Teaching learning optimization-based sliding mode control for frequency regulation in microgrid

This paper presents a novel approach for frequency regulation in Microgrids (MGs) using a Teaching Learning (TL) optimization-based Sliding Mode Control (SMC). The primary focus of this study is to enhance frequency stability in MGs, which is a critical aspect, especially with an integration of renewable energy sources. The TL algorithm is employed to optimally tune the parameters of the SMC, ensuring system stability with nonlinearities and parameter variations. Simulations are conducted in MATLAB to validate the design under various operational conditions. These simulations take into account random step load disturbance, typical nonlinearities and variations in system parameters to closely mimic real-world scenarios. The results indicate a notable improvement in frequency stability compared to other nature-inspired approaches like Gray Wolf optimization (GWO), Particle Swarm optimization (PSO), Salp Swarm algorithm (SSA) and Whale optimization algorithm (WOA) including others. Using the TLBO method, a peak frequency response of 0.2427 per unit (p.u.) Hz was attained, marking a significant enhancement compared to several other methods. This includes an improvement of 59.1% over GWO, 43.2% over SMC, 41.2% over PSO, 23.3% over ANN-GA, 15.9% over Ziegler-Nichols, 14.2% over SSA, and roughly 6% over WOA. The TL's adeptness in fine-tuning the SMC parameters plays a pivotal role in this enhanced performance, showcasing its potential as a reliable and efficient solution for frequency regulation in the dynamically evolving landscape of MG systems.