Unknown power quality disturbances classification based on transfer learning approach with imbalanced data considerations
Year: 2025
Type: article
Abstract: The integration of various new energy sources and power electronic devices into the power grid system has led to an increase in power quality disturbances (PQDs), necessitating accurate and efficient monitoring. However, maintaining model robustness against unknown disturbances and handling dataset imbalances remains a significant challenge in PQDs classification. In practical scenarios, disturbance occurrences are inherently unpredictable, and the operational conditions of the power system further contribute to their uneven distribution. This imbalance negatively impacts model efficiency. While a well-trained model can achieve high accuracy for a specific set of disturbances, its performance often deteriorates when exposed to unknown ones, necessitating continuous updates. This study addresses these two challenges with the help of transfer learning (TL) and adaptive synthetic sampling (ADASYN). To address dataset imbalance, ADASYN is employed to generate synthetic samples for minority classes, ensuring a more representative training distribution. Leveraging these balanced datasets, a convolutional neural network-long short-term memory (CNN-LSTM)-based model is trained to improve classification accuracy. Beyond data balancing, model uncertainty must also be quantified to determine when updates are necessary. For this purpose, Wasserstein distance (WD) is utilized as an uncertainty metric, where values exceeding 2.65 indicate high uncertainty, signaling the need for model updates. TL is employed to facilitate these updates with a small dataset of unknown samples, significantly reducing the computational burden for online classification. Evaluation results confirm the effectiveness of the proposed approach in PQDs classification.
Source: Results in Engineering
Institution: Shahid Beheshti University
Language: English
Topic: Power Quality and Harmonics
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4411460984