Research on Risk Assessment Technology of Power Monitoring System Based on Machine Learning

TLDR: After establishment and operation of the model, effective and rapid analysis and output of disposal recommendations and corresponding risk levels are carried out, and the original experience is intellectualized and rationalized to the relevant people.

Abstract: with the complexity of the power system and the increasingly severe network security environment, the industry has urgently needed to improve the risk prediction ability of the power system security and the potential safety hazards brought about by the disposal. According to the experience and the features attributes of historical data, K-means unsupervised learning clustering is carried out. For supervised learning classification, this paper chooses SVM-KNN, and the risk assessment portrait after business disposal is constructed. After establishment and operation of the model, effective and rapid analysis and output of disposal recommendations and corresponding risk levels are carried out, and the original experience is intellectualized and rationalized to the relevant people. In order to make sure the stable, efficient and safe operation of the power monitoring system, model could give prompt safety advice as an expert.