Applied Complex Diagnostics and Monitoring of Special Power Transformers

Ivanov, Georgi; Spasova, Anelia; Mateev, Valentin; Marinova, Iliana

doi:10.3390/en16052142

Cited by 7 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is calculated by taking the sum of absolute differences between predicted and actual values and dividing it by the number of observations. The formula for the MAE is given in Equation (2).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…To ensure the stability and reliability of the power system, it is crucial to monitor, maintain, diagnose, and upgrade transformers regularly. This helps prevent costly accidents and damage, extend transformer life, increase grid reliability, and reduce maintenance costs [2][3][4]. Accurately evaluating the condition of power transformers is essential for creating and sustaining a highly reliable power system [5,6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stacked Ensemble Regression Model for Prediction of Furan

Faraji,

Shooshtari,

El-Hag

2023

Energies

View full text Add to dashboard Cite

Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first scenario, a round-robin cross-validation method was used, with 75% of the data for training and the remaining 25% for testing. The second scenario involved training the model entirely on Utility A and testing it on Utility B. In the third scenario, the datasets were merged, and round-robin cross-validation was applied, similar to the first scenario. The findings reveal the effectiveness of machine learning algorithms in predicting furan concentrations, and particularly the stacked generalized ensemble method, offering a non-intrusive and cost-effective alternative to traditional testing methods. The results could significantly impact the maintenance strategies of power and distribution transformers, particularly in regions where furan testing facilities are not readily available.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stacked Ensemble Regression Model for Prediction of Furan

Faraji,

Shooshtari,

El-Hag

2023

Energies

View full text Add to dashboard Cite

show abstract

“…Previous studies have shown the relevance of developing techniques for defining the transformer resource in the smart energy paradigm [18,19]. Ref.…”

Section: The Relevance Of Improving the Rul Forecasting Techniquesmentioning

confidence: 99%

“…Currently, two basic approaches are used to estimate and forecast the equipment state-based on the health index (HI) and the RUL. It is believed that HI is a standard tool for the comprehensive assessment of insulation aging [33,34] and load [35] and the analysis of the performance, risk, and reliability of transformers [19,36,37]. Ref.…”

Section: The Relevance Of Improving the Rul Forecasting Techniquesmentioning

confidence: 99%

“…[66] notes that "online monitoring of dissolved gases and moisture in power transformers provides real-time data allowing for early warning of developing transformer emergencies". Along with fault diagnostics, this type of control is aimed at forecasting health [19,[67][68][69]. Herein, the online DGA results are applied in developing a technique for forecasting the furnace transformer RUL.…”

Section: Online Monitoring Of the Furnace Transformer Healthmentioning

confidence: 99%

See 1 more Smart Citation

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

et al. 2023

View full text Add to dashboard Cite

Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

show abstract