微型电脑应用2026，Vol.42Issue(3)：1-4,4.
基于PMU的电力系统状态估计与卷积神经网络异常检测
PMU-based Power System State Estimation and Convolutional Neural Network-based Anomaly Detection
摘要
Abstract
For power system monitoring,a convolutional neural network(CNN)data filter is proposed to validate phasor meas-urement unit(PMU)data by applying the Nesterov Adam gradient descent algorithm and the categorical cross-entropy loss function to identify anomaly data streams targeting the state estimator.To evaluate the performance of the proposed filter,it is compared with other deep learning algorithms as well as traditional classifiers,and experimental simulations are conducted on IEEE-30 and IEEE-118 bus systems.The results demonstrate that the CNN-based filter effectively detects forged data streams intended to tamper with state estimation,serving as an additional security layer for decision support and stable grid operation.Furthermore,it outperforms the recurrent neural network(RNN),the long short-term memory(LSTM)network,and other conventional classifiers.关键词
卷积神经网络/错误数据注入/混合状态估计/多变量时间序列Key words
convolutional neural network/false data injection/hybrid state estimation/multi-variate time series分类
信息技术与安全科学引用本文复制引用
孙萌,黄宇,王奇,肖耀辉,胡明辉..基于PMU的电力系统状态估计与卷积神经网络异常检测[J].微型电脑应用,2026,42(3):1-4,4.基金项目
国家自然科学基金项目(61602530) （61602530）
