PSO-optimized sensor-less sliding mode control for variable speed wind turbine chains based on DPIG with neural-MRAS observer

Abstract

This research introduces a resilient Sensor-Less 1st Sliding Mode (SL-FOSM) approach employing a novel observer, the Artificial Neural Network with Model Reference Adaptive System-Adaptive (Neural-MRAS), for wind turbine chains. The proposed model is implemented on a Doubly Powered Induction Generator (DPIG) operating under genuine variable speed conditions in the Adrar region in Algeria. The control objective is to independently regulate the active and reactive power of the DPIG stator, achieved through decoupling using the field-oriented control technique and control application via FOSM-C. Notably, this methodology reduces both the control scheme cost and the DPIG size by eliminating the need for a speed sensor (encoder). To enhance the MRAS-PI, an Artificial Neural Network (ANN) is suggested to replace the typical classical Proportional-Integral (PI) controller in the adaptation mechanism of MRAS. The rotor position estimation is scrutinized and discussed across various load conditions in low, zero, and high-speed regions. Optimal controller parameters are determined through particle swarm optimization (PSO). The results demonstrate that the proposed observer (Neural-MRAS) exhibits compelling attributes, including guaranteed finite time convergence, robust performance in response to speed variations, high resilience against machine parameter fluctuations, and adaptability to load variations when compared to the MRAS-PI. Consequently, the estimated rotor speed converges to its actual value, showcasing the capability to accurately estimate position across different speed regions (low/zero/high).