A comparison of liquid-filled and dry-type transformer technologies

Nunn, Tommy

doi:10.1109/citcon.2000.848515

Cited by 15 publications

(8 citation statements)

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“…It requires modifications to the components of the transformers to protect both the transformer and the on-load tap changer from loss and to minimize the damage due to short-circuits. In another study [75], the flammability characteristics of liquid-filled and dry-type transformer technologies were compared and investigated. [76] presented the results of two tests that were conducted in 2002 and 2004 under the worstcase conditions to study transformer explosion and fire prevention.…”

Section: Grid Asset Managementmentioning

confidence: 99%

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

et al. 2021

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Wildfires pose a significant challenge to the natural and the built environments, as well as the safety and economic wellbeing of the communities residing in wildfire-prone areas. The electric power grid is specifically among the built environments most affected by, and contributing to, wildfires. In this paper, we propose a three lines of defense (3LD) framework for wildfire risk management in electric power infrastructure and review the literature from this lens. An overview of the physics and phenomenology of the wildfires as it relates to power grids is presented, and the logic for the proposed 3LD framework is discussed. The reviewed literature based on the 3LD theme includes the most relevant and emerging research work on wildfire prevention as the first line of defense, wildfire mitigation and proactive response as the second line of defense, and wildfire recovery preparedness as the third line of defense. This study reveals that while the state of the art, to a large extent, stands comprehensive in various aspects of power system resilience and wildfire risk management, there is a gap in the literature in addressing this emerging risk in a holistic, interdisciplinary approach.

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Section: Grid Asset Managementmentioning

confidence: 99%

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

et al. 2021

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“…Cast-resin transformers, in contrast to traditional oil-filled transformers, use epoxy resin to cast their core and windings, eliminating the need for insulating oil. This leads to reduced maintenance requirements due to the absence of oil replacement and offers the advantage of lower fire risk, making them suitable for indoor installations [1], [2]. Additionally, they are less susceptible to moisture ingress and have a high resistance to pollutants, making them environmentally safer [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of PRPD Pattern in Cast- Resin Transformers Using CNN and Implementation of Explainable AI (XAI) With Grad-CAM

Kim,

Jung,

Hwang

et al. 2024

IEEE Access

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Cast-resin transformers are affected by deterioration due to manufacturing defects and continuous load. Studying PD, which is capable of detecting defects or degradation in advance, is important. With the rapid advancement of AI technologies, research on PD classification using CNN models is being actively conducted. However, due to the black box problem, it is impossible to explain the reasoning behind the learning outcomes. Therefore, relying solely on predictive outcomes of learning for PD classification raises issues of reliability. Recent studies in various fields are progressing with the application of XAI to address the black box issue of CNNs, aiming to identify the criteria used for making predictions. However, research on applying XAI in AI-based PD classification is currently insufficient. Therefore, further study on the implementation of XAI is necessary. In this paper, an excellent CNN model was applied to image classification for PD classification of cast-resin transformers, and the grad-cam model was used for XAI. This approach proposes a method for humans to comprehend the rationale behind the learning outcomes. The data used for training consists of artificial defects under laboratory conditions and noise measured in castresin transformers via UHF sensors. PD and noise classification due to defects was performed, and the reasons for successful and failed results were analyzed through XAI. Consequently, it was observed that the application of XAI to CNN models leads to the construction of a more reliable model. INDEX TERMSCast-resin transformer, PD, pattern classification, convolution neural network (CNN), explainable artificial intelligence (XAI), gradient weighted class activation mapping (Grad-CAM)

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“…Its use is restricted for economic reasons since dry‐type transformers are more expensive than oil‐immersed transformers. For this reason, this alternative is not available for large power transformers [5].…”

Section: Introductionmentioning

confidence: 99%

Dissolved gas analysis differences between natural esters and mineral oils used in power transformers: a review

Meira

Ruschetti

Álvarez

et al. 2019

IET Generation, Transmission & Distribution

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A comparison of liquid-filled and dry-type transformer technologies

Cited by 15 publications

References 0 publications

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

Classification of PRPD Pattern in Cast- Resin Transformers Using CNN and Implementation of Explainable AI (XAI) With Grad-CAM

Dissolved gas analysis differences between natural esters and mineral oils used in power transformers: a review

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