A New Fault Classifier in Transmission Lines Using Intrinsic Time Decomposition

As nonstationarity exists in fault signals of transmission lines, their classification and quantification remain a challenging issue. This paper presents a new scheme for feature extraction in an attempt to achieve high fault classification accuracy. The proposed scheme consists of three steps: first, the proper rotation components (PRCs) matrix of current signals captured from one end of the protected line is constructed using the intrinsic time decomposition, a fast time-domain signal processing tool with no need for sensitive tuning parameters. Second, the singular value decomposition and nonnegative matrix factorization are employed to decompose the PRCs into its significant components. Finally, eight new normalized features extracted from the output of the data processing techniques are fed into the probabilistic neural network classifier. The data processing techniques employed for classification substantially improve the overall quality of the input patterns classified and increase the generalization capability of the trained classifiers. The proposed scheme is evaluated through two simulated sample systems in the PSCAD/EMTDC software and field fault data. Moreover, the effects of the current transformer saturation, decaying dc component, and noisy conditions are evaluated. The comparison results and discussion regarding the different aspects of the problem confirm the efficacy of the proposed scheme.