Intelligent Machine Learning Techniques for Condition Assessment of Power Transformers

Leauprasert, Kunanya; Suwanasri, Thanapong; Suwanasri, Cattareeya; Poonnoy, Nitchamon

doi:10.1109/icpei49860.2020.9431460

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The collected data must fulfil the requirements to identify power transformer health condition. that identifies the ranking method rules (Leauprasert et al, 2020). These linguistic terms are simplified expressions of the transformer condition.…”

Section: Health Index Formulation: Research Methodologymentioning

confidence: 99%

Health index assessment for power transformer strategic asset management in electrical utilities

Romaimi,

Baglee,

Dixon

2023

IJSEAM

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The use of Health Index (HI) has helped in driving electrical utilities decision making for strategic investment planning including operation, and maintenance (O&M) programmes. This approach increases the ability of the business to implement robust investment decision objectives that makes physical assets safe, productive, efficient, and cost effective. Asset Management (AM) framework is defined by ISO55001 to ensure electrical network operators are delivering best quality of services by operating their network at high performance, low cost while managing unexpected risks.

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Section: Health Index Formulation: Research Methodologymentioning

confidence: 99%

Health index assessment for power transformer strategic asset management in electrical utilities

Romaimi,

Baglee,

Dixon

2023

IJSEAM

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“…There is also a study by the expert to compare the results with numerous types of ML such as Linear, Ridge, Lasso, Random Forest, Support Vector, Deep Neural Network (Regression). In [18], the performance is compared between the different types of ML where the input for each ML is from offline data and insulation of result. Sarajcev et al, use a different type of technique, namely Bayesian method, which is different from scoring and ranking [19].…”

Section: Introductionmentioning

confidence: 99%

Development of Power Transformer Health Index Assessment Using Feedforward Neural Network

Aziz

Talib²,

Abidin

et al. 2023

ARASET

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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.

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“…In addition, online learning/monitoring can be implemented in the present model, which can be used for health management. Regression models are used to evaluate health index (%HI) in terms of percentage for condition assessment, which is discussed in Leauprasert (2020), but they lack comprehensible interpretation of their parameters and explanatory power. Also, the HI values may lie outside the intended range.…”

Section: Introductionmentioning

confidence: 99%

A novel method for life estimation of power transformers using fuzzy logic systems: An intelligent predictive maintenance approach

et al. 2022

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Power transformers are a fundamental component of the modern power distribution network. The fault-free operation of step-up and step-down transformers is of prime importance to the continuous supply of electrical energy to the consumers. To ensure such efficient operation, power distribution companies carry out routine maintenance of distribution transformers through preplanned schedules. The efficacy of such maintenance depends on a proper understanding of the transformer and its components and efficient prediction of faults in these components. There are several components whose condition can be studied to predict transformer failures and therefore the overall health of a transformer. These include transformer windings, insulations, transformer oil, core insulations, and ferromagnetic cores. This work develops a new, simplified fuzzy logic–based method to predict the health of a transformer by taking into account the state of several individual components. Case studies are used to demonstrate the efficacy of the developed method.

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Intelligent Machine Learning Techniques for Condition Assessment of Power Transformers

Cited by 8 publications

References 8 publications

Health index assessment for power transformer strategic asset management in electrical utilities

Health index assessment for power transformer strategic asset management in electrical utilities

Development of Power Transformer Health Index Assessment Using Feedforward Neural Network

A novel method for life estimation of power transformers using fuzzy logic systems: An intelligent predictive maintenance approach

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