The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Q. H. Wu - One of the best experts on this subject based on the ideXlab platform.

  • power transformer fault classification based on Dissolved Gas analysis by implementing bootstrap and genetic programming
    Systems Man and Cybernetics, 2009
    Co-Authors: Almas Shintemirov, W Tang, Q. H. Wu
    Abstract:

    This paper presents an intelligent fault classification approach to power transformer Dissolved Gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected Gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor ( KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.

  • Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers
    IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2009
    Co-Authors: Z. Yang, W H Tang, A. Shintemirov, Q. H. Wu
    Abstract:

    This paper presents a novel association rule mining (ARM)-based Dissolved Gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.

  • A Probabilistic Classifier for Transformer Dissolved Gas Analysis With a Particle Swarm Optimizer
    IEEE Transactions on Power Delivery, 2008
    Co-Authors: W H Tang, Z J Richardson, Q. H. Wu, J. Y. Goulermas, J. Fitch
    Abstract:

    This paper presents a Parzen-Windows (PW)-based classifier for transformer fault diagnosis, which is able to interpret transformer Dissolved Gas analysis (DGA) with a probabilistic scheme. A global optimizer, particle swarm optimizer (PSO), is employed to optimize the parameters of PW to improve fault classification accuracies. First, the essential concept of PW-based classification using PSO is introduced. This probabilistic classification approach is then extended from a simple PW method to classifying fault types on the evidence of various Gas ratios. The proposed approach not only allows an intuitive interpretation of the transformer diagnosis, but also provides a DGA reviewer with quantified confidence to support decision making. It can be seen from the results that both the diagnosis accuracy and computational efficiency are improved compared with a number of fault classification techniques.

  • Transformer Dissolved Gas Analysis Using Least Square Support Vector Machine and Bootstrap
    2007 Chinese Control Conference, 2007
    Co-Authors: Tang Wenhu, Shintemirov Almas, Q. H. Wu
    Abstract:

    This paper presents a least square support vector machine (LS-SVM) approach to Dissolved Gas analysis (DGA) problems for power transformers. Two methods are employed to improve the diagnosis accuracy for DGA analysis. First, bootstrap preprocessing is utilised to equalise the sample numbers for different fault types. Then, the preprocessed samples are inputted to a classier for fault classification. For comparison purposes, four classifiers are utilised, i.e. artificial neural network (ANN), k-nearest neighbour (KNN), simple SVM and LS-SVM. The classification accuracy of LS-SVM is then compared with the ones of ANN, KNN and a simple SVM. The results indicate that the LS-SVM approach can significantly improve the diagnosis accuracies for transformer fault classification.

Syed Islam - One of the best experts on this subject based on the ideXlab platform.

  • impact of load ramping on power transformer Dissolved Gas analysis
    IEEE Access, 2019
    Co-Authors: Huize Cui, A Abusiada, Liuqing Yang, Hao Wang, Syed Islam
    Abstract:

    Dissolved Gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a Gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the Gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary Gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.

  • a new fuzzy logic approach for consistent interpretation of Dissolved Gas in oil analysis
    IEEE Transactions on Dielectrics and Electrical Insulation, 2013
    Co-Authors: A Abusiada, S Hmood, Syed Islam
    Abstract:

    Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced Gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.

  • a new approach to identify power transformer criticality and asset management decision based on Dissolved Gas in oil analysis
    IEEE Transactions on Dielectrics and Electrical Insulation, 2012
    Co-Authors: A Abusiada, Syed Islam
    Abstract:

    Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.

  • fuzzy logic approach to identify transformer criticality using Dissolved Gas analysis
    Power and Energy Society General Meeting, 2010
    Co-Authors: A Abusiada, M Arshad, Syed Islam
    Abstract:

    Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation. DGA interpretation is yet a challenge in the power transformer condition monitoring research area. This paper introduces a novel fuzzy logic approach to help in standardizing DGA interpretation techniques and to identify transformer critical ranking using DGA. DGA has been performed on several oil samples that have been collected from different transformers. Traditional DGA interpretation techniques are used to analyze the results and then compared with the results of the fuzzy logic model. Results show that the fuzzy logic model is very effective in interpreting DGA results identifying the critical ranking of the power transformer based on DGA.

