## 1 Introduction  
Photovoltaic (PV) energy has grown enough to be the most processed sustainable energy source that could be considered as a substitute for fossil-fuel energy, as it is inexpensive, clean, requires little maintenance, and emits no noise, among other properties. Solar cells are the major component that converts light energy from the sun to DC electrical energy [1]. However, PV--like most of the approaches for harvesting clean and sustainable solar energy, such as photocatalysis, electrocatalysis, and photo-electrocatalysis--has non-steady energy extraction [2] and is affected by conditions such as illumination, temperature, and panel age [2; 3; 4; 5]. PV systems can work under both uniform irradiance and partially shaded conditions; in the presence of partial shading conditions, the PV array P-V curve contains numerous power peaks which are called the local maximum power points (LMPPs), while the peak of the highest power is known as the global maximum power point (GMPP). The common target is to maximize the efficiency of the PV system by keeping the PV module at its maximum power operation [6], and for that reason, a maximum power point tracking (MPPT) method is essential to control such problems and check whether the PV system operates at the MPP [7]. Many different MPPT algorithms have been presented in recent years, and the conventional MPPT algorithms, such as Perturb and Observe (P&O) and incremental conductance (IC), are considered economical and easy to implement [8]. Moreover, the soft computing techniques, such as fuzzy logic control [9], artificial neural network [10], and particle swarm optimization [11], are complex techniques and have extended computation time and low convergence speed [12]. However, in uniform weather conditions, the conventional algorithms perform accordingly and can track the MPP, while under partial shading conditions, the conventional methods may fail to track the global peak power with a key drawback such as oscillations at the operating power point [13]. Meanwhile, the soft computing techniques can seek the GMPP but with considerable drawbacks such as long computation time, low convergence speed, high complexity, and low accuracy [14]. In recent years, many research works and proposed algorithms have been presented to overcome the problems related to partial shading conditions, and each algorithm provided a certain process with different aspects such as efficiency, convergence speed, and complexity [15]. Among the conventional techniques, P&O is the most extensively used due to its simplicity and low cost. Besides, this technique is considered very accurate as it is based on the direct measurement of voltage and power, and it has a faster response compared to other conventional techniques [16]. Nevertheless, it suffers from two major disadvantages: the high oscillations that happen when the MPP is achieved, and its failure to obtain the maximum available power from a partially shaded PV array. Many researchers have made modifications to P&O to overcome its drawbacks. In [17], the authors proposed a variable step P&O and global scanning technique (VSPO & GS) which contributed better tracking speed and precision. However, its drawback is that the work did not include any comparison with other techniques, so its effectiveness could not be confirmed. A new enhanced P&O algorithm with variable step size was suggested in [18], using fuzzy logic control to provide variable step size convergence to enhance the efficiency of the PV system. In [19], the suggested approach aimed to avert the drift issue by integrating information of change in current, voltage, and power into the decision procedure to track the MPP precisely. The authors concluded that the simple P&O algorithm suffers from drift problems due to the wrong decision-making ability of the algorithm, particularly in the case of high insolation. The methods presented in [18; 19] are expensive, complicated, and require prior knowledge to handle.