Parameter Estimation of Single Phase Transformer Using Jellyfish Search Optimizer Algorithm

Youssef, Heba; Hassan, Mohamed H.; Kamel, Salah; Elsayed, Salah K.

doi:10.1109/icaacca51523.2021.9465279

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…Youssef et al 106 used JSO to estimate the parameters of a single-phase power transformer from the current and voltage under any load. They consider difference between the estimated and actual values as the main objective function that must be minimized.…”

Section: Applicationsmentioning

confidence: 99%

“…Optimizing hyper-parameters of deep learning JSO Chou et al 102 Optimizing hyper-parameters of LSSVR JSO-LSSVR Chou and Truong 88 Estimating parameters of a single-phase power transformer JSO Youssef et al 106 Optimizing parameters of solar photovoltaic (PV) model JSO Bisht and Sikander 108 Finding optimal coefficients of DWT JSO Dhevanandhini and Yamuna 103 Finding optimal configurations of RVFL JSO Elkabbash et al 104 Identification of parameters of PEMFCs JSO Gouda et al (1) Self-adaption: adaptive or self-adaptive algorithms are those that can self-tune their algorithm-specific and common control parameters. The algorithm-specific parameters in JSO include the number of iterations, population size, spatial distribution coefficients, and motion coefficients.…”

Section: Finetuning Of Artificial Intelligencementioning

confidence: 99%

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Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou

Molla

2022

Sci Rep

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The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.

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Section: Applicationsmentioning

confidence: 99%

Section: Finetuning Of Artificial Intelligencementioning

confidence: 99%

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou

Molla

2022

Sci Rep

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“…Skills of hummingbird leads to reach its food supply goal and nominate the new updated one from the available surrounding sources. The directed food search can be represented as in (22) and (23).…”

Section: Aho Optimizer Proceduresmentioning

confidence: 99%

“…The no load losses have been included in the objective function (OF) using manta rays foraging optimizer (MRFO) and chaotic MRFO 3 . Other optimizers have been proposed to evaluate transformer parameters and conducted practical tests for confirmation as coyote optimizer for three and single transformers 21 , and Jellyfish search optimizer, gravitational search algorithm (GSA) and machine learning approach for SPT with 4 kVA rating in 10,22,23 . Multi-objective evolutionary optimization has been adapted to evaluate the transformer parameters, improved using the FEA and verified by comparing the results with the actual measures and behavior 11 .…”

mentioning

confidence: 99%

Estimation of electrical transformer parameters with reference to saturation behavior using artificial hummingbird optimizer

Kotb

El‐Fergany

Gouda

2022

Sci Rep

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This paper offers an efficient tool to define the unknown parameters of electrical transformers. The proposed methodology is developed based on artificial hummingbird optimizer (AHO) to generate the best values of the transformer’s unknown parameters. At initial stage, the parameters’ extraction of the transformer electrical equivalent is adapted as an optimization function along with the associated operating inequality constraints. In which, the sum of absolute errors (SAEs) among many variables from nameplate data of transformers is decided to be minimized. Two test cases of 4 kVA and 15 kVA transformers ratings are demonstrated to indicate the ability of the AHO compared to other recent challenging optimizers. The proposed AHO achieves the lowest SAE’s value than other competing algorithms. At advanced stage of this effort, the capture of percentage of loading to achieve maximum efficiency is ascertained. At later stage, the performance of transformers utilizing the extracted parameters cropped by the AHO to investigate the principal behavior at energization of these transformer units is made. At the end, it can be confirmed that the AHO produces best values of transformer parameters which help much in achieving accurate simulations for steady-state and inrush behaviors.

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“…Fur-thermore, the numeric fitness function reveals the volume of food. The JFS was recently applied in effective manner for several engineering problems such as selective harmonic elimination in multilevel inverters [27], combined heat and power dispatching [28], maximum power point tracking (MPPT) of solar PV systems [29], design of a solar-powered thermoelectric air-conditioning system [30], control of superconducting magnetic energy storage system [31], PV parameter estimation [32], integration of renewable energy sources in power systems [33].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

et al. 2022

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In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of jellyfish. They are divided into three categories. The jellyfish could flow towards the ocean current or between its own swarm. Meanwhile, whenever the food supply is adequate, the jellyfishes are attracted to its location. It is formulated considering the three reliability indices of SAIFI, SAIUI and TENS, simultaneously in a multi-objective model based on the weight factors. The proposed methodology based on the JFS optimizer is implemented on an IEEE 33-node distribution network. According to the numerical results, the SAIFI, SAIUI and TENS is improved by 36.44%, 34.11% and 33.35%, compared to the initial condition. For comparison purposes, tuna swarm optimizer and tunicate swarm algorithm, besides the JFS algorithm, are implemented as well. The simulation results declare the significant outperformance of the JFS algorithm compared to TUNA and TSA in terms of the obtained improvements and the regarding convergence properties.

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Parameter Estimation of Single Phase Transformer Using Jellyfish Search Optimizer Algorithm

Cited by 18 publications

References 24 publications

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Estimation of electrical transformer parameters with reference to saturation behavior using artificial hummingbird optimizer

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

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