A Fuzzy-Neural Technique for Flashover Diagnosis of Winding Insulation in Transformers
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Artificial intelligence for monitoring and supervisory control of process systems
Engineering Applications of Artificial Intelligence
Comparison of Bayesian and fuzzy ARTmap networks in HV transmission lines fault diagnosis
MMACTEE'10 Proceedings of the 12th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Expert Systems with Applications: An International Journal
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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) techniques provide the ability to classify the faulted phase/phases by identifying different patterns of the associated voltages and currents. 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 technique based on the error backpropagation training algorithm, and it is shown that the former technique is better suited for solving the fault diagnosis problem in complex multicircuit transmission systems