A comparative study between the soft computing MPPT techniques and traditional incremental conductance under arbitrary environmental conditions

The outcome of this research is to study and validate a photovoltaic (PV) module connected to the resistive load using soft computing Maximum Power Point Tracking(MPPT). The MPPTs used in this study are the Artificial Neural Network (ANN) and Fuzzy Logic (FL)which are used to track the maximum power of a 200W PV module. First, the mathematical analysis for the PV module, ANN technique, and FL technique is done. The MATLAB Simulink was investigated to model, verify, and simulate the MPPTs. Second, the studied PV system was tested under step change in load and irradiance conditions to obtain the difference in the performance between the soft computing techniques and conventional Incremental Conductance (IC) techniques in terms of oscillation value, dynamic speed, and method's efficiency. The obtained results from the simulation presents that both ANN and FL techniques have less steady state power oscillation, and they faster than the IC technique in terms of tracking the MPP. Therefore, the performance and efficiency of the studied PV module was improved and then the life time of the system is extended.