A Novel Approach to Fault Diagnosis in Multicircuit Transmission Lines Using Fuzzy ARTmap Neural Networks

Author(s): Aggarwal, R.K. | Bennett, A. | Johns, A.T. | Li, F. | Xuan, Q.Y. |

Year: 1999

Citation: IEEE Transactions on Neural Networks, vol. 10, no. 5

Abstract: The work described in this paper addresses the problems of fault diagnosis in complex multicircuit transmission systems, in particular those arising due to mutual coupling between the two parallel circuits under different fault conditions; the problems are compounded by the fact that this mutual coupling is highly variable in nature. In this respect, artificial intelligence (AI) technique provides the ability to classify the faulted phase/phases by identifying different patterns of the associated voltages and currents. In this paper, a Fuzzy ARTmap (Adaptive Resonance Theory) neural network is employed and is found to be well-suited for solving the complex fault classification problem under various system and fault conditions. Emphasis is placed on introducing the background of AI techniques as applied to the specific problem, followed by a description of the methodology adopted for training the Fuzzy ARTmap neural network, which is proving to be a very useful and powerful tool for power system engineers. Furthermore, this classification technique is compared with a Neural Network (NN) technique based on the error backpropagation (EBP) training algorithm, and it is shown that the former technique is better suited for solving the fault diagnosis problem in complex multicircuit transmission systems.

Topics: Machine Learning, Applications: Industrial Control, Network Analysis, Models: Fuzzy ARTMAP,

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