Review and Implementation of Transformer Health Index Methods in line with the Development of a Condition Assessment Tool

Kittan, Stefan; Kornhuber, Stefan; Kastel, Peter; Nitsche, Gunnar; Valtin, Gerd; Weise, Michael

doi:10.1109/diagnostika.2018.8526034

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…punching) x Ai x 7.55x10 -3 kg …………………. (8) Weight of corners = 6.18 x (width of max. punching) x Ai x 7.55x10 -3 kg………………….…”

Section: Kvamentioning

confidence: 99%

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Expert system for improving and controlling insulation system of service transformers using fuzzy logic controller

David Chigozie Madumere,

Evans C. Ashigwuike,

N.B. Gafai

2024

Global J. Eng. Technol. Adv.

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Implementation of the smart transformer concept is critical for the deployment of IOT-based smart grids. Top manufacturers of power electrics develop and adopt online monitoring systems. Such systems become part of high-voltage grid and unit transformers. However, furnace transformers are a broad category that this change does not affect yet. At the same time, adoption of diagnostic systems for furnace transformers is relevant because they are a heavy-duty application with no redundancy. Creating any such system requires a well-founded mathematical analysis of the facility’s condition, carefully selected diagnostic parameters, and set points thereof, which serve as the condition categories. The goal hereof was to create an expert system to detect insulation breach and its expansion as well as to evaluate the risk it poses to the system; the core mechanism is mathematical processing of trends in partial discharge (PD). This research work examined the acidity of distribution transformer oil in service through laboratory tests using a case study of installed distribution transformers at Abuja metropolis network comprising ten Feeders. The result shows the minimum breakdown voltage of 40KV/mm and maximum breakdown voltage of 58KV/mm. The maximum transformer oil Acidity is 0.1143mgKOH/g and the minimum transformer oil Acidity level is 0.0954mgKOH/g. The transformer efficiency without fuzzy is 82.89% and transformer efficiency with fuzzy controller is 98.10%.

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“…punching) x Ai x 7.55x10 -3 kg …………………. (8) Weight of corners = 6.18 x (width of max. punching) x Ai x 7.55x10 -3 kg………………….…”

Section: Kvamentioning

confidence: 99%

“…The reason is that they are critical for uninterrupted electricity delivery to consumers [7]. Researcher [8] states that power transformers are significant and valuable units, and that this is why condition monitoring is crucial in their case. Should a critical transformer fail in a transmission grid, energy security might be jeopardized.…”

Section: Introductionmentioning

confidence: 99%

Expert system for improving and controlling insulation system of service transformers using fuzzy logic controller

David Chigozie Madumere,

Evans C. Ashigwuike,

N.B. Gafai

2024

Global J. Eng. Technol. Adv.

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“…Nevertheless, establishing an effective TAM program is challenging as it requires a balance between cost, risk, and performance, especially in managing an ageing transformer asset [6]. In the context of the TAM program, the HI facilitates the evaluation of the transformer's health condition, which has been accepted as a standard technique [8].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Influence of Non-Routine and Derived Features in the Development of Early Detection Model for Transformer Health Index Classification

Azlan Mohmad,

Mohd Hatta Mohammed Ariff,

Mohd Ibrahim Shapiai

et al. 2024

ARASET

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Establishing an effective HI model is challenging because it involves balancing cost, risk, and performance. The currently developed Reduced Features Model (RFM) for the transformer Health Index (HI) prediction may lead to late prediction. The RFM utilised non-routine input features to achieve a high-accuracy model where data availability is the primary concern. Hence, the common goal of Transformer Asset Management (TAM) in achieving acceptable availability and reliability of the transformer may not be achieved. In this paper, the primary objective is to investigate the performance of the HI model by considering routine test features as a baseline for developing the Early Detection Model (EDM). The development of EDM is significant, as the model shall provide a sustainable solution to the utility and plant owners in establishing their TAM strategies. Hence, this paper's case studies include performance investigation using routine, non-routine, and derived features from the routine test. Support Vector Machine (SVM) was used for the prediction modelling, and the model's performance was validated based on a 5-fold cross-validation technique to avoid biases. As a result, it was found that the average accuracy performance of 88.4% was obtained by considering only routine test features during the model validation process. However, complementing the routine test with other features, which were non-routine and derived features, increased the average performance accuracy model to 95.3%. Hence, further development of EDM is feasible and crucial for sustainable TAM solutions.

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“…Further discussion and comparison of various contemporary approaches to TAI can be found in [13,27,28].…”

Section: Introductionmentioning

confidence: 99%

Exploitation Perspective Index as a Support of the Management of the Transformer Fleet

Kunicki,

Borucki,

Fulneček

2023

Sensors

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This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters.

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Review and Implementation of Transformer Health Index Methods in line with the Development of a Condition Assessment Tool

Cited by 9 publications

References 14 publications

Expert system for improving and controlling insulation system of service transformers using fuzzy logic controller

Expert system for improving and controlling insulation system of service transformers using fuzzy logic controller

Investigation of the Influence of Non-Routine and Derived Features in the Development of Early Detection Model for Transformer Health Index Classification

Exploitation Perspective Index as a Support of the Management of the Transformer Fleet

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