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Feature extraction methods for neural network-based transmission line fault discrimination

Feature extraction methods for neural network-based transmission line fault discrimination

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The suitability of conventional distance relays to operate correctly under variations in such factors as source impedance, prefault load and fault resistance is still a problem. This paper describes an alternative approach to nonunit protection of transmission lines using artificial neural networks (ANNs). Particular emphasis is placed on describing a methodology whereby the extraction of the input features (from the measured voltage and current signals) to the ANNs is near optimal; with this approach, the results presented clearly demonstrate that the protection technique gives satisfactory performance under a wide variation in practically encountered system operating and fault conditions.

Inspec keywords: feature extraction; power transmission lines; relay protection; power system analysis computing; neural nets; power transmission protection; power transmission faults; power system relaying

Other keywords: power system operating conditions; prefault load; artificial neural networks; computer simulation; distance relays; nonunit protection; power transmission line fault discrimination; fault resistance; input feature extraction methods; protection performance; source impedance

Subjects: Power transmission lines and cables; Power engineering computing; Neural computing techniques; Power system protection

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