基于改进LSTM-VAE的配电网异常负荷检测方法研究

Research on abnormal load detection method for distribution network based on improved LSTM-VAE

针对目前配电网负荷数据异常检测方法准确率低的问题,提出将改进的长短期记忆网络和变分自编码器相结合用的配电网负荷异常检测方法。通过残差结构对长短期记忆网络进行优化,提高特征学习能力,并将优化后的长短期记忆网络替换变分自编码器的BP神经网络层(编码和解码),可以更好地获得负荷数据的时间相关性。通过与常规检测方法的试验对比,验证了所提检测方法的优越性。结果表明,相比于常规负荷数据异常检测方法,所提方法具有更好的检测准确率,异常检测准确率为97.30%,比未引入残差结构提高了1.70%,比LSTM模型提高了7.00%,比PSO-PFCM模型提高了4.80%。可为配电网自动化的发展提供一定的参考。

The accuracy of anomaly detection in current distribution network load data anomaly detection is low,a load anomaly detection method for distribution network is proposed,which combines the improved long short-term memory network with variational auto-encoder.Long short-term memory network is optimized through residual structure to improve feature learning ability,and the optimized long short-term memory network replaces the BP neural network layer(encoding and decoding)of the variational auto-encoder,which can better obtain the time correlation of load data.By comparing with conventional testing methods,the superiority of the proposed detection method has been verified.The results indicate that,compared to conventional load data anomaly detection methods,the proposed method has better detection accuracy,the accuracy rate of anomaly detection reaches 97.30%,compared to not introducing residual structure,it has increased by 1.70%,improved by 7.00%compared to the LSTM model,improved by 4.80%compared to the PSO-PFCM model,which can provide a certain reference for the development of distribution network automation.