A Hybrid Optimization Technique for Fault Classification in Power Transformer Using Dissolved Gas Analysis

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.

This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained that BFGS has better performance for the diagnosis of fault in transformer as compared to LM.

Bayesian network offers a powerful map framework that can process probabilities inference. It can be used in inference and express of uncertainty knowledge. This paper introduce a new electrical equipment fault diagnosis method based on Bayesian network (BN). For example, power transformer is very important in power system as a electrical equipment. But, it's very difficult to diagnose the fault exactly because power transformer's complexity configuration. Now, dissolved gas analysis (DGA) is the most effective and convenient method in transformer fault diagnosis. However, the codes of DGA is too absolute, so this paper advances a new transformer fault diagnosis method based on Bayesian network (BN). This method introduces BN method into transformer fault diagnosis and presents a new idea of finding out transformer faults rapidly and exactly. Then, the transformer fault diagnosis model based on Bayesian network and DGA is constructed. Finally, the application examples in the fault diagnosis of transformer are given which shows that this method is effective.

This paper presents a novel fault diagnosis model for oil-immersed power transformers based on dissolved gas analysis. The model is rooted on the theories of rough set and support vector machine. A fitness function based on attribute dependence is developed to identify fault features to improve classification accuracy of transformer fault samples by using Genetic Algorithm. To get improved classification performance, grid search, genetic algorithm and particle swarm optimization are applied to search parameters of support vector machine. Compared with modified Rogers and back propagation neural network, the superiority of the established model is verified.

• Using SMOTE-ENN to balance data and enhance transformer DGA fault diagnosis. • Applying RobustScaler to preprocess data, improving accuracy against outliers. • Proposing a DNN with RobustScaler, SMOTE-ENN, and an optimal optimizer. • Integrating the deep learning model with IoT for fault visualization. • IoT-based monitoring analyzes gas data for faster transformer fault detection. One of the most vital components of power systems is power transformers, which provide an essential link in the chain of other devices used to supply electricity to consumers. According to the literature, the Duval pentagon method (DPM) is one of the most accurate and reliable dissolved gas analysis (DGA) interpretation methodologies. However, implementing large amounts of data in DPM is still challenging and has several limitations. To overcome these limitations, this paper introduces a robust deep neural network (DNN) method for precise DGA monitoring. Another merit is the proposal of synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) preprocessing to eliminate noise from the imbalanced dataset, resulting in cleaner merged DGA samples. Furthermore, a unique RobustScaler technique is employed to maintain high performance against uncertain data noise. To visualize transformer faults remotely and enhance the acceleration of decision-making regarding the transformer status, this paper utilizes an industrial Internet of Things (IoT) platform. Specifically, the designed deep learning model is hybridized with an IoT platform to analyze the transferred DPM dataset of the gases concentration and send the classification results using the IoT gateway to the cloud for visualizing the detected fault on the IoT dashboard. The empirical results display that the proposed method outperforms several state-of-the-art approaches. The proposed method achieves satisfaction in diagnosing faults for the assessment dataset, with an accuracy of 98.19 %. Besides, the obtained results illustrate the effectiveness of the proposed model against uncertainty noise up to 20 % with a superior prediction diagnosis of the transformer faults.

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.

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.

This article focuses on classifying a variety of faults in power transformers with high precision, using an Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) system designed for fault diagnosis. The process begins with the identification of five distinct gases dissolved in oil, serving as diagnostic features. Minimal output encoding is then used to construct multiple binary support vector machine (SVM), facilitating a multi-class classification of transformer faults. While other studies often combine traditional Particle Swarm Optimization (PSO) algorithms with SVMs, our approach employs an enhanced PSO algorithm. This improved algorithm allows for the optimization of inertia and learning factors, values of which adapt based on iteration counts. The PSO is then leveraged on the optimization of the penalty factor and the radial basis function of SVM, thereby improving its classification performance. Simulation results indicate that our IPSO-SVM methodology achieves 90% and 92% accuracy in training and testing sets, respectively. This method significantly enhances the accurateness oftransformer malfunction diagnosis, exhibiting superior diagnostic precision compared to traditional power transformer malfunction diagnosis methods.

Power transformers are one of the most crucial as well as costly equipment in power grids. Nowadays, a set of factors like the growing load, expanding power consumption, and market liberation affect on the transformer reliability and operational lifetime. At present, numerous approaches are on hand to diagnosis and evaluate their operational conditions. Dissolved gas analysis (DGA) in the insulation oil is an efficient diagnostic scheme which can offer an essential source for transformer failure diagnosis. In this paper, five existing approaches of utilizing dissolved gas data to identify fault kind in power transformers oil are employed. The considered methods are: key gas technique, IEC 60599 technique, Doernenburg ratio scheme, Duval triangle framework, as well as Rogers ratio scheme. These fault diagnosis methods are used to interpret and validate the dissolved gas analysis (DGA) results for four case studies. The experiments are performed on four 15.75 kV/400 kV, 3-Phase, 250 MVA power transformers in a thermal power plant. The obtained DGA results reveal that all considered DGA methods have effectively detected the fault type for each considered case except Duval triangle method which has failed to discover the fault type for two cases.

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.

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.

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.

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.

DGA is the best technique which is used to identify the incipient fault of the power transformer. The recently most advanced technology used for online fault diagnosis of the transformer is dissolved gas analysis (DGA) and it is technically acceptable. There are some methods on DGA based which are used to solve this analysis are Key-Gas method, Dornenburg method, International Electro technical Commission (IEC) ratio codes, Duval triangle and Rogers's ratio method. Also, the most well-known strategies are key gas and Roger's ratio. Artificial Neural Network (ANN), Fuzzy logic (FL), Neuro-Fuzzy, Support vector machine etc are the techniques which are used to solve dissolved gas analysis. Taking the concentrations of key gases(CO, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> & CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) of different fault conditions for the same sample unit, we can be identified the fault by using various techniques. Because of the assortment of gas data and operational natures, sometimes it is difficult to identify fault by the traditional method and hence fails to diagnose. And because of that it is important to build up a preventive maintenance technique for detection of an incipient fault of transformer automatically through dissolved gas analysis in transformer insulating oil. In this paper, we focus on two methods, Roger's ratio and IEC for the fault diagnosis of transformer insulating oil with Fuzzy interface system (FIS). This diagnosis is totally based on the ratio of concentration gases generated from single or numerous faults however with same predominant nature. At the point when gases generated from two or more faults then the relation between different gases becomes too complicated and unable to match the predefined ratio codes and fails to diagnose. Hence, to avoid such kind of issues we briefly focused on fuzzy logic and carried out diagnosis using Roger's ratio and IEC ratio codes.

Multi-filter semi-supervised transformer model for fault diagnosis