Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
