This work presents a machine-learning (ML) algorithm for maximum power point tracking (MPPT) of an isolated photovoltaic (PV) system. Due to the dynamic nature of weather conditions, the energy generation of PV systems is non-linear. Since there is no specific method for effectively dealing with the non-linear data, the use of ML methods to operate the PV system at its maximum power point (MPP) is desirable. A strategy based on the decision-tree (DT) regression ML algorithm is proposed in this work to determine the MPP of a PV system. The data were gleaned from the technical specifications of the PV module and were used to train and test the DT. These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature. The boost converter duty cycle was determined using predicted values. The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m2 irradiance and a temperature of 25°C. The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such as β-MPPT, cuckoo search and artificial neural network results. From the proposed algorithm, efficiency has been improved by >93.93% in the steady state despite erratic irradiance and temperatures.

Operating solar photovoltaic (PV) panels at the maximum power point (MPP) is considered to enrich energy conversion efficiency. Each MPP tracking technique (MPPT) has its conversion efficiency and methodology for tracking the MPP. This paper introduces a new method for operating the PV panel at MPP by implementing the multivariate linear regression (MLR) machine learning algorithm. The MLR machine learning model in this study is trained and tested using the data collected from the PV panel specifications. This MLR algorithm can predict the maximum power available at the panel, and the voltage corresponds to this maximum power for specific values of irradiance and temperature. These predicted values help in the calculation of the duty ratio for the boost converter. The MATLAB/SIMULINK results illustrate that, as time progresses, the PV panel is forced to operate at the MPP predicted by the MLR algorithm, yielding a mean efficiency of more than 96% in the steady-state operation of the PV system, even under variable irradiances and temperatures.

Photovoltaic panels use the sun’s radiation on their surface to convert solar energy into electricity. This process is dependent on the temperature of the surface and the intensity of the sun's radiation. To escalate the energy transformation, the solar system must be functioned at its maximum power point (MPP). Every maximum power point tracking (MPPT) technique has a distinct mechanism for tracking maximum power point (MPP). The support vector machine (SVM) regression algorithm is used in this work to develop a novel method for tracking the MPP of a PV panel. The solar panel technical parameters were used to prepare the data for training and testing the SVM model. The SVM algorithm predicts the PV panel's maximum power and relevant voltage for specific irradiation and temperature. The duty cycle of the boost converter corresponding to the maximum power was evaluated using the predicted values. The result of the simulation shows that the proposed control strategy forces the solar panel to work near the predicted MPP. The SVM regression control strategy gives the MPP tracking efficiency of more than 94% for the solar PV system despite variable climatic conditions during its stable state operation. In addition, a comparative analysis of the proposed method was carried out with the existing approaches to confirm the effective tracking of the proposed technique.

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