Transformer Windings Research Articles

Winding Response under Difference Effective Inductance for Single Phase High Voltage Transformer

Early studies of transformer winding parameters were focused on the determination via its physical dimensions and empirical formulas. In most cases it is divided into several parts namely coil section pairs, coils distance, disc coils diameter and thickness of insulation. Maxwell’s equations are often the solution to the problem, which satisfy related boundary conditions between conductors for mutual inductance and capacitor equations. Such solutions often led to errors and hence its mathematical model. To counter the problem, it was suggested that such approximations must be conducted with experimental model windings at the same time. Frequency domain measurements and time domain measurements can be conducted to effectively determine these parameters. This in turn will investigate the behaviour of transformer winding electromagnetic transient at high frequency. From theoretical point of view, predominantly capacitive winding model often considered to represent its behaviour at high frequency and will give the results of its initial distribution. Under this consideration, a single phase plain winding is considered for investigation. A single rectangular wave was considered to represent infinitive impinge incident wave, injected at one end of transformer winding and the measured response signals of the wavetail were considered for measurement. The experimental response and modelling results were compared and proved to have high agreement between the two.

Variational Mode Decomposition-Based Processing for Detection of Short-Circuited Turns in Transformers Using Vibration Signals and Machine Learning

Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one of the most vulnerable parts, where the damage of turn-to-turn short circuits is one of the most studied faults since low-level damage (i.e., a low number of short-circuited turns—SCTs) can lead to the overall fault of the transformer; therefore, early fault detection has become a fundamental task. In this regard, this paper presents a machine learning-based method to diagnose SCTs in the transformer windings by using their vibrational response. In general, the vibration signals are firstly decomposed by means of the variational mode decomposition method, where a comparison with the empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method is also carried out. Then, entropy, energy, and kurtosis indices are obtained from each decomposition as fault indicators, where both the combination of features and the dimensionality reduction by using the principal component analysis (PCA) method are analyzed for the global effectiveness improvement and the computational burden reduction. Finally, a pattern recognition algorithm based on artificial neural networks (ANNs) is used for automatic fault detection. The obtained results show 100% effectiveness in detecting seven fault conditions, i.e., 0 (healthy), 5, 10, 15, 20, 25, and 30 SCTs.

Hybrid Analytical-FEM Approach for Power Transformer Transient Analysis

The paper describes a high-frequency model for transient calculation in transformer windings with segmentation on a turn level. The windings are represented by lumped RLC parameters given by the analytical approach, whereas the model is solved in the time domain. The accuracy of the analytical calculation is improved with correction factors obtained with a set of numerical calculations. The presented model is verified by measurements on the example of power transformer disc winding.

Strongly Coupled Circuit‐Magnetic‐Structural Effect on Winding Vibration Process of Power Transformer

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.

Structural Deformation and Displacement of a Disc Winding Due to Standard Switching Impulse Voltage via Finite Element Method

Switching operations in a power system network can lead to transient overvoltage in the high voltage (HV) winding of distribution transformers that causes high-stress build-up. This paper presents the relationship between electromagnetic force due to a standard switching impulse (SSI) and mechanical deformation/displacement behaviours for a disc-type transformer. The analysis was carried out based on a three-dimensional (3D) modelling of a continuous HV disc winding configuration whereby it is subjected to the switching transient voltage and force excitations through the finite element method (FEM). The electric transient solver analysed the static and dynamic aspects of the electromagnetic forces associated with the variation of forces versus time. The transient structural solver evaluated the structural behaviours of the disc winding related to the axial height and radial width of the winding under electromagnetic forces. It is found that the positively dominant axial force generated in the winding with a magnitude of 8.7 N causes the top and bottom layers of disc winding to tilt and displace. In addition, the positive average radial force of 1.4 N causes the circumference of the winding to experience hoop tension and outwardly stretch.

Artificial neural network-based cooling capacity estimation of various radiator configurations for power transformers operated in ONAN mode

Power transformers submerged in oil are universally acknowledged as very useful elements in electrical power networks. A fraction of the electrical energy involved in the conversion from high to low voltages is lost as thermal energy, which is produced inside the transformer's windings and core. The effective dissipation of heat is of paramount significance and can be accomplished through the strategic installation of radiators on the tank. This study aims to examine the total cooling capacity of a radiator employed for cooling a power transformer operating in oil, natural air, and natural mode. The investigation is conducted by varying the design parameters, specifically the number of fins per radiator and the radiator length. The study also utilizes computational fluid dynamics results to achieve a substantial convergence with experimental findings. In the context of the verification study conducted for a specific study point, it was found that the simulation outcomes at a mass flow rate of 0.15 kg/s corresponded to about 7.4 % of the experimental results. Additionally, the cooling capacity value obtained was 7.2 %. In the context of machine learning, the performance of the numerical approach was assessed by employing the Bayesian regularization method. The evaluation revealed that the margin of deviation, mean squared error, and coefficient of determination (R2) metrics yielded values of −0.001 %, 1.32E-02, and 0.99930, respectively.

