Power Signal Processing and Feature Extraction Algorithms based on Time-Frequency Analysis

Abstract

This research delves into the amalgamation of power signal processing and feature extraction algorithms within the realm of electricity, particularly emphasizing their symbiotic relationship with linear regression models. The aim is to probe the anticipatory capacities and revelations facilitated by this amalgamated methodology across various contexts in electrical systems. At its core, the study hinges on the fundamentals of time-frequency analysis, enabling the dissection of electrical signals into their elemental frequency constituents across time. Techniques like the Short-Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT) furnish a structure for extracting both temporal and spectral insights from these signals. Capitalizing on this framework, linear regression models are deployed to gauge the associations between extracted features and pertinent target variables. Through a methodical inquiry, the research underscores the effectiveness of this amalgamated approach in telecommunications, environmental monitoring, and structural integrity assessment within the electrical domain. Empirical validation and practical case studies underscore the utility of the proposed methodology in unearthing concealed patterns, forecasting forthcoming trends, and guiding decision-making processes. By elucidating the nuanced interplay between signal dynamics and analytical methodologies, this investigation advances the frontier of signal processing, furnishing valuable insights and resources for researchers, engineers, and practitioners grappling with intricate signal analysis endeavours.