A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

Singla, Manish Kumar; Nijhawan, Parag; Oberoi, Amandeep Singh

doi:10.1108/compel-07-2021-0257

Cited by 16 publications

(16 citation statements)

References 31 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different metaheuristic algorithms for non-parametric statistical tests were used to verify the discovery of the result parameters. According to the experimental observations mentioned in the abstract, the statistical results of this model were more consistent with the real-time experimental results [6]. Sun, Y et al proposed a two-stage approach to improve the PSO.…”

Section: Related Worksupporting

confidence: 69%

Path Planning of Agricultural Information Collection Robot Integrating Ant Colony Algorithm and Particle Swarm Algorithm

Wu,

Chen,

Liu

2024

IEEE Access

View full text Add to dashboard Cite

Faced with complex and ever-changing environmental conditions in the agricultural field, efficient agricultural information gathering is crucial for optimising agricultural output. Therefore, a new path planning algorithm combining ant colony algorithm and particle swarm optimization is proposed in this study. The aim is to achieve fast and accurate path planning for agricultural information gathering robots in diverse agricultural environments. The global search ability of particle swarm optimization algorithm in finding optimal paths and the local search advantage of ant colony algorithm in obstacle avoidance are used to optimise the movement strategy of robots in agricultural environments. The research results showed that the global path planning distance of this method was 19.328m. The execution time was 0.97s. In local path planning, the proposed algorithm had a fitness function value of 30.123 when the number of iterations reached 53. In mixed path planning, the proposed algorithm reduced the movement time by 3.2s. The conclusion shows that the algorithm proposed in this study has high applicability and efficiency in practical applications, providing an effective strategy for path planning of agricultural information gathering robots. It has important practical significance for promoting the development of intelligent agriculture.

show abstract

Section: Related Worksupporting

confidence: 69%

Path Planning of Agricultural Information Collection Robot Integrating Ant Colony Algorithm and Particle Swarm Algorithm

Wu,

Chen,

Liu

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The hybrid algorithm is far better than the standalone algorithm with respect to convergence time, reliability, and memory etc. After the extraction of both the models the Friedman ranking test [ 40 – 44 ] Table 8 and Wilcoxon’s rank sum test [ 45 – 51 ] Table 9 is performed and from this test also it is concluded that the proposed hybrid algorithm is far better than the rest of the compared standalone algorithms.…”

Section: Resultsmentioning

confidence: 99%

A modified particle swarm optimization rat search algorithm and its engineering application

Singla,

Gupta,

Alsharif

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm’s accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population’s diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.

show abstract

“…Table 13 shows the statistical analysis results from the PEMFC parameter estimation done using the Friedman Ranking Test [37][38][39][40] for both of the fuel cell models. First place is secured by HPSOPF, followed by PSOGWO, GWOCS, PF, GWO, and PSO.…”

Section: Discussionmentioning

confidence: 99%

Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm

et al. 2023

Self Cite

View full text Add to dashboard Cite

Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10−9 and for the Avista SR-12 model it was 5.65 × 10−8. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.

show abstract

A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

Cited by 16 publications

References 31 publications

Path Planning of Agricultural Information Collection Robot Integrating Ant Colony Algorithm and Particle Swarm Algorithm

Path Planning of Agricultural Information Collection Robot Integrating Ant Colony Algorithm and Particle Swarm Algorithm

A modified particle swarm optimization rat search algorithm and its engineering application

Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm

Contact Info

Product

Resources

About