Proposed three ratios technique for the interpretation of mineral oil transformers based dissolved gas analysis

Gouda, Osama E.; El-Hoshy, Salah H.; E.L.-Tamaly, Hassan H.

doi:10.1049/iet-gtd.2017.1927

Cited by 51 publications

(31 citation statements)

References 24 publications

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“…The evaluation of these ratios and their percentages are performed empirically from a large number of cases of faults visually inspected in transformers over the last 30 years as reported by Egyptian Electricity Holding Company (EEHC) [22], IEC TC 10 [21] and related databases surveyed from actual incident cases of the mineral oil‐filled transformers (383 cases) [16, 23–29].…”

Section: New Proposed Triangle Diagnosis Technique Using Dissolved Gamentioning

confidence: 99%

“…These boundaries were deduced empirically based on empirical evidence of previous knowledge as well as practical 383 DGA samples databases. These data are collected from different transformers of different rating and different life span reported by Egyptian Electricity Holding Company, IEC TC 10 and related databases [21–29]. These DGA databases were tested on the proposed triangle technique to confirm or re‐adjust slightly these boundaries of each fault zone with maximum accuracy whenever possible.…”

Section: Introductionmentioning

confidence: 99%

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Condition assessment of power transformers based on dissolved gas analysis

Gouda

El-Hoshy

E.L.-Tamaly

2019

IET Generation, Transmission & Distribution

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Section: New Proposed Triangle Diagnosis Technique Using Dissolved Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Condition assessment of power transformers based on dissolved gas analysis

Gouda

El-Hoshy

E.L.-Tamaly

2019

IET Generation, Transmission & Distribution

Self Cite

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“…(a) When z = 5, The number of synthetic fault samples generated from each minority class can be calculated according to the difficulty coefficient as shown in Equation (6). Then, the oversampling weights of fault samples of the minority class are normalized to obtain the difficulty coefficient ∂ i,j , as shown in Equation (5).…”

Section: Adasyn's Principle and Implementationmentioning

confidence: 99%

“…Traditional DGA interpretation methods such as IEC Ratio, Rogers Ratio, and Doernenburg Ratio are simple and easy to implement and have been widely used in engineering practice. Yet, there are still problems, such as the absolute diagnostic boundaries and missing codes [5,6]. In addition, although traditional intelligent methods have achieved certain effects, they still have certain limitations on popularization and application due to solution to the problem of imbalanced data is oversampling, which has become the simplest and most reliable method to solve the imbalance and deficiency of a specific dataset [35].…”

Section: Introductionmentioning

confidence: 99%

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

Duan

2020

Applied Sciences

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With the development of Industry 4.0, as a pivotal part of the power system, large-capacity power transformers are requiring fault diagnostic methods with higher intelligence, accuracy and anti-interference ability. Considering the powerful capability for extracting non-linear features and the sensitivity differences to features of deep learning methods, this paper proposes a deep parallel diagnostic method for transformer dissolved gas analysis (DGA). In view of the insufficient and imbalanced dataset of transformers, adaptive synthetic oversampling (ADASYN) was implemented to augment the fault dataset. Then, the newly constructed dataset was normalized and input into the LSTM-based diagnostic framework. Then, the dataset was converted into images as the input of the CNN-based diagnostic framework. At the same time, the problem of still insufficient data was compensated by the introduction of transfer learning technology. Finally, the diagnostic models were trained and tested respectively, and the Dempster-Shafer (DS) evidence theory was introduced to fuse the diagnostic confidence matrices of the two models to achieve deep parallel diagnosis. The results of the proposed deep parallel diagnostic method show that without complex feature extraction, the diagnostic accuracy rate could reach 96.9%. Even when the dataset was superimposed with 3% random noises, the rate only decreased by 0.62%.Appl. Sci. 2020, 10, 1329 2 of 18 their shortcomings in learning ability, processing efficiency, and feature extraction ability. For example, the learning ability of the fuzzy methods are not satisfying [7,8]. Neural networks (NN) tend to fall into local optimal solutions [9,10]. The K-nearest neighbor (KNN) method is inefficient in high-dimensional space [11]. The support vector machine (SVM) is essentially a two-classifier, which makes it more troublesome to deal with multi-classification problems [12,13]. There are also some relevant diagnosis studies on combining traditional ratio methods and intelligent methods, which are combinations of previous researches, and have achieved certain effects [14][15][16]. However, the features extracted by the ratio methods are still limited. And the anti-interference ability of the combined models need further testing. In recent years, due to the strong ability of deep learning to extract complex non-linear features, some papers have tried to introduce it into the field of transformer fault diagnosis. And the fault diagnostic accuracy of deep learning methods has been significantly improved compared to traditional machine learning diagnostic models'. The authors in [17] proposed a DBN-based fault diagnostic method that used the uncoded ratio of DGA data as the model input. Compared with the traditional methods', the accuracy was significantly improved. The authors in [18] introduced a method for identifying and locating winding faults based on CNN and transformer impulse tests. And the results showed that the method was effective. The authors in [19] studied the internal fault diagnosis of...

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“…Since cellulose degradation is specific in its gas generation, it can be dealt separately by diagnostic methods. Many conventional methods are proposed to identify the transformer faults based on DGA such as key gas method [1], ratio methods [5]- [7], and graphical methods [8]- [12]. Some of these methods were targeting diagnosis of main fault types in transformer oil and others were developed to include cellulose degradation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

2020

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The transformer fault diagnosis based on dissolved gas analysis is greatly affected by the uncertainties existing in measured data during oil sampling, handling and storage. This work aims to develop an efficient code matrix based on dissolved gas percentages for accurate fault diagnosis considering measurement uncertainties. Fuzzy system is utilized to produce the rules that map the limits of gas ratios for different fault types. Each gas percentage range is divided into three regions represented by three fuzzy memberships. The fuzzy system is then developed to relate the gas percentages to the fault type. The membership limits are then optimized by using heuristic algorithms in order to maximize the accuracy. Hybrid Grey Wolf Optimization (HGWO) algorithm is utilized to produce the diagnostic code matrix. The proposed code limits the impact of measurement uncertainties on the output fault diagnosis. Different levels of measurement uncertainties up to 20% are considered to validate the effectiveness of the new code in improving the diagnostic accuracy. The accuracies during the training and testing processes attained 97.45% and 95.45%, respectively, with the maximum uncertainty level of 20%. Moreover, randomly selected case studies representing various fault types are investigated using the proposed method. For each case study, various levels of uncertainties are imposed on the original data. The proposed method proved its easiness towards diagnosing transformer faults and robustness against measurement uncertainties.

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Proposed three ratios technique for the interpretation of mineral oil transformers based dissolved gas analysis

Cited by 51 publications

References 24 publications

Condition assessment of power transformers based on dissolved gas analysis

Condition assessment of power transformers based on dissolved gas analysis

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

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