Transformer Research Articles

Seismic Response Prediction of Porcelain Transformer Bushing Using Hybrid Metaheuristic and Machine Learning Techniques: A Comparative Study

Although seismic response predictions are widely used for engineering structures, their applications in electrical equipment are rare. Overstressing at the bottom of the porcelain insulators during seismic events has made power transformer bushings in substations prone to failure. Thus, this paper proposed and compared six integrated machine learning (ML) models for seismic stress response predictions for porcelain transformer bushings using easily monitored acceleration responses. Metaheuristic algorithms such as particle swarm optimization were employed for architecture tuning. Prediction accuracies for stress response values and classifications were evaluated. Finally, shaking table tests and simulation analyses for a 1100 kV bushing were implemented to validate the accuracy of the six ML models. The results indicated that the proposed ML models can quickly forecast the maximum stress experienced by a porcelain bushing during earthquakes. Swarm intelligence evolutionary technologies could quickly and automatically aid in the retrofitting of architecture for the ML models. The K-nearest neighbor regression model had the best level of prediction accuracy among the six selected ML models for experimental and simulation validations. ML prediction models have clear benefits over frequently used seismic analytical techniques in terms of speed and accuracy for post-earthquake emergency relief in substations.

Solvent-free synthesis of nickel doped ruthenium for efficient hydrogen evolution

The efficient transformation of chemicals and electricity via electrocatalytic hydrogen evolution reaction (HER) plays a pivotal role in green hydrogen production. Both in academia and industry, the pursuit of designing and fabricating catalysts for HER that are not only efficient and robust but also environmentally friendly is of significant interest. In this work, a ruthenium catalyst doped with nickel was synthesized using a procedure-minimal and solvent-free method facilitated by microwave-assisted pyrolysis. The incorporation of alloying and optimized electronic structures has resulted in Ru-based nanocrystals that change the HER mechanism and enable enhanced activity. The optimal RuNi/C catalyst demonstrates an overpotential of only 15 mV at a current density of 10 mA cm−2, with a Tafel slope of 24 mV dec−1, surpassing the performance of the state-of-the-art Pt/C catalyst. Remarkably, the prepared catalyst exhibits admirable corrosion resistance, making it a highly promising candidate for green hydrogen production through seawater splitting.

Effect of anions on formation of ferrite coatings on carbon steel by hydrothermal method

The major concerns in Primary Heat Transport circuits of water cooled nuclear power plants are corrosion of structural materials and radiation field buildup on out-of-core surfaces due to accumulation of activated corrosion products. To address these concerns, hot conditioning and metal ion passivation are employed, which lead to the formation of protective magnetite coating on structural alloys like carbon steel. In this study, investigations were undertaken to modify the magnetite/ferrite films hydrothermally in the presence of zinc (Zn2+) and nickel (Ni2+) ions by using different salts of these ions containing anions such as acetate, nitrate and sulphate. Mixed ferrite coatings containing Zn2+ and Ni2+ ions were developed on carbon steel in lithiated water in presence of these anions at 250 °C by hydrothermal method and subsequently characterized by grazing incidence-X-ray diffraction for phase identification and scanning electron microscopy for surface morphology. The effects of these anions were studied by comparing the corrosion resistance properties of ferrite coated carbon steel specimens. The corrosion properties were studied in lithium hydroxide medium at ambient temperature by electrochemical techniques and the results of the investigation are discussed in this paper.

Fluctuation of fine motor skills throughout the menstrual cycle in women

The effect of the menstrual cycle on fine motor skills is unclear. This study determined whether the menstrual cycle affected fine motor skills and related neural activities. Nineteen women with regular menstrual cycles were tested for fine motor skills using two types of tasks: grooved pegboard task (GPT), which evaluates motor control with high freedom of movements, and force modulation task (FMT), which evaluates more complex and fine motor control with low freedom of movements. We also assessed primary motor cortex intracortical circuits and sensorimotor integration using paired-pulse transcranial magnetic stimulation to reveal why the menstrual cycle affects fine motor skills. The present study indicated that fine motor skills assessed by FMT varied throughout the menstrual cycle while those measured by GPT did not. These results suggest that fine motor skills requiring more complex and fine control may be affected by the menstrual cycle. Additionally, changes in fine motor skills throughout the menstrual cycle may be associated with the severity of menstruation-related symptoms.

