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A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data

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Energy and AI 2023年第4期14卷 598-611页

作者： ani harish Prince Asok Jayan M.V. APJ Abdul Kalam Technological University Rajiv Gandhi Institute of TechnologyKottayam686501KeralaIndia APJ Abdul Kalam Technological University CET CampusThiruvananthapuram695016KeralaIndia

Smart grid is envisaged as a power grid that is extremely reliable and *** electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system *** development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement *** research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is ***,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning *** substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%*** computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 *** emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 *** efficacy of the models is demonstrated with data from the IEEE 39 bus test system.

关键词： Machine learning Fault detection Fault classification Auto-Encoder Transmission line Smart grid Neural network Extreme Learning Machine

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