Optimization of a hydro kinetic power generator using genetic algorithm /

Abstract

The paper discusses the optimization of a renewable energy harvester which converts kinetic energy of slow moving currents into electricity. The metaheuristic method of genetic algorithm is adopted to optimize the process parameters of the Hydro Vortex Power Generator (HVPG). The study is conducted in a three folded manner. The device was optimized for the range of Reynolds number 0.3 × 105 < Re < 2.5 × 105 based on equations derived analytically from vibration theory and then using an empirical equation derived from experimental data for two regimes of flow (250 < Re < 3.8 × 104 and 0.3 × 105 < Re < 2.5 × 105. Empirical optimization model is observed to predict the maximum amplitude of oscillation with in a realistic range with the theoretical model showing a three time over prediction. A comparison with experimental results suggests that the effect of added mass on the amplitude of oscillation is of enhanced damping and hence a more realistic prediction is obtained from empirical model optimization. The most significant result from this analysis is that the empirical model predicts maximum amplitude at lowest value of mass ratio (m*), whereas the theoretical model predicts it at the highest value.