A combined technique for power transformer fault diagnosis based on k‐means clustering and support vector machine

AbstractThis contribution presents a two‐step hybrid diagnostic approach, combining k‐means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

Transformer oil electrical–thermal characteristics analysis and evaluation

Transformer oil plays the role of heat dissipation and insulation in power transformers, and its electrical and thermal characteristics are directly related to the stable operation of the transformer. At the same time, it is important to master the electric–thermal characteristics of transformer oil for transformer state evaluation, latent fault prediction, and maintenance planning. Based on this, the gas production rule and electrical characteristics of transformer oil under accelerated aging are studied in this paper. First, the experimental platform for power transformer aging is established to accelerate the aging of power transformers. Second, the accelerated aging transformer oil is taken as the research object, and the power frequency, DC, lightning, and gas chromatography are measured and analyzed. Finally, a transformer oil aging evaluation model is established, which takes into account four factors: carbon and oxygen gas factors in transformers, life loss factors calculated by hotspot temperature, hydrocarbon gas factors dissolved in transformer oil, and oil quality factors, and conducts aging evaluation. The conclusion shows that the T parameter proposed in this paper can effectively evaluate the quality of transformer oil. When T is less than or equal to 0.6, there is no aging of the oil. When T is greater than 0.6 and less than 1, the transformer oil has been aging, but it is not manifested. Those are parameters whose chromatography and breakdown voltage are not far beyond the IEEE standard. When T = 1, the aging of oil with significant changes in gas content and breakdown voltage can be observed. When T is greater than 1, the oil has undergone serious aging.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Power frequency magnetic field interference suppression method for online frequency response analysis of power transformers

AbstractFrequency response analysis is widely used for the offline diagnosis of winding deformations in power transformers. To apply this approach to a working transformer, the magnitude of the response current needs to be measured by using Rogowski coil sensors across a load current. The saturation of the power frequency magnetic field in these current sensors must be prevented to ensure accurate measurement of such small response currents. Here, a method is presented to suppress the power frequency magnetic field using a sensing system including a special connection of three‐phase current sensors based on the sum of the three‐phase power frequency load currents of the transformer being close to zero. Each sensor comprises two secondary coils: a measuring coil and an anti‐saturation coil. The anti‐saturation coils are connected in parallel with one another through small inductors to eliminate the power frequency magnetic field in the cores of the sensors. Theoretical analysis is used to derive a solution for this system. The experimental results verify the proposed method as enabling the sensors to function with a transformer carrying a load current of 2333 A.

Transformer Fault Diagnosis Based on Intelligent Algorithm and Partial Discharge

Abstract As an important part of power transmission and transformation equipment and power systems, transformers’ operating conditions will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper uses the pulse current method to detect the pulse signal generated by the discharge model based on the partial discharge experimental platform data, analyzes and studies the discharge characteristics of different discharge defects, and draws For the partial discharge phase distribution (PRPD) spectrum of each defect type, the statistical characteristic parameters characterizing the characteristics of the spectrum were extracted. The partial discharge statistical characteristics were used as classification feature quantities and input into three network models: RBF, GRNN, and PSO-GRNN. The results show that PSO-GRNN has a good recognition effect and is suitable for partial discharge type identification. It has important practical significance and application value for the all-round health management of power transformers.

DC Bias Content Extraction of Power Transformer Under AC and DC Environment and its Suppression Measures

DC Bias Content Extraction of Power Transformer Under AC and DC Environment and its Suppression Measures

Response of piled raft foundations using the disturbed state concept theory: Analysis and interpretation

In nearshore and marine environments, challenges arise from loading conditions, subsoil profiles, and dynamic soil states influenced by continuous wave action. Piles in such settings face complex interactions due to soil vulnerability to loading and over-consolidation. Accurate estimation of pile shaft and base resistances, as well as raft-soil interface stiffness, is crucial. The paper introduces the disturbed state concept for analysing piled raft foundations under vertical loading. This theory assumes interface strength between piles and soil follows an exponential distribution, expressed through the Weibull function. Interaction between piles is established using a modified displacement factor, which is a modified form of Mindlin's solution. Raft-soil interaction is modelled with springs connected to the pile head. The load transfer model exhibits both softening and hardening responses. Validation against numerical and field experimental studies demonstrates close agreement within a 5%–15% error range. Parametric investigation analyses load-settlement response, percentage of load on pile base with settlement increase, and axial load transfer along pile length under varying conditions. The impact of limiting pile-soil relative movement on piled raft response is also explored.

