Real-Time Harmonics Detection in Power Grids Using Wavelet-Based Approaches

The capacity to detect harmonics in power grids in real-time is now essential for maintaining operational reliability, power quality, and stability. The capacity of wavelet-based approaches to give localized time-frequency information has made them well-known as powerful instruments for non-stationary signal analysis. Nevertheless achieving good harmonic identification in noisy environments, with frequency drift, and with transient disruptions has been a difficulty for previous studies. When it comes to real-time applications, conventional methods that rely on Fourier analysis or use fixed windows sometimes fall short in accurately capturing interharmonics and fast grid variations. This research presents a new framework for robust harmonic, interharmonic, and transient detection called the Adaptive Wavelet Harmonic Real-Time Tracker (AWHR-TT), it aims to address these constraints. For the purpose of to achieve sub-cycle latency with excellent precision, the technique includes a lifting-scheme wavelet packet transform, adaptive best-basis selection, synchrosqueezed time-frequency mapping, and state-space tracking. Even in cases of a poor grid or distorted data, its adaptive denoising and event detection layers guarantee dependability. When compared to more traditional approaches, AWHR-TT outperforms them in terms of total harmonic distortion (THD) estimation, detection delay, and accuracy of harmonic identification. With the objective to help utilities keep their smart grids stable and in line with power quality requirements, this research has real-world applications in real-time monitoring and quality assessment.