Robustness Analysis of Generalized Regression Neural Network-based Fault Diagnosis for Transmission Lines

Protecting the high voltage transmission lines has been one of the most significant problems in the power systems. Precise and timely detection, identification, and location estimation of line-to-ground, line-to-line, line-to-line-to-ground, and line-to-line-to-line faults can considerably enhance the speed of a recovery process of transmission lines and hence reduce the costs associated with the downtime of a power system. Consequently, having a robust, affordable, and accurate fault diagnosis system is crucial to perform these tasks within an acceptable time window after a fault occurs in the presence of system uncertainties. Mistakenly detected or undetected faults can be expensive in the conventional techniques and this fact has motivated us to present a robust detection, identification, and location estimation system by using a machine learning method called generalized regression neural networks. The robustness of this technique is tested with respect to the variations of fault resistance, phase difference between two connected buses, fault inception angle, local bus voltage fluctuations, source inductance fluctuations, and measurement noise. Besides, the effect of noise on the GRNN method is revealed in this paper. Its comparison with the existing state-of-the-art methods shows its outstanding performance in the accurate fault classification and location estimation for transmission lines.