Application of Logistic Regression Algorithm in the Interpretation of Dissolved Gas Analysis for Power Transformers

Almoallem, Yousuf D.; Taha, Ibrahim B. M.; Mosaad, Mohamed I.; Nahma, Lara; Abu-Siada, A.

doi:10.3390/electronics10101206

Cited by 15 publications

(3 citation statements)

References 25 publications

(27 reference statements)

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“…[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%

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

et al. 2023

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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.

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Section: Online Monitoring Of the Furnace Transformer Healthmentioning

confidence: 99%

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

et al. 2023

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“…In addition to neural networks, SVM and clustering, researchers have also applied other AI techniques in fault diagnosis, such as logistic regression (Almoallem et al, 2021) and association rules (Lee et al, 2013), with good results.…”

Section: Other Techniquesmentioning

confidence: 99%

Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

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Transformer is one of the important components of the power system, capable of transmitting and distributing the electricity generated by renewable energy sources. Dissolved Gas Analysis (DGA) is one of the effective techniques to diagnose early faults in oil-immersed transformers. It correlates the concentration and ratio of dissolved gases with transformer faults. Researchers have proposed many methods for fault diagnosis, such as double ratio method, Rogers method, Duval triangle method, etc., but all of them have some problems. Based on the strong data mining capability and good robustness of AI techniques, many researchers introduced AI techniques to mine the features of DGA data. According to the characteristics and scale of DGA data, researchers select appropriate AI techniques or make appropriate improvements to AI techniques to improve diagnostic performance. This paper presents a systematic review of the literature on the application of artificial intelligence techniques for DGA-based diagnosis and for solving intractable problems in early transformer fault diagnosis, which include neural networks, clustering, support vector machines, etc. In addition to reviewing the applications of these intelligent techniques, the diagnostic thinking proposed in this literature, such as the introduction of temporal parameters for comprehensive analysis of DGA data and the extraction of optimal features for DGA data, is also reviewed. Finally, this paper summarizes and prospects the artificial intelligence techniques applied by researchers in transformer fault diagnosis.

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“…Carbon monoxide (CO) and carbon dioxide (CO 2 ) exhibit unstable gas concentrations during faults and are typically excluded when selecting characteristic gases. Currently, hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2) are commonly chosen as characteristic variables for fault diagnosis [6,7]. However, the analysis of dissolved gas in oil is significantly influenced by gas component thresholds, and deviations in threshold settings can hinder the timely detection of potential transformer faults.…”

Section: Introductionmentioning

confidence: 99%

Identifying dissolved gas characteristic parameters in transformer oil based on trapezoidal model

Yi,

Jie,

Zhuo

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Application of Logistic Regression Algorithm in the Interpretation of Dissolved Gas Analysis for Power Transformers

Cited by 15 publications

References 25 publications

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

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

Fault diagnosis of transformer using artificial intelligence: A review

Identifying dissolved gas characteristic parameters in transformer oil based on trapezoidal model

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