Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
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
Artificial recognition system for defective types of transformers by acoustic emission
Expert Systems with Applications: An International Journal
Artificial identification system for transformer insulation aging
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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A top-down experimental procedure for defect type recognition of epoxy-resin transformers by Partial Discharge (PD) is proposed. Most of the PD detection methods could be performed only at the shutdown period of equipments. By using Acoustic Emission (AE), the real-time and online detection could be reachable. Therefore, this paper conducted high voltage test of pre-faulty transformers and measured those PD signals for recognition needed. Afterward, the selected features that proposed in this paper can be extracted from these collected PD signals. According to these features, effective identification of their faulty types can be done using the proposed particle swarm optimization combined with neural network. Finally, with a view to apply in field, this research adds different noise levels to distort the original data. These distorted data are entered for subsequent testing. Research shows encouraging results that with 30% noise per discharge count, an 80% successful recognition rate can be achieved.