Hilbert spectral analysis for PQ disturbances detection in Sustainable Energy Systems

As the integration of renewable energy sources into power grids increases, ensuring power quality in sustainable energy systems has become a critical challenge. This paper presents a novel approach for identifying power quality (PQ) disturbances using Hilbert-Huang Transform (HHT), a powerful time-frequency analysis tool. Unlike traditional Fourier-based methods, HHT is highly effective in analyzing non-linear and non-stationary signals, making it particularly suitable for complex power quality events such as voltage sags, swells, harmonics, and transients. The proposed method first applies Empirical Mode Decomposition (EMD) to decompose the power signal into a set of intrinsic mode functions (IMFs), followed by the application of Hilbert spectral analysis to extract instantaneous frequency and amplitude features. These features are used to identify and classify different types of disturbances in real-time. The performance of the Hilbert-Huang Transform is validated through simulations and case studies, demonstrating its superior ability to handle complex and timevarying power quality disturbances with higher accuracy compared to conventional techniques. This method offers significant improvements in both diagnostic precision and computational efficiency, providing a robust solution for realtime power quality monitoring and disturbance mitigation in smart grids.