A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Romero-Ternero, M. C.; Oviedo-Olmedo, David; Carrasco, Alejandro; Luque, J.

doi:10.3390/s19224998

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…After the EMD process, every resulting IMF does satisfy the conditions expressed in Section 2.4 for having a meaningful Hilbert Transform (HT). The electricity demand Hilbert-Huang Transform (HHT) is then no more than the ensemble of the HT of each IMF, as reflected in Equation (17), which is depicted in Figure 10a. The plot represents, for each IMF, the instantaneous frequency (vertical axis) versus time (horizontal axis), while the instantaneous amplitude corresponding to each point is also color-coded.…”

Section: Hilbert-huang Spectrum Of Electricity Demandmentioning

confidence: 99%

“…Autoregressive integrated moving average (ARIMA) has been proposed in forecasting solar production [13], long-term energy demand [14] and electricity prices [15]. Other uses of data analytics in smart grids include heterogeneous data integration [16] or estimation of battery state-of-charge in solar farms [17]. A comprehensive and updated review of issues regarding the role of data analysis in the development of intelligent energy networks can be found in [18].…”

Section: Introductionmentioning

confidence: 99%

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Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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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.

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Section: Hilbert-huang Spectrum Of Electricity Demandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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“…State of Charge (SOC) is the key index closely related to lithium-ion battery performance and safety to reflect the characteristics the power supply system of electric vehicles. Online and high precision estimate SOC is required by a battery management system to prevent battery overcharge and discharge, low power current limiting threshold, vehicle control strategy threshold, etc [ 1 ]. Generally, SOC cannot be measured directly; hence, it is usually estimate by detecting terminal voltage, current, impedance, temperature, and other parameters [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Lin

Tu³

et al. 2023

Sensors

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The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.

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A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Cited by 2 publications

References 39 publications

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

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