10 kV电缆接头局部放电趋势分析及预警方法研究

Research on partial discharge trend analysis and early warning method of 10 kV cable connector

电缆接头是局部放电频发的位置,针对目前对局部放电趋势研究的不足和预警不及时的问题,提出了一种基于Mann-Kendall检验法和长短期神经网络(LSTM)的局部放电的趋势分析和预警方法。为了清晰地揭示局部放电数据的趋势特征,文中采用Mann-Kendall检验法对采集的暂态地电压(TEV)数据进行处理,定量计算趋势变化及突变点检测。文中提出基于Mann-Kendall检验法和LSTM算法的综合预警模型,该模型利用LSTM预测TEV序列幅值,并用Mann-Kendall计算预测值的趋势参数,通过综合考虑TEV幅值大小和趋势参数实现了电缆接头局部放电主动预警。算例结果表明,Mann-Kendall能清晰揭示局部放电变化趋势,LSTM对局部放电数据预测效果良好,基于二者构建的预警模型能较好地对局部放电进行预警。

Cable joints are the location where partial discharges frequently occur.Aiming at the current insufficiency of partial discharge trend research and untimely early warning problems,a trend analysis and early warning method of partial discharge based on Mann-Kendall test method and long short-term memory(LSTM)neural network is proposed in this paper.Firstly,in order to clearly reveal the trend characteristics of the partial discharge volume,the Mann-Kendall test method is used to process the collected transient earth voltage(TEV)data,and quantitatively calculate the trend change and mutation point detection.Secondly,this paper proposes a comprehensive early warning model based on Mann-Kendall test method and LSTM algorithm.The model utilizes LSTM to predict TEV sequence amplitude,and Mann-Kendall is used to calculate trend parameters of predicted values,and the active early warning of partial discharge in cable joints is achieved through comprehensively considering TEV amplitude and trend parameters.The example results show that Mann-Kendall can clearly reveal the development trend of partial discharge,and the prediction effect of partial discharge data based on LSTM is good.The early warning model based on the two can better warn the partial discharge.