Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

Dagal, Idriss; Akın, Burak; Akboy, Erdem

doi:10.1002/er.7753

Cited by 41 publications

(19 citation statements)

References 49 publications

(73 reference statements)

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“…In order to obtain the optimal dynamic model, an improved quantum particle swarm optimization algorithm [9,10] is used to optimize the delay parameters of the dynamic model. The number of particle dimensions is optimized to be 7.…”

Section: Modeling Resultsmentioning

confidence: 99%

Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction

Ma,

Zhang,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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Aiming at the problem that it is difficult to effectively predict the concentration of nitrogen oxide emissions from boilers under the changing load conditions of power plants, this paper proposes a dynamic modeling method of nitrogen oxide (NOx) emissions from power plants, which considers the delay-feature extraction of input variables. This method combines principal component analysis and Gaussian regression modeling, and uses principal component analysis to extract the feature information extracted from the input variable data, and the current value and historical sequence value of extracted information are used as the input of Gaussian regression model. In addition, the historical time series value of boiler NOx concentration is added to the input of the model as feedback data. Taking the combustion system of a 1,000MW ultra-supercritical unit boiler as an object, and combining with the actual operation data on site, a dynamic model of boiler NOx emission is established. The experimental results show that the built boiler NOx emission dynamic model has high prediction accuracy and strong generalization performance, which has a certain reference value for boiler NOx emission modeling and intelligent control research.

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Section: Modeling Resultsmentioning

confidence: 99%

Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction

Ma,

Zhang,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…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

Self Cite

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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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“…[16] proposed a novel hybrid series salp particle Swarm optimization (SSPSO) which is highly required for suburb area applications where the partial shading conditions are frequent. [17] has explored an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems. [7] The PSO traditional technique was supposed to be the most efficient method of tracking the maximum power in the pattern shading situations.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved particle swarm optimization based on buck-boost converter (IPSO-BBC) for photovoltaic system applications

AKIN¹

2022

Recent Adv Sci Eng

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Nowadays, energy access and demands are of high importance. In some remote areas where the main energy grids are difficult to access, photovoltaic solar standalone systems are highly recommended for utility operation. In order to achieve and maintain successfully the required output power at maximum levels under any variable environmental conditions, high PV solar powers are necessary to feed efficiently the output. In this paper, an improved particle swarm optimization-based buck-boost converter (IPSO-BBC) for optimum standalone load charging is investigated. The proposed IPSO-BBC algorithm utilizes the PV solar voltage and current as two inputs based on PSO parameters. Solarex-MSX photovoltaic solar panel of 60 watts maximum power is used for the whole system operation. In order to harvest the maximum power for the output satisfaction, therefore; for this purpose, the IPSO-BBC algorithm approach is implemented. The PSO is used to monitor the PV voltage and current and BBC for the duty cycle settlement in various environmental conditions. The IPSO-BBC approach optimally presents a very good efficiency of 98.24% and a tracking factor of 0.93 with PV solar power generation performance under partial shading conditions. The entire simulation is carried out by MATLAB/Simulink.

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Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

Cited by 41 publications

References 49 publications

Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction

Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction

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

Improved particle swarm optimization based on buck-boost converter (IPSO-BBC) for photovoltaic system applications

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