A novel metaheuristic MPPT technique based on enhanced autonomous group Particle Swarm Optimization Algorithm to track the GMPP under partial shading conditions - Experimental validation

Refaat, Ahmed; Khalifa, Abd-Elwahab; Elsakka, Mohamed; Elhenawy, Yasser; Kalas, Ahmed E.; Elfar, Medhat H.

doi:10.1016/j.enconman.2023.117124

Cited by 42 publications

(7 citation statements)

References 52 publications

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“…If ℱ 𝒫𝑟𝑒𝑑𝑎𝑡𝑜𝑟 𝒿 is greater, it indicates that the predator or intruding entity is at a greater distance from the hippopotamus's territory Eq. (12). In this case, the hippopotamus turns towards the predator but with a more limited range of movement.…”

Section: 𝜒 𝒾mentioning

confidence: 94%

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Amiri,

Hashjin,

Montazeri

et al. 2023

Preprint

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The novelty of this article lies in introducing a novel nonparametric metaheuristic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 132 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both local search and exploitation, as well as in global search and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. The performance of the HO consistently surpassed that of the top 3 algorithms in achieving optimal value, except for 29 functions. However, although it did not exhibit strong convergence in these 29 functions, the standard deviation for them was lower than the other investigated algorithms, illustrating its ability to manage the functions effectively. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The Wilcoxon signed test demonstrates that HO exhibits a notable and statistically significant advantage over the investigated algorithms in effectively addressing the optimization problems examined in this study.

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Section: 𝜒 𝒾mentioning

confidence: 94%

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Amiri,

Hashjin,

Montazeri

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…In the PI-INC MPPT algorithm, a proportional-integral (PI) controller plays a crucial role in fine-tuning the operating point towards the maximum power point (MPP). It utilizes equation (12) as the error signal and aims to minimize it. The proportional term Kp adjusts the duty cycle based on the current error, providing a response proportional to the magnitude of the error.…”

Section: Fig 6 Flowchart Of the Ic-pi Algorithmmentioning

confidence: 99%

“…Another notable technique is particle swarm optimization (PSO), a heuristic optimization algorithm inspired by the social behavior of bird flocking [12,13]. In PSO, a swarm of "particles" representing potential solutions moves through the search space, influenced by their own best positions and the best positions found by other particles in the swarm.…”

Section: Introductionmentioning

confidence: 99%

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Velpula,

Kona

2024

Preprint

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This paper proposes a novel brain inspired intelligent maximum power point tracking (BIIM) algorithm to enhance the performance of grid-connected solar photovoltaic (PV) system. A computational model for the algorithm is developed and implemented by mimicking the functionalities of amygdala, thalamus, orbitofrontal cortex and sensory cortex, present in the limbic system of the human brain. Conventional maximum power point tracking (MPPT) algorithms, such as perturb and observe (P&O) and incremental conductance (IC), often encounter challenges in achieving efficient and accurate maximum power point (MPP) tracking under varying environmental conditions. These algorithms exhibit slow convergence speed, oscillations around the MPP, and reduced tracking accuracy, leading to power losses and suboptimal photovoltaic system performance. To overcome these limitations, a novel brain-inspired intelligent MPPT (BIIM) algorithm has been proposed and implemented. The proposed BIIM algorithm incorporates the limbic system's ability to process sensory inputs, learn from past experiences, and make rapid decisions to achieve efficient MPPT. The efficacy of the proposed BIIM algorithm is comprehensively evaluated under various critical conditions, such as irradiance variations, temperature fluctuations, and grid faults. To validate the effectiveness of the proposed MPPT approach, comparative analysis was conducted with conventional MPPT algorithms such as P&O, proportional-integral (PI) controlled incremental conductance (IC-PI) and particle swarm optimization (PSO). The simulation results reveals the superior dynamic performance of the BIIM algorithm by reducing the response time, undershoot, along with increased efficiency and robustness.

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“…Therefore, researchers have developed a series of enhanced particle swarm techniques. These include the adaptive factor selection particle swarm algorithm (FMSPSO) 42 , enhanced autonomous group particle swarm algorithm (EAGPSO) 43 , hybrid tandem particle swarm optimization algorithm (SSPSO) 44 , and hybrid particle swarm optimization with Salp Swarm Algorithm (PSOSSO) 45 . These improvement strategies improve the convergence performance of PSO to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions

Mai,

Zhang,

Chao

et al. 2024

Sci Rep

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Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.

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A novel metaheuristic MPPT technique based on enhanced autonomous group Particle Swarm Optimization Algorithm to track the GMPP under partial shading conditions - Experimental validation

Cited by 42 publications

References 52 publications

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions

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