Transmission Line Fault Classification Using Hidden Markov Models

The maintenance of power quality in electrical power systems depends on addressing the major disturbances that may arise during generation, transmission and distribution. Many studies aim to investigate these disturbances by analyzing the behavior of the electrical signal through the classification of short circuit faults in power transmission lines as a way to assist the administration and maintenance of the electrical system. However, most fault classification methods generate a high computational cost that do not always yield satisfactory results; these methods utilize front ends in data processing before being processed by conventional classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) that are adopted into the Frame Based Sequence Classification (FBSC) architecture that uses the front ends Waveletenergy, Waveletconcat, RAW, Root Mean Square front ends (RMS) and ConcatFrontEnd. An alternative method for classifying faults without having to use front ends employs the UFPAFaults database and the Hidden Markov Model (HMM) algorithm that directly treats the electrical signal in the form of multivariate time series. The results indicate the HMM algorithm as a potential classifier because its comparatively low error rate of 0.03% exceeds the performance of the conventional classifiers ANN, SVM, KNN and RF as used with the FBSC architecture. When the statistical test with a significance of alpha = 5% is applied, only the ANN and RF classifiers present a result close to what the HMM algorithm provides. Another relevant factor is that the HMM algorithm considerably decreases the computational cost by more than 90% of processing time as compared to the conventional classifiers of the FBSC architecture, thereby validating its potential in the direct classification of faults in electric power system transmission lines.