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In power systems, potential transformers (PTs) are responsible for stepping down high voltage to low voltage. However, a short circuit between turns on the primary side of a generator outlet PT can significantly reduce the secondary phase voltage, leading to voltage imbalances and generating fundamental zero-sequence voltage. This situation is analogous to a stator winding ground fault, often resulting in incorrect protective operations. To prevent such malfunctions, this paper analyzes the causes of false tripping through simulation and proposes a fault diagnosis model based on the Circular Sparrow Search Algorithm (CSSA)-optimized Gaussian Mixture Model (GMM), referred to as the CSSA-GMM model. A fault simulation model was established using Simulink to verify the differences between turn-to-turn short circuits and stator ground faults, and their electrical characteristics were studied. The results indicate that under different fault types, parameters such as the three-phase primary current and three-phase secondary voltage exhibit varying relationships and fault variations. By optimizing the GMM parameters using CSSA and comprehensively analyzing the voltage and current characteristics, this model can effectively diagnose turn-to-turn short circuit faults at various short-turn ratios, achieving an accuracy rate of up to 98%. This approach clearly distinguishes PT turn-to-turn short circuits from generator outlet stator ground faults, providing new insights for fault recognition and supporting the intelligent development of relay protection systems. [ABSTRACT FROM AUTHOR]

To eliminate the problem of the aging of cellulose insulation in the manufacturing stage, a new drying method is being developed based on the use of methanol vapors. Previous studies have shown that the complete removal of methanol from the cellulose insulation after the drying process is very difficult. Therefore, it is necessary to check how the remaining methanol after drying affects the properties of both the cellulose materials and mineral oil. To conduct such studies, it is necessary to know the methanol content in oil that can be expected depending on its initial content in the cellulose materials and the temperature of the insulation system. Therefore, the main goal of this work is to develop methanol equilibrium curves for oil–paper insulation. To achieve the assumed goal, three-stage studies were conducted. A gas chromatograph equipped with a flame ionization detector was used in all stages of these studies. The gas partition coefficient between oil and air was determined for a temperature of 70 °C. The key experimental finding was the development of methanol equilibrium curves for oil–paper insulation. Thanks to this achievement, it is possible to estimate the methanol content in cellulose materials and mineral oil depending on the insulation temperature. Such data are necessary, among others, to plan appropriate studies aimed at assessing the impact of methanol content on the dielectric and physicochemical properties of these materials, important from the point of view of the operation of power transformers. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model's optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model. [ABSTRACT FROM AUTHOR]

The study determines the frequency–temperature dependence of the conductivity of a moist solid insulation component of power transformers, impregnated with the innovative bio-oil NYTRO® BIO 300X, manufactured from plant-based raw materials. The research was conducted for six moisture levels ranging from 0.6% to 5% by weight, within a frequency range from 10−4 Hz to 5 · 103 Hz and measurement temperatures from 20 °C to 70 °C, with a 10 °C step. The conduction model for both DC and AC, based on the quantum mechanical phenomenon of electron tunneling between water nanodroplets, was used to analyze the obtained results. It was determined that the frequency dependence of the conductivity of pressboard-bio-oil-moisture composites is influenced by two factors as follows: the activation energy of conductivity and the activation energy of relaxation time. For each moisture content, 16 values of the activation energy of the relaxation time and 16 values of the activation energy of conductivity were determined. It was found that the values of activation energy of conductivity and relaxation time are equal and independent of moisture content, frequency, and temperature. Based on 192 residual activation energy values, the mean generalized activation energy value for the relaxation time and conductivity was calculated with high precision, resulting in ΔE ≈ (1.02627 ± 0.01606) eV. The uncertainty of its determination was only ±1.6%. This indicates that electron tunneling from the first nanodroplet to the second, causing AC conductivity, and their return from the second nanodroplet to the first, determining the relaxation time, occur between the same energy states belonging to the water nanodroplets located in the pressboard impregnated with bio-oil. For each moisture content, the curves obtained for different measurement temperatures were recalculated to a reference temperature of 20 °C using the generalized activation energy. It was found that the shifted curves obtained for different temperatures perfectly overlap. Increased moisture content shifts the recalculated curves toward higher conductivity values. It was established that for all moisture contents in the lowest frequency range, conductivity is constant (DC conductivity). A further increase in frequency causes a rapid rise in conductivity. The increasing period can be divided into two stages. The first stage occurs up to about 100 Hz–101 Hz, depending on the moisture content. In the second stage, the rate of conductivity increase is higher, and its value depends on moisture content. The lower the moisture content, the faster the conductivity increases. Recalculation using the generalized activation energy eliminated the effect of temperature on the curves. It was found that the shapes of the recalculated curves and their position relative to the coordinates depend only on the moisture content in the composite. The equality of the activation energy of the relaxation time and conductivity established in the study, as well as their independence from frequency and moisture content in the pressboard impregnated with NYTRO® BIO 300X bio-oil, allows for recalculating the curves of electrical parameters determined at any operating temperatures of the transformer to a reference temperature, for example, 20 °C. Comparing the curve obtained for the transformer, recalculated to the reference temperature, with reference curves determined by us in the laboratory for different moisture contents, will allow for the precise determination of the moisture content of the solid insulation component impregnated with NYTRO® BIO 300X bio-oil. This will contribute to the early detection of approaching critical moisture content, threatening catastrophic transformer failure. [ABSTRACT FROM AUTHOR]

