Dissolved Gas Analysis Research Articles

Construction of Transformer Fault Diagnosis and Prediction Model Based on Deep Learning

The current intelligent diagnosis and prediction methods for transformer faults are prone to low diagnostic accuracy and insufficient trend prediction ability when the fault categories are imbalanced. Therefore, a fault diagnosis and prediction model for transformers was constructed using a deep learning framework. The fault diagnosis model was constructed using a focus loss stack sparse noise reduction autoencoder on the deep learning framework. The prediction model was constructed by fusing long and short term memory networks on the basis of tree structure Parzen optimization, and the two models were validated. The results obtained through validation of the diagnostic model indicate that, when the actual hidden layer is set to 3 and the quantity of neurons is 58, the model accuracy during training and testing reaches 97.5% and 92.5% respectively. After adding 0.001 times the Gaussian white noise, the model accuracy was significantly lifted, so this study set the Gaussian noise coefficient to 0.001. In the comparison with baseline models, the actual classification ability of the research model samples is strong, significantly improving the fault diagnosis ability. In the validation of the prediction model, the three error index values of the research model in the single prediction step of CH4 concentration were 0.0699, 0.0540, and 0.8481%, respectively, and proved to be were lower than in the case of the baseline model. The three error values in the two-step prediction are 0.0194, 0.0161, and 0.6535%, which are also lower than in case of the baseline model. Overall, the diagnosis and prediction model proposed in this paper can provide real-time future numerical predictions of dissolved gas analysis and monitoring data in transformer oil. Furthermore, the research outilnes the future development trend of monitoring and measurement through application of tensor flow deep learning framework in transformer fault diagnosis. The attained prediction results are innovative, and could well complete the purpose of actual transformer fault diagnosis and early warning.

A feature selection and ensemble learning based methodology for transformer fault diagnosis

Dissolved gas analysis (DGA) data are generally used to diagnose a transformer fault. However, the measurement errors in DGA data are inevitable and will affect the accuracy and reliability of the diagnosis results. Nevertheless, so far, only a few efforts have been devoted to addressing this issue. To provide an accurate and stable transformer fault diagnosis system, a feature selection and ensemble learning based methodology is proposed. Firstly, an Overlapping Information Feature Selection (OIFS) method is proposed to select efficient features from the given feature set for classifiers. Secondly, an Intelligent Voting Ensemble Learning (IVEL) method is proposed to generate a diagnosis model based on the feature combination from the OIFS method. The reported test results show that the OIFS method outperforms existing methods in 9 out of 10 tested classifiers. Additionally, the IVEL outperforms three popular ensemble learning methods, including random forest (RF), gradient boosting decision tree (GBDT), and LightGBM, in terms of both accuracy and robustness performances. Finally, the proposed methodology (OIFS-IVEL) is applied to diagnose the transformer faults in the IEC TC 10 database, achieving a 100% accuracy in recognizing fault types and a 92.6 % accuracy in evaluating fault severity.

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis.

Application of improved support vector machine model in fault diagnosis and prediction of power transformers

AbstractPower transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K‐means algorithm into the model and constructs a high‐precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

Partial Discharge Localization through k-NN and SVM

Power transformers are essential for the distribution and transmission of electricity, but they are prone to degradation due to faults early on. Partial Discharge (PD) is the most significant pointer of insulation breakdown in high-voltage apparatus. Dissolved Gas Analysis (DGA) is a commonly used technique for detecting and diagnosing PD. However, DGA data often contain missing values, which can significantly affect the accuracy of PD diagnosis. To mitigate the issues of missing values, this paper proposes using the k-Nearest Neighbors (kNN) technique to impute the missing values in the dataset. Further, it combines kNN with a Support Vector Machine (SVM) to detect the possibility of a PD source in the high-voltage apparatus. The approach was evaluated on a real-world DGA dataset and achieved high classification performance and discriminatory power for distinguishing between PD and non-PD instances. The effectiveness of the missing value imputation technique was evaluated, and the proposed approach demonstrated improved accuracy and precision compared to methods without imputation. The proposed approach offers a current solution for PD analysis in power transformers using DGA data with missing values.

Adsorption properties of noble-metal (Ag, Rh, or Au)-doped CeO2(1 1 0) to CO: A DFT + U study

Dissolved gas analysis (DGA) is an effective method for fault detection of oil-immersed transformers, and CO is one of the characteristic gases in insulation oil. Therefore, based on density functional theory, the adsorption properties of three noble metals (Ag, Rh, or Au)-doped CeO2 (110) to CO were studied in this paper. The adsorption energy, charge transfer, density of states (DOS), and deformation charge density (DCD) were calculated to analyze the adsorption behavior of CO on the material surface. The frontier molecular orbital, energy band structure, and desorption time were also calculated to evaluate the potential of the three doping systems as CO sensors. The results show that the adsorption properties of Ag-CeO2, Rh-CeO2, and Au-CeO2 to CO are significantly higher than that of pristine CeO2, with adsorption energies are −54.61, −85.78, −205.57 kJ/mol, respectively. However, Rh-CeO2 exhibits superior sensitivity and satisfactory recovery time towards CO compared with Ag-CeO2 and Au-CeO2. Rh-CeO2 also shows excellent selectivity to CO, which is a promising candidate material for CO sensors. The research conducted in this work is intended to contribute to dissolved gas analysis (DGA).

Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

Conventional Dissolved Gases Analysis in Power Transformers: Review

Transformers insulated with mineral oil tend to form gases, which might be caused by system faults or extended use. Based on an evaluation of the main failure analysis techniques using combustible gases, this study reviewed the conventional techniques for Dissolved Gas Analysis (DGA), present in the norms IEC 60599 and IEEE Std C57.104, and their failure analysis tendency. Furthermore, to illustrate distinct technique performances and failures, the performance of the following techniques was analyzed based on the IEC TC10 database: Dornenburg, Duval Triangle, Duval Pentagon, IEC ratio method, Key Gas, and Rogers. The objective of this work was to present relevant information to support students and professionals who work in failure analysis and/or assist in the development of new tools in the DGA field.

Multiplexed fiber-optic photoacoustic sensors for simultaneous detection of multi-point gases

A multiplexing scheme of fiber-optic photoacoustic (PA) gas sensors is demonstrated for simultaneous detection of multi-point gases. By sharing the PA demodulation device, the average cost of single point measurement can be significantly reduced. The passive fiber-optic PA gas sensing probe is integrated by a PA tube and a Fabry-Perot (F-P) interferometric (FPI) cantilever. The laser excited the PA signals is simultaneously incident into two sensing probes. The superimposed F-P interference spectrum containing multi-point gas concentration information is received by a high-speed spectrometer. For synchronous measurement of multi-point gases, a white-light interferometry (WLI) based frequency division multiplexing (FDM) spectral demodulation method is exploited. A multi-point acetylene gas remote monitoring system is established by two PA gas sensing probes with 3 km fiber cable. The experimental results show that the sensing probes have achieved the detection limit of sub-ppm. The crosstalk between the two sensors is characterized by the gas concentrations at the two points, indicating a crosstalk of about − 35 dB. This multiplexing scheme of fiber-optic PA sensing probes has the merits of remote monitoring, low crosstalk, high sensitivity, intrinsic safety and low cost. It can be applied for coal spontaneous combustion monitoring, dissolved gas analysis and gas micro-leakage monitoring.

DFT study on the adsorption and sensing properties of dissolved gases (H2, CO and CH4) in transformer oil on PdO-doped In2O3 (1 1 0) surfaces

Density functional theory (DFT) was used to explore the adsorption and sensing behaviors of H2, CO, and CH4 on PdO-doped In2O3 (110) surfaces. Adsorption energy, adsorption distance, transferred charge, band structure, density of states (DOS), deformation charge density (DCD), and desorption time are calculated and analyzed for each gas. The results show that PdO-In2O3 can be used as a low power consumption gas-sensitive sensor for H2 detection as well as a CO solid adsorbent. The findings of this study give theoretical support for improving In2O3 gas sensors in dissolved gas analysis (DGA).

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively.

Hybrid fuzzy and gated recurrent network based artificial intelligence approach for fault diagnosis and prognosis of transformers using dissolved gas analysis

In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset.

Transformer fault classification for diagnosis based on DGA and deep belief network

Power transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dissolved gas analysis (DGA) technology is used to identify fault types through dissolved gas in transformer oil, and its reliability has been proved. In order to analyze these gases and diagnose transformer fault types with the results, many methods have been developed, such as Key Gas Method, Method of Duval, IEC 60599 Method, Method of Dornenburg and Method of Rogers, etc. In some cases, the accuracy of these traditional methods is reduced and cannot be applied for diagnosis, since they have fixed input features and is not flexible for input combination. In order to achieve the propose of solving this defect, this paper introduces a deep belief network-based DGA method to diagnose the faults and states of power transformers with customized input features. For this work, six fault classifications were considered based on the nine characteristics extracted from the gases precipitated from the insulating oil of power transformers. The deep belief network was tested using oil samples collected from power transformers. Experiments have shown that the performance of the network has obtained relatively good accuracy results.

Dissolved Gas Analysis of Transformer: An Approach Based on ML and MCDM

In the electric power business, power transformers are one of the most common and expensive components. The conventional diagnostic tool for understanding insulation incipient failures is the dissolved gas analysis (DGA) of transformers. Nonetheless, interpreting DGA fault gases remains a significant difficulty for engineers. Along with offline methods, a number of computational approaches have been created for DGA fault classification. Still there exist significant hurdles in applying those methods for DGA fault classification. To establish an effective fault classification system, very diversified and massive DGA data sets of in-service transformers were collected from various utilities for this study. The dataset consists of 3147 instances with four classes: No fault, Thermal fault, Low energy discharge and high energy discharge. Models were built using various machine learning approaches like Quadratic Discriminant Analysis (QDA), Gradient Boosting (GB), Extra Trees (ET), Light Gradient Boosting Machine(LGBM), Random Forest (RF), K Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), Ada Boost (AB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Ridge Classifier, and SVM - Linear Kernel. The proposed work adopts Analytic Hierarchy Process (AHP) technique for the estimation of weights of the criteria. Based on the generated weights, the performance of the various classifiers is assessed and ranked using Multi-Objective Optimization based on the Ratio Analysis (MOORA) approach. QDA is selected as the best classifier model by the proposed technique.

