Multi-objective optimization of a PMSG control system through small-signal analysis

Turbine manufactures interest in permanent-magnet synchronous generators (PMSGs) with full-scale converters is rapidly increasing, hence, it becomes important to study the stability and performance of such systems. As the industry interest in PMSGs is recent, literature in the subject is scarce. The main contribution of this paper is the introduction of a structured multi-objective optimization process of the control structure of a PMSG applied in wind energy. A small-signal model is derived for the PMSG and the fullrated voltage-source converters (VSCs) connected in a back-toback arrangement. Thereafter, the linear model is used in a multiobjective genetic algorithm (MOGA) to fine-tune the proportional and integral (PI) gains of the control structure. The results demonstrate the effectiveness of the control optimization method. The system is stable and performs well in the conducted case study. The responses from both, the small-signal and the non-linear models, are similar. In this work, is it shown that it is possible to straightforwardly fine-tune and obtain optimal PI gains for the control structure of turbines, equipped with PMSGs interconnected to VSCs in a back-to-back arrangement.