Direct single‐phase isolated improved gamma Z‐source AC–AC converter suitable for DVR application

AbstractA direct single‐phase isolated improved gamma Z‐source AC–AC converter is designed and manufactured in proposed paper. This converter contains an impedance source structure and its advantages. In general, these advantages are as follows. Firstly, the proposed structure has a wide range of output voltage as boost and buck as well as in‐phase or out‐of‐phase states. Secondly, the proposed converter is electrically isolated thanks to a high frequency transformer (HFT), which significantly reduces the volume of the structure; therefore, in applications such as dynamic voltage restorer (DVR), there is no need to install a coupling transformer with the grid frequency which is very bulky and full of losses. Furthermore, the HFT of the proposed converter is a three winding transformer which also acts as the inductance needed for impedance source network. In addition, the safe commutation method has been utilized, which reduces the voltage spikes on the power switches. Finally, the analysis and design of this converter has been done, and then confirmed by a small‐scale laboratory prototype.

Automatic identification of transformer excitation inrush based on second-order Taylor coefficient

In order to solve the time-consuming problem of the existing automatic identification methods of transformer inrush current, an automatic identification method of transformer inrush current based on second-order Taylor coefficient was designed. Ignore the leakage resistance and winding resistance of transformer winding, get the second harmonic characteristic quantity, assume the transformer variable ratio value is fixed, identify the transformer winding parameters, introduce the decay coefficient to describe the characteristic of excitation inrush current gradually decaying with time, construct the excitation inrush current Taylor expansion phase model, and set the automatic identification mode based on the second order Taylor coefficient. Experimental results: The designed transformer excitation inrush current automatic identification method and the other two transformer excitation inrush current automatic identification method of the elapsed time were: 55.32ms, 71.52ms, 71.04ms, indicating that after the integration of the second-order Taylor coefficient, the designed transformer excitation inrush current automatic identification method better performance.

Fault diagnosis and analysis of a 110 kV transformer

This paper aims to provide valuable insights and assistance for ensuring the safe operation of a 110 kV main transformer by conducting an in-depth analysis of tap changer breather oil leakage defects. By employing transformer testing, disassembly analysis, and other methods, this study successfully identifies and prevents the possibility of transformer failure. The analysis reveals that the deformation of the transformer winding is caused by insufficient short-circuit resistance, which leads to the deformation of the medium-voltage windings under the continuous action of electrodynamic forces during near-area short-circuit currents. Near-area short-circuits are a significant contributing factor to transformer failures. Therefore, it is recommended to enhance operation and maintenance management for older main transformers and optimize protection settings to reduce the risk of near-area short-circuit failures, thus ensuring the safe and stable operation of the main transformer.

An Efficient and Compact Equalizer Based on Forward-Flyback Conversion for Large-Scale Energy Storage Systems

An effective battery equalizer is of great significance to eliminate inconsistencies and improve the performance of large-scale battery packs. Nevertheless, the existing balancing circuits have various problems such as slow balancing speed, high cost, bulky size, complex control, etc. Therefore, this paper designs an efficient and compact transformer-based equalizer, which works at forward-flyback conversion. Distinguished from the conventional transformer-based equalizers, the proposed equalizer requires only one MOSFET and one transformer winding equipped with each cell. Two complementary PWM signals can control all MOSFETs used in the proposed equalizer. The equalizer can automatically equalize all cells without the need for additional voltage sensing and demagnetization circuits, reducing the cost and volume. It can directly transfer energy between any two adjacent modules, improving the equalization efficiency and speed. An experimental prototype of eight series-connected cells was set up. The experimental results and systematic comparison with the existing balancing topologies show that the proposed equalizer is appropriate for balancing large-scale energy storage systems. The energy delivery efficiency among cells can be as high as 93.15% over various loading conditions.

