Novel prediction‐reliability based graphical DGA technique using multi‐layer perceptron network & gas ratio combination algorithm

Dissolved gas analysis (DGA) is a well-known method for diagnosis of incipient faults in power transformers. Some traditional criteria of the dissolved gas analysis are published in standards and technical reports which are still in use in many electrical utilities around the world. This paper describes a hybrid algorithm using neural-fuzzy system for incipient fault detection in power transformers. In order to reach a higher degree of reliability with respect to each technique individually, the proposed method is based on the combined use of six standardized criteria. Six neural networks are trained based on randomly generated data considering the individual standards and the results are mixed to give the better results. The proposed method is tested using realistic data. The experiments results showed that the proposed algorithm is accurate, reliable and robust in identifying incipient faults in power transformers.

The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger’s ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in “RAWAT” NTDC grid station, Islamabad, and “UCH-II” power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer.

Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational variables. A novel extension method, which is based on the matter-element model and extended relation functions, is presented for fault diagnosis of power transformers. Thus, incipient faults in power transformers can be identified directly by the degree of relation. The application of this new method to some transformers has given promising results.

The power transformer is a valuable asset of the electrical system. A damage causes the interruption of electrical service and high repair costs for companies. Therefore, the detection of faults in incipient conditions is essential. In the recent literature Dissolved Gas Analysis (DGA) is the best accepted technique to the diagnosis of incipient faults in power transformers. This paper presents an approach to diagnosis fault by DGA using deep neural network, the drawbacks of the number of training patterns (amount of data) is satisfactory solved with using the Mean Shift algorithm. Likewise, the input and output parameters are conveniently selected, the input parameters being the gas relations established in the IEC 60599 standard acting in parallel with a new ratio of proposed gas (Rnew=C2H2 / C2H6) and a binary output. The proposed approach achieved an accuracy of 100%, both in the training and validation process as well.

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

In an electric power system, a power transformer is one of the most critical equipment and cannot be separated from possible abnormal conditions due to fault. Dissolved Gas Analysis (DGA) is a reliable technique for detecting the presence of a fault condition that just occurred in oil immersed transformer. Basically DGA is a process to calculate the levels or values of hydrocarbon gases that are formed due to abnormalities. The gas inside the transformer can function as a marker for various types of faults. In this paper, for DGA testing and evaluation of the type of fault in the power transformer using the interpretation of IEEE std C57.104 and IEC 60599. The method used for DGA testing according to the IEEE interpretation is total dissolved combustible gas (TDCG), key gas, doernenburg ratio, and roger ratio. While the method used for DGA testing at IEC 60599 is the duval triangle, the basic gas ratio and CO2/CO ratio. From the results of DGA tests that have been carried out, all of these methods will be used to ascertain the type of fault that occurs in the power transformer.

Dissolved gas analysis (DGA) is a prevailing methodology being widely used to detect incipient faults in power transformers. Although various methods have been developed to interpret DGA results, they may sometimes fail to diagnose precisely, especially when solid insulation deterioration is involved. This study presents a time series correlation technique, in which the sampled data of dissolved gases in the transformer oil are considered as a time series and the series correlation scheme in statistics is adopted to explore and manipulate the fault information. Both the constant and variable characteristic parameters are initially chosen based on analysis in terms of frequency histograms. With quantitative correlation analysis between the constant and variable characteristic parameters, a new criterion for solid insulation diagnosis is thereby proposed, which can be used to diagnose whether solid insulation deterioration is involved in a transformer fault, as well as further distinguish the electrical faults from the thermal ones. According to the proposed technique, explorative tests regarding two power transformers have shown promising results. With regard to the 91 fault samples collected from China Power Grid, the diagnosis accuracies for electrical and thermal faults were 86.5 and 77.7%, respectively, whereas it was 61.5% for the prevailing CO 2 /CO criterion.

Missing values are a common occurrence in a number of real world databases, and statistical methods have been developed to deal with this problem, referred to as missing data imputation. In the detection and prediction of incipient faults in power transformers using dissolved gas analysis (DGA), the problem of missing values is significant and has resulted in inconclusive decision-making. This study proposes an efficient nonparametric iterative imputation method named FINNIM, which comprises of three components: 1) the imputation ordering; 2) the imputation estimator; and 3) the iterative imputation. The relationship between gases and faults, and the percentage of missing values in an instance are used as a basis for the imputation ordering; whereas the plausible values for the missing values are estimated from k-nearest neighbor instances in the imputation estimator, and the iterative imputation allows complete and incomplete instances in a DGA dataset to be utilized iteratively for imputing all the missing values. Experimental results on both artificially inserted and actual missing values found in a few DGA datasets demonstrate that the proposed method outperforms the existing methods in imputation accuracy, classification performance, and convergence criteria at different missing percentages.

