Performance enhancement of grid‐connected photovoltaic systems using Ant Lion optimisation and Genetic Algorithm‐based optimisation techniques

Abstract

Abstract One of the most common issues in renewable energy applications is modelling and regulating grid‐connected PV systems. The model reference adaptive control (MRAC) technique has been analysed in this paper. It's difficult to develop an MRAC that performs well in both transient and steady‐state conditions; hence, the primary goal of this paper is to create a modified MRAC that has excellent steady‐state and transient responses. A modified MRAC scheme has been proposed, where an Ant lion optimisation technique and a well‐known algorithm that is Genetic Algorithm (GA) is used to optimise the controller gain and compared it with each other and also with the conventional MRAC technique. The optimisation techniques are used to tune the controller parameters so that errors should be minimised. The time response analysis of MRAC, MRAC‐GA, and MRAC‐ALO controllers are shown and evaluated using common control criteria such as rising time, settling time, peak overshoot, steady‐state, and error parameters. The simulation findings reveal that the proposed modified MRAC controller, both MRAC‐ALO and MRAC‐GA, outperforms in relation to transient performance and steady‐state performance compared to conventional MRAC controllers.

Keywords

• adaptive control
• Ant Lion Optimisation (ALO)
• Genetic Algorithm (GA)
• optimisation method
• photovoltaic (PV) system
• Proportional Resonant (PR) controller