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.

[1]  P. Bauer,et al.  Comparison of direct voltage control methods of multi-terminal DC (MTDC) networks through modular dynamic models , 2011, Proceedings of the 2011 14th European Conference on Power Electronics and Applications.

[2]  Shuhui Li,et al.  Optimal and Direct-Current Vector Control of Direct-Driven PMSG Wind Turbines , 2012, IEEE Transactions on Power Electronics.

[3]  Jonathan E. Fieldsend,et al.  Using unconstrained elite archives for multiobjective optimization , 2003, IEEE Trans. Evol. Comput..

[4]  Henk Polinder,et al.  Dynamic modelling of a wind turbine with doubly fed induction generator , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[5]  Zhe Chen,et al.  Power control of permanent magnet generator based variable speed wind turbines , 2009, 2009 International Conference on Electrical Machines and Systems.

[6]  Y. Wang,et al.  Offset-Free Predictive Control for Variable Speed Wind Turbines , 2013, IEEE Transactions on Sustainable Energy.

[7]  S. Mishra,et al.  Small-Signal Stability Analysis of a DFIG-Based Wind Power System Under Different Modes of Operation , 2009, IEEE Transactions on Energy Conversion.

[8]  Hee-Sang Ko,et al.  Modeling and control of DFIG-based variable-speed wind-turbine , 2008 .

[9]  O. Anaya-Lara,et al.  Performance of Doubly Fed Induction Generator (DFIG) during Network Faults , 2005 .

[10]  Hui Huang,et al.  Small-signal modelling and analysis of wind turbine with direct drive permanent magnet synchronous generator connected to power grid , 2012 .

[11]  R.G. Harley,et al.  Control of IPM Synchronous Generator for Maximum Wind Power Generation Considering Magnetic Saturation , 2007, 2007 IEEE Industry Applications Annual Meeting.

[12]  Fernando Tadeo,et al.  Control for a Variable Speed Wind Turbine Equipped with a Permanent Magnet Synchronous Generator (PMSG) , 2012 .

[13]  Xiao-Ping Zhang,et al.  Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator , 2007 .

[14]  Abdenour Abdelli,et al.  Optimization of a small passive wind turbine generator with multiobjective genetic algorithms , 2007 .

[15]  Ujjwal Maulik,et al.  Multiobjective Genetic Algorithms for Clustering - Applications in Data Mining and Bioinformatics , 2011 .

[16]  Xiao-Ping Zhang,et al.  Small signal stability analysis and control of the wind turbine with the direct-drive permanent magnet generator integrated to the grid , 2009 .

[17]  Pavol Bauer,et al.  Offshore transnational grids in Europe: the North Sea Transnational grid research project in relation to other research initiatives , 2010 .