The Evaluation of Over Current Relay and Ground Fault Relay Settings on Power Transformers

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.

Enhancing Power Transformer Oil Quality Weight Factor using A Genetic Algorithm

Power transformers are critical to electrical power systems but are prone to failures due to factors such as heat, electricity, chemical reactions, mechanical stress, and adverse environmental conditions. Moni-toring the insulating oil effectively is key to preventing these failures. A major challenge in this process is determining the optimal weights for the oil quality index, which lacks a standardized benchmark and often relies on subjective expert assessments. To reduce expert bias and subjectivity, this research utilizes a genetic algorithm to optimize the weightings for five essential parameters: color, water content, break-down voltage (BDV), interfacial tension (IFT), and acidity. The algorithm operates through three stages: crossover, mutation, and selection, and analyzes data from 504 oil tests across various transformers. The mean absolute percentage error (MAPE) is used as the fitness value to assess the algorithm's effective-ness. The optimization process determined the best conditions as 132 iterations, a population size of 180, a crossover rate of 0.2, and a mutation rate of 0.8. These parameters achieved an average MAPE of 1.799% over ten trials, indicating high accuracy. This research not only optimizes the weighting of the oil quality index but also significantly reduces the need for expert input and subjective judgments in trans-former maintenance. The findings are expected to improve the efficiency and reliability of power trans-formers, thereby minimizing failures and associated economic costs.

Thermal Analysis of Power Transformer Using 2D and 3D Finite Element Method

An accurate simulation and computational analysis of temperature distribution in large power transformers used in power plants is crucial during both the design and operational phases. This study introduces a thermal modeling analysis encompassing two- and three-dimensional (2D and 3D) approaches for power transformers. The mathematical model for heat diffusion follows the Finite Element Method (FEM) approach. Validation of the computed results involves comparing them against measurements from Hyundai’s test report for both 2D and 3D models, aiming to identify the most effective solution. Additionally, the thermal dynamics of power transformers under diverse operational conditions, specifically oil-immersed ones, are examined. The efficacy of this model is confirmed through testing on a step-up transformer at Kureimat station in Egypt, with specifications including three-phase, 50 Hz, 16.5/240 KV, nine taps, and cooling type (ONAN/ONAF1/ONAF2).

Adsorption sensitivity and sensing performance of Au-doped Ti3C2O2 gas sensor for fault characteristic gases in power transformer

By analyzing the compositions and concentration of dissolved gases in power transformer oil, it is possible to effectively identify and predict the types and degrees of internal faults in oil-immersed transformers. This technology is currently an important method for fault diagnosis and prevention of large oil-immersed power transformers. This study established two different adsorption systems, namely Ti3C2O2 and Ti3C2O2 doped with Au atom, for the detection of four fault characteristic gases (CO, CO2, C2H4, CH4) in power transformer oil. The research results indicate that Ti3C2O2 exhibits inadequate adsorption capabilities for the above gases. The maximum adsorption energy is only −0.440 eV, which belongs to physical adsorption. After doping with Au atoms, the adsorption sensitivity of Ti3C2O2 for the above gases was significantly improved, with a significant increase in adsorption energy and charge transfer. The average adsorption energy has increased by 435%, and the minimum adsorption energy is also −0.660 eV. The adsorption process changed from physical adsorption to chemical adsorption. These research results provide a theoretical basis for Au-doped Ti3C2O2 gas sensor in detecting fault characteristic gases in power transformer.

