Abstract
The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.
Highlights
Spectrum (MHHS)the according to obtained obtained different frequency scales are new harmonics pointed in green in the graphics
Besides the harmonics obtained by frequency version ofis the Transform (FFT), the Hilbert–Huang
Besides the harmonics obtained by FFT, the Hilbert–Huang analysis reveals new harmonics pointed in green in the graphics
Summary
Modern electrical networks are one of the main scenarios in which a widespread deployment of sensors is being achieved [1], acquiring a large amount of information of different types.Processing that information fosters making more efficient and intelligent decisions, in such a way that resulting power networks are usually known as smart grids [2].The large number of sensors used in today0 s smart grids has become economically and technically feasible due to a significant reduction in their cost, the availability of higher capacity communication systems, and the greater accessibility to storage and processing equipment [3].The straightforward availability of greater datasets poses a challenge on the data analytic side [4]making possible new and more efficient approaches to several applications such as fault detection [5], predictive maintenance [6], transient stability analysis [7], electric device state estimation [8], power quality monitoring [9], topology identification [10], renewable energy forecasting [11], and non-technical loss detection [12].Especially relevant are the smart grid applications in which a high ratio of renewable and distributed energies has to be considered. Processing that information fosters making more efficient and intelligent decisions, in such a way that resulting power networks are usually known as smart grids [2]. The large number of sensors used in today0 s smart grids has become economically and technically feasible due to a significant reduction in their cost, the availability of higher capacity communication systems, and the greater accessibility to storage and processing equipment [3]. Making possible new and more efficient approaches to several applications such as fault detection [5], predictive maintenance [6], transient stability analysis [7], electric device state estimation [8], power quality monitoring [9], topology identification [10], renewable energy forecasting [11], and non-technical loss detection [12]. Autoregressive integrated moving average (ARIMA) has Sensors 2020, 20, 2912; doi:10.3390/s20102912 www.mdpi.com/journal/sensors
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