Dynamic just-in-time learning based model predictive control for variable pitch wind energy conversion system

Abstract

The pitch adjustment is critical to maintain the stable operation of the wind energy conversion system (WECS) under high wind speed, which can reduce mechanical load and provide safe and reliable power input for the power grid. It makes the design of a pitch controller arduous because the WECS is the high order system with nonlinear, multi-variable, and strong coupling. Based on this, a novel pitch controller is presented by employing dynamic just-in-time learning (JITL) based model predictive control to investigate wind turbine performance in a high wind speed operating region. This method captures the wind power process nonlinearities by the set of local models available online via JITL technology. Moreover, in order to meet the requirements of the real-time operation of the wind power process, a three-dimensional space data classification algorithm is employed to classify the data by calculating the local density and the minimum distance. Furthermore, considering the multi-objective and strongly nonlinear optimal control characteristics of model predictive control, the proposed dynamic linear model is introduced into the design of the nonlinear wind power model predictive control system. The optimal control rate of the controller is determined by solving the convex optimization problem within the design framework. Finally, the feasibility and superiority of the strategy are demonstrated by a typical wind power process.