A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm (PSO), Cuckoo (CSO), Flower Pollen Algorithm (FPA), Gray Wolf (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic (GA), PSO-trained Learning. modelled in MATLAB/Simulink environment under different operating conditions, example, step changes temperature, irradiance, partial shading. improved up 0.72% reduced settling time 76.4%. findings of research highlight that PSO_ML-FSSO is potential approach outperforms all algorithms tested herein