The operational stability of the power transformer is essential for maintaining the symmetry, balance, and security of power systems. Once the power transformer fails, it will lead to heightened instability within grid operations. Accurate prediction of oil temperature is crucial for efficient transformer operation. To address challenges such as the difficulty in selecting model hyperparameters and incomplete consideration of temporal information in transformer oil temperature prediction, a novel model is constructed based on the improved whale optimization algorithm (IWOA) and long short-term memory (LSTM) neural network with self-attention (SA) mechanism. To incorporate holistic and local information, the SA is integrated with the LSTM model. Furthermore, the IWOA is employed in the optimization of the hyper-parameters for the LSTM-SA model. The standard IWOA is improved by incorporating adaptive parameters, thresholds, and a Latin hypercube sampling initialization strategy. The proposed method was applied and tested using real operational data from two transformers within a practical power grid. The results of the single-step prediction experiments demonstrate that the proposed method significantly improves the accuracy of oil temperature prediction for power transformers, with enhancements ranging from 1.06% to 18.85% compared to benchmark models. Additionally, the proposed model performs effectively across various prediction steps, consistently outperforming benchmark models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The operation and maintenance management of transformers has accumulated a large amount of unstructured defect recording data in the form of text. However, the lack of effective mining method has led to an extremely low utilization rate. A text mining method for transformer defect recording text based on a character-word level ensemble integrated model is proposed in this paper. Firstly, the transformer defect recording texts are preprocessed with text segmentation, stop word removal, text augmentation, and text feature representation to convert the data into mathematical vectors for input. By integrating multiple word- and character-level classification models, the method can realize accurate identification and classification of transformer defect types through the synergistic and complementary effects of meta-learners on the individual base learners. Compared to single-text classification algorithms, this method can obtain the semantic features of the text more comprehensively, achieving a classification precision of 91% and F1 score of 0.9, which is the comprehensive evaluation score for model precision and recall. By applying natural language processing technology to precise power equipment defect recoding text classification and efficient fault recognition, data resources are awakened, and the intelligent management level of power transformers is significantly improved.

The operation of power transformer in an electric system is the cause of noise in form of sound. At a certain level, this noise can be considered as pollution, interfering with the comfort and health of human hearing. The phenomenon shows the need to understand load noise that is generated during the design process of power transformer. However, a major related problem is the unavailability of an accurate load noise model capable of precise prediction during the design stage. Therefore, this research aimed to develop load noise model based on an artificial neural network for power transformer to predict the generated load noise value. The development process was carried out using a trained backpropagation neural network (BPNN) with the Levenberg-Marquardt algorithm. Before training for neural network, input parameters such as power, impedance, and winding geometry factors were selected and normalized. The linear regression method was used to assess the quality of neural network model training results. For performance comparison, the multiple linear regression (MLR) model and the Reiplinger method were also developed. The results showed that load noise model was developed based on BPNN with seven hidden layers and nine neurons for each layer. Model showed acceptable output variables, with mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) of 0.007, 0.464, 0.708, and 0.998, respectively. Furthermore, the prediction of load noise achieved through BPNN showed significantly high accuracy compared to the existing standard formulas.

