applied sciences Article  
Maximum Power Point Tracking Algorithm of Photo-Voltaic Array through Determination of Boost Converter  
Conduction Mode  
[PERSON],* \(\copyright\)6, [PERSON], [PERSON], \(\copyright\)6, [PERSON]. [PERSON], [PERSON] \(\copyright\)6 and [PERSON]  
Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia  
Faculty of Engineering, University of Central Punjab, Lahore 54000, Pakistan  
Correspondence: [EMAIL_ADDRESS]  
Systems can be attached in a microgrid to fulfill neighborhood capacity. For larger systems, the use of string inverters and a central inverter is preferred [13; 14]. Standalone PV systems, on the other hand, work without needing to be connected to a grid, and are capable of supplying electricity in the absence of a grid using excess energy stored in a battery [15; 16]. This implies that the requirement of energy storage is imperative for such PV solutions. The most widely recognized setup for a standalone PV system involves a PV array, charge regulator, predetermined load, and battery storage [9], as shown schematically in Figure 2. The load should be part of the PV system's design in order to ensure that the required capacity can be met under all climatic conditions. When the load profile is variable, a hybrid framework can be utilized. Along with the numerous benefits of PV systems, there are associated drawbacks, such as the initial high capital cost of PV plants, the low conversion efficiency of PV cells, and the unique MPP point on the I-V curve, which varies with weather condition. In light of these constraints, MPP algorithms are commonly installed in PV system in order to extract maximum power from the PV modules and deliver it to the load. Because MPP algorithms work through power electronic converters, the efficient operation of the power converter in terms of the conduction mode, i.e., continuous conduction mode (CCM), must be ensured as well. Numerous MPPT algorithms have been proposed in the past [17]. The nature of these algorithms involves the following mechanisms: simple \(P\&O\), hybrid algorithms, parametric estimation-based MPP methods, artificial intelligence schemes, meta-heuristic algorithms, and bio-inspired methods. A variable step size-based \(P\&O\) method was presented in [18; 19] for MPP tracking. These algorithms though achieve better performance than \(P\&O\); however, they do not sense the conduction mode of converter, and always assume the dc-dc converter is in CCM. In [20], a fractional short-circuit scheme was used with a \(P\&O\) algorithm. Under various weather conditions, the algorithm [20] achieved better dynamic and steady-state efficiencies compared to \(P\&O\). The major downside of this algorithm is the need to install an extra switch in dc-dc converter, which increases its hardware complexity. Figure 1: A generic PV system for AC load or grid connection. Figure 2: A generic standalone PV system. Using the Lyapunov control scheme, in [21] the authors estimated a PV system's parameters using an MPP algorithm. Their proposed scheme delivers good results; however, the implementation of the extensive mathematical expressions involved in the parametric estimations is not straightforward, and a high-tech embedded system is required for the installation of such a scheme. To improve the performance of fractional open-circuit voltage (FOCV) MPPT, a genetic algorithm (GA) was utilized in [22]. This algorithm, although able to increases the performance of FOCV, concurrently increases the computation burden of the system due to the GA. An ANN-based MPPT method using ripple correlation current (RCC) was presented in [23]. Again, this method requires a large amount of computational power for its practical realization. A metaheuristic-based robust unified control MPPT algorithm was presented in [24]. The high performance of this algorithm relies on a highly complex algorithmic mechanism, however, resulting in increased computation burden.