A Hybrid Hilbert-Huang Method for Monitoring Distorted Time-Varying Waveforms

The electric power systems together with the entire energy sector are rapidly evolving towards a low-carbon, secure, and competitive economy facing revolutionary transformations from technical structure to economic value chain. Pathways achieve sustainability led development of new technologies, accommodation larger shares unpredictable stochastic electricity transfer sources end-users without loss reliability, business models services, data management, so on. technologies incentives for local communities along large microgrids main forces driving evolution low voltage changing context paradigm rigid contractual binding between utilities end-user customers (now progressing flexible prosumers generation storage capabilities). flexibility operation prosumer can be enhanced by non-intrusive time-frequency analysis distorted quality waveforms both demand at point common connection. Therefore, it becomes importance discriminate among successive quasi-steady-state given system using only aggregated information available in PCC. This paper focuses on Hilbert–Huang method modifications such as empirical mode decomposition improved masking signals based Fast Fourier Transform, Hilbert spectral analysis, post-processing separating components their amplitudes frequencies within low-voltage operation. is used time-frequency-magnitude representation promising localization capabilities enabling efficient prosumers.