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ABSTRACT This paper concentrates on the output power smoothing and the grid dynamic response enhancement of a grid-interactive MW-class permanent magnet synchronous generator-based wind energy conversion system (WECS). A simple fuzzy controller method is applied to improve the overall performance of the WECS. The proposed method can retrieve the storing kinetic energy from the inertia of a wind turbine, perfectly. As a result, it can ensure a proficient power smoothing of the variable speed WECS. On the other hand, the grid side inverter is controlled by the fuzzy controller. This approach can reduce the fluctuation of DC link voltage and can deliver a smooth power to the power grid. The proposed method is compared with two other methods such as the maximum power point tracking control method and the without fuzzy controller method. A simple shunt circuit also includes in the DC link circuit. Therefore, during the system fault condition, the WECS can perform a stable operation. Effectiveness of the proposed method is verified by numerical simulations. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy-logic-based binary particle swarm optimization (BPSO) method for solving short-term thermal unit commitment problem integrated with an equivalent wind-battery system. As a renewable power source, wind power is injected stochastically with the model. To handle the uncertainty and intermittency due to the wind power integration, the trivial crisp problem formulations are modified by introducing fuzzy logic. Moreover, since it is also forecast, load demand along with the spinning reserve and production cost are taken under fuzzification. The potential solutions are distributed among several clusters based on a clustering scheme which exploits their associated fitness values. The fitness value is functionalized by combining the objective function, penalty function, and the aggregated fuzzy membership function. After clustering, each solution is updated according to the velocity and position BPSO refinement functions. The clustering scheme inherently introduces multipopulation-based search space exploration in PSO. Therefore, this algorithm allows the particles to explore a larger search space in the problem domain by diversification of particles. Simulation results are provided to show the effectiveness of the proposed method by scrutinizing two different power systems. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]