A novel hybrid series salp particle Swarm optimization (SSPSO) for standalone battery charging applications

Dagal, Idriss; Akın, Burak; Akboy, Erdem

doi:10.1016/j.asej.2022.101747

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…The overall simulation was illustrated at 25 ms, and the results were observed and indicated that the proposed technique curves paired well with that of the preceding literature 32 under weather‐ varying conditions. However sudden decrease and increase occurred at 45 and 50 ms, respectively, it has accurately tracked the global maximum power at 72 ms for two power values of 80 and 60 W, whereas the conventional method failed to track the global maximum power (GMP) 33‐35 from the origin zero with enormous decay until 138 and 116 ms for different powers of 80 and 60 W, respectively and has presented rough oscillations.…”

Section: The Proposed Trt‐bbc Working Principlementioning

confidence: 95%

Transformer rail‐tapped buck‐boost converter design‐based feedback controller for battery charging systems

Dagal

Akın

2022

Energy Storage

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In this article, a transformer rail‐tapped buck‐boost converter (TRT‐BBC) with minor loss of power transfer from a photovoltaic solar panel to a lead‐acid battery for battery charging systems is designed. The TRT‐BBC has been utilized to inter‐match the PV direct power to the battery. The 24‐V lead acid battery bank with a storage energy of approximately 168 Wh is designed. The photovoltaic solar panel of 200 W maximum power is used for the whole system operation. The proposed TRT‐BBC topology solves the conventional buck‐boost converter inverted output problem with apex conversion efficiencies of 93.25% and 91.97% for continuous current mode (CCM) and discontinuous current mode (DCM) respectively at standard test conditions (STC) and full load conditions. The whole system can operate either in DCM or CCM according to the needs at a switching frequency of 100 kHz and the maximum power point tracking (MPPT) with proportional integration (PI) based constant voltage‐constant current (CV‐CC) feedback control loop used to monitor the switching duty cycle of the TRT‐BBC.

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Section: The Proposed Trt‐bbc Working Principlementioning

confidence: 95%

Transformer rail‐tapped buck‐boost converter design‐based feedback controller for battery charging systems

Dagal

Akın

2022

Energy Storage

Self Cite

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“…When the operating temperature or irradiance of the PV array changes, the position of the MPP changes accordingly. Therefore, a restart strategy 44 , 45 is introduced in the algorithm to ensure its adaptability to dynamically changing weather. The algorithm is initialized with the following conditions: …”

Section: Dbo-based Mppt Techniquementioning

confidence: 99%

“…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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“…This algorithm utilized a hybrid series salp particle optimization algorithm to achieve maximum power point tracking. Simulation results demonstrated that the proposed algorithm is highly efficient, with an average tracking efficiency of 99.99% [11]. Chalh et al proposed a new method for MPPT based on Gull optimization algorithm to solve the problem of using the maximum power point tracking controller.…”

Section: Related Workmentioning

confidence: 99%

Modeling and simulation of complex emergency dispatch based on BIPSO

Sun,

Huang

2024

Int. J. Simul. Multidisci. Des. Optim.

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In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

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A novel hybrid series salp particle Swarm optimization (SSPSO) for standalone battery charging applications

Cited by 15 publications

References 18 publications

Transformer rail‐tapped buck‐boost converter design‐based feedback controller for battery charging systems

Transformer rail‐tapped buck‐boost converter design‐based feedback controller for battery charging systems

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

Modeling and simulation of complex emergency dispatch based on BIPSO

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