Abstract This paper presents a novel Particle Swarm Optimisation (PSO) -based Electronic Load Controller (ELC) with intelligent energy recovery capabilities for Self-Excited Induction Generator (SEIG) systems in off-grid micro-hydro applications. Unlike conventional resistive dump loads, which dissipate excess energy as waste heat, the proposed system employs multi-objective PSO algorithms to simultaneously optimise voltage regulation, frequency stability, harmonic minimisation, and energy recovery through an adaptive water pumping mechanism. The PSO algorithm optimises PI controller gains, PWM switching parameters, and power distribution strategies using a comprehensive fitness function incorporating voltage regulation error, frequency deviation, total harmonic distortion, and energy recovery efficiency. Experimental validation on a 2. 2 kW laboratory prototype demonstrates superior performance with voltage regulation accuracy of 1. 8\% compared to 8\% for conventional methods, frequency stability of 0. 9\% versus a 3\% baseline, and energy recovery efficiency of 92. 1% through intelligent water pumping. The PSO algorithm achieves rapid convergence within 15. 2 iterations while maintaining computational feasibility with an execution time of 0. 83 ms. Total harmonic distortion is reduced to 5. 8% experimentally, ensuring IEEE 519 compliance while eliminating resistive energy waste. The system maintains a stable DC-link voltage of 586 V and generates an optimal 240 V RMS single-phase output for induction motor operation. Economic analysis reveals 1567 annual savings with a 2. 1-year payback period and 5. 2 tons CO ₂ emission reduction annually. The proposed intelligent ELC demonstrates 99. 7% system availability with a productive water storage capability of 3. 2 million litres annually, establishing a new paradigm for sustainable micro-hydro energy management.
Sinha et al. (2026) studied this question.