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- W3040442605 abstract "Undesirable operation of a distant relay at the occurrence of stressed conditions is a reason for blackouts. There are a few computational intelligent methods available in the literature for avoiding relay maloperations. However, because of variations in the system parameters and expansions of the network, the performance of these techniques can be degraded. To solve this issue, data mining approaches have been introduced. The existing data mining approaches need improvement in terms of accuracy and error rate while discriminating fault and stressed conditions. In this paper, a Convolutional Neural Network (CNN) based classifier is proposed for identifying various faults and differentiating fault and power swing situations. The data are collected from the IEEE-9 bus system by Phasor Measurement Units (PMU) and the proposed CNN classifier model classifies system conditions like normal, fault, and power swing. The outcome shows that the classifier has high accuracy and low error rate compared to other classification models such as Naïve Bayes, Decision Tree, and K-Nearest Neighbor. Furthermore, the proposed CNN model is validated with the use of TensorFlow framework to demonstrate its superior performance." @default.
- W3040442605 created "2020-07-10" @default.
- W3040442605 creator A5013897567 @default.
- W3040442605 creator A5085097428 @default.
- W3040442605 date "2020-06-07" @default.
- W3040442605 modified "2023-09-24" @default.
- W3040442605 title "Classification of Electrical Power System Conditions with Convolutional Neural Networks" @default.
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- W3040442605 doi "https://doi.org/10.48084/etasr.3512" @default.
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