Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition

Karandaev, A. S.; Yachikov, I. M.; Radionov, Andrey A.; Liubimov, Ivan V.; Druzhinin, N.N.; Khramshina, Ekaterina A.

doi:10.3390/en15103519

Cited by 13 publications

(5 citation statements)

References 70 publications

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“…Obviously, the RUL can also be defined by other informative parameters. These first include the PD characteristics (intensity and apparent charge) controlled by the TDMS system [2,61]. The destructive process development rate is also considered, which is among the technique's advantage.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…Implementing the developed technique for assessing the furnace transformer RUL based on the results of online monitoring of partial discharges, provided in [2,64], is also relevant. This will expand the list of coordinates used in forecasting the RUL and facilitate implementing techniques for the multi-parameter diagnostics of the transformer state.…”

Section: Discussionmentioning

confidence: 99%

“…Ref. [2] develops a technique for assessing the diagnostic sensitivity of PD parameters to technical condition changes.…”

Section: Online Monitoring Of the Furnace Transformer Healthmentioning

confidence: 99%

“…The ETTsNKV-40,000/110 Transformer Parameters [2,61] The modular TDMS (transformer diagnostics monitor special) diagnostic system performed the health monitoring. The system specifications, purpose, and functionality are considered in [58,84].…”

Section: Specifics Of the System For Online Monitoring Of The Furnace...mentioning

confidence: 99%

“…Ref. [2] stresses the relevance of the 'smart transformer' concept in the paradigm of digital development of industry and energy. Its implementation is urgent for the transformers of arc furnaces and secondary steel processing units (furnace transformers).…”

Section: Introductionmentioning

confidence: 99%

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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: Discussion Of the Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Ref. [2] develops a technique for assessing the diagnostic sensitivity of PD parameters to technical condition changes.…”

Section: Online Monitoring Of the Furnace Transformer Healthmentioning

confidence: 99%

Section: Specifics Of the System For Online Monitoring Of The Furnace...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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Construction and optimization of data model based on knowledge feature in transmission line equipment state recognition

Chen,

Zhang,

Zhang

et al. 2024

International Journal of Low-Carbon Technologies

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Transmission lines are a vital component of the power system, and their operational status directly affects the safety and stability of the entire grid. This study utilizes the association rules algorithm to conduct an in-depth analysis of the historical data of the lines' status, achieving the mining of knowledge features. Based on this, a key state quantity system is constructed, and a transmission line operational status recognition model is established, combined with the random forest algorithm, and further optimized. Through the selection of association rules and confidence, 8 components and 81 indicator state parameters that can reflect the operational status of transmission lines are determined, quantified into a key parameter system. For the improved model, the accuracy of the evaluation of the four state levels is more than 90%. Through comparative experiments with classical models on the national grid data set, the applicability and rationality of the proposed classification algorithm are verified. The model based on the random forest algorithm is capable of efficiently handling large-scale data sets and high-dimensional features, and resisting overfitting in the classification task of transmission line equipment status recognition, while also effectively managing imbalanced data sets. With an increase in the amount of collected data and improvement in data quality, future research can continue to focus on optimizing the model to improve recognition accuracy.

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A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

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Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field.

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Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition

Cited by 13 publications

References 70 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

Construction and optimization of data model based on knowledge feature in transmission line equipment state recognition

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

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