Efficient Classification of Power Quality Using Long Short-Term Memory Network Technique

This study aims to apply a new deep-learning technique to detect and categorize individual and complex PQ issues such as swell, flickers, surges, interruptions, and sags. The suggested technique, the long short-term memory (LSTM) network, is a novel artificial intelligence technique and an identifiable form of recurrent neural network. This technique is utilized to detect and identify power quality (PQ) issues based on three principal solutions: automatic feature extraction, voltage/current magnitude calculations, and PQ problem duration. Simulated PQ problems generated by the Matlab simulation and many real field data sets are used to authorize the proposed technique's capability. The real data contain voltage and current waveforms that are measured, recorded, and analyzed in medium-voltage and high-voltage (MV/HV) substations by using a data acquisition device. The simulation results show that the proposed method is capable of detecting and classifying PQ problems more accurately compared with other artificial intelligence techniques.