A Abusiada - One of the best experts on this subject based on the ideXlab platform.

  • impact of load ramping on power transformer Dissolved Gas analysis
    IEEE Access, 2019
    Co-Authors: Huize Cui, A Abusiada, Liuqing Yang, Hao Wang, Syed Islam
    Abstract:

    Dissolved Gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a Gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the Gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary Gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.

  • a new fuzzy logic approach to identify power transformer criticality using Dissolved Gas in oil analysis
    International Journal of Electrical Power & Energy Systems, 2015
    Co-Authors: A Abusiada, S Hmood
    Abstract:

    Abstract Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced Gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key Gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

  • a new fuzzy logic approach for consistent interpretation of Dissolved Gas in oil analysis
    IEEE Transactions on Dielectrics and Electrical Insulation, 2013
    Co-Authors: A Abusiada, S Hmood, Syed Islam
    Abstract:

    Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced Gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.

  • a new approach to identify power transformer criticality and asset management decision based on Dissolved Gas in oil analysis
    IEEE Transactions on Dielectrics and Electrical Insulation, 2012
    Co-Authors: A Abusiada, Syed Islam
    Abstract:

    Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.

  • fuzzy logic approach to identify transformer criticality using Dissolved Gas analysis
    Power and Energy Society General Meeting, 2010
    Co-Authors: A Abusiada, M Arshad, Syed Islam
    Abstract:

    Dissolved Gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation. DGA interpretation is yet a challenge in the power transformer condition monitoring research area. This paper introduces a novel fuzzy logic approach to help in standardizing DGA interpretation techniques and to identify transformer critical ranking using DGA. DGA has been performed on several oil samples that have been collected from different transformers. Traditional DGA interpretation techniques are used to analyze the results and then compared with the results of the fuzzy logic model. Results show that the fuzzy logic model is very effective in interpreting DGA results identifying the critical ranking of the power transformer based on DGA.

Larry J Weber - One of the best experts on this subject based on the ideXlab platform.

  • spillway jet regime and total Dissolved Gas prediction with a multiphase flow model
    Journal of Hydraulic Research, 2019
    Co-Authors: Yushi Wang, Marcela Politano, Larry J Weber
    Abstract:

    A numerical model, based on the open source code OpenFOAM, was developed to predict jet regimes and total Dissolved Gas downstream of spillways. The model utilizes the volume of fluid method to tra...

  • a multiphase model for the hydrodynamics and total Dissolved Gas in tailraces
    International Journal of Multiphase Flow, 2009
    Co-Authors: Marcela Politano, Pablo M Carrica, Larry J Weber
    Abstract:

    Abstract Elevated supersaturation of total Dissolved Gas (TDG) has deleterious effects in aquatic organisms. To minimize the supersaturation of TDG at hydropower dams, spillway flow deflectors redirect spilled water horizontally forming a surface jet that prevents bubbles from plunging to depth in the stilling basin. A major issue regarding the prediction of the hydrodynamics and TDG in tailraces is the effect of the spillway bubbly surface jets on the flow field. Surface jets cause significant changes on the flow pattern since they attract water toward the jet region, a phenomenon called water entrainment. Bubbles create interfacial forces on the liquid, reduce the effective density and viscosity, and affect the liquid turbulence increasing the water entrainment. Most numerical studies on dams use standard single-phase models, which have demonstrated to fail to predict the hydrodynamics and TDG distribution. In this paper, an anisotropic two-phase flow model based on mechanistic principles capable of predicting water entrainment, Gas volume fraction, bubble size and TDG concentration is presented. Good agreement between model results and field data is found in the tailrace of Wanapum Dam. The simulations capture the measured water entrainment and TDG distribution. The effect of the bubbles on the hydrodynamics and TDG distribution is analyzed.

