Enhanced Particle Swarm Optimization for Minimizing Governor Actuation in Hydropower Plants Under Renewable Energy Intermittency

The growing integration of variable renewable energy (VRE) sources into small-scale, isolated, low-inertia power systems has intensified frequency fluctuations, placing increased stress on conventional high-inertia generators and their governors. This study addresses the urgent need for a cost-effective, intermediate solution to mitigate these fluctuations, particularly in systems with limited access to high-capital technologies such as battery storage. A modified Particle Swarm Optimization (PSO) algorithm is proposed, integrating a non-linear inertia weight function and a storming mechanism to overcome the local optimum limitations of conventional PSO. The algorithm was applied to the Laxapana Hydro Complex in Sri Lanka, a key power generation and frequency regulation facility, and an ideal example of a small-scale isolated power system. Real-world system data were used in simulations conducted in MATLAB and PSSE environments. Simulation results across three operational scenarios: general generation fluctuations, solar intermittency, and wind intermittency, demonstrate that the optimized governor settings significantly reduce long-term mechanical movement while maintaining system stability. Specifically, under solar intermittency, governor actuator exhibited approximately a 20% reduction in movement compared to the baseline configuration. This reduction minimizes wear and tear, leading to lower maintenance costs and improved long-term reliability. These findings highlight the potential of the proposed method as a low-cost, adaptable solution for improving governor performance in renewable-integrated systems, offering a pathway toward more reliable and cost-effective power system operations.