Competency of improved artificial ecosystem optimizer in parameters identification of small and medium sized distribution transformers

Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 32421 - 21
Hlavní autori:	Draz, Abdelmonem, Ashraf, Hossam, El Shamy, Ahmed R., El-Fergany, Attia A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 12.09.2025 Nature Publishing Group Nature Portfolio
Predmet:	639/166 639/166/987 Accuracy Algorithms Artificial ecosystem optimizer Behavior Distribution transformers Efficiency Exploitation Genetic algorithms Humanities and Social Sciences Metaheuristic algorithms parameters estimation multidisciplinary Normal distribution Objective function Optimization algorithms Optimization techniques Parameter estimation Parameter identification Science Science (multidisciplinary) Statistical analysis Artificial ecosystem optimizer Statistical analysis Metaheuristic algorithms parameters estimation Distribution transformers
ISSN:	2045-2322, 2045-2322
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Abstract	Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs with different four sizes (i.e. 4, 15, 112.5 and 167 kVA ratings). Comparison with well-known literature optimizers, including genetic algorithm, particle swarm optimizer, coyote optimization algorithm, artificial hummingbird optimizer, and others, validates the performance of the proposed IAEO. The IAEO demonstrates its superiority by getting the lowest possible value of the sum of absolute errors (SAEs) between measured and calculated values, which serves as the objective function (OF) to be optimized. Moreover, three additional optimizers are employed and compared to IAEO for all study cases: firefly algorithm, political optimizer, and exponential distribution optimizer. It is found that IAEO attains the minimum SAEs values of 1.12e-5 and 0.0322, outperforming the best competitors for 4 kVA and 15 kVA TXs, respectively. Furthermore, IAEO accurately captures the steady state fingerprint of all studied TXs in terms of efficiency and voltage regulation (VR). This way, the peak efficiency occurs at 36.2% loading in 112.5 kVA TX while the negative VR may reach -8% when the 167 kVA TX is loaded with its rated leading power factor. Finally, all executed optimizers are analyzed using several statistical indices, including t-test, where the proposed IAEO gets the smoothest and fastest OF minimization trend.
AbstractList	Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs with different four sizes (i.e. 4, 15, 112.5 and 167 kVA ratings). Comparison with well-known literature optimizers, including genetic algorithm, particle swarm optimizer, coyote optimization algorithm, artificial hummingbird optimizer, and others, validates the performance of the proposed IAEO. The IAEO demonstrates its superiority by getting the lowest possible value of the sum of absolute errors (SAEs) between measured and calculated values, which serves as the objective function (OF) to be optimized. Moreover, three additional optimizers are employed and compared to IAEO for all study cases: firefly algorithm, political optimizer, and exponential distribution optimizer. It is found that IAEO attains the minimum SAEs values of 1.12e-5 and 0.0322, outperforming the best competitors for 4 kVA and 15 kVA TXs, respectively. Furthermore, IAEO accurately captures the steady state fingerprint of all studied TXs in terms of efficiency and voltage regulation (VR). This way, the peak efficiency occurs at 36.2% loading in 112.5 kVA TX while the negative VR may reach -8% when the 167 kVA TX is loaded with its rated leading power factor. Finally, all executed optimizers are analyzed using several statistical indices, including t-test, where the proposed IAEO gets the smoothest and fastest OF minimization trend. Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs with different four sizes (i.e. 4, 15, 112.5 and 167 kVA ratings). Comparison with well-known literature optimizers, including genetic algorithm, particle swarm optimizer, coyote optimization algorithm, artificial hummingbird optimizer, and others, validates the performance of the proposed IAEO. The IAEO demonstrates its superiority by getting the lowest possible value of the sum of absolute errors (SAEs) between measured and calculated values, which serves as the objective function (OF) to be optimized. Moreover, three additional optimizers are employed and compared to IAEO for all study cases: firefly algorithm, political optimizer, and exponential distribution optimizer. It is found that IAEO attains the minimum SAEs values of 1.12e-5 and 0.0322, outperforming the best competitors for 4 kVA and 15 kVA TXs, respectively. Furthermore, IAEO accurately captures the steady state fingerprint of all studied TXs in terms of efficiency and voltage regulation (VR). This way, the peak efficiency occurs at 36.2% loading in 112.5 kVA TX while the negative VR may reach -8% when the 167 kVA TX is loaded with its rated leading power factor. Finally, all executed optimizers are analyzed using several statistical indices, including t-test, where the proposed IAEO gets the smoothest and fastest OF minimization trend.Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs with different four sizes (i.e. 4, 15, 112.5 and 167 kVA ratings). Comparison with well-known literature optimizers, including genetic algorithm, particle swarm optimizer, coyote optimization algorithm, artificial hummingbird optimizer, and others, validates the performance of the proposed IAEO. The IAEO demonstrates its superiority by getting the lowest possible value of the sum of absolute errors (SAEs) between measured and calculated values, which serves as the objective function (OF) to be optimized. Moreover, three additional optimizers are employed and compared to IAEO for all study cases: firefly algorithm, political optimizer, and exponential distribution optimizer. It is found that IAEO attains the minimum SAEs values of 1.12e-5 and 0.0322, outperforming the best competitors for 4 kVA and 15 kVA TXs, respectively. Furthermore, IAEO accurately captures the steady state fingerprint of all studied TXs in terms of efficiency and voltage regulation (VR). This way, the peak efficiency occurs at 36.2% loading in 112.5 kVA TX while the negative VR may reach -8% when the 167 kVA TX is loaded with its rated leading power factor. Finally, all executed optimizers are analyzed using several statistical indices, including t-test, where the proposed IAEO gets the smoothest and fastest OF minimization trend. Abstract Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs with different four sizes (i.e. 4, 15, 112.5 and 167 kVA ratings). Comparison with well-known literature optimizers, including genetic algorithm, particle swarm optimizer, coyote optimization algorithm, artificial hummingbird optimizer, and others, validates the performance of the proposed IAEO. The IAEO demonstrates its superiority by getting the lowest possible value of the sum of absolute errors (SAEs) between measured and calculated values, which serves as the objective function (OF) to be optimized. Moreover, three additional optimizers are employed and compared to IAEO for all study cases: firefly algorithm, political optimizer, and exponential distribution optimizer. It is found that IAEO attains the minimum SAEs values of 1.12e-5 and 0.0322, outperforming the best competitors for 4 kVA and 15 kVA TXs, respectively. Furthermore, IAEO accurately captures the steady state fingerprint of all studied TXs in terms of efficiency and voltage regulation (VR). This way, the peak efficiency occurs at 36.2% loading in 112.5 kVA TX while the negative VR may reach -8% when the 167 kVA TX is loaded with its rated leading power factor. Finally, all executed optimizers are analyzed using several statistical indices, including t-test, where the proposed IAEO gets the smoothest and fastest OF minimization trend.
ArticleNumber	32421
Author	El-Fergany, Attia A. Ashraf, Hossam Draz, Abdelmonem El Shamy, Ahmed R.
Author_xml	– sequence: 1 givenname: Abdelmonem surname: Draz fullname: Draz, Abdelmonem email: aaderaz@zu.edu.eg organization: Electrical Power and Machines Department, Faculty of Engineering, Zagazig University – sequence: 2 givenname: Hossam surname: Ashraf fullname: Ashraf, Hossam organization: Department of Electrical Engineering and Information Systems, The University of Tokyo – sequence: 3 givenname: Ahmed R. surname: El Shamy fullname: El Shamy, Ahmed R. organization: Advanced Power and Energy Center, Department of Electrical Engineering, Khalifa University, Faculty of Engineering, Zagazig University – sequence: 4 givenname: Attia A. surname: El-Fergany fullname: El-Fergany, Attia A. organization: Electrical Power and Machines Department, Faculty of Engineering, Zagazig University
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Keywords	Artificial ecosystem optimizer Statistical analysis Metaheuristic algorithms parameters estimation Distribution transformers
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Snippet	Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications.... Abstract Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications....
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SubjectTerms	639/166 639/166/987 Accuracy Algorithms Artificial ecosystem optimizer Behavior Distribution transformers Efficiency Exploitation Genetic algorithms Humanities and Social Sciences Metaheuristic algorithms parameters estimation multidisciplinary Normal distribution Objective function Optimization algorithms Optimization techniques Parameter estimation Parameter identification Science Science (multidisciplinary) Statistical analysis
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Title	Competency of improved artificial ecosystem optimizer in parameters identification of small and medium sized distribution transformers
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