Citation :

Hetal Desai, Shweta Dour, Pramod Modi, "Dynamic Performance Optimization of Grid-Forming Inverters using PSO-Based Tuning in Hybrid Power Systems," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 1, pp. 54-64, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I1P104

Abstract

"Traditional frequency and voltage stability paradigms are challenged by the integration of inverter-based renewable energy sources into power grids, especially in hybrid systems that combine Grid-Forming Inverters (GFMs) and Synchronous Generators (SGs). In order to improve dynamic performance during severe load disturbances, this research proposes a Particle Swarm Optimization (PSO)-based methodology for simultaneously tweaking 18 control parameters across both GFM and SG subsystems. A 100% step load increase is applied to a MATLAB/Simulink model of a hybrid power system that consists of a 100 MVA SG and 100 MVA GFM supplying a 75 MW base load. By minimizing a multi-objective cost function that balances frequency deviation, voltage regulation, power-sharing accuracy, and settling time, the PSO algorithm optimizes PI controller gains, droop coefficients, AVR settings, and governor time constants. Transformative improvements are demonstrated by comparison with traditional trial-and-error tuning in voltage settling time, decrease in frequency dip, and improvement in accuracy of power sharing. Algorithm robustness is confirmed by statistical validation across ten separate PSO runs. By concurrently optimizing multi-domain parameters in hybrid GFM-SG systems, the suggested methodology fills important gaps in the literature and offers a scalable solution for upcoming low-inertia, inverter-dominated grids. The findings prove metaheuristic optimization as a useful method for next-generation power system control and set new performance benchmarks.

Keywords

Hybrid system, Frequency stability, Particle Swarm optimization, Grid forming Inverter, AC current limiter.

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