Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Trappey, Amy J.C.; Trappey, Charles V.; Ma, Lin; Chang, Jimmy C.M.

doi:10.1016/j.cie.2014.12.033

Cited by 77 publications

(53 citation statements)

References 29 publications

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“…The planned corrective maintenance is a technique that ensures a reduction in costs and duration of the repair. For this reason, classification algorithms [11] or neural networks [15] [16] have been applied to address problems of ice accumulation [18] [10] as well as the prediction of physical deterioration of machinery (generators and transformers) [20] [12]. Next, (ii) preventive maintenance consists of reducing equipment failures by seeking solutions to problems before they happen.…”

Section: Related Workmentioning

confidence: 99%

Agreement technologies applied to transmission towers maintenance

Chamoso

Paz

Prieta

et al. 2017

AIC

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Among the most indispensable elements required by a city is electric power and the transmission towers used for its distribution. These towers have underground electrodes which must be reviewed on a regular basis by controlling different parameters to ensure that electrical resistance is in a secure range in order to avoid problems and risks. Using artificial intelligence, it is possible to ensure proper maintenance of the towers by estimating the required values and proposing a re-duction in the size of the population sample, which will min-imize the cost of operation. The use of an intelligentagent virtual-organization based architecture is proposed within this working environment. By using mathematical estimation models and agreement based negotiations, the architecture is capable of maximizing estimations and minimizing associ-ated costs. The proposed model has been evaluated with the developed software through a real case study, which permit-ted us to validate the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Agreement technologies applied to transmission towers maintenance

Chamoso

Paz

Prieta

et al. 2017

AIC

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“…The BP algorithm is motivated to minimize the sum-squared error following the gradient descent approach [16,17].…”

Section: The Ann Modelmentioning

confidence: 99%

“…Furthermore, several soft computing techniques have also been used in various studies to overcome the shortcomings of the different standard methods. Some of the popular soft computing methods that have been adopted to improve the reliability of DGA-based transformer incipient fault identification are fuzzy logic [11][12][13][14], ANNs [15][16][17], wavelet networks [18,19] and the adaptive neuro-fuzzy inference system (ANFIS) [20][21][22][23]. However, these intelligent techniques have their own limitations and hence the degree of the reliability of the method would depend on how these methods are adopted to circumvent their limitations.…”

Section: Introductionmentioning

confidence: 99%

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Equbal¹,

Khan²,

Islam³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a transformer incipient fault diagnosis model has been developed with the help of an artificial neural network (ANN), taking into account the difference in the energy required to produce the different fault gases.The key fault gases are indicative of the fault type prevailing in the transformer. However, in conventional studies, the energy difference in fault gas formation is not considered while adopting the key gas method for fault diagnosis. In this work, a weighting factor has been used to take into account this relative difference in energy requirement for various fault gas formations. The fault gas concentrations have been suitably weighted by their respective weighting factors before being used in the incipient fault diagnosis process. A backpropagation ANN has been appropriately trained using the weighted fault gas concentration for transformer incipient fault identification. The model has been trained to identify fault types as enlisted in the transformer fault-interpreting standard IEC-599. The developed ANN model has been tested for its diagnostic capability using a reported fault database. The comparative diagnosis results presented here show clear improvement in the diagnosis of transformer internal faults using the energy-weighted ANN model over the unweighted ANN model.

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“…Power grid construction concerns people's social life and national energy security, which plays an important role in promoting economic development, pushing industrial structure upgrades, and optimizing resource allocation [1,2]. The power grid interconnection project holds the characteristics of long construction period, intensive technical and economic investments, and wide covering range, the construction of which can be influenced by the external environment factors [3,4].…”

Section: Introductionmentioning

confidence: 99%

Risk Evaluation of Qinghai–Tibet Power Grid Interconnection Project for Sustainability

Liu

2016

Sustainability

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Tel./Fax: +86-1061773129 † These authors contributed equally to this work. Abstract:The Qinghai-Tibet power grid interconnection project is the first power transmission project with the highest altitude, longest transmission lines, longest distance running across the plateau frozen ground, and highest iron tower in the world. The risk evaluation on it can identify the overall risk level and key risk factors, which can reduce risk-induced loss and promote sustainable construction. In this paper, the risk of the Qinghai-Tibet power grid interconnection project was evaluated by employing a matter-element extension model under a fuzzy environment. After building the risk evaluation index system, the performances and weights of criteria were qualitatively judged by three groups of experts in different fields, and then the risk of the Qinghai-Tibet power grid interconnection project was rated by employing matter-element extension model. Meanwhile, the sensitivity analysis was performed to identify key risk criteria. The empirical results indicate the risk of the Qinghai-Tibet power grid interconnection project belongs to the "stronger" grade, tending to the "strongest" grade. "Social stability risk", "altitude sickness seizure risk", "permafrost-induced risk", "severe weather-induced risk", and "ecological destruction risk" are key sub-criteria, which should be paid more attention to when taking risk management measures. Finally, some countermeasures for key risks of the Qinghai-Tibet power grid interconnection project were given. The findings in this paper can provide references for engineering managers and related stakeholders.

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Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Cited by 77 publications

References 29 publications

Agreement technologies applied to transmission towers maintenance

Agreement technologies applied to transmission towers maintenance

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Risk Evaluation of Qinghai–Tibet Power Grid Interconnection Project for Sustainability

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