Anomaly detection method for intelligent power system

Abstract

The invention discloses an anomaly detection method for an intelligent power system, and effectively improves the accuracy of anomaly detection and classification of the power system. When training data is insufficient, compared with a traditional supervised classification method, the method apparently improves the detection effect and the classification precision; when the training data is sufficient, the method employs an improved multi-granularity cascade forest for replacing traditional deep learning, so the problems that a deep learning algorithm model is complex and the training time istoo long are solved. Compared with a traditional detection classifier, the method has the advantages of less required training data, lower algorithm complexity, shorter training time, faster convergence speed and faster response time.

Description

technical field [0001] The invention relates to the field of electric equipment monitoring, in particular to an abnormal detection method of an intelligent substation. Background technique [0002] The power system is an electric energy production and consumption system composed of power plants, power transmission and transformation lines, power supply and distribution stations, and power consumption links. Its function is to convert the primary energy in nature into electric energy through the power generation device, and then supply the electric energy to various users through power transmission, transformation and distribution. [0003] The stability of the power system is mainly affected by unexpected events related to weather and equipment failures, which may cause interruptions in power transmission, causing abnormal power outages and interruptions at the user end. In addition to the impact of unexpected events such as equipment failures, cyber attacks against power s...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides an abnormality detection method for an intelligent power system to solve the problem that the existing machine learning technology cannot quickly and accurately detect and classify equipment faults from the monitoring data in the operation and control of the current power system Power system anomalies such as cyber attacks and cyber attacks generally have problems of low accuracy, poor generalization ability, and high resource usage

Images