High-Accuracy Transfer Learning-Based Power Quality Disturbance Recognition Under A Noisy Environment

The smart and sustainable development of power grids has brought about numerous power quality disturbances (PQD) that threaten the efficiency and reliability of the smart grids. Detecting and classifying these disturbances is crucial to taking appropriate corrective action to mitigate possible impacts. While machine learning (ML) algorithms are at the center of attention for most PQD detection problems, their performance is limited due to insufficient samples. Therefore, this paper proposes a new model based on continuous wavelet transform (CWT) and transfer learning to overcome the data challenge. In this regard, raw voltage signals are First transformed to 2D contour images using the CWT. Then, converted images are used to fine-tune a pre-trained model on the Imagenet dataset through transfer learning. The simulation results over 29 groups of PQD under different noise levels show its effectiveness over other ML models. Finally, our code is publicly available in a git repository for enthusiastic readers.