Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest

Dongdong Yang (), Shixuan Lü, Junming Wei, Lijun Zheng and Yunguang Gao
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Dongdong Yang: Electric Power Science Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Shixuan Lü: Electric Power Science Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
Junming Wei: Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China
Lijun Zheng: Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China
Yunguang Gao: College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Energies, 2025, vol. 18, issue 15, 1-17

Abstract: The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration.

Keywords: power quality disturbance; improved adaptive S-transform; random forest; feature extraction; detection and classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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