Abstract
The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.
Highlights
The global electricity demand is expected to increase 49% from 2007 to 2035 [1]
This paper presents a grid-connected hybrid power system (HPS) consisting of wind turbine, PV, Solid Oxide Fuel Cells (SOFC), electrolyzer, battery storage system, SC and MT generating sources to satisfy a dynamic residential load
The results prove that power is well managed in the HPS under rapid change of atmospheric and dynamic load conditions
Summary
The global electricity demand is expected to increase 49% from 2007 to 2035 [1]. At present, most of the electricity demand is met by fossil fuels. Numerous intelligent and modern soft computing techniques are widely used to obtain the PV MPPT and swift response of SOFC, which include evolutionary algorithms, artificial neural network (ANN), fuzzy logic and their hybrids. In the stated HPS, tracking the maximum power point (MPP) for the PV system and obtaining the swift response of the SOFC are quite perplexing issues, because this system is greatly characterized by nonlinearity. The Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT control of PV is characterized by nonlinearity, which operates on the instantaneously captured nonlinear dynamics of the system. The Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of SOFC is embodied to respond swiftly to the nonlinear identified dynamics of the system due of sudden load changes.
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