Power transformer incipient faults diagnosis using Dissolved Gas Analysis and Rough Set

2012 ◽  
Author(s):  
Noor Akhmad Setiawan ◽  
Sarjiya ◽  
Zenith Adhiarga
Keyword(s):  
Rough Set ◽  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Incipient Faults ◽  
Faults Diagnosis

Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis

2016 ◽  
Vol 1 (1) ◽  
pp. 10 ◽  
Author(s):  
Osama E. Gouda ◽  
Saber M. Saleh ◽  
Salah Hamdy El-hoshy
Keyword(s):  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Insulation System ◽  
Matlab Program ◽  
Incipient Faults ◽  
Faults Diagnosis ◽  
Incipient Fault ◽  
Paper Insulation ◽  
Incipient Fault Diagnosis

Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized  into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

scholarly journals Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis

2015 ◽  
Vol 16 (3) ◽  
pp. 409
Author(s):  
Osama E. Gouda ◽  
Saber M. Saleh ◽  
Salah Hamdy EL-Hoshy
Keyword(s):  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Insulation System ◽  
Matlab Program ◽  
Incipient Faults ◽  
Faults Diagnosis ◽  
Incipient Fault ◽  
Paper Insulation ◽  
Incipient Fault Diagnosis

Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

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

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1206
Author(s):  
Yousuf D. Almoallem ◽  
Ibrahim B. M. Taha ◽  
Mohamed I. Mosaad ◽  
Lara Nahma ◽  
Ahmed Abu-Siada
Keyword(s):  
Logistic Regression ◽  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Incipient Faults ◽  
Regression Approach ◽  
Industry Practice ◽  
Interpretation Process ◽  
Logistic Regression Algorithm

Dissolved gas analysis (DGA) is one of the regular routine tests accepted by worldwide utilities to detect power transformer incipient faults. While the DGA measurement has fully matured since the development of offline and online sensors, interpretation of the DGA results still calls for advanced approaches to automate and standardize the process. Current industry practice relies on various interpretation techniques that are reported to be inconsistent and, in some cases, unreliable. This paper presents a new application for the advanced logistic regression algorithm to improve the reliability of the DGA interpretation process. In this regard, regularized logistic regression is used to improve the accuracy of the DGA interpretation process. Results reveal the superior features of the proposed logistic regression approach over the conventional and artificial intelligence techniques presented in the literature.

scholarly journals Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2223 ◽  
Author(s):  
Sayed A. Ward ◽  
Adel El-Faraskoury ◽  
Mohamed Badawi ◽  
Shimaa A. Ibrahim ◽  
Karar Mahmoud ◽  
...  
Keyword(s):  
Power Transformer ◽  
Partial Discharge ◽  
Electrical Power ◽  
Diagnostic Techniques ◽  
Gas Analysis ◽  
Economic Losses ◽  
Dissolved Gas ◽  
Electricity Transmission ◽  
Dissolved Gas Analysis ◽  
Precise Interpretation

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

scholarly journals Fuzzy Algorithm for Power Transformer Diagnostics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde
Keyword(s):  
Gas Chromatography ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Inference System ◽  
Incipient Faults ◽  
The World

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

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