Abstract
Complex power quality disturbances (CPQDs) classification can be regarded as a typical application of multi-label (ML) learning. In this study, we propose a new recognition method for CPQDs based on S-transform (ST) and a hybrid kernel function-based extreme learning machine (ELM) for ML learning (HKEML). The signal processing techniques S-transform is utilized to extract the distinctive features of the CPQDs. A novel ML classifier called HKEML is constructed by combining hybrid kernel function-based multiclass ELM and a thresholding learning method-based kernel ELM. Finally, a test study was conducted using Matlab synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with several recent state-of-the-art ML learning algorithms, HKEML achieved better classification performance but with greatly superior computational speed.
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Zhao, C., Li, K., Xu, X. (2020). A Method Based on S-transform and Hybrid Kernel Extreme Learning Machine for Complex Power Quality Disturbances Classification. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_33
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DOI: https://doi.org/10.1007/978-3-030-23307-5_33
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