Stating Diagnosis of Current State of Electric Furnace Transformer on the Basis of Analysis of Partial Discharges

Karandaeva, Olga I.; Yakimov, Ivan A.; Filimonova, A.A.; Gartlib, Ekaterina A.; Yachikov, I. M.

doi:10.3390/machines7040077

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…Similar methods of feature extraction are performed by other groups of scientists. For example, Time features and Frequency features were determined according to reference [47,48] and [15]. In [15], the authors classify AE signals processed with the use of wavelet transformations and using random forest and KNN algorithms to locate corrosion in devices applied in the chemical industry.…”

Section: Data Analysis and Classification Methodsmentioning

confidence: 99%

Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification

Wotzka

Cichoń

2020

Sensors

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The principal objective of this study is to improve the diagnostics of power transformers, which are the key element of supplying electricity to consumers. On Load Tap Changer (OLTC), which is the object of research, the results of which are presented in this article, is one of the most important elements of these devices. The applied diagnostic method is the acoustic emission (AE) method, which has the main advantage over others, that it is considered as a non-destructive testing method. At present, there are many measuring devices and sensors used in the AE method, there are also some international standards, according to which, measurements should be performed. In the presented work, AE signals were measured in laboratory conditions with various OLTC defects being simulated. Five types of sensors were used for the measurement. The recorded signals were analyzed in the time and frequency domain and using discrete wavelet transformation. Based on the results obtained, sets of indicators were determined, which were used as features for an autonomous classification of the type of defect. Several types of learning algorithms from the group of supervised machine learning were considered in the research. The performance of individual classifiers was determined by several quality evaluation measures. As a result of the analyses, the type and characteristics of the most optimal algorithm to be used in the process of classification of the OLTC fault type were indicated, depending on the type of sensor with which AE signals were recorded.

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Section: Data Analysis and Classification Methodsmentioning

confidence: 99%

Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification

Wotzka

Cichoń

2020

Sensors

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“…Refs. [59][60][61] provide an overview of known systems for the online monitoring of the furnace transformer's health. Ref.…”

Section: Online Monitoring Of the Furnace Transformer Healthmentioning

confidence: 99%

“…Ref. [61] develops a technique and considers an example of diagnosing the health of a ladle furnace unit transformer (studied herein) based on the statistical processing of continuously measured partial discharges (PD). Ref.…”

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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“…One of the directions of obtaining a forecast of the power equipment state is the development of probabilistic models based on measurements of the load and temperature of the equipment [3]. A general trend in the development of condition monitoring systems is the increasingly widespread use and analysis of equipment diagnostic data, which can be obtained online [4,5]. One of the most promising methods is the acoustic method for diagnosing the state [6,7], the method for determining partial discharges [8].…”

Section: State Of the Issuementioning

confidence: 99%

Use of sensor networking technology to build a power transformer monitoring system

et al. 2020

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The paper discusses the technology of designing a monitoring system for power transformers and power supply devices, based on the use of sensor networks, describes the creation of a Bayesian network to predict the probability of failure. Conditions for achieving the maximum diagnostic efficiency with the required accuracy are formulated. A diagnostic system is proposed that allows assessing the influence of each parameter on the accuracy of predicting failures. Using the method of expert assessments, the possibility to calculate the probabilities of failures with a lack of information is shown.

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Stating Diagnosis of Current State of Electric Furnace Transformer on the Basis of Analysis of Partial Discharges

Cited by 11 publications

References 23 publications

Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification

Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification

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

Use of sensor networking technology to build a power transformer monitoring system

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