Xinjia Li, Yahong Li, Lei Fang, Liwei Liu, Ke Wang
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引用次数: 0
摘要
随着海量电力测量数据的流通,由安全攻击引起的数据异常给智能电网的可靠运行带来了安全隐患。基于长短期记忆(LSTM)的数据流通监控和安全风险异常评估已得到深入研究。然而,一些问题仍未得到解决,包括学习过拟合和预测误差过大。在本文中,我们研究了模糊学习来推断安全风险的异常级别。具体而言,本文提出了一种基于自适应灰狼优化-LSTM-模糊 petri 网络(AGWO-LSTM-FPN)的电气测量数据循环监测和安全风险异常评估算法。具体来说,AGWO 用于优化 LSTM 参数更新,提高流量预测精度。此外,还将 FPN 与多维监测指标相结合,以加强异常级别评估。仿真结果表明了 AGWO-LSTM-FPN 的卓越性能。
Fuzzy Learning-Based Electric Measurement Data Circulation Monitoring and Security Risk Anomaly Evaluation
With the circulation of massive electric measurement data, data anomaly caused by security attacks imposes security risks on reliable operation of smart grid. Long short-term memory (LSTM) based data circulation monitoring and security risk anomaly evaluation has been intensively studied. However, some issues remain unsolved, including learning overfitting and large prediction error. In this paper, we investigate fuzzy learning to infer the abnormal level of security risk. In particular, an adaptive grey wolf optimization-LSTM-fuzzy petri network (AGWO-LSTM-FPN) based electrical measurement data circulation monitoring and security risk anomaly evaluation algorithm is proposed. Specifically, AGWO is utilized to optimize LSTM parameter updating and improve traffic prediction accuracy. Furthermore, FPN is combined with multi-dimensional monitoring indicators to enhance anomaly level evaluation. Simulation results illustrate the excellent performance of AGWO-LSTM-FPN.