Estimation of the transformer parameters from nameplate data using turbulent flow of water optimization technique

Hassan, Amir Y.; Said, Mokhtar; Saleh, Saber M.

doi:10.11591/ijeecs.v25.i2.pp639-647

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…This mining technique finds applications in marketing, bioinformatics, finance, and other domains [Milhem et al 2022]. The UP-Tree, a data structure that efficiently stores high-utility item sets, is commonly used in various high utility pattern mining algorithms [Hassan et al 2022]. It leverages a tree structure to represent item sets and their utility values, enabling efficient pruning and search operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Ghaib,

Alsalhi,

Hayder

et al. 2023

JCSTS

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High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large datasets. To address this challenge, we propose an optimized method called improving the efficiency of Distributed Utility itemsets mining in relation to big data (IDUIM). This technique improves upon the Distributed Utility item sets Mining (DUIM) algorithm by incorporating various refinements. IDUIM effectively mines item sets of big datasets and provides useful insights as the basis for information management and nearly real-time decision-making systems. According to experimental investigation, the method is being compared to IDUIM and other state algorithms like DUIM, PHUI-Miner, and EFIM-Par. The results demonstrate the IDUIM algorithm is more efficient and performs better than different cutting-edge algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Ghaib,

Alsalhi,

Hayder

et al. 2023

JCSTS

View full text Add to dashboard Cite

show abstract

Characterization of electrical 1-phase transformer parameters with guaranteed hotspot temperature and aging using an improved dwarf mongoose optimizer

Rizk‐Allah

El‐Fergany

Gouda

et al. 2023

Neural Comput & Applic

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Parameters identification of Electric Power Transformer (EPT) models is significant for the steady and consistent operation of the power systems. The nonlinear and multimodal natures of EPT models make it challenging to optimally estimate the EPT’s parameters. Therefore, this work presents an improved Dwarf Mongoose Optimization Algorithm (IDMOA) to identify unknown parameters of the EPT model (1-phase transformer) and to appraise transformer aging trend under hottest temperatures. The IDMOA employs a population of solutions to get as much information as possible within the search space through generating different solution’ vectors. Furthermore, the Nelder–Mead Simplex method is incorporated to efficiently promote the neighborhood searching with the aim to find a high-quality solution during the iterative process. At initial stage, power transformer electrical equivalent extraction parameters are expressed in terms of the fitness function and its corresponding operating inequality restrictions. In this sense, the sum of absolute errors (SAEs) among numerous factors from nameplate data of transformers is to be minimized. The proposed IDMOA is demonstrated on two transformer ratings as 4 kVA and 15 kVA, respectively. Moreover, the outcomes of the IDMOA are compared with other recent challenging optimization methods. It can be realized that the lowest minimum values of SAEs compared to the others which are 3.3512e−2 and 1.1200e−5 for 15 kVA and 4 kVA cases, respectively. For more assessment for the proposed optimizer, the extracted parameters are utilized to evaluate the transformer aging considering the transformer hottest temperature compared with effect of the actual parameters following the IEEE Std C57.91 procedures. It is proved that the results are guaranteed, and the transformer per unit nominal life is 1.00 at less than 110 °C as per the later-mentioned standard.

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Estimation of the transformer parameters from nameplate data using turbulent flow of water optimization technique

Cited by 2 publications

References 18 publications

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data

Characterization of electrical 1-phase transformer parameters with guaranteed hotspot temperature and aging using an improved dwarf mongoose optimizer

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