Fault Classification and Faulty Section Identification in Teed Transmission Circuits Using ANN

An accurate fault classification algorithm for Teed transmission Circuit based on application of artificial neural networks (ANN) is presented in this paper. The proposed algorithm uses the voltage and current signals of each section measured at one end of teed circuit to detect and classify Double line to ground faults. ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. The adaptive protection scheme based on application of ANN is tested for double line to ground faults, varying fault location, fault resistance and fault inception angle. An improved performance is experienced once the neural network is trained adequately, gives accurate results when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within one cycle; thus the proposed adaptive protection technique is well suited for teed transmission circuit fault detection and classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.