operation of the power network, a reliable operating condition recognition system of distribution networks is necessary. To solve the problem of multi-condition recognition, an operating condition recognition system based on the workflow of decision-making tree is proposed. Big data of waveforms acquired by an online recording system is transformed into characteristics through time-domain, frequency-domain and wavelet transformation, and ANN(Artificial Neural Networks) models is automatically built with the training of those characteristics of waveform data. As shown by the experimental results, this recognition system can accurately recognize operating conditions and improve the automatic operating capacity of distribution networks.
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