Power Quality Disturbances (PQDs) Classification Analyzed Based on Deep Learning Technique
Keywords:DWT, MRA, Deep learning, CNN, Power Quality Disturbance
Power Quality (PQ) problems in a distributed generation are mainly appeared due to excess non-linear load in the system. Identification and classification are necessary to ensure the reliability of Power Quality Disturbances (PQDs). This study proposed a signal processing and deep learning approach classify the PQDs by applying Discrete Wavelet Transform (DWT), Multi-Resolution Analysis (MRA) and a one-dimensional Convolutional Neural Network (CNN). For speed up in training, the performance of model a signal processing-based DWT-MRA extracted 54 features and fed it into 1D-CNN. Implementation of 1D-CNN seems more reliable than other machine learning approaches. Simulation results showed good performance and classification of data efficiently. Hence, the proposed approach could open a new era for PQDs in PV/wind smart grid in the near future to obtain more efficient outcomes.
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This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License