An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.
Renewable energy sources experience problems such as deregulation when they are used as stand-alone energy sources. This paper presents an optimal power sharing and power control strategy combining a photovoltaic (PV) array, a fuel cell (FC) stack, an ultra-capacitor (UC) module, and a set of loads. The photovoltaic is the prior energy source while the fuel cell (FC) system is added as a backup source to meet the excess power demand. The ultra-capacitor (UC) is utilized as a buffer storage to compensate the slow dynamic response of the FC during transient and regulate the DC-bus voltage. The power control strategy is designed to work on a two-level arrangement. The top level controls the entire power management, which generates references to low level individual subsystems depending upon solar radiation, temperature, and load conditions. Based on the command signals, each local controller controls the PV, FC, electrolyzer, and UC. The top level also controls the load scheduling during low solar radiation in order to sustain the system operation for 24 h. The performance of the system is tested under real-world record of solar radiation, temperature, and load conditions for Bahria town at Islamabad, Pakistan. The effectiveness of the proposed model in terms of voltage regulation, power transfer, load tracking, and grid stability is verified by Matlab simulation results.
Abstract:The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station (CS) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking (MPPT) controlled subsystem, photovoltaic (PV) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles (PHEVs) simultaneously to fulfil the grid-to-vehicle (G2V), vehicle-to-grid (V2G), grid-to-battery storage system (G2BSS), battery storage system-to-grid (BSS2G), battery storage system-to-vehicle (BSS2V), vehicle-to-battery storage system (V2BSS) and vehicle-to-vehicle (V2V) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate (AdapPID) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.
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