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Fuzzy ART neural network algorithm for classifying the power system faults (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Vasilic, Slavko Kezunovic, Mladen |
| Abstract | Abstract—This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios. Index Terms—Adaptive resonance theory, clustering methods, fuzzy logic, learning systems, neural networks, pattern classification, power system faults, protective relaying, testing, training. I. |
| File Format | |
| Journal | IEEE Trans. Power Delivery |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Power System Fault Neural Network Fuzzy Art Neural Network Algorithm Fuzzy Logic Combined Use Input Signal Preprocessing Step Algorithm Selectivity Resonance Theory Neural Network Output Previous Version Supervised Adaptive Resonance Theory Index Term Improved Algorithm Recognition Capability Protective Relaying Algorithm Classification Capability Real Event Fuzzy Decision Rule Advanced Pattern Recognition Algorithm Simulation Result Implemented Scenario Transmission Line Fault Pattern Classification Art Algorithm |
| Content Type | Text |
| Resource Type | Article |
