Adaptive Laplacian Continuous Mixed-Norm Control Approach for Dynamic Performance Improvement of Wind Energy Systems

Abstract

This paper introduces an adaptive filtering algorithm based on the Laplacian continuous mixed-norm (LCMN) as a control methodology to improve both transient and dynamic wind energy systems (WESs) performances. The foundation of this wind system is a variable-speed wind turbine that powers a permanent magnet synchronous generator. The proposed LCMN algorithm automatically updates all interface circuits' proportional-integral (PI) controllers gains. It has several advantages compared with other algorithms such as higher algorithm stability, lower fluctuations and steady-state errors. The efficacy of the LCMN-based PI control approach is validated by a fair comparison with other control methods such as the least mean square, robust mixed norm and continuous mixed p-norm when the WES is subjected to severe symmetrical and various unbalanced conditions. Furthermore, the applicability of the suggested technique is examined in typical operational circumstances utilizing actual wind speed measurements obtained from Hokkaido Island. The results indicate preferable performance of dynamic analyses even though the wind speed profile is intermittent.. The dynamic responses exhibit an actuating error of less than 2% across numerous profiles. Therefore, the LCMN-based PI control methodology is considered as an effective solution for online adjustment of controller gains in the course of system nonlinearities and uncertainties. A key advantage of this method is its independence from constructing system transfer functions and the avoidance of optimization methods. This can save substantial time and effort typically required for optimization processes.