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.

Electrical energy usage has drastically increased in recent decades, resulting in significant demand for renewable energy sources, especially solar. With the development of technology, extracting energy from photovoltaic (PV) modules has become easier and more economical. The performance of PV array decreases under an intermittent environment such as partial shading conditions (PSCs), causing fluctuations in PV array power output. This paper presents the analysis of a 4×4 PV array configuration under different PSCs. The power output of PV array depends on factors such as the type of configuration, size of array, and shading patterns. The performance of various types of 4×4 PV array configurations under different shading situations are compared and analyzed in this study, and the results presented.

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Partial shading conditions (PSC) are unavoidable and are the main reason for the reduction in power from a photovoltaic (PV) array. With proper arrangement, the impact of PSC can be somewhat mitigated. There are distinct types of configurations, including series, parallel, series-parallel (SP), honeycomb, total cross-tied (TCT), etc. This article presents a novel SP–TCT configuration to maximize output power from PV panels under different shading conditions. The proposed configuration performance has been examined considering a 4 × 4 PV array under long-narrow and long-wide, short-narrow, short-wide and uniform shading conditions. The results of the proposed configurations are compared with existing configurations in terms of performance measures such as maximum power, fill factor, efficiency and mismatch losses. In all the cases, performance of the proposed configuration is nearer to the TCT configuration performance. The percentage improvement in terms of efficiency for the proposed novel SP–TCT configuration and TCT configuration is nearly 1.6% compared to other methods.

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