Abstract
Electric power quality is an issue for utilities, end users, manufacturers, and other customers. Poor power quality is the primary source of economic losses. The extensive use of sensitive electronic equipment devices has expanded as the globe has become more industrialized. Due to electronic system maintenance, power quality disturbances (PQD) may cause security difficulties and loss. To minimizing power quality events, the events must be identified and classified, so that appropriate preventive action must be taken. In this paper, preprocessing of power signals has been carried out using the dual-tree complex wavelet transform which increases classification accuracy by localizing disturbances based on phase information connected to time and frequency. “Phase space reconstruction using neural networks are used to reconstruct two-dimensional data in order to increase classification accuracy.” The best structure has been developed by testing and implementing the proposed technique in various network configurations with reduced complexity which has been authenticated by its accuracy in classifying the disturbances.
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Likhitha, R., Aruna, M., Hussaian Basha, C.H., Prathibha, E. (2023). Classification of PQDs by Reconstruction of Complex Wavelet Phasor and a Feed-Forward Neural Network—Fully Connected Structure. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_28
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DOI: https://doi.org/10.1007/978-981-99-4577-1_28
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