Abstract
This paper proposes a load-oriented control parameters optimization strategy for Doubly Fed Induction Generator (DFIG) to enhance Low-Frequency Oscillation Damping (LFOD) and improve stability of a power system. Enabled by the smart grid measuring technologies, frequency deviations of generators of interest are obtained and employed as the input signals of the designed Supplementary Damping Controller (SDC) of DFIG. In order to acquire the optimal load-oriented control parameters, an hour-ahead load-forecasting scheme is devised, using Artificial Neural Network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique for the purpose of minimizing the Critical Damping Index (CDI) of the power system. Numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller in smart grids integrated with wind power generators.
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