Unsupervised Detection of Mixed Power Quality Events at the Grid Edge Using Feature-Engineered Discrete Wavelet Transform
Conference
Roy, S, Sarwat, A. (2025). Unsupervised Detection of Mixed Power Quality Events at the Grid Edge Using Feature-Engineered Discrete Wavelet Transform
. 10.1109/IAS62731.2025.11061504
Roy, S, Sarwat, A. (2025). Unsupervised Detection of Mixed Power Quality Events at the Grid Edge Using Feature-Engineered Discrete Wavelet Transform
. 10.1109/IAS62731.2025.11061504
Grid edge power quality (PQ) issues are receiving growing attention due to the increasing complexity, decentralization, and digital connectivity of modern energy systems. Ensuring stable and high-quality power at the grid edge is essential to prevent equipment damage, enhance energy efficiency, and maintain the reliable operation of bidirectional systems such as microgrids, electric vehicles (EVs), and smart homes - all of which require fast and accurate detection and classification of PQ disturbances. This work proposes an unsupervised classification method for detecting mixed PQ events at the grid edge, leveraging feature-engineered discrete wavelet transform (DWT) applied to real-field PQ meter data. A five-level DWT decomposition is used to extract rich statistical features from real-time voltage and current waveforms, which are subsequently clustered using a domain knowledge-driven K-Means approach. The classification results demonstrate that the proposed method effectively distinguishes between normal and abnormal conditions, as well as differentiates multiple simultaneous PQ disturbances - improving event characterization without the need for synthesized labeled datasets.