  • a multidimensional two phase flow model for the total Dissolved Gas downstream of spillways
    Journal of Hydraulic Research, 2007
    Co-Authors: Marcela Politano, Pablo M Carrica, Cagri Turan, Larry J Weber
    Abstract:

    Elevated levels of Dissolved Gas in the spillway stilling basin, which are responsible for Gas bubble disease in fish, constitute an important negative environmental effect of dams. Bubbles, entrained when a plunging jet impacts the tailwater pool, plunge beneath the surface and transfer mass to the liquid, causing an increase in the total Dissolved Gas (TDG) concentration. Most of the numerical studies onTDG downstream of spillways found in the literature are based on experimental correlations for the Gas volume fraction.Abetter approach involves the use of a two-phase flowmodel. In this paper, a two-fluid model is used to calculate the Gas volume fraction and velocity of the bubbles. A polydisperse model is used in which a Boltzmann transport equation predicts the bubble size distribution, to account for the different bubble sizes found in the flow downstream of spillways. The bubble mass is discretized considering groups of bubbles of variable mass, with the mass of the bubbles changing due to bubble/l...

  • modeling total Dissolved Gas production and transport downstream of spillways three dimensional development and applications
    International Journal of River Basin Management, 2004
    Co-Authors: Larry J Weber, Heqing Huang, Yong Lai, Andrew P Mccoy
    Abstract:

    Abstract Challenges a resource manager encounters concerning water quality downstream of dam spillways are well documented. The plunging and air‐entraining nature of spillway flows can lead to supersaturated water downstream of the dam. A multi‐dimensional numerical model is developed for predicting total Dissolved Gas (TDG) production and transport downstream of a hydropower dam spillway. The model is based on the Reynolds averaged Navier‐Stokes equations for flow hydrodynamics and a total Dissolved Gas model that incorporates the Gas production, exchange and transport physics. A three‐dimensional version of the model is calibrated using one set of available field data with a moderate flow discharge. The applicable range of the model is established by applying the calibrated model to different spillway discharges and comparing results with field data. The model is then validated with TDG field data available for Wanapum Dam and applied to two practical engineering problems. The simulations successfully r...

Sherif S.m. Ghoneim - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent prediction of transformer faults and severities based on Dissolved Gas analysis integrated with thermodynamics theory
    IET Science Measurement & Technology, 2018
    Co-Authors: Sherif S.m. Ghoneim
    Abstract:

    Most presented Dissolved Gas analysis (DGA) techniques were interested in determining the fault types (FTs), but few articles discussed the corresponding severity of these faults. Here, the thermodynamic theory is utilised to evaluate the fault severity based on the energy associated with each FT. Therefore, energy weighted DGA is proposed, where the individual Gas concentration is multiplied by a relative factor that relates to the enthalpy change of reaction. A fuzzy logic system is built based on the IEC code rules, the transformer condition code that is reported in IEEE Standard C57.104-2008, and the thermodynamic theory. For enhancing the network fault diagnosis of the power transformers all over the distribution network, the proposed fuzzy logic approach is employed for its integration in accordance with the distributed agents of the distribution substations. This smart system facilitates evaluating decisions of the distributed agents as well as providing a higher decision level if needed. That is achieved by sending the important information about transformers attained by the proposed fuzzy approach such as the FT, its severity, the total Dissolved combustion Gases condition, the recommended action, in addition to the period of incoming action to the primary substation.

  • Conditional probability-based interpretation of Dissolved Gas analysis for transformer incipient faults
    IET Generation Transmission & Distribution, 2017
    Co-Authors: Ibrahim B.m. Taha, Diaa-eldin A. Mansour, Sherif S.m. Ghoneim, Nagy I. Elkalashy
    Abstract:

    In this study, a conditional probability scheme is designed to diagnose transformer incipient faults based on the percentage of five Dissolved Gases (hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2)) with respect to their summation. Based on the fault features, the point probabilities of each fault type occurrence and non-occurrence are computed. Then, the conditional probability of certain fault occurrence is estimated to specify the probabilistic indication of each fault type occurrence. Multivariate normal probability density function is considered to point out the conditional probability of the fault occurrence. It is implemented in different scenarios to define the fault type (partial discharge, low energy discharge, high energy discharge, low thermal, medium thermal, and high thermal) where the best scenario is selected. The proposed technique has the merits of simplicity and ease of implementation. It is assessed through the analysis of 403 Dissolved Gas sample dataset collected from the Egyptian electric utility as well as from credited literatures. The proposed probabilistic technique is evaluated in comparison with other methods in literatures. Also, it is validated against uncertainty in DGA data up to 20%. The results reveal the ability and reliability of the proposed technique for transformer fault diagnosis.

