Experimentally investigated machine learning based MPPT for PV panel systems

This manuscript uses a supervised-based machine learning algorithm to predict the maximum power point in a PV system. The PV panel has been integrated with a boost converter. The input parameters are irradiance, temperature, PV current, PV voltage, proportional–integral (PI) and Power value. Sixty thousand data have been collected using MATLAB Simulink and are applied for modelling. It has been tested on several algorithms with accuracy like, Fine tree (96%), Medium Tree (82.9%), Coarse tree (56.9%), Fine KNN (93.8%), Medium KNN (93%), Coarse KNN (90.4%), Cosine KNN (92.9%), Cubic KNN(92.6), Weighted KNN (94.3%), Ensemble Boosted Trees (94.6%), Ensemble Bagged Trees (98.1%) and Ensemble Subspace KNN (97%). The model with better accuracy is used to track the MPP (Maximum Power Point). The system has been studied using a confusion matrix, parallel coordinates plots, scattering plots and receiver operator characteristics (ROC) curve. The proposed algorithm has been tested on a hardware prototype where data have been collected using the KG045 voltage sensor and ACS712 current sensor. The ML-based MPPT is configured in MATLAB and then tested on a hardware prototype using a PV panel under varying power conditions. The system has been tested at different temperatures like 28°C, 39°C, 25°C, 32°C and 36°C. The model has been successfully tested on hardware as well as software results.