The failures of the power system are caused by insulation damages of HV apparatus including transformers, HV cables and generators. They are expensive. In the beginning, insulation failures occure in limited regions of insulation, which is called partial discharge (PD). When PDs are not detected online, they will spread along the insulation and bridge the whole of the insulation that eventually results in total breakdown. Thus, the HV apparatus fails. In this research, different sensors such as HFCT and coupling capacitor required to detect the PD of different HV devices including power transformers, HV cables, switchgears, motors and generators are introduced. The properties of PD signals occurred in HV apparatus is determined by experimental results related to PD signals detected from these HV apparatus. Then, an approach uses the correlation between signals energy is suggested to determine the location of PD occurred in the HV devices. The suitabality of the proposed approach is satisfied by simulating the PD signals in the EMTP-RV software and processing the detected signals by MATLAB software. It is concluded from the experimental outcomes that the suggested sensors can accurately detect the PD signals occurred in the XLPE cables and transformers. The outcomes shown that the suggested method based on the correlation between signals energy can accurately determine the location of PD source in HV devices.

The article discusses the impact of reactive power load on additional power losses in a high-power transformer operating at a VAr static compensation station. The presence of an almost pure capacitive load on the secondary side together with the thyristor switches is the cause of high-frequency components in the phase currents. The additional effect of electrical resonance between the transformer inductance and the load capacitance is also taken into account. The analysis is achieved using a three-dimensional finite element approach. [ABSTRACT FROM AUTHOR]

Power transformers are crucial and require continuous monitoring to prevent failures. Partial discharge (PD), a significant phenomenon, emits electromagnetic waves, acoustic waves, and light, and causes chemical decomposition of insulation materials. Current technology allows a real-time PD monitoring using the Ultra-High Frequency (UHF) method. This contribution aims to design a small printed antenna for internal UHF sensing within the transformer tank, using low-cost Printed Circuit Board (PCB) materials. A printed antenna less than 4 cm x 4 cm was designed and optimized through parameter sweeps to achieve a proper return loss below 1 GHz. Impedance matching is important to achieve an appropriate return loss, and smaller antennas face high impedance mismatching challenges. Adding a resistive loading can solve the impedance mismatching. To study the impact of the transformer tank model as a cavity resonator on the impedance matching of the printed antenna, the antenna was inserted into the tank model through different valves, and the return loss measurement was conducted. Furthermore, a frequency analysis was performed using a spectrum analyzer to identify the optimal frequency range for PD detection, ensuring a high signal-to-noise ratio. Validation of the performance of printed the antenna in PD detection for different PD models was done through a comparison of the Phase-Resolved Partial Discharge (PRPD) patterns obtained through conventional electrical PD measurements according to IEC 60270 and acquired by the printed antenna using the UHF method. This study demonstrates the feasibility of using a small printed antenna for real-time PD monitoring in high-voltage transformers. [ABSTRACT FROM AUTHOR]

Power transformers are vital and costly components in power systems, essential for ensuring a reliable and uninterrupted supply of electrical energy. Their protection is crucial for improving reliability, maintaining network stability, and minimizing operational costs. Previous studies have introduced differential protection schemes with harmonic restraint to detect internal transformer faults. However, these schemes often struggle with computational inaccuracies in fault detection due to neglecting current transformer (CT) saturation and associated uncertainties. CT saturation during internal faults can produce even harmonics, disrupting relay operations. Additionally, CT saturation during transformer energization can introduce a DC component, leading to incorrect relay activation. This paper introduces a novel feature extracted through advanced wavelet transform analysis of differential current. This feature, combined with differential current amplitude and bias current, is used to train a deep learning system based on long short-term memory (LSTM) networks. By accounting for existing uncertainties, this system accurately identifies internal transformer faults under various CT saturation and measurement uncertainty conditions. Test and validation results demonstrate the proposed method's effectiveness and superiority in detecting internal faults in power transformers, even in the presence of CT saturation, outperforming other recent modern techniques. [ABSTRACT FROM AUTHOR]

