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A new procedure to optimally identifying the prediction equation of oil breakdown voltage with the barrier parameters’ effect is presented. The specified equation is built based on the results of experimental works to link the response with the barrier parameters as the inputs for hemisphere-hemisphere electrode gap configuration under AC voltage. The AC HV is applied using HV Transformer Type (PGK HB-100 kVAC) to the high voltage electrode in the presence of a barrier immersed in Diala B insulating oil. The problem is formulated as a nonlinear optimization problem to minimize the error between experimental and estimated breakdown voltages (VBD). Comprehensive comparative analyses are addressed using three recent innovative marine predators, grey wolf, and equilibrium optimization algorithms to reduce the error between the experimental and estimated breakdown voltages. In addition, the experimental results are expressed in two different models via grey and real coding. Finally, simulation results are conducted with statistical indices that show the effectiveness of the proposed models for experimental verification. The total percentage errors of the tested samples between the observed and estimated VBD are 1.207, 1.222, and 1.207 for EO, GWO, MPA, respectively. The marine predator algorithm has the best performance compared with the other two competitive algorithms for grey and actual codes. [ABSTRACT FROM AUTHOR]

In this paper, maintenance process for on-load tap changer (OLTC) would be enhanced using nanofluid that developed by adding nanoparticles (titanium dioxide, TiO2) to the insulating oil. The occurrence of the internal arcs inside OLTC resulted in the formation of carbon atoms on the inner bonding points during its operation. Over time, these points became unusable since the contact resistance value was high. To increase the efficiency of the maintenance process, infrared radiation was used to excite the additive TiO2 atoms to attract carbon atoms. Electrical tests were performed before and after the proposed maintenance process according to IEC standards. The results showed significant differences in the maintenance process with and without nanoparticles. A prediction model to identify the OLTC state was constructed based on support vector machine classifier. The results revealed the ability of the predicted model to predict the OLTC state with accuracy 100%. [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]

The insulating oil performance could be enhanced in high-voltage apparatus using barriers. The importance of the barrier in increasing the dielectric strength of the insulating oils in order to reduce the oil failure stresses had not been sufficiently studied. In this paper, the effects of the barrier variables on the insulation performance of the transformer oil for point-plate and plate-plate gaps were demonstrated. These variables are: gap space (d), the barrier location relative to the high-voltage electrode (a / d) %, barrier diameter (D), barrier thickness (e), electrode configurations (EC), the presence of contaminating particles, the weight of the contaminating particles (W) and the temperature of the insulating oil (T). The statistical t test was used to explain whether the effect of these parameters was significant or not. Furthermore, the above-mentioned variables were used as training variables to construct the prediction model of oil breakdown voltage considering barrier effect based on the artificial neural networks (ANN). The ANN model was developed based on the results from experimental works. Therefore, 784 samples were used as training data set and other 25 samples were taken as testing and validating samples. The results explained that the prediction ANN model had a high ability to expect the breakdown voltage for other different experiment cases. The average errors of the training and testing samples were 1.6%, and 2.66%, respectively. Therefore, the prediction accuracy could be considered as 98.4% for training and 97.34% for testing. [ABSTRACT FROM AUTHOR]

Some costly repairs are recited when faults occur in transformer, therefore, an early detection of the transformer faults results in a reduction of the transformer maintenance and it maintains the continuous operation of the power networks. Nowadays, Dissolved Gas Analysis (DGA) is the most common diagnostic tool to determine the faults occurred in insulating transformer oils by analyzing the gas evolved from it. In this paper, the Fuzzy Logic Transformer Fault Diagnostic System (FLTFDS) is used as an application of the dissolved gas analysis to diagnose the dominant transformer fault generated by the electrical and thermal stresses. FLTFDS is constructed on the basis of three common criteria: the International Electro-technical Commission standard (IEC Code Standard), the Central Electric Generating Board (CEGB) Standard based on Rogers four ratios, and Finally Duval Triangle Method; these criteria are used to interpret the transformer faults using fuzzy logic. There are three main transformer diagnostic faults: thermal, arcing and partial discharge faults. The FLTFDS output is a dominant fault based on the output of the three criteria. The FLTFDS solves the conflict among the outputs of the three common criteria because such criteria, give different results for the same case study. Data samples (386 samples) are collected from the electrical utility laboratory in Egypt and credited literature. These data samples are used as an input to the three criteria and the outputs of the three criteria are an input to the Fuzzy-based decision making system (FDMS)in order to determine the dominant fault. The results demonstrate that the accuracy of FLTFDS output is greater than the accuracy of the three common criteria. [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]

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 continuity of transformer operation is very necessary for utilities to maintain a continuity of power flow in networks and achieve a desired revenue. Most failures in a transformer are due to the degradation of the insulating system, which consists of insulating oil and paper. The degree of polymerization (DP) is a key detector of insulating paper state. Most research in the literature has computed the DP as a function of furan compounds, especially 2-furfuraldehyde (2-FAL). In this research, a prediction model was constructed based on some of most periodical tests that were conducted on transformer insulating oil, which were used as predictors of the insulating paper state. The tests evaluated carbon monoxide (CO), carbon dioxide (CO2), breakdown voltage (VBD), interfacial tension (IF), acidity (ACY), moisture (M), oil color (OC), and 2-furfuraldehyde (2-FAL). The DP, which was used as the key indicator for the paper state, was categorized into five classes labeled 1, 2, 3, 4, and 5 to express the insulating paper normal aging rate, accelerating aging rate, excessive aging danger zone, high risk of failure, and the end of expected life, respectively. The classification techniques were applied to the collected data samples to construct a prediction model for the insulating paper state, and the results revealed that the fine tree was the best classifier of the data samples, with a 96.2% prediction accuracy. [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]