Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Abstract

Purpose

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.

Design/methodology/approach

The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.

Findings

The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.

Originality/value

The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

Citation

Li, X. and Liu, S.s. (2020), "Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models", International Journal of Intelligent Computing and Cybernetics, Vol. 13 No. 1, pp. 81-101. https://doi.org/10.1108/IJICC-11-2019-0124