The operation of power transformer in an electric system is the cause of noise in form of sound. At a certain level, this noise can be considered as pollution, interfering with the comfort and health of human hearing. The phenomenon shows the need to understand load noise that is generated during the design process of power transformer. However, a major related problem is the unavailability of an accurate load noise model capable of precise prediction during the design stage. Therefore, this research aimed to develop load noise model based on an artificial neural network for power transformer to predict the generated load noise value. The development process was carried out using a trained backpropagation neural network (BPNN) with the Levenberg-Marquardt algorithm. Before training for neural network, input parameters such as power, impedance, and winding geometry factors were selected and normalized. The linear regression method was used to assess the quality of neural network model training results. For performance comparison, the multiple linear regression (MLR) model and the Reiplinger method were also developed. The results showed that load noise model was developed based on BPNN with seven hidden layers and nine neurons for each layer. Model showed acceptable output variables, with mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) of 0.007, 0.464, 0.708, and 0.998, respectively. Furthermore, the prediction of load noise achieved through BPNN showed significantly high accuracy compared to the existing standard formulas. [ABSTRACT FROM AUTHOR]

The ultra-high frequency (UHF) technique offers significant advantages over the conventional partial discharge (PD) measurement method, particularly for online monitoring, 3D localization, and immunity against noise. However, its primary limitation lies in the challenge of calibration due to the impact of various factors such as PD source locations, antenna characteristics, and transformer structures including, active part and tank wall, on the received UHF signals. Currently established parameters such as signals peak-to-peak and energy of signals do not provide a meaningful correlation between received UHF signals strength and factors such as distance and antenna radiation pattern. Addressing these gaps, this paper introduces a novel parameter: the first arrived signal (FAS), derived from the short-time Fourier transform (STFT) of UHF signals. Experimental results demonstrated the capability of the FAS to correlate meaningfully between signal strength and distance from the source, as well as antenna radiation pattern and polarization. The proposed parameter is then utilized to estimate conventional transferred charge using the received UHF signals. Results indicate promising estimation accuracy, particularly when electromagnetic waves directly reach the antenna. This approach offers the potential for a more precise estimation of conventional PD transferred charge, enhancing the capabilities of the UHF method in assessing insulation system health conditions. [ABSTRACT FROM AUTHOR]

Power transmission and transformation are important parts of power distribution, which is increasingly necessary in modern society; this is particularly important due to the green transition. Consequently, increasing the efficiency of power transformers is a priority to improve the economic and environmental aspects of electrical transformation. Lightweight graphene coatings on transformer wire rods represent a valid solution improving thermoelectrical performances. In this work, a graphene coating was applied by electrophoretic deposition to copper wire rods through a sustainable deposition bath, studying the impact of the deposition current density. The morphology of the coated wire rods, the adhesion of the coating to the substrate, and their thermoelectric properties were evaluated. This work demonstrates the application of EPD to coat copper wire rods with graphene, resulting in a 5.58% reduction in electrical resistivity and an 88% enhancement in thermal performance. In addition, relationships between coating performances and deposition parameters were highlighted. An analysis of variation (ANOVA) confirmed the main role of the current density on the examined outputs. [ABSTRACT FROM AUTHOR]

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning. [ABSTRACT FROM AUTHOR]

The power transformer is an invaluable piece of device in the power system. To prevent catastrophic failures and the ensuing power outages, the status of a transformer linked to a system must be examined for any possible faults. Despite using DGA as a global tool for detecting faults, it is limited by the inability to accurately solve the problem associated with results variability due to the intrinsic nature of the IEC TC 10 database. This study proposed a data-driven fault/defect diagnostic model using four ensemble models with three base classifiers respectively. The base classifiers are comprised of SVM, C4.5 decision tree, and naive Bayes while the ensemble methods are comprised of stacking, voting, boosting and bagging respectively. The DGA dataset used comprises seven features and 168 instances split into training (i.e. 56%) and test (i.e. 44%) datasets respectively. The results indicate that C4.5 obtained a 98.33% accuracy while stacking obtained a 99.89% accuracy as the bestperforming base and ensemble models respectively. The high classification performance accuracy achieved by our proposed models indicates its capacity for real-world applications. It can be applied to advance automation in mobile-based technology. [ABSTRACT FROM AUTHOR]

