Robust variable-step perturb-and-observe sliding mode controller for grid-connected wind-energy-conversion systems

Toumi, Ilham; Meghni, Billel; Hachana, Oussama; Azar, Ahmad Taher; Boulmaiz, Amira; Humaidi, Amjad J; Ibraheem, Ibraheem Kasim; Kamal, Nashwa Ahmad; Zhu, Quanmin; Fusco, Giuseppe; Bahgaat, Naglaa K

Abstract

In order to extract efficient power generation, a wind turbine (WT) system requires an accurate maximum power point tracking (MPPT) technique. Therefore, a novel robust variable-step perturb-and-observe (RVS-P&O) algorithm was developed for the machine-side converter (MSC). The control strategy was applied on a WT based permanent-magnet synchronous generator (PMSG) to overcome the downsides of the currently published P&O MPPT methods. Particularly, two main points were involved. Firstly, a systematic step-size selection on the basis of power and speed measurement normalization was proposed; secondly, to obtain acceptable robustness for high and long wind-speed variations, a new correction to calculate the power variation was carried out. The grid-side converter (GSC) was controlled using a second-order sliding mode controller (SOSMC) with an adaptive-gain super-twisting algorithm (STA) to realize the high-quality seamless setting of power injected into the grid, a satisfactory power factor correction, a high harmonic performance of the AC source, and removal of the chatter effect compared to the traditional first-order sliding mode controller (FOSMC). Simulation results showed the superiority of the suggested RVS-P&O over the competing based P&O techniques. The RVS-P&O offered the WT an efficiency of 99.35%, which was an increase of 3.82% over the variable-step P&O algorithm. Indeed, the settling time was remarkably enhanced; it was 0.00794 s, which was better than for LS-P&O (0.0841 s), SS-P&O (0.1617 s), and VS-P&O (0.2224 s). Therefore, in terms of energy efficiency, as well as transient and steady-state response performances under various operating conditions, the RVS-P&O algorithm could be an accurate candidate for MPP online operation tracking.

• Journal Article Type: Article
• Acceptance Date: May 16, 2022
• Online Publication Date: May 20, 2022
• Deposit Date: Aug 2, 2022
• Publicly Available Date: Aug 2, 2022
• Journal: Entropy
• Electronic ISSN: 1099-4300
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 5
• Pages: 731
• DOI: https://doi.org/10.3390/e24050731
• Keywords: super-twisting algorithm, robust variable-step perturb and observe, normalization, systematic step size, second-order sliding mode controller
• Public URL: https://uwe-repository.worktribe.com/output/9645665
• Publisher URL: https://www.mdpi.com/1099-4300/24/5/731