Signal processing approaches for power quality disturbance classification: A comprehensive review
Madgula Satyanrayana, Venkataramana Veeramsetty, Durgam Rajababu
Abstract
This paper discusses various investigative methods for power quality disturbance (PQ) classification, and these issues have become more severe as a result of the transition from centralized to distributed power generation, mostly because of the incorporation of modern grid technologies and renewable energy sources (RES). This study presents a comprehensive review of signal processing methods, such as Fourier transform (FT) methods and their advanced derivatives, such as Short Time Fourier Transform (STFT), Wavelet Transform (WT), and Discrete Wavelet Transform (DWT), which are reviewed for the analysis and diagnosis of PQ problems. It highlights the strengths and limitations of these techniques, focusing on their effectiveness in addressing non-stationary signal disruptions such as voltage sags, swells, harmonics, and transients. This paper underscores the superiority of notch filters and DWT in handling time-frequency localization, making it essential for real-time PQ disturbance classification in dynamic power systems. This work contributes to enhancing the reliability and stability of modern power systems surrounded by evolving PQ challenges. • Mathematical modelings of various PQ issues discussed. • Literature on PQ issues and diagnosis methods are discussed.