Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Expert Systems with Applications: An International Journal
Particle Swarm Trained Neural Network for Fault Diagnosis of Transformers by Acoustic Emission
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
How initial conditions affect generalization performance in large networks
IEEE Transactions on Neural Networks
Determination of neural-network topology for partial discharge pulse pattern recognition
IEEE Transactions on Neural Networks
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An artificial identification system to classify the insulation aging status of cast-resin transformer through current impulse method of partial discharge (PD) is proposed. The aging phenomenon of insulation materials belongs to a natural property and has strongly influences with the stability of power systems. Therefore, an effectively insulating identification technology plays an important role to enhance the system operating reliability. Since PD is a well known evidence of insulation degrading, a series of high voltage test with acceleration aging process to collect PD signals for identification system are conducted. Some selected statistical PD features instead of waveform are then extracted from these experimental PD signals as input data of the identification system. Also, an artificial neural network that combined particle swarm optimization is presented as the effectively identification tool. To demonstrate the effectiveness and feasibility of the proposed approach, the artificial identification system is applied on both noisy and noiseless circumstance. The experiment showed promising results with over 94% identification rate and even with 30% noise per discharge signal, an 85% successful identification rate can still be achieved.