An Intelligent Model Based on Phase Space Analysis for Fault Classification in Single Circuit Transmission Lines

Abstract

Two important issues in the modern transmission lines protection are the speed and accuracy of the fault type classification, which have a great impact on the duration of fault clearing time and the accuracy of fault detection by the distance relay. The purpose of this study was to use the phase space analysis and decision tree-learning algorithm to classify the fault type in single circuit transmission lines. Accordingly, an algorithm is developed in which the three-phase current and voltage signals are measured and sampled on one side of the transmission line, firstly. Then, after the phase space analyzing of the current and voltage samples, the statistical feature vector of the output of the analysis is calculated. In the end, the feature vector is fed to the pre-trained intelligent model, to determine the type of fault occurred. The proposed algorithm has been investigated and tested on the sample network in different fault conditions, including different values of fault resistance, fault inception time, the amount of the transferred power on the transmission line, and the fault location. The results show that the proposed algorithm can determine the fault type with a length of post-fault data window less than 2 ms and accuracy of 100 percent.

Keywords

• single circuit transmission lines
• transmission line protection
• intelligent model
• fault classification
• phase space analysis
• decission tree