Development and evaluation of a new DGA diagnostic method based on thermodynamics fundamentals

Cruz, V. G. M.; Costa, André L.H.; Paredes, Márcio L.L.

doi:10.1109/tdei.2015.7076789

Cited by 31 publications

(13 citation statements)

References 13 publications

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“…During normal operation of the transformer, due to aging and cracking of insulation oil and solid insulation, a very small amount of gas will be decomposed, mainly including hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), acetylene (C 2 H 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), etc. [2][3][4]. When a fault or abnormality occurs inside the transformer, the contents of some components in these gases will increase rapidly.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…During the long operation of the transformer, due to equipment aging, discharge fault, thermal fault, and other reasons, a small amount of gas will be produced in the insulation oil, and the content of various components of dissolved gas and the proportion of components in the oil are closely related to the operation condition of the transformer. Through dissolved gas analysis (DGA) [1][2][3][4][5], some latent faults in the transformer and their development degree can be found. DGA is an internationally recognized and effective method of diagnosing early transformer faults that has been proven in practice by many fault diagnoses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

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Section: Parameter Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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“…Therefore, the dissolved gas analysis (DGA) is an important method to find the transformer defects and latent faults. It is highly feasible and accurate to predict transformer running states and make future fault classifications based on the trend of each historical gas concentration and the ratio between gas concentrations [2][3][4]. Current methods include oil gas ratio analysis [5][6][7], SVM [8,9] and artificial…”

Section: Introductionmentioning

confidence: 99%

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Lin

Yan

et al. 2018

Energies

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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

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“…The DGA investigative methods like the established IEC and IEEE standard methods, are theoretically based on Halstead simplified thermodynamic and compositional representations for the thermal decomposition of transformer oil and on experimental data . Though DGA has been widely accepted method for the past few decades, it has certain drawbacks like it necessities carrier gas, regular calibration, and lack of expert personnel . To overcome these shortcomings with DGA, in this work, optical spectroscopy methods such as ultraviolet‐visible (UV‐Visible) spectrometer and Fourier transform infrared (FTIR) spectroscopy and Nuclear magnetic resonance (NMR) techniques have been proposed and implemented .…”

Section: Introductionmentioning

confidence: 99%

“…10 Though DGA has been widely accepted method for the past few decades, it has certain drawbacks like it necessities carrier gas, regular calibration, and lack of expert personnel. 11 To overcome these shortcomings with DGA, in this work, optical spectroscopy methods such as ultraviolet-visible (UV-Visible) spectrometer and Fourier transform infrared (FTIR) spectroscopy and Nuclear magnetic resonance (NMR) techniques have been proposed and implemented. [12][13][14] The test has been conducted on different transformer oil samples, which have undergone thermal stress, using these methods.…”

Section: Introductionmentioning

confidence: 99%

Analysis of transformer oil degradation due to thermal stress using optical spectroscopic techniques

Kalathiripi

Karmakar

2017

Int Trans Electr Energ Syst

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Summary The power transformers are continuously under the impact of electrical and thermal stresses. These stresses are primarily responsible for the occurring incipient faults such as partial discharge, arcing, and pyrolysis. The incipient faults, if not taken care at the earliest, cause the insulating transformer oil to degrade and transformer failure over a period of time. Therefore, monitoring and diagnosing the power transformer have become an inevitable task for its effective functioning. In this proposed work, thermal analysis on different transformer oil samples has been performed by using optical methods such as ultraviolet‐visible spectroscopy, Fourier transform infrared spectroscopy and Nuclear magnetic resonance (NMR) spectroscopy. The obtained results with UV‐visible spectroscopy method exhibit proportional degradation of the oil samples with temperature rise. The Fourier transform infrared method identifies the dissolved gases (ie, CH4, C2H6) released during the decomposition of hydro carbon present in the transformer oil. Finally, NMR spectroscopy method also confirmed that it has the potential to monitor the decomposed oil by investigating the region under an NMR signal which is proportional to the number of absorbing protons. The employed photo‐spectroscopic methods can be best alternative next to so called dissolved gas analysis method.

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Development and evaluation of a new DGA diagnostic method based on thermodynamics fundamentals

Cited by 31 publications

References 13 publications

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Analysis of transformer oil degradation due to thermal stress using optical spectroscopic techniques

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