Optimal control of a wind generator system using non-squares estimators

The control of eolic and solar energy systems demands methods and technics adapted to the high degree of environment non-stationarities whose adjustments are carried out via adaptive filters. Among the best known are least mean square (LMS) and the recursive least square (RLS) algorithms [1] and [2]. However, those algorithms still fail to respond quickly to the optimal control of the doubly fed induction generator (DFIG) as required in online learning [3]. Here we propose a methodology based on approximate solutions to the linear quadratic regulator (LQR) by using a family of non-squares approximations [4], [5]. We show experimentally that the RLNS provides more accurate estimates for DLQR when compared to the RLS while showing a convergence speed to the actual solution in less than 50% of the iterations as required by the standard RLS estimator for approximating Ricatti equation solution via Heuristic Dynamic Programming (HDP) [6].