###### Abstract  
In this paper, a new maximum power point tracking (MPPT) framework for photovoltaic (PV) systems is presented based on the remora optimization algorithm (ROA) subjected to standard and partial shading conditions. The studied system includes a PV array, a DC/DC converter, and a load and MPPT control system. The control variable is the voltage, and the optimization variable is the converter duty cycle, which is optimally determined using the ROA that is inspired based on the parasitic behavior of remora for achieving the maximum power of the PV system. In this study, the ability of the ROA is compared with manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) methods for the MPPT solving of different shading patterns in view of extracted power, efficiency, and tracking rate. The results show that the ROA is a competitive method with higher efficiency in maximum power tracking and convergence accuracy than the MRFO and PSO for the MPPT solving of different patterns with higher exploration power. Moreover, an examination of the two partial shading patterns also showed that the power extracted using the ROA is higher than the MRFO and PSO while also reaching the global power value more quickly. The ROA achieved a tracking efficiency of 99.97% in a partial shading condition, with faster tracking in comparison with the MRFO and PSO methods. Therefore, the ROA is a high-speed tracking optimization method for enhancing the PV system's efficiency in standard and especially in shading conditions. Keywords:photovoltaic system; maximum power point tracking; global power; partial shading condition; remora optimization algorithm +
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The V-I characteristic of the PV cell changes with the variation of irradiance and operating temperature, and this has a great effect on the optimal MPP. Additionally, under specific irradiance, there is a single operational point for the PV plate where the output power is maximized [7; 8; 9; 10]. Therefore, to achieve the MPP of the PV system, controlling the maximum power point tracking (MPPT) is important to improve the efficiency of PV systems [8]. Moreover, partial shading conditions (PSCs) lead to multiple peaks in the characteristics of the PV configuration [8; 9]. Hence, it is vital to evaluate the acquisition of the MPP under the PSCs. Among these peaks, the point with the highest value is called the global MPP (GMPP). The others are called local MPPs (LMPPs) [8; 9]. Traditional MPPT methods, which assume only one peak, are unable to obtain convergence to the GMPP; these methods are often trapped with one peak because of their instability in distinguishing between local and global points [9]. This requires the application of robust optimization methods that are able to achieve the MPP under PSCs [7; 8; 9; 10]. Today, intelligent optimization methods have replaced traditional methods due to their high tracking power and efficiency in achieving the GMPP; these methods have been widely welcomed. In [11], an MPPT algorithm for a PV system is studied to track the MPP under PSC while considering the maximum voltage corresponding to the GMPP. In [12], a two-stage method is proposed. At first, the MPP close to the GMPP is moved via the load line. Then, convergence of the exploitation point is performed for the GMPP. When the GMPP is on the left side of the load line, the method is ineffective. In [13], a variable step using the P&O method is suggested, and the step size is found through the Fibonacci sequence. In this method, the GMPP is not traceable under all conditions. In [14], the MPPT algorithm is presented as a two-step process, and a sweeping process is used to identify the GMPP in the first step and then implemented using the P&O technique to converge to the GMPP. This method needs more time to determine the GMPP because all MPPs have to be identified to find the GMPP. In [15], a control algorithm for finding the GMPP is presented under different PSC conditions.