Power quality (PQ) assessment is crucial in modern multi-source grids that accommodate thermal, solar, and wind power. The systems tend to exhibit nonlinear and intermittent characteristics and cause disturbances in the shape of voltage sags, swells, harmonics, and transients. Rule-based and traditional signal-processing systems are unable to categorize these disturbances due to high noise and variability present in actual data. In this paper, an efficient PQ analysis is proposed with a hybrid ensemble machine learning technique. A synthetic database of 8000 signals for 16 single and composite PQ disturbances based on IEEE and IEC standards was established. Continuous Wavelet Transform (CWT) was employed to transform 1D signal into 2D time–frequency images to provide better feature extraction. Three ensemble models, Ada-Boost, Light-GBM, and XG-Boost, were trained and tested using clean and noisy (20 dB) data. Ada-Boost demonstrated the maximum accuracy of 99.92% with zero noise and 99.86% with 20 dB noise. Light-GBM and XG-Boost were also satisfactory, indicating accuracies of 95.65%–98.73% and 96.46%– 98.64%, respectively. The results authenticate that ensemble learning methods offer a reliable and scalable solution to real-time PQ monitoring in smart grid systems that work better than traditional approaches in noisy and complex situations.
Power quality assessment, Machine Learning, CWT, Light-GBM.
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