IoT based monitoring of distribution transformer

As transformer does not have any rotating parts hence it is called as static device, therefore it does not have mechanical losses, but internal stresses arose from abnormal system conditions like Over currents due to overloading on Transformer and short circuits, The faults can cause mechanical and thermal stresses inside the transformer winding and its connecting terminals. Thus transformer must be protected from above stresses. The aim of this project is to obtain real time data of transformer from remote places with the help of Internet of Things[ IOT] and then designing and implementing a Global system of Mobile communication [GSM] based technology to measure overvoltage, load current, and winding temperature. By measuring the condition of Transformer will help us to point out an any abnormalities before any serious damage that in turn leads to a greater credibility and significant cost savings

Research on uninterruptable power supply technology for auxiliary winding of electric locomotive while passing the neutral section

PurposeAuxiliary power system is an indispensable part of the train; the auxiliary systems of both electric locomotives and EMUs mainly are powered by one of the two ways, which are either from auxiliary windings of traction transformers or from DC-link voltage of traction converters. Powered by DC-link voltage of traction converters, the auxiliary systems were maintained of uninterruptable power supply with energy from electric braking. Meanwhile, powered by traction transformers, the auxiliary systems were always out of power while passing the neutral section of power supply grid and control system is powered by battery at this time.Design/methodology/approachUninterrupted power supply of auxiliary power system powered by auxiliary winding of traction transformer was studied. Failure reasons why previous solutions cannot be realized are analyzed. An uninterruptable power supply scheme for the auxiliary systems powered by auxiliary windings of traction transformers is proposed in this paper. The validity of the proposed scheme is verified by simulation and experimental results and on-site operation of an upgraded HXD3C type locomotive. This scheme is attractive for upgrading practical locomotives with the auxiliary systems powered by auxiliary windings of traction transformers.FindingsThis scheme regenerates braking power supplied to auxiliary windings of traction transformers while a locomotive runs in the neutral section of the power supply grid. Control objectives of uninterrupted power supply technology are proposed, which are no overvoltage, no overcurrent and uninterrupted power supply.Originality/valueThe control strategies of the scheme ensure both overvoltage free and inrush current free when a locomotive enters or leaves the neutral section. Furthermore, this scheme is cost low by employing updated control strategy of software and add both the two current sensors and two connection wires of hardware.

Analysis of overvoltage impact on shell-type on-board traction transformer by multi-physics field simulation

With the rapid development and increasing demands for the high-speed railway in China, it is of great significance to examine the defects of the high-speed train and ensure the safety of the operating railway. In the actual operation of the high-speed train, there is a split-phase section to transit the train to the next substation power supply arm. In this particular stage, operational overvoltage would occur as the VCB and the pantograph switch on and off, which threatens the safety of the operation of high-speed trains and contributes to the breakdown of transformer windings, which is indispensable in transformer operation. In this work, a finite element model of shell type on board traction transformer is constructed and multi-physics field simulation results, including electromagnetic field, temperature field, and flow velocity field, are analyzed to investigate the potential risky spots in the practical operation of high-speed train under overvoltage impulse, which could be supportive in maintaining the devices and detecting the winding defects.

Isolation‐based bidirectional generalized step‐up multilevel converter with optimized device count

SummaryThis article describes a generalized single‐phase multiwinding step‐up transformer‐based multilevel converter with optimized device count. The proposed topology comprises of a single dc source, eight switches (four unidirectional and four bidirectional) and a single high‐frequency multiwinding transformer. Phase disposition pulse width modulation (PDPWM) is used for the level creation. The proposed converter is examined qualitatively and quantitatively using the total number of transformers, PWM control method, switch count, voltage stress, and the number of gate drivers, all of which are favorable. All elements of transformer selection, winding requirements, and comparative study have been thoroughly examined. A thorough Lagrange multiplier method has been applied to minimize the copper losses and optimize the multiwinding transformer (MWT) winding. The proposed converter simulations are performed using the MATLAB/SIMULINK environment. Further, the validation of the result is confirmed by a hardware setup of 0.9 kW.

Toward unique electrical ladder network model synthesis of a transformer winding high-frequency modeling using K-means and metaheuristic-based method

