The Stand-Alone Hybrid Power Systems Analysis

This paper presents a new Hierarchical and Robust Fuzzy Control Scheduler of Energy Management Strategy (HRFCSEMS) for the Stand-Alone Hybrid Power Systems (SAHPS) is presented. It consists of two level named Centralized Fuzzy Supervisory Control (CFSC) which choose the power references for each Decentralized Robust Fuzzy Scheduler Control (DRFSC). SAHPS comprises: a photovoltaic panel (PV) and Wind Turbine (WT) as renewable sources, a micro turbine generator and a battery storage system. The proposed control strategy is able to satisfy the load requirements based on a fuzzy supervisor controller and manage power flows between the different energy sources and the storage unit by respecting the State of Charge (SOC) and the variation of wind
Many of researchers proposed several State of Charge (SOC) estimation methods for the batteries based on fuzzy logic, neural network and Kalman extended filters [9], [10].
Micro turbine with a hybrid power generation system is presented to serve the load demands in remote areas because micro turbine instead of diesel generator because of more fuel flexibility and to less: weight, maintenance, noise, and pollution. The micro turbine and the battery storage are used as a support for the hybrid system of the wind turbine and to the solar array. Stability condition is more difficult when the system is nonlinear and subject to parameter uncertainties because there is no methodical way to find sufficient stability condition to secure good performance. PV and WT are nonlinear systems exposed to variable temperature, irradiation, and wind profiles which makes PV and WT controllers design face difficult duty [11]. Takagi-Sugeno (TS) fuzzy control play important role when to design robust nonlinear control systems [12]. Stability conditions is studied based on Lyapunov stability theory is proposed in [12], fuzzy scheduling controllers is given in [13], [14], switching controllers is presented in [15] and other methods studied the robust fuzzy control are given in
DRFSC maximize power ex-traction PV and WT and stabilize a nonlinear system in the presence of the parameter uncertainties. A DRFSC consists of three units: a uncertain nonlinear plant, a fuzzy regenera-tor, and a fuzzy gain scheduler, as shown in section 3. The derived stability conditions for DRFSC are used to analyze the stability of the TS fuzzy control systems with uncertain-ty which can be regarded as a generalized class of uncertain nonlinear systems. Sufficient design conditions are derived for robust asymptotic tracking in terms Linear Matrix Inequalities