Fuzzy Learning-Based Electric Measurement Data Circulation Monitoring and Security Risk Anomaly Evaluation

Abstract

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.