Improved Frequency Stability in Autonomous AC Microgrids through Modified Whale Optimization Algorithm-Based PID Controllers

The integration of naturally replenished sources (NRS) has hastened micro grid development, owing to environmental concerns and rising electrical demand. This study looks at the modelling and control of a self-governing AC micro grid that includes solar thermal power generation, wind turbines, tidal power generation, micro-turbines, biomass, diesel engines, and energy storage devices. Load frequency control (LFC) is critical for ensuring frequency stability by balancing generation and demand, reducing frequency variations. To improve LFC, this study substitutes traditional approaches with metaheuristic-optimized control. The Whale Optimisation Algorithm (WOA) is a relatively new swarm intelligence optimisation technique that is employed in a variety of scientific and engineering disciplines. This work presents a modified WOA, known as MWOA, to solve the original algorithm's slow convergence, tendency to stagnate at local minima, and poor stability. The changes include using a tent map function to optimise initial population distribution, new iteration-based strategies for updating the convergence factor and inertia weight to balance global and local searches, and an ideal feedback strategy to improve global search performance. MATLAB and OPAL-RT simulations show that MWOA-optimized controllers outperform in terms of performance and precision, representing a breakthrough in micro grid frequency stability and demonstrating their suitability for real-world power systems.