Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 204, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2021.107682
Keywords
Power quality disturbance(PQD); Deep belief network(DBN); Extreme learning machine(ELM); Adaptive wavelet threshold; Restricted boltzmann machine(RBM)
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Funding
- National Natural Science Foundation of China [51777061]
- Postgraduate Scientific Research Innovation Project of Hunan Province [CX20200432]
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A new method combining adaptive wavelet threshold denoising and deep belief network fusion extreme learning machine (DBN-ELM) is proposed to solve the problems of noise interference and artificial feature extraction in power quality disturbance (PQD) classification. The simulation result and experimental verification show that the proposed method can effectively suppress PQD noise and performs well on DBN-ELM classification.
To solve the problems of noise interference and artificial feature extraction in power quality disturbance (PQD) classification, a new method combining adaptive wavelet threshold denoising and deep belief network fusion extreme learning machine (DBN-ELM) is proposed. Firstly, the noise content of the layer is determined by calculating the energy ratio of the wavelet coefficients of each layer, and an adaptive wavelet threshold is constructed based on the energy ratio to denoise the PQD signals. Secondly, the feature extraction capability of DBN is used to extract the feature from the PQD signals after denoising. Finally, a novel PQD classifier called DBN-ELM is constructed by integrating an ELM into a DBN, which avoids global fine-tuning of DBN and improves PQD classification efficiency. The simulation result and experimental verification show that the proposed method can effectively suppress PQD noise and performs well on DBN-ELM classification.
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