**1. Introduction**

Recently, power generation from renewable sources such as solar and wind is receiving more attention as their operation is pollution free to reduce the environmental impact of fossil fuels [1,2]. Photovoltaic (PV) is the fastest-growing renewable system, and it directly converts solar energy to electrical energy. The power generated from the PV source can also be utilized for chemical energy transformation, such as hydrogen fuel cells [3–5]. The power generated from a PV system varies according to the temperature and irradiation received

**Citation:** Worku, M.Y.; Hassan, M.A.; Maraaba, L.S.; Shafiullah, M.; Elkadeem, M.R.; Hossain, M.I.; Abido, M.A. A Comprehensive Review of Recent Maximum Power Point Tracking Techniques for Photovoltaic Systems under Partial Shading. *Sustainability* **2023**, *15*, 11132. https://doi.org/10.3390/ su151411132

Academic Editor: Yagang Zhang

Received: 7 June 2023 Revised: 8 July 2023 Accepted: 10 July 2023 Published: 17 July 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

at any instant [6–8]. To generate the maximum available power from the PV system under varying irradiation and temperature, maximum power point tracking (MPPT) methods are integrated [9,10].

On the other hand, the optimal tilt and orientation of the PV panels can improve the solar yield, as reported by a study conducted in the United Arab Emirates (UAE) [11]. However, the optimal tilt and orientation are region-dependent and vary considerably. The Kingdom of Saudi Arabia in the Sun Belt region experiences high irradiance levels between 4.479 kWh/m2 and 7.004 kWh/m2, depending on the geographical location [12]. This available abundance of solar power is being utilized and integrated into the grid by the kingdom. The 300 MW Sakaka PV power plant is the first renewable-based power source covering an area of six square kilometers in Saudi Arabia [13]. Power generation based on PV is growing fast, and different developing countries are generating and integrating this power into their respective grids [14,15].

In the case of uniform irradiance, one maximum power point appears in the PV array characteristics curve that the conventional MPPT techniques can track. However, due to shadows and clouds, PV arrays receive non-uniform irradiation, creating multiple maximum points in the PV array curve. Many modern MPPT techniques are proposed to handle the numerous maximum points since most conventional MPPT methods fail under such circumstances. One of the most crucial factors in choosing a proper MPPT method mainly lies within three specifications. The first factor is performance, which is the tracking speed and accuracy. The second factor is the complexity of the control system, voltage and current sensors, parameter tuning or perturbation, and partial shading detections. The third factor is the cost of the entire MPPT system.

Several MPPT techniques for PV systems have been proposed in the last decade, and the methods developed so far can be broadly classified into MPPT-based and circuitbased methods. The MPPT-based method is classified as conventional, soft computing, and hybrid techniques. Some of the techniques classified under the traditional approach include fractional short circuit (FSC) [16,17], fractional open circuit (FOC) [18], perturb and observe (P&O) [19–24], incremental conductance (IC) [25–32], hill climbing (HC) [33–35], curve fitting (CF) [36], constant voltage (CV) [37,38], and ripple correlation control (RCC) [39]. FSC and FOC methods are less accurate and perform better only in low-power applications. Although the popular MPPT techniques such as P&O, HC and IC can track the maximum power under uniform irradiation, they fail to operate under partial shading properly and have slow tracking speed, poor convergence, and high steady-state oscillations. Hence, conventional methods should work with other methods to track the maximum power under partial shading conditions [40–43].

Since PV systems have nonlinear characteristics, soft computing methods have been proposed by researchers to handle non-linearity and are considered the prime choice for nonlinear optimization. Numerous soft computing techniques for MPPT application are proposed, including fuzzy logic control (FLC) [44–48], artificial neural network (ANN) [49–52], genetic algorithm (GA) [53–56], particle swarm optimization (PSO) [57–60], nonlinear control [61], chaotic approach (CA) [62], differential evolution (DE) [63–65], simulated annealing (SA) [66], grey wolf optimization (GWO) [42,67–69], cuckoo search [70–72], bat search algorithm, bee colony search algorithm [73,74], ant colony optimization [75–78], firefly algorithm [79], and random search methods [80]. PSO is the most popular and widely used optimization technique to track the maximum power in PV systems. Although FLC and ANN effectively track the maximum power, they require large memory and data for training and implementation. They also need detailed knowledge of the system while implementing the algorithm.

To enhance the performance of MPPT methods under partial shading, researchers combined conventional techniques with soft computing techniques to form hybrid strategies. Some of the developed hybrid techniques include incremental conductance combined with firefly algorithm (IC-FFA) [81], perturb and observe with artificial neural network (P&O-ANN) [82], perturb and observe with fireworks (P&O- FWA) [83], perturb and

observe with grey wolf (P&O-GWO) [84], perturb and observe with genetic algorithm (P&O-GA) [85], perturb and observe with bat search algorithm (P&O-Bat) [86] and perturb and observe with particle swarm optimization (P&O-PSO) [87,88]. Two or more intelligent algorithms such as particle swarm optimization and simulated annealing (PSO-SA) [89], particle swarm optimization and fish swarm [90,91], differential evolution with Jaya algorithm (DE-Jaya) [92], and differential evolution with whale optimization (DE-WO) [93] can also be combined to form hybrid methods.

This paper presents a detailed, organized, and up-to-date review of the different maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems. The advantages and disadvantages of each method are presented to assist power utilities and power engineers in choosing the proper MPPT method while designing a PV generation system under partial shading conditions. Hybrid MPPT methods based on the combination of soft computing and conventional methods are more efficient than the other methods. However, reducing the complexity of practical implementation is a challenge and a research direction that should be addressed. Moreover, MPPT methods based on optimization face challenges with respect to periodic tuning, accuracy, stability, and the number of send parameters.

The rest of the paper is organized as follows; Section 2 describes the PV configuration, Section 3 presents the details of PV under partial shading, Section 4 describes the dynamic tracking and classification of MPPT methods under partial shading, Section 5 is the discussion, and Section 6 concludes the paper.
