Distribution Transformer Losses Evaluation: A New Analytical Methodology and Artificial Neural Network Approach

Leal, Adriano Galindo; Jardini, J.A.; Magrini, L.C.; Ahn, Se Un

doi:10.1109/tpwrs.2008.2012178

Cited by 32 publications

(7 citation statements)

References 12 publications

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“…It can be observed that ANN can provide a reliable result but this method requires lots of data, especially for architectures with many layers. The same approach was also applied in other related works [47,48].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids

et al. 2020

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Due to the large number of distribution transformers in the distribution grid, the status of distribution transformers plays an important role in ensuring the safe and reliable operation of the these grids. To evaluate the distribution transformer health, many assessment techniques have been studied and developed. These tools will support the transformer operators in predicting the status of the distribution transformer and responding effectively. This paper will review the literature in the area, analyze the latest techniques as well as highlight the advantages and disadvantages of current methodologies.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids

et al. 2020

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“…The authors in [37] utilized kWh meters joined to a power transformer to compute the annual kWh consumption and kW demand. In [38], the load profile data were collected from monthly load curve readings, and electricity consumption was recorded for a month. Subsequently, the artificial neural network was applied to determine the load profile characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Residential Neighborhoods’ Electricity Demand in Iraq Distribution Power Grids

et al. 2021

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In Baghdad City's distribution power grid, a massive number of 630 kV distribution transformers (DTs) are used in residential neighborhoods. Each DT is joined to nine low-voltage 0.415 kV distribution feeders. Each feeder has a designated size of 1 × 240 mm 2 and is joined to a specified number of residential dwellings (= 30) fixed in the initial design stage. The size and number of low-voltage 0.415 kV distribution feeders are set with no change. In this investigation, we use a new approach for modeling electricity demand in residential neighborhoods in Baghdad City and overcome this constraint by finding the optimum number of residential dwellings joined to the same low-voltage 0.415 kV distribution feeder. Two sets of the experimental equations are created to compute the number of residential dwellings that are required to be joined to the low-voltage 0.415 kV distribution feeder. The multi-gradient particle swarm optimization algorithm is used as an optimization tool to handle these experimental equations. Results show that each low-voltage 0.415 kV distribution feeder can be loaded with 50 dwellings instead of 30 due to the diversity among residential dwellings. Several facts about the load profile characteristics of

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“…The range of techniques is broad. Artificial neural networks (ANNs) have been used (Leal et al 2009, Hong-Rui et al 2007, Ni and Yu 2009, Lee et al 2011 to estimate technical losses in a distribution system. In other research (Lasso et al 2006), a technique named "stratified sampling" was used to estimate technical losses in the secondary energy distribution network.…”

Section: Introductionmentioning

confidence: 99%

Influence of Clandestine Connections on Energy Loss Evaluation in Electrical Distribution Networks

Donadel

Sousa

Varejão

2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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In the literature, there are several methodologies to estimate technical losses in electrical distribution networks. The range of techniques is broad, ranging from basic techniques (based on loss factor, for example) to sophisticated ones (based on artificial intelligence). These methodologies are important, because the costs of technical losses represent a huge part of the total operation costs of distribution network operators (DNOs). However, the presence of clandestine connections, common in developing countries, was not considered in the methodologies encountered in the literature. Clandestine connections occur when a consumer has made his/her connection without DNO permission. In these cases, the amount of energy consumed by a clandestine "consumer" is a nontechnical loss (and, therefore, should be correctly computed as nonbilled energy). Therefore, a new methodology is proposed to consider the presence of clandestine connections in energy loss estimation in distribution systems.

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Distribution Transformer Losses Evaluation: A New Analytical Methodology and Artificial Neural Network Approach

Cited by 32 publications

References 12 publications

A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids

A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids

A Novel Approach for Residential Neighborhoods’ Electricity Demand in Iraq Distribution Power Grids

Influence of Clandestine Connections on Energy Loss Evaluation in Electrical Distribution Networks

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