Assessment of High Voltage Power Transformer Aging Condition Based on Health Index Value Considering Its Apparent and Actual Age

Tamma, Winanda Riga; Prasojo, Rahman Azis; Suwarno, Suwarno

doi:10.1109/icitee49829.2020.9271778

Cited by 10 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method divided the population into four groups, taking into account diagnostic indicators and the critical operations of individual transformers. Consideration of the transformer's apparent and actual age and their potential influence on the assessment of its aging condition was presented in [18]. The authors calculated the health index for 130 units and used them to model the curve of the heath index decrease in the entire population.…”

Section: Introductionmentioning

confidence: 99%

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

Kunicki,

Borucki,

Fulneček

2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Kunicki,

Borucki,

Fulneček

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The transformer status is available on the display after evaluation by ANN in the OCMS system. However, the research conducted by Wang Jian et al,in [27] is more advanced as the realtime data is collected from online sensors installed in the transformer. The real-time data from various sensors and devices such as vibration sensor, partial discharge sensor, insulation oil and grounding current of the iron core provide the input for the calculation of HI.…”

Section: Introductionmentioning

confidence: 99%

Development of Power Transformer Health Index Assessment Using Feedforward Neural Network

Aziz

Talib²,

Abidin

et al. 2023

ARASET

View full text Add to dashboard Cite

The role of a power transformer is to convert the electrical power level and send it to the consumer, making it an essential component of a power system. In addition, transformer asset management is essential for monitoring the functioning of transformers in the system to prevent failure and anticipating the health state of transformers, using a technique known as the health index (HI). However, the calculation and computation to determine the transformer HI based on a scoring and ranking technique is complex and required expert validation. Therefore, this paper presents a transformer HI prediction using a feedforward neural network (FFNN) to improve the existing complex scoring and ranking technique. Levenberg–Marquardt (LM), Bayesian Regularized (BR), and Scaled Conjugate Gradient (SCG) are the FFNN training techniques presented in this study to forecast the transformer HI. To validate the techniques, the HI values generated by different FFNN techniques were compared to the scoring and ranking system. Then, the performance of the proposed ANN was evaluated using the correlation coefficient and mean square error (MSE). As a result, the transformer HI was successfully predicted by employing three FFNN techniques, namely the LM, BR, and SCG techniques, which were able to determine whether the transformer's condition is very good, good, fair, or poor. In conclusion, the ANN suggested in this study has also been validated with the ranking and scoring approach, which provides high similarity score in comparison to the transformer health index.

show abstract

“…Several techniques (Romero-Quete et al, 2017;Scatiggio et al, 2018;Vasquez & Jayaweera, 2020;Zeinoddini-Meymand & Vahidi, 2016) have been developed to assess the condition of PT. Health Index (HI) is one of these techniques, it uses as input parameters different criteria such as ageing of oil-impregnated paper (Liang et al, 2017), apparent age (Tamma et al, 2020), or the condition of other PT subsystems (e.g., load tap changer, bushings, cooling system) (Jian et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Even though HI tends to use almost the same input framework, employed algorithms take different approaches to process input data. For instance, main algorithms include systems based on Weighted Sum Models (WSM) (Jahromi et al, 2009;Jian et al, 2020;Naderian et al, 2008;Tamma et al, 2020), systems based on the Markov chain model (Yahaya et al, 2017), fuzzy logic systems (Cerón et al, 2015;Mharakurwa & Goboza, 2019;Rosero-Z et al, 2018), and systems based on artificial neural networks (Islam et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Health Index for Power Transformer Condition Assessment: A Comparison of Three Different Techniques.

Zaldivar

Romero

2022

JART

View full text Add to dashboard Cite

In practice, the condition state of Power Transformers (PT) is quantified by using Health Index (HI). This paperanalyzes and compares three different state-of-the-art algorithms to compute HI. The first one uses a Weighted SumModel (WSM), the second is based on a Fuzzy Inference System (FIS), and the third combines both techniques, i.e.,WSM and FIS. These three approaches are tested in a PT fleet composed of 30 units. Results show that eachapproach produces different HI values for the same PTs. Therefore, decision making regarding the PT fleet willdepend on the selected approach for HI calculation. This work proposes merging the knowledge involved in eachanalyzed approach by using a K-means clustering technique to overcome this drawback. This solution could help theasset manager to make adequate decisions regarding the maintenance scheduling of PT when there is uncertaintyabout the appropriate approach to be selected.

show abstract

Assessment of High Voltage Power Transformer Aging Condition Based on Health Index Value Considering Its Apparent and Actual Age

Cited by 10 publications

References 10 publications

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

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

Development of Power Transformer Health Index Assessment Using Feedforward Neural Network

Health Index for Power Transformer Condition Assessment: A Comparison of Three Different Techniques.

Contact Info

Product

Resources

About