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This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The power transformer is a vital element in power system assets. When failure occurs in transformers, it leads to more revenue loss for the electrical utilities. Power transformer failures may occur due to high stresses on the insulating systems (oil and paper). These stresses categorize as electrical, thermal, and mechanical stresses. The main contribution of the current work is to increase the contribution of essential test parameters, which contribute to determining the insulating transformer state. In this work, principal component analysis (PCA) is not used as a prediction or classification tool. Still, it preserves only the necessary information and marginalizes or reduces the role of parameters with fewer contributions. PCA is a statistical method to compress an extensive data set of variables to a smaller number of orthogonal factors unrelated to interpreting the correlation matrix. One hundred forty-seven data samples were collected from the Saudi Electricity Company and used in this study. The data include the dissolved gas analysis (DGA) test, the breakdown voltage (BDV), moisture content (MC), Acidity (AC), interfacial tension (IFT), oil color (OC), dissipation factor (DF) or tan □, furan content (2-FAL). Based on PCA, some test variables can be selected as indicators for the insulating paper state from more test variables groups, reducing the cost of all tests. [ABSTRACT FROM AUTHOR]

In this paper, the dielectric characteristics of the power transformer oil are enhanced by dispersing newly synthesized nanoparticles. Two nanocrystalline powders, barium titanate (BaTiO3) and nickel ferrite (NiFe2O4), are synthesized via the tartaric acid precursor method. The change in the crystal structure, crystalline size, and microstructure of the synthesized nanoparticles as a function of annealing temperatures are characterized using X-ray diffraction and transmission electron microscope. These nanoparticles are mixed with the transformer oil considering different concentrations such as 25, 50, and 75 mg/l where the sonicator is used to disperse the nanoparticles in the oil samples. The dielectric breakdown strength is tested based on IEC standard. The results confirm the efficiency of the NiFe2O4 than BaTiO3 for enhancing the dielectric characteristics of the power transformer oils. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In modern power systems, power transformers are considered vital components that can ensure the grid's continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects. [ABSTRACT FROM AUTHOR]

The aging of power transformers causes several defects and damages in the insulating system, especially in the insulating paper. The degradation of the insulating paper generates dissolved gases in the insulating oil, which are measured by gas chromatography and used as an indicator of the insulation status. The state of the insulating paper can be identified based on the degree of polymerization (DP) measurement. In some cases, when the measurement of DP is difficult, estimating DP can be accomplished through gathering information about some of the testing parameters, such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldhyde (FA)). The statistical tools (correlation and multiple linear regression), based on 131 transformer samples, can be used to build a relation linking DP and one or more of the previous parameters to identify the insulating paper status and the percentage of remaining life of the transformer. The results indicated that it is difficult to build a mathematical model to relate between the DP and the testing variables, except with FA, where the trend of DP with FA is more obvious than with other variables. The empirical formula to compute DP based on the FA concentration was developed and gave promising results to compute DP and the remaining life of the power transformers. [ABSTRACT FROM AUTHOR]