Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks

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Title: Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks
Author: Vasilic, Slavko
Abstract: This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line . The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions . The new concept relies on a principle of pattern recognition and detects the existence of the fault , identifies fault type , and estimates the transmission line faulted section . The approach utilizes self -organized , Adaptive Resonance Theory (ART ) neural network , combined with fuzzy decision rule for interpretation of neural network outputs . Neural network learns the mapping between inputs and desired outputs through processing a set of example cases . Training of the neural network is based on the combined use of unsupervised and supervised learning methods . During training , a set of input events is transformed into a set of prototypes of typical input events . During application , real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule . This study introduces several enhancements to the original version of the ART algorithm : suitable preprocessing of neural network inputs , improvement in the concept of supervised learning , fuzzyfication of neural network outputs , and utilization of on -line learning . A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events . Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm , Multilayer Perceptron (MLP ) neural network algorithm , and impedance based distance relay . Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied , as well as superior classification capabilities compared to the other solutions . Consequently , it is demonstrated that the proposed ART solution may be used for accurate , high -speed distinction among faulted and unfaulted events , and estimation of fault type and fault section .
URI: http : / /hdl .handle .net /1969 .1 /436
Date: 2004-09-30

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Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks. Available electronically from http : / /hdl .handle .net /1969 .1 /436 .

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