Abstract
In this article, a novelty control structure of grid-connected doubly-fed induction generator (DFIG) based on a function link (FL)-based Wilcoxon radial basis function network (FLWRBFN) controller is proposed. The back-propagation (BP) method is used online to train the node connecting weights of the FLWRBFN. To improve the online learning capability of FLWBFN, differential evolution with particle swarm optimization (DEPSO) is used to tune the learning rates of FLWRBFN. For high randomness of wave energy generation, the transmission power between generators and electrical grids is easy to unstable and AC bus voltage and DC voltage will also lose constant under the conditions of variable generator speed and variable load. Therefore, the proposed intelligent controller can maintain the above power balance and voltage constant and reduce fluctuation. Finally, PSCAD/EMTDC software is used to simulate and study various cases to confirm the robustness and usefulness of the proposed intelligent control technology applied to an ocean wave energy conversion system.
Highlights
Published: 6 April 2021As the world’s energy demand continues to grow and conventional fossil energy such as oil, natural gas and coal are increasingly exhausted
To test the robustness of the proposed function link-based Wilcoxon radial basis function network (FLWRBFN) with differential evolution with particle swarm optimization (DEPSO), proportional-integration (PI), RBFN and FLWRBFN techniques are compared through various tests
The simulation cases are to analyze the effect of doubly-fed induction generator (DFIG) for rotor speed response, power change, and bus voltage transient process when serious load disturbance and short circuit faults suddenly occur in the electrical grid
Summary
As the world’s energy demand continues to grow and conventional fossil energy such as oil, natural gas and coal are increasingly exhausted. Some intelligent control schemes based on fuzzy logic with sliding mode controller, direct power and torque control (DPTC), perturb and observe (P&O) and Grey-based Elman neural network have been proposed for the external controllers of wind energy conversion. These intelligent controllers can improve control performance, but there is still room for improvement [18,19,20]. The Wilcoxon neural network studied in this paper is robust to the uncertainties, randomness, and parameter changes of the WECS, and can effectively restrain the external disturbance to the system It is widely used in control systems to achieve better dynamic performance. Energies 2021, 14, 2027 power oscillation in power system changes and short-circuit faults, and ensure the dynamic and transient performance of WECS in a wide range of different conditions
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