A Comprehensive and Practical Method for Transformer Fault Analysis With Historical Data Trend Using Fuzzy Logic

Dissolved Gas Analysis (DGA) of transformer oil is an important tool to identify incipient faults of transformer. The challenge with the existing research on DGA is that only a single sample is used for the analysis which might lead to inaccurate results. In this paper a comprehensive three stages fuzzy logic approach is proposed to emulate best practices in the industry for transformer health analysis using the current as well as historical data from previous samples. In the first stage, a fuzzy logic is developed for an oil sampling pre-check for better lab acceptance rate which helps to save time and cost. In the second stage fuzzy, the latest IEEE Std C57.104 – 2019 is used to determine the status of transformer health by considering the gas formation rate from past samples and observing the trend. Finally, the third stage fuzzy is used to identify the transformer fault type and determine the corresponding down time. The proposed approach is tested on real data from the industry and the results demonstrate accurate transformer health identification with additional advantages of saving time and cost.

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

Transformer Dissolved Gas Analysis for Highly-Imbalanced Dataset Using Multiclass Sequential Ensembled ELM

Dissolved gas analysis (DGA) has been a critical technique for transformer diagnosis. DGA is a typical multi-class imbalance problem where most of the samples correspond to healthy state transformers or units. Though numerous works have been carried out on this issue, the diagnosis accuracy is still unsatisfactory when the status of health and multiple faults are considered. Multi-class imbalance problem is also a tough task from the view of algorithm development. Previous works underestimate this issue in some sakes such as lacking investigation of the highly imbalanced dataset, and lacking consideration of health data. This paper presents a comprehensive study of the mentioned issues. A novel algorithm called sequential ensembled extreme learning machine (SE-ELM) is proposed. SE-ELM adopts a novel multi-class undersampling strategy followed by a sequentially updated ensemble, which achieves both accuracy and efficiency. The proposed method is validated on both an open IEC dataset and a highly imbalanced private dataset. The comparison with popular algorithms proves the efficiency of SE-ELM.

Dissolved Gas Analysis of Transformer Oil Based on Bayesian Regularized BP Neural Network

The hidden fault diagnosis of the transformer will play an important role in the future smart grid, as the safe operation of the transformer is an important guarantee of power system stability. The attention value method which is being applied to actual substations is introduced, according to a real case that took place in 2010, after detailed experiments, the method proposed in this paper has been verified, that is, a model based on Bayesian regularized BP neural network has been successfully established. The hidden fault in the transformer can be found earlier by DGA based on Bayesian regularized BP network model.

Enhancement of Adsorption Performance of Gases in Oil on a Cr3-Modified SnS2 Monolayer Based on the First Principles.

Dissolved gas analysis (DGA) is the most commonly used transformer fault diagnosis technology at present. In this paper, according to the method of first principles of density function theory (DFT), the gas-sensitive mechanisms of four oil-soluble characteristic gases (H2, CO, C2H2, C2H4) on intrinsic SnS2 and Cr3-SnS2 were studied. The adsorption energy and electron transfer were calculated, and the density of states, energy bands, and recovery times were analyzed. It was concluded that H2 and C2H4 were physisorbed on the Cr3-SnS2 monolayer, while CO and C2H2 were chemisorbed. It is believed that the Cr3-SnS2 material can be used in gas sensing for CO and C2H2. Cr3-SnS2 is expected to serve as a gas detector for the detection of CO with both a good response and reusability. Therefore, Cr3-SnS2 has very promising applications in the evaluation of the operation of oil-immersed transformers. This study will provide some help and inspiration for the development of the Cr3-SnS2 monolayer in gas-sensitive materials.

Programming code using the Duval method for diagnosing power transformers and direct way

A transformer is a piece of equipment of great value in a public or private power substation in that it allows power to be supplied to the production processes. Thus, as it is used, the equipment is exposed to different environmental conditions such as level of contamination, salinity. , height, humidity, among others, as well as energy demand and quality, causing different types of electrical and thermal stress inside. That is why the objective of this study is to implement a programming code using the Duval method for the diagnosis of power transformers. For this, programming based on the supervised learning algorithm is used as a methodology to process the gas concentration levels contained in the dielectric oil obtained from the analysis of dissolved gases and for its interpretation the Duval method is used, which is presented in IEEE C57.104™-2019 standard. The designed code individually processes the dissolved gas analysis reports as well as large quantities of reports from the same transformer or from different ones, allowing the comparison of historical data of the equipment or equipment and the evaluation of its status according to the electrical stress to which it is subjected.