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<span>There are many types of generators used within wind energy such as doubly fed induction generator (DFIG). Particle swarm optimization (PSO) algorithm is simple, robust and easy to implement. In addition to the privilege of PSO, autonomous groups particle swarm optimization (AGPSO) has the advantages of using diverse autonomous groups which result in more randomized and directed search. Applying AGPSO to tune PI controller to control DFIG is proposed in this paper. An implemented laboratory prototype consists of brushless DC motor (BLDC) for simulating the various wind speeds. Wound rotor induction machine, working as DFIG. This system is a stand-alone system. System identification strategy was introduced in this work. In this study, AGPSO is suggested for tuning the PI controller. Different case studies are performed, such as step changes in both speed and electrical load for showing the effectiveness of the proposed algorithm. For comparison PSO is used to tune the PI controller. Results from experiments clarify the feasibility of the proposed methodology. It is approved that AGPSO achieves the prevalent control execution (quicker transient response and more modest steady state error (ess)) contrasted with the PSO in tuning PI controller when applied to be used with off-grid systems. </span>

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Brushless Direct Current (BLDC) motor becomes at the top of motors in high performance drive systems such as Electric vehicle (EV). This paper presents a modern approach of speed control for BLDC using particle swarm optimization (PSO) algorithm. to optimize the scaling factors of fuzzy logic controller (FLC). The overall system is simulated under various operating conditions and simulation results have been examined at different control techniques. At the same operating conditions, these simulation results are compared with those obtained using fuzzy logic. The investigated results illustrated that the use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. The system is tested for a step change in load and the simulation results showed good dynamic response with fast recovery time.