Abstract
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 to achieve sustainability led to the development of new technologies, accommodation of larger shares of unpredictable and stochastic electricity transfer from sources to end-users without loss of reliability, new business models and services, data management, and so on. The new technologies and incentives for local energy communities along with large development of microgrids are main forces driving the evolution of the low voltage energy sector changing the context and paradigm of rigid contractual binding between utilities and end-user customers (now progressing to flexible prosumers with generation and storage capabilities). The flexibility and operation of a prosumer can be enhanced by a non-intrusive time-frequency analysis of distorted power quality waveforms for both generation and demand at the point of common connection. Therefore, it becomes of importance to discriminate among successive quasi-steady-state operation of a given local system using only the aggregated waveforms information available in the PCC. This paper focuses on the Hilbert–Huang method with modifications such as empirical mode decomposition improved with masking signals based on the Fast Fourier Transform, Hilbert spectral analysis, and a post-processing method for separating components and their amplitudes and frequencies within distorted power signals for a low-voltage prosumer operation. The method is used for a time-frequency-magnitude representation with promising localization capabilities enabling efficient operation for prosumers.
Highlights
The operation of current distribution grids is impacted by the high variability of the energy transfer
As can be seen in the table, the components do not appear on the whole-time window ( Tw ), but on some intervals only, their amplitudes being expressed in percentages of the RMS value of the original signal
The method proposed in this paper is based on two different methods that were adapted and improved: The enhanced Hilbert–Huang method based on masking signals for identification of oscillation modes existing in distorted time-varying waveform and the synthesis of a steady-state signal as presented in the Quasi-Steady-State Identification (QSSI) algorithm
Summary
The operation of current distribution grids is impacted by the high variability of the energy transfer. The state-of-the-art in power quality measurements and associated signal processing is applied in emerging control algorithms dedicated to microgrids (including DC and hybrid) and energy communities [3], to overcome unprecedented operational constraints [4]. Many of those constraints are linked to measurement processes and they might fail to meet the needs of the user [5,6,7] unless a careful analysis of the model uncertainties is performed. The interested reader is directed to the primary source of the modified Hilbert-Huang method [21], where the comparative performances are highlighted
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