Design Optimization of Micro Hydro Turbine Using Artificial Particle swarm Optimization and Artificial Neural Network

Turbines are used to convert potential energy into kinetic energy. The blades installed on the turbine edge influence the amount of energy generated. Turbine blades are designed expertly with specific curvature angles. The number, shape, and angle of the blades influence the turbine efficiency. The particle swarm optimization (PSO) algorithm can be used to design and optimize micro-hydro turbines. In this study, we first optimized the formula for turbine using PSO. Second, we input the PSO optimization data into an artificial neural network (ANN). Third, we performed ANN network simulation testing and training. Finally, we conducted ANN network error analysis. From the 180 PSO data records, 144 were used for training, and the remaining 40 were used for testing. The results of this study are as follows: MAE = 0.4237, MSE = 0.3826, and SSE = 165.2654. The lowest training error was achieved when using the trainlm learning algorithm. The results prove that the ANN network can be used for optimizing turbine designs