Short‐circuit forces, which result in violent vibration of power transformer windings, are induced by the interaction between the short‐circuit currents and the leakage magnetic induction. During vibration, the rearrangements of disks affect the magnetic flux distribution, which leads to time‐varying equivalent reactance and further changes of currents and forces. When a short‐circuit accident occurs, these processes happen simultaneously and constantly. In this article, the influence of circuit parameters on short‐circuit currents is analyzed. The strongly coupled phenomenon between equivalent circuit, magnetic field and structural field is explicated, and the method to obtain vibration process with strongly coupled phenomenon considered is proposed. Then the strongly coupled effect on vibration process is investigated. The vibration process is characterized by the time‐varying currents, electromagnetic forces and displacements. The space coefficient is defined to represent the magnetic flux distribution. The changes of maximum value and its occurrence time of the characteristic parameters are obtained. The peak values of the short‐circuit forces and displacements increase and the waveforms change, which is caused by the distortions of leakage magnetic flux and the changes of spatial ampere‐turn balance during vibration. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The present article provides a comprehensive analysis of the aging phenomenon of transformer insulation and the corresponding changes in insulation performance that occur over time. The present manuscript outlines diverse insulating materials that are employed in transformers, the fundamental aging mechanism of the insulation, the function of space charges in the aging of the insulation, and various simulation methodologies utilized for diagnosing the insulation status of the transformer. The primary focus of this paper is to examine the various numerical techniques employed in modeling and analyzing the aging characteristics of transformer insulation. The mechanisms underlying the accumulation of charge and distortion of electric fields in insulation are explained through the utilization of numerical modeling techniques. This paper focuses on understanding the mechanism of insulation aging, thereby facilitating the optimization and maintenance of insulation performance throughout its operational lifespan. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) fault diagnosis is of great importance for ensuring the safe and stable operation of power transformers. To address the issues of low accuracy in traditional PD fault diagnostic methods, this paper proposes a novel method for the power transformer PD fault diagnosis. It incorporates the approximate entropy (ApEn) of symplectic geometry mode decomposition (SGMD) into the optimized bidirectional long short-term memory (BILSTM) neural network. This method extracts dominant PD features employing SGMD and ApEn. Meanwhile, it improves the diagnostic accuracy with the optimized BILSTM by introducing the golden jackal optimization (GJO). Simulation studies evaluate the performance of FFT, EMD, VMD, and SGMD. The results show that SGMD–ApEn outperforms other methods in extracting dominant PD features. Experimental results verify the effectiveness and superiority of the proposed method by comparing different traditional methods. The proposed method improves PD fault recognition accuracy and provides a diagnostic rate of 98.6%, with lower noise sensitivity. [ABSTRACT FROM AUTHOR]

This paper proposes an operation modal analysis (OMA)-based approach to obtain a transformer's mechanical condition from vibrations during the transformer's transient processes, such as short-circuit and power-off conditions. Such processes generate a short transient vibration response, which yields a spectrum with poor frequency resolution. Vibration before the transient process could be included to increase the signal length; however, this would introduce a forced vibration component into the spectrum, making it challenging to distinguish the modal frequencies. To overcome these problems, a time–frequency analysis-based algorithm is proposed to extract the modal frequency spectrum from the vibration signal with the high-frequency resolution, providing clearer insight into the mechanical condition of the transformer. A faulty-state indicator is then proposed based on the similarity between the extracted modal spectrum and the initial modal spectrum obtained under a healthy state. To validate the proposed method, laboratory experiments were conducted under short-circuit and power-off conditions. Two mechanical faults—the looseness of the winding clamping and winding deformation—are introduced. The results show that both faults will cause variations in the modal frequency spectrum, leading to significant decreases in the indicator value. In summary, the proposed method can effectively evaluate a transformer's mechanical condition in an OMA setting. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electric Power Science & Technology is the property of Changsha University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractThis article calculates and studies the oil flow distribution, temperature distribution and electric field distribution of vegetable and mineral Insulating oil in the low-voltage winding of the transformer with finite element method (FEM). The results show that the maximum flow rates of vegetable Insulating oil and mineral oil are located on the side wall of low-voltage winding at 0.1, 0.3 and 0.6 m/s inlet velocities, and the maximum flow rate of mineral oil is higher. In addition, the increase of inlet flow rate will also cause the temperature inside the low voltage (LV) winding to drop. Due to the low viscosity of mineral oil, its heat transfer efficiency is higher. In addition, at the same flow rate, the pressure of mineral oil in the low-voltage winding is smaller, which is also due to the low viscosity and strong fluidity of mineral oil.

