Sensing of Power System Disturbances Using CNN-Aided Tailored BiLSTM Model
Chandan Jana, Sannistha Banerjee, Subhajit Maur, Sovan Dalai · IEEE Sensors Journal · 2024
Fast and accurate detection of power system disturbance (PSD)-creating events is very much essential for the safe and reliable operation of today’s power distribution network. This task becomes more challenging when two events occur simultaneously. In this work, a deep learning (DL) network-based classifier has been proposed to classify the events that are likely to occur in the power system. Here, a few normal events such as switching, load changing, all feasible faults, and five dual events have been considered. All acquired disturbance signals are allowed to go through three major stages. At the initial stage, signal-to-image conversion takes place using continuous wavelet transform (CWT) followed by a couple of convolution operations for feature extraction, and then, the extracted time-series data are fed to the input of bidirectional long short-term memory (BiLSTM) module. Finally, the softmax output classifier has been applied. Few other popular classifiers have also been studied for comparison. Using this proposed method, 98.57% accuracy has been achieved considering 21 feasible events.