Open AccessPerformance validation of battery management system under prediction error for photovoltaic based distribution system
Author(s) -
Satapathy Prachitara,
Dhar Snehamoy,
Dash Pradipta Kishore
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2017.0286
Subject(s) - photovoltaic system , battery (electricity) , computer science , reliability engineering , automotive engineering , engineering , electrical engineering , power (physics) , physics , quantum mechanics
An effective reduction in power prediction error profile and an improved battery management system design for photovoltaic (PV) based microgrid application are presented in this study, where battery life and power loss are considered to be effectiveness measures. For local energy management the prediction error has a direct influence on distributed generator (DG) control reference calculation and thus in system stability. The silent effect of prediction error in battery energy storage life deterioration is highlighted in terms of battery temperature and power losses. The PV power prediction challenge (null versus positive volatility nature) is addressed with effective error reduction by kernel‐based feature mapping function. To obtain fast prediction (operational references to DG primary control) in an online manner, a new fast reduced Morlet kernel‐based online sequential extreme learning machine is proposed in this study. The battery (lithium‐ion) temperature effect is addressed by introducing a new secondary controller, which comprises battery temperature reference model (model reference) along with rule‐based temperature tolerance switching of stacks. The effectiveness of the proposed design is presented by rigorous case studies (MATLAB and TMS320 C6713), where extreme performance is achieved by simultaneous prediction error and local uncertainty.