Oil transformers are one of the most widely used and vital equipment in power distribution networks. Therefore, in order to maintain the service of the electricity distribution system, special attention should be paid to the health and repairs of these equipment's in order to avoid possible costs due to their failure. Since the mineral oil used in these transformers is one of the strongest insulators, it needs a series of maintenance and periodic tests to increase the lifespan of the transformer. This paper proposes a new approach to provide an appropriate management decision and predict the remaining life of the mineral oil transformer using artificial intelligence. Health index formulation is a quick and efficient approach to combining various data and creating an indicator for asset management planning. While the health method using DGA is well presented in the literature, more attention should be paid to the combination with other parameters. Thus, in the proposed plan, evaluating the transformer's life with the actual health index formulation method includes additional factors such as load history, bush condition, ageing, and physical observations, along with common factors such as DGA, oil quality and dissolved gases in oil. Also, AI-based techniques have been used to increase accuracy. Furthermore, an integrated inspection and repair model is provided for maintenance management. Therefore, an optimal strategy is presented to determine the type of repair and inspection interval so that the transformer's life is managed by minimizing the total costs. Moreover, transformer faults are selected to manage the life of transformers and reduce blackouts. A model using transformer failure data has been developed to classify seven types of transformer faults. In order to validate the proposed procedure, the data collected for different transformers have been used. The results show that the proposed model is reliable and can provide a timely asset management decision with minimal cost.

In power systems, potential transformers (PTs) are responsible for stepping down high voltage to low voltage. However, a short circuit between turns on the primary side of a generator outlet PT can significantly reduce the secondary phase voltage, leading to voltage imbalances and generating fundamental zero-sequence voltage. This situation is analogous to a stator winding ground fault, often resulting in incorrect protective operations. To prevent such malfunctions, this paper analyzes the causes of false tripping through simulation and proposes a fault diagnosis model based on the Circular Sparrow Search Algorithm (CSSA)-optimized Gaussian Mixture Model (GMM), referred to as the CSSA-GMM model. A fault simulation model was established using Simulink to verify the differences between turn-to-turn short circuits and stator ground faults, and their electrical characteristics were studied. The results indicate that under different fault types, parameters such as the three-phase primary current and three-phase secondary voltage exhibit varying relationships and fault variations. By optimizing the GMM parameters using CSSA and comprehensively analyzing the voltage and current characteristics, this model can effectively diagnose turn-to-turn short circuit faults at various short-turn ratios, achieving an accuracy rate of up to 98%. This approach clearly distinguishes PT turn-to-turn short circuits from generator outlet stator ground faults, providing new insights for fault recognition and supporting the intelligent development of relay protection systems.

Power transformer is an important component of the substation and has an important role in the electric power distribution system. During operation the power transformer can’t be isolated from distribution system when abnormal conditions or faults occur. A fault in the power transformer will have huge impact on the power transformer itself. The substation has an installed capacity of 60 MVA of power transformers, and during operation there is a possibility of relay failure, which may interrupt the power distribution process to the load. To avoid this failure, it is necessary to analyse the settings of Over Current Relay (OCR) and Ground Fault Relay (GFR) on the 60 MVA transformers. The analysis was carried out by simulating short circuit faults of the electrical system in the ETAP software application, and then configure the setting of the relay by referring to the magnitude of the short circuit current value for phase to phase and phase to ground. The results of this analysis are used as a reference to determine the OCR and GFR relay values in the power transformer. The results obtained show that the recommended value for OCR setting on 150 kV power transformers is 0.92 second while for GFR is 0.96 second, and for the 20 KVA power transformers the OCR and GFR setting values are 0.40 and 0.20 seconds respectively. Comparison based on the results of the setup data and the analysis data has carried out, there is difference in the working time of the relay, with a difference of 5%, where the value of the analysis results is not much different from the conditions in the field.

This paper proposes a residual flux detection method based on the different polarities of response currents. When a positive–negative alternating DC voltage is loaded, the direction of the residual flux can be determined by the difference between the various polarities of the response current waveforms. As a result, residual flux value can be calculated using the empirical formula that relates residual flux to the responce current. In this formula, unknown parameters can be obtained using the field-circuit coupling method. Finally, this paper employs a closed iron core as an example to obtain the corresponding formula and then verifies its accuracy through experiments. The results show that the accuracy of the proposed method is less than 5%, which is higher than that of other existing methods. The method presented in this paper not only accurately detects the residual flux of a transformer but also requires no additional energy from the transformer.

