Transformer Windings Research Articles

Innovative Automatic Winding for Transformers: An Efficient Solution for Modern Transformer Production

The advancement of electronic technology has significantly impacted human lifestyles, including the development of transformers. Although transformer winding machines have existed previously, the process remains manual and prone to errors. To address this issue, this study focuses on developing an automated copper wire winding system for transformers. The research employs a research and development method, resulting in the design and construction of an automatic transformer winding system. The device utilizes an Arduino microcontroller as its processor and a proximity sensor to detect the number of wire turns. During testing, voltage and current measurements were conducted on transformers produced by the automatic winding machine. The results indicate that the voltage error ranges from approximately 0.16% to 0.9%, while the current error is around 1.5%. By using this automated transformer winding machine, the transformer production process becomes more efficient, accurate, and time-saving. Thus, this tool has the potential to serve as an effective solution for the production of power and distribution transformers for electrical networks.

Determination of winding destroy limit area for a power series of amorphous core transformers

The amorphous steel core transformer features a unique structure with a rectangular winding, resulting in an uneven distribution of electromagnetic force compared to the circular winding of silicon core transformers. Consequently, evaluating the electromagnetic added value and identifying the destructive areas of the winding is crucial. In this paper, the combination of Matlab and the finite element technique is developed to calculate the magnetic field, short-circuit current, and electromagnetic force for a 3-phase amorphous transformers (22/0.4 kV) during short-circuit faults. The findings establish a relationship between the electromagnetic force value during a short circuit and the winding radius, as well as identify the areas where the winding damage is confined based on the transformer power sequence. These research results assist designers, manufacturers, and transformer operators in determining the appropriate harmful conditions of short-circuit electromagnetic force for the windings.

CEEMDAN-MRAL Transformer Vibration Signal Fault Diagnosis Method Based on FBG

In order to solve the problem that the vibration signal of transformer is affected by noise and electromagnetic interference, resulting in low accuracy of fault diagnosis mode recognition, a CEEMDAN-MRAL fault diagnosis method based on Fiber Bragg Grating (FBG) was proposed to quickly and accurately evaluate the vibration fault state of transformer.The FBG sends the wavelength change in the optical signal center caused by the vibration of the transformer to the demodulation system, which obtains the vibration signal and effectively avoids the noise influence caused by strong electromagnetic interference inside the transformer. The vibration signal is decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet threshold denoising algorithm improves the signal-to-noise ratio (SNR) to 1.6 times. The Markov transition field (MTF) is used to construct a training and test set. The unique MRAL-Net is proposed to extract the spatial features of the signal and analyze the time series dependence of the features to improve the richness of the signal feature scale. This proposed method effectively removes the noise interference. The average accuracy of fault diagnosis of the transformer winding core reaches 97.9375%, and the time taken on the large-scale complex training set is only 1705 s, which has higher diagnostic accuracy and shorter training time than other models.

Interpretable analysis of transformer winding vibration characteristics: SHAP and multi-classification feature optimization

Interpretable analysis of transformer winding vibration characteristics: SHAP and multi-classification feature optimization

Innovative diagnosis of transformer winding defects using fuzzy and neutrosophic cross entropy measures

Innovative diagnosis of transformer winding defects using fuzzy and neutrosophic cross entropy measures

Fault Diagnosis Method for Transformer Winding Based on Differentiated M-Training Classification Optimized by White Shark Optimization Algorithm

Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate the accuracy limitations of single-classifier approaches in winding status assessment, this paper proposes a differentiated M-training classification algorithm based on White Shark Optimization (WSO). The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. For each base classifier, a parameter optimization method leveraging WSO is developed to enhance diagnostic precision. Second, an experimental platform for transformer fault simulation is constructed, capable of replicating various fault types with programmable severity levels. Through controlled experiments, frequency response curves and associated characteristic parameters are systematically acquired under diverse winding statuses. Finally, the model undergoes comprehensive training and validation using experimental datasets, and the model is verified and analyzed by the actual transformer test results. The experimental findings demonstrate that implementing WSO for base classifier optimization enhances the M-training algorithm’s diagnostic precision by 8.92% in fault-type identification and 8.17% in severity-level recognition. The proposed differentiated M-training architecture achieves classification accuracies of 98.33% for fault-type discrimination and 97.17% for severity quantification, representing statistically significant improvements over standalone classifiers.

Simulation Analysis and Experiment Research of Transformer Vibration Based on Electric–Magnetic–Mechanic Coupling

To research a transformer’s vibration characteristics, a simulation and an experiment are conducted on a 10 kV transformer. The theoretical model for core and winding vibration is established. The electric–magnetic–mechanic multi-physical field coupling model for the transformer core and winding is constructed, yielding voltage and current waveform and magnetic field distributions. The simulation results show that the amplitude of the main flux for core is 1.79 T, the amplitude of vibration acceleration for core is 0.005 m/s2, the magnetic flux leakage is 0.31 T, the amplitude of the vibration acceleration on the side of the winding is 0.0795 m/s2, and the amplitude of vibration acceleration on the front midpoint of winding is 0.0387 m/s2. The transformer vibration experimental platform is constructed, and no-load and load tests are conducted. Empirical findings demonstrate that the acceleration of core vibration is 0.0047 m/s2, and the simulation deviation is 6.38%. The maximum winding vibration acceleration at the side midpoint of phase A is 0.0714 m/s2, and at the front midpoint of Phase B is 0.0416 m/s2. Compared with experiment results, the simulation deviations are 2.1% and 3.3%, respectively. These conclusions indicate an alignment between the experiment and simulation results, thereby confirming reliability of the methodology.

