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Introduction. In recent times, there has been a growing popularity of photovoltaic (PV) systems, primarily due to their numerous advantages in the field of renewable energy. One crucial and challenging task in PV systems is tracking the maximum power point (MPP), which is essential for enhancing their efficiency. Aim. PV systems face two main challenges. Firstly, they exhibit low efficiency in generating electric power, particularly in situations of low irradiation. Secondly, there is a strong connection between the power output of solar arrays and the constantly changing weather conditions. This interdependence can lead to load mismatch, where the maximum power is not effectively extracted and delivered to the load. This problem is commonly referred to as the maximum power point tracking (MPPT) problem various control methods for MPPT have been suggested to optimize the peak power output and overall generation efficiency of PV systems. Methodology. This article presents a novel approach to maximize the efficiency of solar PV systems by tracking the MPP and dynamic response of the system is investigated. Originality. The technique involves a multivariate linear regression (MLR) machine learning algorithm to predict the MPP for any value of irradiance level and temperature, based on data collected from the solar PV generator specifications. This information is then used to calculate the duty ratio for the boost converter. Results. MATLAB/Simulink simulations and experimental results demonstrate that this approach consistently achieves a mean efficiency of over 96 % in the steady-state operation of the PV system, even under variable irradiance level and temperature. Practical value. The improved efficiency of 96 % of the proposed MLR based MPP in the steady-state operation extracting maximum from PV system, adds more value. The same is evidently proved by the hardware results.

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We studied an automatic control system with two-channel fuzzy controller under conditions of non-stationarity of parameters of the control object’s model. The relevance of present research is in the application of more complex control structures to achieve small deviations in quality control parameters under conditions of change in the parameters of control model. The application of circuits with predictors and adaptation algorithms is limited to the object of thermal power engineering. A common approach is implementing the PID control algorithm using a fuzzy controller. Then by selecting the membership functions and building the rule base the control algorithm is improved. In contrast to the described scheme the basis of building a two-channel fuzzy controller is not the PID law but the knowledge and experience of the operator in control the technological parameter in manual mode. The ranges of input and output variables of the fuzzy controller are defined. Based on expert knowledge and analysis of the actions of the operator a rule base for both blocks of fuzzy logic is formed. Mathematical modeling of the designed system was performed. We compared a single-circuit system at constant settings of the controller and the system with two-channel fuzzy controller for different states of an object that are determined by variable parameters of its model. Parameters of operation quality of both systems were calculated. Application of two-channel fuzzy controller warrants the constancy of the automation control system operation quality. Also in this case ensures the robustness of the automatic control system for stability in terms of parametric nonstationarity of the plant. [ABSTRACT FROM AUTHOR]