Statistical and Machine Learning Technique to Detect and Classify Shunt Faults in a UPFC Compensated Transmission Line
AbstractIn this paper, machine learning technique is used to detect and classify all shunt faults in a UPFC compensated transmission line. A four-bus three-machine system with detailed modelling of UPFC has been used for fault simulation studies in MATLAB/Simulink. Instantaneous voltage and current signals obtained at local bus terminal are processed with DFT and statistical method for feature extraction. The input features of the ANN is minimized using the statistical method. Generated features are used to train the ANN module. Trained ANN modules are used for testing different fault conditions in the time domain. Rigorous simulation studies have been performed with a wide variety of different possible fault situations. Simulation results bring out the superiority of the scheme. Moreover, the error introduced due to CT, CCVT and Dynamic behaviour of the UPFC has been considered for testing the trained ANNs by varying the different operating modes of UPFC, and different compensation levels, wherein all the cases, the performance is found reliable.
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