Factorial analysis for ageing assessment of in‐service transformers

Wang, Dongmiao; Tee, Sheng Ji; Liu, Qiang; Wang, Zhongdong

doi:10.1049/iet-gtd.2017.1531

Cited by 8 publications

(5 citation statements)

References 15 publications

(26 reference statements)

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“…Since only the substation systems are involved, the 102 buses network can be simplified as a radial system. Considering a potential impact of too many tap switching operations on OLTCs [31], the limited tap changer switching operation numbers TClimit has been set to 4 (i.e. M 4).…”

Section: Stimulation Results and Comparisonsmentioning

confidence: 99%

“…This paper presents an improved optimal load demand reduction control strategy for the operation of primary transformer tap-changers in distribution networks, provided that the upstream transmission system requires active load demand reduction services during periods of low generation production or generation loss. Since frequent operation of OLTCs would impact the asset health of OLTC [31], the objective is thus to find an optimal dispatch solution of transformer OLTCs to achieve the required load demand reduction service by minimizing OLTC tap switching operations and the network power losses. Similar to the optimization method used in [32], but this paper optimizes transformer tap changers switching operations and network active power loss to provide load demand reduction supports to the upstream transmission network.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

Gao

Chen

2022

IEEE Access

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To mitigate the low frequency problem in a transmission system in an event of a power station failure or during low renewable generation production, UK National Grid (NG) Electricity System Operator has balancing mechanism in place with generators to provide temporary extra power, or with large energy users to reduce load demand or so call fast reserve services. This paper presents an alternative method to aggregately control the existing distribution network primary on load transformer tap changers as a voltageled customer load active service. The main benefits of the proposed method are (i) to unlock the distribution network load demand flexibility without causing any negative impact on customers, and (ii) to provide the lowest cost of fast reserve service from a distribution network to transmission network. In this paper an optimal control strategy based on genetic algorithm is proposed and developed to achieve an optimized voltage-led customer load active service with the aim of finding the optimal dispatch of on load transformer tap changers by minimizing each transformer tap switching operation as well as network losses. Two practical 102 buses and 222 buses UK distribution networks have been modelled and used to demonstrate and compare the effectiveness of the proposed control methods under different operating conditions. The performances of the proposed method are also compared with both the rule-based and the branch-and-bound methods. The results show that the proposed optimal control strategy based on the genetic algorithm is more effective by achieving more accuracy and a faster solution for a large distribution network than other two methods. These are important findings as the fast reserve service by transmission network requires the accuracy of the load demand reduction delivery within 2 minutes.INDEX TERMS Fast reserve, customer active load service, load demand reduction management, aggregately control of transformer tap changers, genetic algorithm, and optimizations.

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Section: Stimulation Results and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

Gao

Chen

2022

IEEE Access

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“…This approach presents higher accuracy in fault diagnosis Shrivastava & Choubey (2012). In 2018, the factor analysis based on ANOVA is exploited to evaluate the aging of transformers in service Wang et al (2018). Reference Ardi et al (2019) for diagnosing incipient faults in power transformers, uses analytical incremental learning (AIL) based on dissolved gas analysis.…”

Section: Some Applications Of Extraction Techniquesmentioning

confidence: 99%

Knowledge Extraction Techniques for Power Transformer Maintenance Data: Review

Manyol,

Olong,

Eke

et al. 2023

Int. J. Res. Granthaalayah

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The maintenance of power transformers is time or condition-based and at the end of this one, analysis reports are produced to give its status. These different reports produced over time form a large database called the transformer maintenance asset bank. Extracting knowledge from this power transformer maintenance data is now an important subject for the scientific community given the importance of the transformer in the electric power generation chain. The science of data mining finds a field of application for its analysis techniques, the most used in preventive maintenance are predictive techniques. This work reviews knowledge extraction techniques from power transformer maintenance data. For this purpose, 80 articles from a platform are identified and 7 of them are retained at the end after meeting the criteria. Among the predictive analysis techniques namely regression, classification, and prediction, classification is the most used with its ANN (Artificial Neural Network) algorithm. On the other hand, association rule mining (ARM) has the highest accuracy, 98.21% in 2020. In addition, the combination of a classification algorithm preceded by the descriptive one, namely the principal component analysis (PCA), could offer higher accuracy than when they are used individually.

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“…El análisis factorial es una herramienta sumamente útil, puede ayudar a los administradores de activos a analizar grandes bases de datos, validar constructos y comprender más sobre los factores subyacentes que influyen en las variables y así planificar posteriormente la gestión de estrategias (Wang, Ji, Liu y Wang 2018;Hernández-Sampieri, Fernández, Baptista, 2010;Montgomery y Runger, 2010). Mediante esta técnica se encuentran grupos de variables homogéneas a partir de un conjunto numeroso de ellas (Pérez y Medrano, 2010).…”

Section: Microempresas Comercializadorasunclassified

Análisis factorial para la validación de las variables en un modelo de alianzas estratégicas en microempresas mexicanas

Castellanos

Cano‐Olivos

Flores

et al. 2019

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Introducción: El objetivo de esta investigación es validar las variables que son consideradas en un Modelo Conceptual de Alianzas Estratégicas para Microempresas Comercializadoras. Para tal efecto, se utiliza el análisis factorial exploratorio.Método: Para el estudio se recolectaron datos a través de una encuesta, la cual se aplicó a 204 microempresas comercializadoras, ubicadas en Hermosillo, Sonora. Los datos fueron analizados mediante el programa de software SPSS versión 23.0Resultados: Los resultados muestran que el instrumento utilizado para la recolección de datos es aceptable, pues se tiene un valor de alfa de Cronbach de 0.855. Por otro lado, con el análisis factorial se hizo evidente que las variables a considerar en el Modelo Conceptual de Alianzas Estratégicas para Microempresas Comercializadoras son las correctas, y se agruparon por factores.Discusión o Conclusión: Las microempresas pueden formar parte de una red de alianzas vinculadas a empresas más grandes. Por la simplicidad del Modelo Conceptual de Alianzas Estratégicas para Microempresas Comercializadoras, los empresarios pueden utilizar este modelo como guía para generar ventajas competitivas.

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Factorial analysis for ageing assessment of in‐service transformers

Cited by 8 publications

References 15 publications

Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

Knowledge Extraction Techniques for Power Transformer Maintenance Data: Review

Análisis factorial para la validación de las variables en un modelo de alianzas estratégicas en microempresas mexicanas

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