Analysis of Determining the Surge Arrester Protective Distance for Protection on 60 MVA Power Transformers at the 150 KV Main Substation in Surabaya Barat

Because it provides electrical energy to customers that require protection from lightning interference, the substation is a crucial location. An electrical equipment's defense against lightning strikes is provided by a lighting arrestor. The arrester should be placed as near to the transformer as feasible for optimal protection. In order to restrict the installation of arresters to safeguard equipment, the maximum distance for arrester installation must be established. This study only looks at one 60 MVA transformer in the Surabaya Barat Main Substation, and its goal is to determine the minimum amount of error in transformer protection. Based on the maximum possible surge arrester surge arrester jarak with a power transformer at the Surabaya Barat Main Substation, the maximum possible surge arrester distance is 8,135 meters. Based on surge arrester capability, the transformer may be protected from over voltage since the maximum voltage that can occur is just 150.83 kV, with the maximum voltage remaining below the transverse impulse drop of around 650 kV.

Recent development of grain oriented electrical steel in Shougang steel

Shougang Steel has been pursuing the development of low iron loss and high permeability grain oriented electrical steels for more than a decade. Grain oriented electrical steel requires a very strong Goss {110}〈001〉 texture and as such a comprehensive control of thermo-mechanical processing parameters is required to achieve a strict control of texture. In this paper the key metallurgical problems associated with manufacture of grain oriented electrical steel are discussed, including the factors that affect both primary recrystallization and secondary recrystallization leading to the development of a strongly textured final sheet. The metallurgical factors affecting magnetostriction of electrical steels are also analyzed, and a technological approach for domain refinement, and its effect on magnetic properties, is described. Examples of magnetic properties under non-sinusoidal magnetization conditions are also reported and analyzed. As a result of continuous research and development efforts the high permeability low iron loss grain oriented electrical steel of thin gauge products of 0.2 mm/0.18 mm thickness have recently been developed to meet the requirements of the new China national standard of energy efficiency grades for power transformers.

Analytical design and computational fluid dynamics analysis for optimizing fixed ventilation systems for power transformer: Numerical study and experimental validation

Despite the recognized fact that power transformers (PT) are highly efficient technology, part of their electrical energy is counted as heat losses. Therefore, the cooling system is a critical factor in the life span and performance of the PTs, and hence the adopted thermal energy design can be strongly effective. This study revolves around the innovative development of a novel generation of fixed ventilation fans for PTs, achieved through Computational Fluid Dynamics (CFD) simulations and practical experiments. The proposed CFD model was developed adopting ANSYS FLUENT 19.2. The proposed CFD model was verified by comparing the numerical and experimental results of the cooling air temperature and velocity. A standard k-ε model was used to simulate the airflow of the investigated cooling system numerically. A thermal analysis of a four-sided power transformer was performed, and an analysis of the optimum position to install the cooling fan on the radiator was presented. The most effective number of used fans was also determined. The main results showed that two fans were found to be the best performance among the tested candidate alternatives. The two cooling fans’ size, material, and cover shape were also studied and added to the proposed model. Besides, the utilization of a thermoelectric generator was considered in this study in order to recycle some of the lost heat into output power. An experimental prototype mimicking the actual cooling system for the power transformer was designed and fabricated. Results showed that the selected material has achieved a high Calculated Factor of Safety (FoS) equal to 237, indicating that the utilization of the designed parts is of high safety.

Partial Discharge Fault Diagnosis in Power Transformers Based on SGMD Approximate Entropy and Optimized BILSTM.

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.

Revisiting the dry friction-like magnetic vector hysteresis model

Physically correct simulations of magnetic devices require precise and energy-consistent hysteresis models. Electrical steel sheets of, e.g., power transformers or rotating machines represent one crucial element in such a simulation since the hysteresis is revealed uni- and multi-axial, and the steel sheets may have anisotropy. A dedicated vector hysteresis model based on dry friction-like pinning is revisited and derived in terms of thermodynamic state equations in a theoretical manner. The model is then linked to an incremental energy minimization procedure used in elasto-plasticity theory. A numerically efficient two-dimensional solution scheme of the hysteresis model is extended to the three-dimensional case. Rotational losses are also in the scope of this paper since the model cannot describe this loss type by nature. Therefore, numerical adaptations are discussed to account for vanishing rotational losses in saturation.