<![CDATA[This paper presents an integrated optimization framework for pico-hydro power systems using a Genetic Algorithm (GA) approach to improve energy harvesting and efficiency. The study focuses on a crossflow turbine configuration designed for rural applications in East Java, Indonesia. Field measurements were performed on a prototype installation with a net head of 10.2 m and discharge of 0.028 m³/s. The optimization simultaneously adjusts turbine geometry and electronic load controller (ELC) capacity to maximize total system efficiency. The GA model was developed in MATLAB with multi-parameter evaluation across 120 generations and 50 population members. Results indicate that the optimized configuration (nozzle diameter 10 mm, jet angle 22.1°, runner speed 942 rpm, ELC 405 W) achieves a 229 % increase in output power, raising overall efficiency from 18 % to 30 %. The study demonstrates that GA provides a powerful metaheuristic tool for non-linear parameter optimization in pico-hydro systems, enhancing both mechanical and electrical performance. The framework supports sustainable rural electrification by enabling efficient, low-cost renewable energy deployment. This study contributes to the advancement of intelligent optimization methods for small-scale renewable energy systems in developing countries.]]>
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