Maximum Power Point Tracking Strategy for Large-Scale Wind Generation Systems Considering Wind Turbine Dynamics

Abstract

Under the global trend of renewable energy development, various advanced techniques such as forecasting algorithm, intelligent computation, and optimal control are expected to make the complex and uncertain renewable energy system stable and profitable in the near future. This paper presents a new control strategy for large-scale wind energy conversion systems to achieve a balance between power output maximization and operating cost minimization. First, an intelligent maximum power point tracking (IMPPT) algorithm is proposed such that short-term wind speed prediction, wind turbine dynamics, and MPPT are collectively considered to improve system efficiency. Second, in view of a spatial and temporal distribution of wind speed disturbances, a box uncertainty set is embedded in the forecast wind speed, which is likely more realistic for practicing engineers. Then, the IMPPT and box uncertainties are applied to the wind energy conversion system (WECS) control strategy, which is formulated as a min-max optimization problem and efficiently solved with semi-definite programming (SDP). Finally, a comparison with the conventional MPPT control method demonstrates that the proposed approach can obtain higher efficiency, which validates this paper.