This paper presents a comparative study of neural network (NN) efficiency for the detection of incipient faults in power transformers. The NN was trained according to five diagnosis criteria commonly used for dissolved gas analysis (DGA) in transformer insulating oil. These criteria are Doemenburg, modified Rogers, Rogers, IEC, and CSUS. Once trained, the NN was tested by using a new set of DGA results. Finally, NN diagnosis results were compared with those obtained by inspection and analysis. The study shows that the NN rate of successful diagnosis is dependant on the criterion under consideration, with values in the range of 87-100%.

Dissolved gas analysis (DGA) approach is extensively applied to detect incipient faults of power transformers. This paper presents a novel DGA method for power transformer incipient fault diagnosis based on integrated adaptive neuro fuzzy inference system (ANFIS) and Dempster-Shafer Theory (DST). Four out of seven common conventional methods which are studied and compared for better consistency and accuracy, are used to develop new ANFIS based fault diagnosis models. To promote fault diagnostic performance further and make fault decision process more reliable and reasonable, an improved DST is introduced to integrate outputs of each ANFIS based model, and to provide comprehensive and convincing fault diagnosis results. The fault diagnosis capability of the proposed method is validated by a reported fault dataset and 10-fold cross validation experiment. The performance of the proposed method is compared with conventional approaches and ANFIS based models which demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy and consistency.

Power transformers’ condition monitoring using neural modeling and the local statistical approach to fault diagnosis

The dissolved gas-in-oil analysis (DGA) is a prevailing methodology being widely used to detect incipient faults in power transformers. However various methods have been developed to interpret DGA results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI) based methodology is varied with variation of input variable. Thus, selection of input variable to an AI model is major research area. In this paper, principle component analysis using RapidMiner software is applied to IEC TC10 and related datasets to identify most relevant input variables for incipient fault classification. Thereafter, extreme learning machine (ELM) is implemented to classify the incipient faults of power transformer and its performance is compared with fuzzy-logic and artificial neural network. The compared results shows that ELM provides better diagnosis results with proposed input variables.

The most common fault diagnosis method for oil-immersed power transformers is dissolved gas analysis (DGA). Doernenburg ratios, Rogers ratios, IEC (International Electrotechnical Commission) ratios, and Duval's triangle are conventional DGA techniques for insulating oil in power transformers. In this study, Scikit-learn known as a popular open-source free machine learning tool for Python programming language has been used to develop different machine learning (ML) classifiers to effectively detect defects in oil-immersed power transformers. These classifiers are Decision Trees, Support Vector Machines, Gaussian Naive Bayes, k-Nearest Neighbours, Random Forests, and Multi-Layer Perceptron. The input vector of each classifier has been formed by Doernenburg ratios, Rogers ratios, IEC ratios, and CSUS (California State University Sacramento) method. After these classifiers are completely trained, unseen DGA data sets are then used to evaluate their performances. Based on a statistical analysis, the study can indicate the most effective type of the input vector and ML classifier for precisely detecting faults in power transformers.

PurposePower transformers are vital components of an electrical network. A defective transformer can cause instability and blackouts in parts of the network. An accurate classification of different transformer faults results in a relatively accurate fault diagnosis and timely corrective actions. It is possible to increase productivity and reduce costs by using fault detection of power transformers through the analysis of gases dissolved in oil. The proposed technique is a suitable tool to help the utilities and engineers in charge of preventive maintenance by reducing the costs of different fault diagnosis tests for power transformers.Design/methodology/approachIn this paper, the IEC 60599 standard along with clustering and classification methods are used to classify power transformer’s fault types. K-means and Fuzzy C-means clustering methods are used for clustering, and the support vector machine (SVM) method is used for classification of different types of faults in ‎power ‎transformers. The performance of K-means and SVM methods is improved by using the Grasshopper Optimization Algorithm (GOA). The efficiency of the proposed methods is evaluated using real field data of power transformers. The purpose of this study is to propose hybrid methods including K-means-GOA clustering and SVM-GOA classification for accurate fault diagnosis. These methods have been used for the first time in fault diagnosis determination of power transformers through gas analysis. The Silhouette criteria is used in this paper to compare the efficiency of different clustering methods.FindingsSimulation results of the paper are based on the gas chromatography data related to 266 different real power transformers. They show the high accuracy and high-performance speed of intelligent clustering and classification methods compared to conventional ones. This analysis would be helpful in performing the required maintenance check and plan for repairs.Originality/valueThe applicability and efficiency of the proposed hybrid K-means-GOA and SVM-GOA models are verified for transformer fault detection using the experimental diverse data set including 266 set of real field test parameters of power transformers.

Dissolved Gas Analysis (DGA) is one of the most widely used monitoring technology for oil immersed power transformers. The Duval Pentagon method is a graphic interpretation method of DGA. Besides the six typical faults usually interpreted by DGA, this method also can be used alone to bring auxiliary information about the internal faults of power transformers. Based on Duval Pentagon method, this paper presents a detailed software implementation process of automatic fault identification for power transformers. Moreover, this paper also offers clear threshold conditions which indicate the abnormality of DGA results before applying the Duval Pentagon method to improve the practical use of this DGA interpretation method. Thus, the program developed by this paper can provide a commercial system of DGA interpretation.