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Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers' ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults' diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon's rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases. [ABSTRACT FROM AUTHOR]

The prediction of fault type in transformers at an early stage is an important aspect for power system reliability. Checking the transformer status begins with the dissolved gas analysis (DGA) test. Rogers' four-ratio and IEC 60599 standard methods are commonly DGA-based techniques that used to detect the transformer faults. These methods are very easy to implement, but their diagnostic accuracies are very poor. The target of this work is to build a new PSO-FS optimization platform identifying the optimal limits of gas ratios and corresponding rules for each fault type to enhance the diagnostic accuracies of Rogers' four-ratio and IEC 60599 code methods. In the proposed platform, PSO optimization technique specifies the gas ratio limits and corresponding rules where the fuzzy system identifies the fault types. Based on the results of the optimization process, the new codes that used to diagnose the fault types were developed. The performance of the proposed approach investigated using 481 collected datasets. The overall accuracies of the Rogers' four-ratio and IEC code models enhanced from 47.19 and 55.09 to 85.65 and 85.03, respectively. However, when datasets due to mixtures of faults (samples with undetermined or misidentified faults) are removed from the calculations, the accuracies of the PSO-FS methods are not significantly better than when using existing DGA methods. [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]

The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer's health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO2), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO2, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10−7. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system's performance is compared with reported work and has been found to provide encouraging outcomes. [ABSTRACT FROM AUTHOR]

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter "λ" and penalty margin parameter "C" were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers' accuracy was evaluated and compared with several DGA techniques in the literature. [ABSTRACT FROM AUTHOR]