A Novel Approach to Investigate the Effect of Maintenance on the Replacement Time for Transformers

Aldhubaib, Hani A.; Salama, M.M.A.

doi:10.1109/tpwrd.2014.2321409

Cited by 24 publications

(14 citation statements)

References 38 publications

(52 reference statements)

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“…Data requirements for probabilistic concepts in asset management are huge and range from inspection rates and mean times to failure to probabilities of state transitions . For example, the effect of maintenance on the replacement time for transformers was studied in . The study used RCM and a genetic algorithm to optimally schedule maintenance activities for the transformer.…”

Section: Domains Of Asset Management and Advent Of Data Managementmentioning

confidence: 99%

A literature survey on asset management in electrical power [transmission and distribution] system

Khuntia

Rueda

Bouwman

et al. 2016

Int. Trans. Electr. Energ. Syst.

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SUMMARYAsset management is one of the key components in a transforming electric power industry. Electric power industry is undergoing significant changes because of technical, socio-economical and environmental developments. Also, because of restructuring and deregulation, the focus has been on transmission and distribution assets that include transmission lines, power transformers, protection devices, substation equipment and support structures. This study aims to provide a detailed exposure to asset management classification, various interesting maintenance methods and theories developed. The work encompasses the issue of data management in recent years. Because of the use of various smart metering devices, large amounts of information are being collected. The advent of data-mining techniques has changed the asset management scenario, and it has been covered in this survey paper. In the end, it also discusses various risk assessment techniques in asset management developed and used for academic research and industries. It is accompanied with survey results from pan-European Transmission System Operator (TSOs) on various aspects in asset management.

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Section: Domains Of Asset Management and Advent Of Data Managementmentioning

confidence: 99%

A literature survey on asset management in electrical power [transmission and distribution] system

Khuntia

Rueda

Bouwman

et al. 2016

Int. Trans. Electr. Energ. Syst.

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“…Al-Chalabi et al [16] presented a model-based ANN method to determine the economic replacement time (ERT) of production machines. Aldhubaib and Salama [17] illustrated an approach to link maintenance and replacement decisions. They used GA to optimally schedule maintenance activities.…”

Section: Introductionmentioning

confidence: 99%

Determining optimal machine part replacement time using a hybrid ANN-GA model

Gökler

Boran

2020

Scientia Iranica

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Companies must determine the replacement time of machine parts correctly since it affects their production costs and efficiencies. For this, it is aimed to determine the most appropriate replacement time to minimize cost per unit. In this study, it is proposed to develop a hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) model to predict replacement time without using a cost model. At first, a replacement cost model is developed to calculate replacement times to use in training the neural network. Nevertheless, the cost model needs complex mathematical calculations. GA is used instead of the cost model to determine replacement time, and thus, to achieve fast learning for the neural network. The hybrid ANN-GA model was applied to predict replacement time of bladder in tire manufacturing. Furthermore, ANN and GA models, which were developed to increase the prediction accuracy of the hybrid model, were used. The hybrid ANN-GA model showed better solution according to the performance statistics than the other ANN and GA models. The values indicate that the hybrid model is in good agreement with the cost model. Thus, it is recommended that the hybrid model is used instead of the cost model.

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“…Those systems usually cooperate with technical assessment decision support systems (DSS) [13,14]. Artificial intelligent based algorithms, such as artificial neural networks, fuzzy logic, or machine learning, have been willingly and successfully applied in DSS for the last decade [15][16][17][18]. Apart from numerous other indicators, temperature is one of the fundamental parameters that deliver valuable information about potential abnormalities in apparatus operation: Typically, if temperature is higher than expected (or typical) it may be a symptom of some potential faults.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet

et al. 2019

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A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.

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A Novel Approach to Investigate the Effect of Maintenance on the Replacement Time for Transformers

Cited by 24 publications

References 38 publications

A literature survey on asset management in electrical power [transmission and distribution] system

A literature survey on asset management in electrical power [transmission and distribution] system

Determining optimal machine part replacement time using a hybrid ANN-GA model

Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet

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