Accuracy Performance of Open-Core Inductive Voltage Transformers at Higher Frequencies

The new revision of the main instrument transformer standard, IEC 61869-1:2023, premiered requirements for the performance of instrument transformers in terms of transfer accuracy at higher frequencies. Five accuracy class extensions were introduced to establish an explicit performance level. Each of the extension levels has a distinct bandwidth and accuracy performance associated with it. While these requirements are mainly aimed at non-conventional instrument transformers, the hypothesis of this paper is that conventional high-voltage instrument transformers can have a performance conformant to the above-mentioned requirements. Specifically, the focus of this paper will be on open-core inductive voltage transformers, which inherently exhibit an improved frequency response in comparison to their conventional closed-core counterparts. The main aim of this paper is to present a relevant transformer model based on a lumped parameter equivalent diagram. This model considers the actual mutual coupling (both capacitive and inductive) of the transformer windings. The model is created in EMTP software, and the output yields a frequency response characteristic of the transformer. The model will be validated with test results obtained through measurements on actual 123 kV, 245 kV, and 420 kV inductive voltage transformers.

Mechanical and electrical properties of ALTEK alloy electrical foil for transformer windings

Mechanical and electrical properties of ALTEK alloy electrical foil for transformer windings

Digital protection scheme based on Durbin Watson and Pearson similarity indices for current signals practically applied to power transformers

Electrical faults can change the power quality parameters of power systems. A numerical protection technique for fault detection and imbalance assessment based the Durbin-Watson (DW) factors for three phase currents is proposed in this paper. The approach integrates two protection functions based on the Durbin-Watson and Pearson similarity algorithms into one protection scheme. This strategy can figure out online faults located on the three-phase power transformer windings, such as turn-to-turn, winding-to-neutral, and winding-to-winding. Moreover, it can distinguish between balanced and imbalanced currents. To assess the validity of the protection scheme, it is practically examined on a three-phase power transformer with tapped windings connected to a three-phase load. Comprehensive tests are conducted to investigate the efficacy and efficiency of the suggested scheme. The analog-to-digital converter is integrated with LABVIEW software to process and analyze the two algorithms of the suggested scheme. The results of the experiments reveal that the security, dependability and precision ratios of the developed protection are almost 99%. Additionally, the protection system can immediately identify electrical faults, triggering a tripping signal to both the annunciator panel and the circuit breaker trip coil of the equipment, but it remains inactive under normal operating conditions and acceptable current unbalance. In the fault events, the numerical approach can respond quickly using a limited data set within a single cycle of the foundation frequency, and operate effectively using a pair of algorithms based on DW and Pearson similarity. It is also robust against the condition of sound transformer windings. Besides, it can determine and estimate the severity rate of perturbation and unbalance in power transformer currents, and it has a protection redundancy. Furthermore, the scheme is extremely sensitive to light fault currents, and has a unique set of tripping curves.

Design and Modeling Guidelines for Auxiliary Voltage Sensing Windings in High-Voltage Transformers and Isolated Converters

This paper provides guidelines for designing and modeling sensing coils in high-voltage, high-frequency transformers to enable a cost-efficient design of isolated converter topologies. The objective is to design a magnetic structure in which an additional sensing coil, placed on the main transformer, can be used to precisely measure the voltage on the secondary, despite fast changes in the voltage and current. This is usually a challenging task since high-voltage transformers will always require considerable isolation, which will give rise to significant leakage fields, which in turn will distort the measurement, especially at high frequencies. Our main finding is that this problem can be avoided if the sensing winding is carefully routed to maintain a certain ratio between the transformer’s coupling coefficients, which is achieved by placing this winding in an area within the core in which the magnetic field is low. In principle, this leads to a linear relationship between the voltages of the secondary and sensing windings despite non-ideal leakage inductances. The results are demonstrated experimentally using a 10 kW transformer, with 60 kV isolation, demonstrating a coupling coefficient of about 0.99, which reflects an error of less than 1.5% in the sensed secondary voltage.

Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation

Winding deformities in distribution transformers pose significant risks to operational reliability and system safety. Frequency response analysis (FRA) is a well-established technique for identifying mechanical faults; however, its diagnostic reliability is hindered by subjectivity in interpreting response signatures. This study proposes a novel diagnostic technique, termed FRA6σ, which integrates Six Sigma (6σ) statistical tools with FRA to enable objective fault detection. The methodology employs control charts (X¯ chart, R¯-chart) to monitor deviations from baseline signatures and utilizes process capability indices (Cp and Cpk) to quantify the severity of deviations. Three transformer cases were evaluated across five defined frequency regions (10 Hz to 2 MHz), each associated with distinct physical fault types. The FRA6σ approach successfully identified early-stage faults across all cases. In one instance, axial and radial winding deformation was detected with a Cp of 1.0 and corresponding range chart violations, preceding any visible damage. Another case revealed inter-turn insulation degradation in the 100 kHz–1 MHz band with Cpk values below 0.9, prompting immediate intervention. Compared to traditional FRA interpretation, the proposed method improved diagnostic sensitivity by 31.25% and enabled fault detection earlier based on retrospective physical inspection benchmarks. The integration of Six Sigma with FRA provides a structured, quantifiable, and repeatable approach to transformer fault diagnostics. FRA6σ enhances early detection of winding deformities and dielectric issues, offering a robust alternative to subjective analysis and supporting predictive maintenance strategies in power systems.

Algorithm for sequences exploration and optimization of a Multi-Active-Bridge

Algorithm for sequences exploration and optimization of a Multi-Active-Bridge

Terminal-based method for efficient inter-turn fault localization and severity assessment in transformer windings

Terminal-based method for efficient inter-turn fault localization and severity assessment in transformer windings

Detection algorithm for induced voltage in primary winding of transformer

Detection algorithm for induced voltage in primary winding of transformer

Towards Automated Frequency Response Analysis of Power Transformers with Deep Learning

ABSTRACT Frequency response analysis (FRA) has emerged as one of the standard techniques for monitoring the integrity of the mechanical structure of power transformer windings. Interpreting FRA test results, though, is still largely dependent on expert identification of graphical features. Machine learning, however, presents an opportunity to automate and improve this feature identification process. In this study, FRA measurements were simulated and then image and series data representations were used to train three neural networks. The Xception network, trained with image magnitude data, obtained the best performance, with an F1 score of 98.6%. The ResNet and Fully Connected Neural Network, trained with series magnitude data, obtained F1 scores of 94.6% and 91.4%, respectively. Results revealed that networks trained using image-encoded FRA data outperformed those trained using series FRA data.

Accumulative Effect of Bipolar Oscillation Impulse Voltage on Interturn Insulation of Transformer Winding

Accumulative Effect of Bipolar Oscillation Impulse Voltage on Interturn Insulation of Transformer Winding

Simulation study on converter transformer windings stress characteristics under harmonic current and temperature rise effect

Simulation study on converter transformer windings stress characteristics under harmonic current and temperature rise effect

A Control Technique for Galvanically Isolated DC–DC Converters with a Single Channel

This paper presents an on–off power control technique for galvanically isolated dc–dc converters, which implements a feedback control loop for power regulation on the same isolation transformer used for power transfer. To this aim, the power oscillator is controlled with a PWM scheme, and the control signal is transmitted through the galvanic barrier by using an ASK modulation that acts on the secondary winding of the isolation transformer. The key building block of the proposed architecture is a PLL that allows the reconstruction of the PWM control signal when the power oscillator is turned off and data transmission is disabled. The effectiveness of the proposed power control architecture is validated by designing an isolated dc–dc converter based on a thick polyimide transformer. It complies with reinforced isolation while addressing the power requirements of applications such as low-power sensor interfaces, medical devices, and housekeeping power, e.g., gate drivers or controllers for power converters. At a 20 V output voltage, 110 mW isolated output power is delivered. The dc–dc converter also provides PWM power regulation against PVT variations.

Sensing Performance of Crn (n = 1-3) Clusters Doped WSe2 and WTe2 for Gases in Power Transformer Winding Deformation Fault by DFT Calculation.

In order to verify the adsorption effect of WTe2 on the fault gas of power transformer winding deformation, this paper uses density functional theory (DFT) to study and compare the adsorption mechanism and sensing characteristics of three fault gases (CO, CO2, C2H2) on the surface of WSe2 and WTe2 substrates after cluster doping with Crn (n = 1-3). By analyzing the binding energy, band structure, adsorption distance, adsorption energy, DCD and DOS of the two substrate systems, the adsorption effect of Cr cluster doped WSe2 and WTe2 on fault gas is compared. Higher stability and conductivity indicate that Cr cluster doping is beneficial for WSe2 and WTe2 substrates. CO and CO2 have the best adsorption characteristics and sensing performance among the three doping systems on Cr2-WSe2, while C2H2 can be best captured by Cr3-WTe2. This study also analyzes the work function, band gap and energy gap, further verifying the conductivity changes described in the previous article. In addition, the recovery time and sensitivity are used to discuss the practical application prospects of the two substrates after Cr cluster doping, and corresponding conclusions are obtained. This study provides a theoretical basis for evaluating and preventing winding deformation failures in power transformers, and also provides new insights into whether WTe2 can be used as a mainstream gas-sensitive material in engineering fields.