PurposeAlthough, numerous optimization algorithms have been devoted to construct an electrical ladder network model (ELNM), they suffer from some frail points such as insufficient accuracy as well as the majority of them are unconstrained, which result in optimal solutions that violate certain security operational constraints. For this purpose, this paper aims to propose a flexible-constraint coyote optimization algorithm; the novelty lies in these points: penalty function is introduced in the objective function to discard any unfeasible solution, an advanced constraint handling technique and empirical relationship between the physical estimated parameters and their natural frequencies.Design/methodology/approachFrequency response analysis (FRA) is very significant for transformer winding diagnosis. Interpreting results of a transformer winding FRA is quite challenging. This paper proposes a new methodology to synthesize a nearly unique ELNM physically and electrically coupled for power transformer winding, basing on K-means and metaheuristic algorithm. To this end, the K-means method is used to cluster the setting of control variables, including the self-mutual inductances/capacitances, and the resistances parameters. Afterward, metaheuristic algorithm is applied to determine the cluster centers with high precision and efficiency.FindingsFRA is performed on a power transformer winding model. Basing on the proposed methodology, the prior knowledge in selecting the initial guess and search space is avoided and the global solution is ensured. The performance of the abovementioned methodology is compared using evaluation expressions to verify its feasibility and accuracy.Originality/valueThe proposed method could be generalized for diagnosis of faults in power transformer winding.

Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis

Accurately diagnosing various winding deformation faults is significant in power transformer maintenance. Among the different fault diagnosis methods, frequency response analysis (FRA) is widely used but still poses challenges. Artificial intelligence (AI)-based methods have recently been proposed to interpret FRA data. Nevertheless, these approaches are either complicated or exhibit limited generalization performance due to real-world FRA fault data scarcity. Inspired by AI-generated content (AIGC), this study proposes a data augmentation technique named conditional Wasserstein generative adversarial network with gradient penalty (Conditional-WGAN-GP) combined with fault diagnosis model. Numerous FRA-based data are automatically generated using the proposed data augmentation technique based on real FRA data obtained from a specially designed 10 kV transformer. The augmented dataset is then used to train fault diagnosis models to detect winding deformation faults. The trained fault diagnosis model is subsequently applied to assess two actual transformers. Experimental results demonstrate that when combined with the proposed method, even simpler fault diagnosis models can achieve high accuracy, exhibiting an improvement of approximately 5 % compared to the previous baseline model. The fault diagnosis models combined with the proposed data augmentation technique demonstrate improved generalization and robustness. (GitHub code: https://github.com/cy1034429432/Diagnosing-Transformer-Winding-Deformation-Fault-Types-from-FRA-Based-on-Conditional-WGAN-GP-/tree/main).

Research on identification method of transformer winding material based on vibration characteristic

At present, there are shoddy transformers in the market, of which the windings are replaced by aluminum and copper clad aluminum. The commissioning of these transformers may cause poor power supply performance and excessive winding heating. The existing detection methods of windings are complex, time-consuming to operate and destructive. Therefore, a winding material identification method based on vibration characteristics is proposed. Firstly, the vibration accelerations of transformer cores and windings with different winding materials are theoretically derived. Furthermore, through the coupling simulation of magnetic field and structure field of distribution transformers, the calculated vibration characteristics of copper, aluminum, and copper clad aluminum are verified. Finally, by comparing the time domain, the frequency domain and time-frequency domain of acceleration signals, preliminary identification of winding materials is conducted, which lays a theoretical foundation for establishing a precise identification model for winding materials in the future. This work provides guarantee for the safe operation of the distribution network.

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

AbstractThis 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.

Improved thermal network model for hot spot location in traction transformer windings

Obtaining accurate location information of the hot spot in advance is a prerequisite for locating the hot spot. To tackle this problem, we propose an improved thermal network model, which provides a quantitative method for placing fiber optic sensors at hot-spot locations. The initial step is to construct an enhanced thermal network model that takes into account the thermal resistance of the boundary layer using heat transfer theory. Then, we compare this model with Computational Fluid Dynamics (CFD) in example calculations to validate its effectiveness and precision. The results show that our proposed model can accurately and rapidly calculate temperature distribution within winding areas, enabling the identification of hot spots. This information is reliable for installing monitoring equipment to measure transformer temperatures.

Research on Real-Time Calculation Method of Transformer Temperature Field Based on Reduced Order Model and Sparse Measurement

The lifespan of transformers is closely related to their operating temperature. Among the existing temperature acquisition methods, the measurement method cannot obtain the complete temperature field temperature, while the numerical calculation method presents the challenge of significant computational expenses. This study theoretically derived how to determine the real-time temperature distribution of transformer windings based on sparse measurement data and reduced order models and solved sensor layout issues, including the number and placement position of sensors. In this study, the maximum calculation error of the temperature distribution is 1.8 K, and the calculation time is 0.38 seconds. The research findings suggest that multiple modes can contribute to the temperature distribution of a transformer. Under a constant load condition, the calculation of transformer temperature can be achieved by utilizing a temperature sensor positioned at the hot spot in conjunction with the first mode.