Ezhilmathi Nagarathinam¹, Buvana Devaraju², Karthiyayini Jayamoorthy³, Padmavathi Radhakrishnan⁴, Santhana Lakshmi ChandraMohan⁵, Vijayakumar Perumal⁶, Karthikeyan Balakrishnan^7,*

Energy Engineering, Vol.121, No.8, pp. 2129-2142, 2024, DOI:10.32604/ee.2024.052280

Abstract Maximum Power Point Tracking (MPPT) is crucial for maximizing the energy output of photovoltaic (PV) systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions. This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer (DGWO). It dynamically adjusts the parameters of the MPPT controller, specifically the duty cycle of the SEPIC converter, to efficiently track the Maximum Power Point (MPP). The proposed system aims to enhance the energy harvesting capability of solar PV More >

Bo Yang^1,2, Rui Xie¹, Zhengxun Guo^3,4,*

Energy Engineering, Vol.121, No.8, pp. 2009-2022, 2024, DOI:10.32604/ee.2024.049423

Abstract Maximum power point tracking (MPPT) technology plays a key role in improving the energy conversion efficiency of photovoltaic (PV) systems, especially when multiple local maximum power points (LMPPs) occur under partial shading conditions (PSC). It is necessary to modify the operating point efficiently and accurately with the help of MPPT technology to maximize the collected power. Even though a lot of research has been carried out and impressive progress achieved for MPPT technology, it still faces some challenges and dilemmas. Firstly, the mathematical model established for PV cells is not precise enough. Second, the existing… More > Graphic Abstract

Jian Zhong, Lei Zhang^*, Ling Qin

Energy Engineering, Vol.121, No.4, pp. 951-971, 2024, DOI:10.32604/ee.2023.041433

Abstract Partial shading conditions (PSCs) caused by uneven illumination become one of the most common problems in photovoltaic (PV) systems, which can make the PV power-voltage (P-V) characteristics curve show multi-peaks. Traditional maximum power point tracking (MPPT) methods have shortcomings in tracking to the global maximum power point (GMPP), resulting in a dramatic decrease in output power. In order to solve the above problems, intelligent algorithms are used in MPPT. However, the existing intelligent algorithms have some disadvantages, such as slow convergence speed and large search oscillation. Therefore, an improved whale algorithm (IWOA) combined with the More >

Muhannad J. Alshareef^*

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 297-313, 2023, DOI:10.32604/iasc.2023.046722

Abstract Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using… More >

B. Jegajothi^1,*, Sundaram Arumugam², Neeraj Kumar Shukla³, I. Kathir⁴, P. Yamunaa⁵, Monia Digra⁶

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2417-2437, 2023, DOI:10.32604/csse.2023.038074

Abstract Renewable energy sources like solar, wind, and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment. Because, Since the production of renewable energy sources is still in the process of being created, photovoltaic (PV) systems are commonly utilized for installation situations that are acceptable, clean, and simple. This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking (MPPT) in solar systems with the help of an embedded controller. The adaptive method incorporates both the Whale Optimization Algorithm (WOA) and the Artificial… More >

Tao Hou¹, Shan Wang^1,2,*

Energy Engineering, Vol.120, No.1, pp. 87-106, 2023, DOI:10.32604/ee.2022.022122

Abstract The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The More > Graphic Abstract

Ceren Baştemur Kaya¹, Ebubekir Kaya^2,*, Göksel Gökkuş³

Computer Systems Science and Engineering, Vol.45, No.1, pp. 69-84, 2023, DOI:10.32604/csse.2023.030598

Abstract It is one of the topics that have been studied extensively on maximum power point tracking (MPPT) recently. Traditional or soft computing methods are used for MPPT. Since soft computing approaches are more effective than traditional approaches, studies on MPPT have shifted in this direction. This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT. The meta-heuristic training algorithms used are particle swarm optimization (PSO), harmony search (HS), cuckoo search (CS), artificial bee colony (ABC) algorithm, bee algorithm (BA), differential evolution (DE) and flower pollination algorithm (FPA). The… More >

N. Kanagaraj^*, Mohammed Al-Ansi

Computer Systems Science and Engineering, Vol.45, No.1, pp. 769-784, 2023, DOI:10.32604/csse.2023.029457

Abstract In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power… More >

Thamatapu Eswara Rao^*, S. Elango

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1783-1798, 2023, DOI:10.32604/iasc.2023.028835

Abstract Global energy demand is growing rapidly owing to industrial growth and urbanization. Alternative energy sources are driven by limited reserves and rapid depletion of conventional energy sources (e.g., fossil fuels).Solar photovoltaic (PV), as a source of electricity, has grown in popularity over the last few decades because of their clean, noise-free, low-maintenance, and abundant availability of solar energy. There are two types of maximum power point tracking (MPPT) techniques: classical and evolutionary algorithm-based techniques. Precise and less complex perturb and observe (P&O) and incremental conductance (INC) approaches are extensively employed among classical techniques. This study More >

N. Rajeswari^1,*, S. Venkatanarayanan²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1311-1322, 2023, DOI:10.32604/iasc.2023.028552

Abstract Due to the enormous utilization of solar energy, the photovoltaic (PV) system is used. The PV system is functioned based on a maximum power point (MPP). Due to the climatic change, the Partial shading conditions have occurred under non-uniform irradiance conditions. In the PV system, the global maximum power point (GMPP) is complex to track in the P-V curve due to the Partial shading. Therefore, several tracking processes are performed using various methods like perturb and observe (P & O), hill climbing (HC), incremental conductance (INC), Fuzzy Logic, Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO)… More >