Fuzzy Logic Control of Maximum Power Point Tracking Controller in an Autonomous Hybrid Power Generation System by Extended Kalman Filter for Battery State of Charge Estimation

Abstract

Autonomous power generation systems are designed to operate independently from the public power grid. Batteries constitute the important element in stand-alone PV system. They are used to store electricity produced by solar energy at overnight or for emergency use during the non-constant load demand. This paper has three major parts.The first pertains the design of an intelligent method for maximum power point tracking based on fuzzy logic controller to improve the efficiency of a standalone solar energy system. The second part describes the battery state of charge (SOC). The proposed model, which better reflects the real SOC response of the lithium battery, is constructed by using the extended Kalman Filter (EKF) states estimator. This proposed method can be considered as a more accurate and reliable method to estimate the battery state of charge. The third part integrates a management system for the above two renewable energy sources. The performance of the proposed management system by using a fuzzy logic controller based maximum power point tracking FLC-MPPT and the EKF estimator have been simulated in Matlab/Simulink at different solar irradiation and temperature for a given no constant load energy request.

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A Robust Renewable Energy Source-oriented Strategy for Smart Charging of Plug-in Electric Vehicles Considering Diverse Uncertainty Resources

“…In real-world power systems, the planning/operation process encounter with several economic/technical uncertainties. To model such uncertainty resources, the familiar probability distribution function (PDF) [9][10][11] for probabilistic approaches and fuzzy membership function [12][13][14] for possibilistic techniques have been recurrently employed in recent studies. Nevertheless, the PDF of the uncertain variables is not reachable or not appropriate for precise uncertainty modeling in most practical cases.…”

“…In this way, to ensure a reliable and robust decicison outputs hedged against the uncertain characteristics of the mentioned parameters, an efficient uncertainty modeling approach should be utilized. Generally speaking, the well-known probabilistic [9][10][11] and possibilistic methods [12][13][14] are usually observed in recent research works to model the various uncertainty resources. In the former, the probability distribution function (PDF) of the uncertain variables is mandatory while the latter characterize the uncertain resources via their membership function.…”