  • integrated ann based proactive fault diagnostic scheme for power transformers using Dissolved Gas analysis
    IEEE Transactions on Dielectrics and Electrical Insulation, 2016
    Co-Authors: Sherif S.m. Ghoneim, Ibrahim B.m. Taha, Nagy I. Elkalashy
    Abstract:

    This paper focuses on a Smart Fault Diagnostic Approach (SFDA) based on the integration among the output results of recognized Dissolved Gas analysis (DGA) techniques. These techniques are Dornenburg method, Electro-technical Commission standard (IEC) Code, the Central Electricity Generating Board (CEGB) Code based on Rogers' four ratios, Rogers method given in IEEE-C57 standard, and the Duval triangle. The artificial neural networks (ANN) model is constructed to monitor the transformer fault conditions and trained for each technique individually. The fault decision of each ANN model supplies the proposed integrated SFDA. The integration between these DGA approaches not only improves the fault condition monitoring of the transformers but also overcomes the individual weakness and the differences between the above methods. Toward a better diagnostic scheme, a new SFDA is developed based on the integration of the most three appropriate DGA methods. Further Gas concentrations have been considered as raw data (California State University Sacramento (CSUS) as an example) to enhance the proposed SFDA performance. Comparison of each DGA concept with respect to the proposed one is reported, where the results provide evidences of the efficacy of the proposed SFDA.

  • artificial neural networks for power transformers fault diagnosis based on iec code using Dissolved Gas analysis
    2015
    Co-Authors: Sherif S.m. Ghoneim, Ibrahim B.m. Taha
    Abstract:

    Transformer is the main important equipment in electrical power system. Early stage detection of the transformer faults has great economic significance because it considered expensive equipment and it helps to maintain the continuous operation of the electrical power system. Transformer oil is used for two main purposes, one for insulating liquid and the other for cooling. Some physical- chemical tests are carried out to determine the physical and chemical properties of the oil. Dissolved Gas Analysis (DGA) is now considered a common practice method for detection of the transformer incipient fault. This paper focuses on the employment of the Artificial Neural Network techniques (ANN) to diagnose Dissolved Gas in transformers, in order to determine the fault causes based on the IEC standard method. The ANN on IEC Code results meets the similar results of the other techniques that use to diagnose the transformer fault. Therefore, this method is very reliable to use as a diagnostic tool for transformer fault detection.

  • Improvement of Rogers four ratios and IEC Code methods for transformer fault diagnosis based on Dissolved Gas Analysis
    2015 North American Power Symposium (NAPS), 2015
    Co-Authors: Ibrahim B.m. Taha, Sherif S.m. Ghoneim, Hatim G. Zaini
    Abstract:

    Early detection of the generated Dissolved Gases due to different electrical and thermal stresses in transformer oil will prevent the malfunction of the transformer and hence maintain the continuity of the power system network operation. Dissolved Gas analysis (DGA) is widely spread technique that is very useful in detecting the incipient faults in the oil filled power transformers. There are many interpretation techniques based on DGA such as Dornenburg method, Key-Gas method, IEC Standard Code, Duval triangle and Rogers methods. The diagnostic accuracy of these techniques is very limited and a significant instability is occurring with data uncertainty. In this paper a modification will be made on the IEC code and Rogers four ratios method based on the case study dataset (320 samples) to improve the accuracy of them. The data is collected from the central chemical laboratory of Egyptian electricity utility and from literatures. The results point out the accuracy improvement using the proposed form of IEC Code and Rogers four ratios methods.