1. Particle swarm optimisation technique to improve energy efficiency of doubly-fed induction generators for wind turbines
- Author
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Cherngchai Sompracha, Dilan Jayaweera, and Pietro Tricoli
- Subjects
particle swarm optimisation ,asynchronous generators ,power generation control ,wind turbines ,wind power plants ,energy conservation ,power generation reliability ,machine control ,error compensation ,parameter estimation ,angular velocity control ,electric current control ,stators ,reactive power control ,energy management systems ,power system management ,maximum power point trackers ,numerical analysis ,energy efficiency ,doubly-fed induction generators ,wind energy conversion systems ,variable-speed fixed-pitch wind turbines ,variable-pitch wind turbine systems ,stator reactive power management ,maximum power point tracking control ,soft-stalling control ,wecs ,dfig ,particle swarm optimisation control system ,reliability ,circuit parameter estimation ,electrical loss minimisation ,doubly-fed generator ,optimal d-axis rotor current ,numerical simulation ,model-based loss minimisation ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Wind energy conversion systems (WECSs) require a suitable control to maximise the power generated by wind turbines independently on the wind conditions. Variable-speed fixed-pitch wind turbines with doubly-fed induction generators (DGIG) are used in WECSs for their higher reliability and efficiency compared to variable-pitch wind turbine systems. This study proposes an effective control algorithm to maximise the efficiency of fixed-pitch wind turbines with DFIGs using particle swarm optimisation control to compensate for the errors in the estimation of the circuit parameters of the generator. The proposed control algorithm generates an optimal speed reference to optimise the mechanical power extracted from the wind and the optimal d-axis rotor current through stator reactive power management to minimise the electrical losses of the doubly-fed generator. The optimal speed reference is provided by a maximum power point tracking control below the rated wind speed and a soft-stalling control above the rated wind speed, while the optimal d-axis rotor current is searched by a particle swarm optimisation algorithm. The proposed control system has been verified by numerical simulations and it has been demonstrated that the energy generated for typical wind speed profiles is greater than that of a traditional control based on a model-based loss minimisation.
- Published
- 2019
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