IEEE Access · 2022 · 34 citations · 25 references
AgricultureComputational EngineeringOptimizationPower ElectronicsRenewable Energy SystemsDistributed Energy ResourcesHeuristic OptimizationPower Electronics For Renewable EnergyRenewable Energy Power ConversionWind Energy SystemsWind Turbine TechnologyMaximum Power Point TrackingMaximum Power Point Tracking AlgorithmsParticle Swarm ControlParticle Swarm OptimizationPower Conversion For Wind Energy SystemsRotor Speed OptimizationWind Energy Conversion SystemsWind Turbine Power Converters
In this work, a searching space minimization-based particle swarm optimization (SSM-PSO) scheme has been proposed for maximum power point tracking (MPPT) in a doubly fed induction generator (DFIG) based wind energy conversion system (WECS). DFIG displays non-linearity in <i>P</i> – ω characteristics. So different types of conventional and optimization-based schemes are developed for MPPT. The drawbacks in the conventional perturb and observe (P&O) scheme has been successfully abolished by the proposed SSM-PSO method. Because of its weather-insensitive nature, the conventional P&O MPP tracking scheme results in the fluctuation of DFIG output under a sudden change in wind speed. To avoid this problem, maximum and minimum limits for the optimal rotor speed have been determined in the proposed SSM-PSO scheme. Further, the obtained limits for rotor speed are employed to improve the searching space within the non-linear <i>P</i> – ω curve. This initial confinement of particles to a limited searching space in SSM-PSO results in a faster response of the system. Since the proposed SSM-PSO is atmosphere sensitive, it avoids fluctuations under an abrupt variation in wind velocity. The improved initialization part of SSM-PSO leads to better dynamic characteristics compared to existing P&O and optimization-based schemes. The proposed SSM-PSO scheme is implemented for a 2MW DFIG system in MATLAB Simulink atmosphere and showed satisfactory results.
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