Abstract This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data.

Inrush current in high-temperature superconducting (HTS) transformers is a little-studied phenomenon. After connecting to the power grid, a current flow through the windings which exceeding the critical current value of the superconducting tape. It may cause significant overheating and thermal damage of winding. The purpose of the study is to develop a mathematical model for calculating inrush current pulses in a HTS transformer and its verification by physical experiments. To achieve the goal of the study, a mathematical model has been developed that accurately represents the electromagnetic and thermal transient processes after HTS transformer is turned on at idling or under load. The model considers the critical parameters of the HTS tapes, the process of heating and cooling of the windings, quench characteristics, and the electrical and magnetic parameters of the transformer. Good compliance of the experimental results and mathematical modeling with a deviation of 1.99 % allowed us to verify the model. The most important result is the creation of a mathematical model of the HTS transformer at the moment of connecting to power grid. This model represents the temperature changing of the windings during the loss of superconductivity. The developed model can be used in the analysis and modeling of inrush current in designed and operating HTS transformers for any power. The obtained results are significant for determine the optimal starting characteristics, geometric and electrical parameters of HTS transformers. The proposed methods for reducing the inrush current ensure safe and reliable operation of the HTS transformer when switched on at idling or under load.

Abstract The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Abstract Power transformer is widely used in the power system with the rapid development of the power converters connected to the grid. When a transformer operates under DC bias conditions, its iron core loss increases significantly, causing local overheating and threatening the proper operation of the transformer. However, there are persistent difficulties in accurately assessing the core loss when the induction waveform is influenced by a DC bias. This paper first proposes improvements to the J–A and Preisach models to evaluate the core loss of the iron core under DC bias. Additionally, we incorporate the hysteresis models into the finite element method (FEM) by modifying the fundamental constitutive equations in the FEM model in order to perform a precise core loss/distribution calculation. To verify the accuracy of prediction, a transformer prototype with a laminated core is developed. The improved J–A-FEM and Preisach-FEM models were directly compared in terms of calculation accuracy, numerical implementation, and computational burden.

The electrification of society, increasing renewable energy sources and mobility charging lead to new loading patterns for power transformers. Dynamic load conditions induce enhanced mechanical stress on the transformers' windings, potentially causing degradation of the solid insulation over time and compromising the transformer's short-circuit withstand capability. Thermal expansion of the windings, caused by losses in the copper conductors, occurring as the transformer is loaded, increases the stress. Conversely, magnetic losses in the core and tie plate expansion contribute to a reduction in stress. This paper presents the effect of step changes in core losses and copper losses by on-line measurements of the clamping pressure, to better understand the mechanical stresses acting upon the solid insulation cellulose materials. Energisation is found to decrease the clamping pressure following warming up of the transformer, and loading the transformer increased the pressure as the windings increased in temperature. The converse effect was found when unloading and de-energising. The on-line monitoring system provides a new and important step towards ensuring the short-circuit performance of power transformers. [ABSTRACT FROM AUTHOR]

Accurately describing the impact of air bubbles generated in the oil on the insulation performance between windings during inter-turn short-circuit faults in power transformers is of great significance for their stable operation. For the transformer winding turn-to-turn short circuit fault, firstly, a multi-physical field coupling model of transformer “ circuit-magnetic field” in transient mode was developed, while the current and electromagnetic characteristics of transformer windings under fault conditions were investigated. Then the modeling of the transformer turn-to-turn short circuit, the magneto-electric coupling, and the field strength distribution were established, and the characteristics of the discharge of air bubbles in the transformer oil were further analyzed. Finally, COMSOL software was utilized to establish the simulation model. The collected data of continuous operation of an oil-immersed transformer and the selected data of low voltage short circuit fault were simulated. The simulation results show that the electric field will produce distortion when passing through the bubbles, the field strength value increases instantly, and the distortion of the electric field increases with the increase of the number of bubbles, the insulation strength of the transformer oil between the turns decreases, and the insulation performance of the transformer is greatly affected. [ABSTRACT FROM AUTHOR]