Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions

Yang, Bo; Wu, Shaocong; Huang, Jianxiang; Guo, Zhengxun; Wang, Jiarong; Zhang, Zijian; Xie, Rui; Shu, Hongchun; Jiang, Lin

doi:10.1016/j.enconman.2023.117410

Cited by 38 publications

(4 citation statements)

References 32 publications

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“…Reference 36 proposed an MPPT technique based on the Horse Herd Optimization Algorithm (HOA) and experimentally validated it on a real PV system. Similar approaches include the Marine Predator Optimization Algorithm (MPA) 37 , Salp Swarm Algorithm (SSA) 38 , Search and Rescue Algorithm (SRA) 39 , and Tuna Swarm Optimization (TSO) 40 . The MPPT technique based on the Particle Swarm Optimization (PSO) algorithm 41 is considered one of the more classical techniques, but the original Particle Swarm Optimization technique is slow in tracking and easily falls into local optimal solutions.…”

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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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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“…Yang et al developed the salp swarm optimization (SSO) based MPPT to enhance the system's ability to capture MPP under both UIC and PSC. This approach synergizes with a solar power system that incorporates an integrated thermoelectric generator and harnessing excess heat to generate additional electricity [ 24 ]. Naseem et al discussed the use of fuzzy control (FC) to improve the system performance by analyzing the MPP based on the relationship between the slope of PV module's output voltage and output power.…”

Section: Introductionmentioning

confidence: 99%

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

Chen,

Farooqui,

Liu

et al. 2024

Heliyon

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“…In the multitude of algorithms proposed for path planning, bio-inspired algorithms are a powerful solution since they are flexible, scalable, and capable of finding global optima in multi-dimensional spaces [67][68][69][70][71][72][73][74][75][76][77][78]. For example, the Salp Swarm Algorithm (SSA), which is based on the swarming behavior of salps in the ocean, is one of the new bio-inspired optimization techniques that have demonstrated potential in different optimization problems [79][80][81][82][83]. Its mode of operation, imitating the navigation pattern of the salp chain, provides an equilibrium between the stages of exploration and exploitation, and so it is an interesting candidate for UGV path planning [84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100].…”

Section: Introductionmentioning

confidence: 99%

Path planning for a UGV using Salp Swarm Algorithm

AlShabi,

Ballous,

Bou Nassif

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.

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Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions

Cited by 38 publications

References 32 publications

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

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

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

Path planning for a UGV using Salp Swarm Algorithm

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