## 1 Introduction  
Energy is a vital component designed to provide economic development and progress [1]. At the same time, however, the increase in population and standard of socio-economic development puts us on the brink of an acute energy crisis amid the exhaustion of conventional energy sources. Consequently, one of the significant challenges among researchers is developing renewable green energy production technology [1] and reducing distribution system operation costs. The latter challenge comes from neglecting the renewable energy sources that can diminish the costs in terms of energy consumption [2]. Solar energy has several remarkable qualities, and it represents the primary source of free generated power. This energy is available in unlimited quantities, but it has some disadvantages related to weather conditions and geographical location [3]. Relying on the advantages, we can consider solar energy a possible alternative to solve the energy crisis by aiming for sustainable development [3]. The design of a system for converting solar energy into thermal or electrical energy is based on the correct assessment of solar radiation at the geographic location and knowledge of properties of solar radiation [3]. Improving the efficiency of solar panels and inverters is challenging, and the output depends on available technologies. Moreover, the system's design may require better performance components, dramatically increasing installation costs [4]. Instead, improving the maximum power point tracking (MPPT) with a new control algorithm is more straightforward, avoiding high costs. This can be performed even in installations already in use by updating existing control algorithms, which leads to an immediate increase in electricity generation and, consequently, the costs are minimized [4]. MPPT algorithms are necessary because PV arrays have a nonlinear voltage-current characteristic with a unique point where the generated power is maximum. The maximum power point (MPP) depends on both temperature and irradiation conditions which can change rapidly with the fluctuation of weather conditions. It is essential to track the MPP accurately, in all possible atmospheric conditions, to reach a maximum performance of the power conversion [5]. The rapid evolution of artificial intelligence is strongly present in all industries, including sustainable energy. Both literature study and research experiments have shown that artificial intelligence (AI) techniques perform exceptionally well in nonlinear systems, such as photovoltaic systems. Among the existing AI techniques, artificial neural networks (ANN) and fuzzy logic (FL) techniques are the most widely used MPPT methods in the literature [5]. For instance, [PERSON] and [PERSON] [6] present a completely new design procedure to convert the maximum power point tracking algorithm (MPPT) into a PV control system with a buck-boost converter. In their work, they adopt Bode diagrams using mode and phase conditions. The design procedure is applied to perturb and observe (P&O) in incremental conductance (IC) algorithms. As a result, the authors demonstrated that the suggested design modality can optimize energy capture, allowing the algorithms to achieve outstanding performance in maximum power point tracking and obtain adaptivity characteristics. [PERSON] and [PERSON] [7] developed a PV system with MPPT based on self-predictive incremental conductance (SPInC) and applied a DC-DC boost converter. According to these authors' Matlab/Simulink simulation results, the SPInC algorithm outperforms the classical InC, and the output power has a minimal ripple. [PERSON] and [PERSON] [8] explored different converter configurations applied to a PV system using conventional MPPT techniques, such as the perturb and observe method with variable step size (VSS-P&O), modified incremental conductance (MIC) and fractional open-circuit voltage (FOCV).