MATLAB Implementation of Hybrid Intelligent Algorithm for Power System Optimization

This article presents the application of a hybrid intelligent algorithm that combines artificial neural networks (ANN) with particle swarm optimization (PSO). The ANN component handles function approximation for uncertain parameters and constraints, while PSO optimizes solutions for stochastic programming models in power systems. We implement this approach specifically for IEEE 33-node systems incorporating multiple wind power nodes. The hybrid algorithm demonstrates superior performance in complex scenarios and efficiently processes large datasets. We detail the algorithm's underlying principles and advantages, explaining its particular effectiveness for power system optimization problems. The implementation involves training neural networks using MATLAB's Neural Network Toolbox for uncertainty modeling, while employing PSO with adaptive inertia weights for global optimization. Furthermore, we explore parameter tuning strategies to enhance algorithm performance and discuss practical applications for solving real-world energy system challenges through MATLAB-based implementations.