Power transformer integrity is critical for system reliability as mechanical winding deformations can alter electrical behavior. Sweep Frequency Response Analysis (SFRA) is widely used to detect such alterations; however, fault localization remains challenging due to the terminal nature of conventional measurements. This paper characterizes the distributed electromagnetic behavior of a transformer winding using spatially resolved transfer functions along its axial position. Controlled faults are introduced by modifying inter-disc capacitance, and magnitude and phase data are used to construct three-dimensional representations as a function of frequency and winding position. The proposed framework visualizes how localized perturbations redistribute modal energy within the winding. Selected diagnostic frequencies correspond to spatial electromagnetic modes, providing meaningful features for the classification model. A two-stage Support Vector Machine (SVM) is implemented to localize faults into predefined winding regions using only terminal measurements. The method achieves 94.4% overall classification accuracy. Results demonstrate that conventional SFRA data contain sufficient information for faulty localization when interpreted within a distributed-modal framework.
Sweep Frequency Response Analysis (SFRA) serves as a highly sensitive diagnostic technique for identifying mechanical deformations within power transformer windings. The inherent magnetic and capacitive inter-winding couplings in transformers mean that a fault localized to a single winding can propagate detectable anomalies across the frequency responses observed at the terminals of all windings. This study systematically explores this interdependency through detailed simulations and case studies. A comprehensive six-winding equivalent circuit model of a 40 MVA, 132/11 kV power transformer, incorporating both magnetic and capacitive coupling effects is simulated to validate the underlying hypothesis. Complementing this, faults comprising an open-circuit condition and radial buckling were deliberately induced in the low-voltage (LV) winding of a 20 MVA, 66/11 kV oil-immersed transformer for experimental validation. The resultant frequency response traces reveal pronounced deviations in both LV and high-voltage (HV) signatures attributable to faults within a single phase winding. The study concludes that, due to unavoidable coupling, analyzing a single winding signature is sufficient to ensure the presence of mechanical deformation, whereas comparison of multiple signatures retains value for fault localization and severity assessment.
Mayur V. Gojiya, Mehul Dansinh Solanki, Ashokkumar Parmar et al.· ITEGAM- Journal of Engineeri...· 0 citations
Power transformer windings can be damaged by internal faults, such as short-circuit currents and the forces they generate, which can lead to, e.g., internal shorts due to damaged insulation. If these faults are not detected early, they can lead to catastrophic failures. In general, distributed-parameter models can simulate the performance of a transformer in both time and frequency domains more accurately than conventional lumpedparameter models, especially at higher frequency range (> 1 MHz). In this work, a multiconductor transmission line (MTL) model is used to simulate transformer windings in the frequency domain. Two typical winding configurations, continuous disk and interleaved disk, are studied. Internal shorts and buckling faults are applied to both models, and their frequency responses are compared using the frequency response analysis (FRA). The results show that each fault causes a noticeable change in the resonant frequency and admittance curve, except in the case of buckling in the interleaved-disk winding. The results demonstrate that combining MTL simulations with FRA can effectively identify the type of internal faults in transformer windings, offering a more comprehensive diagnostic tool than conventional frequency-domain analyses using lumped-element models.
Amir Ali Kaabi Nejadian, Hamed Moradi Tavasani, Waldemar Ziomek et al.· Electrical Insulation Confer...· 0 citations
The winding structure is a critical component of a power transformer. Assessing its structural condition during operation poses significant challenges for grid maintenance. Unlike static windings, the on-load windings undergo periodic stiffness changes under electromagnetic forces (EMFs). In this study, a novel parametric vibration model based on the time-varying stiffness is investigated. Next, the Mathieu equation is employed to analyze the sideband vibration response, and the spectral correlation (SC) method is applied to detect weak signals. This study presents a transfer learning framework employing a knowledge separator and a domain-adversarial network to address the challenge of limited fault samples. In laboratory experiments, the structural degradation of windings was simulated by adjusting the clamping force. Both numerical simulations and experimental results show that reducing the clamping pressure from 4.0 to 2.0 MPa causes the characteristic frequency to shift from approximately 46–20 Hz. The performance of the transfer learning is assessed based on the prediction accuracy of the unlabeled samples in the target domain following training in the source domain. Finally, vibration monitoring was performed on a field transformer, which served to verify the correlation between the observed 16.1 Hz low-frequency sideband vibration and the defects in the winding structure.
Kaixing Hong, Zichen Zhang, Yichuan Ye et al.· IEEE Transactions on Instrum...· 0 citations
In practical power systems, transformers are continuously involved in voltage conversion and electrical-energy transmission, so once abnormal operating conditions appear inside the equipment, the influence may gradually spread to the stability of the entire grid. Because early-stage transformer faults are usually weak and not easy to observe directly from conventional electrical quantities, this paper investigates a fault-monitoring method based on current-signal trajectories and discusses its application to transformer operating-state analysis. In the proposed approach, the primary-side and secondary-side current signals are taken as the basic data source, and the corresponding Lissajous trajectories are then constructed under different operating conditions. Instead of analyzing only waveform amplitude or phase separately, the method converts the current relationship into geometric trajectory characteristics, making the variation process easier to observe visually. To examine whether the method remains effective under different fault levels, a transformer equivalent-circuit model was built in Multisim, and several abnormal operating conditions were reproduced by changing equivalent impedance parameters and related electrical quantities. During the simulation analysis, several ellipse-related quantities were extracted from the generated trajectories, mainly including ellipse area together with the ratio between the major axis and minor axis. These geometric quantities were then compared under healthy and faulty operating conditions. The obtained results show that even under relatively weak parameter disturbances, such as ±3% variations, the trajectory shape had already changed slightly compared with the normal state, and the corresponding feature deviation approached about 5% in some simulation cases. After the disturbance level continued to increase, especially when the parameter variation reached around ±30%, the deformation of the ellipse became much more obvious, while part of the geometric feature variation exceeded 32%. From the overall simulation results, it can be seen that the trajectory characteristics still maintain visible differences under different transformer operating conditions, including relatively weak abnormal states appearing at an early stage. Compared with directly using conventional electrical signal quantities for analysis, the proposed method represents operating-state variation through geometric trajectories, so some small parameter changes can be reflected more intuitively. The study therefore suggests that current-signal trajectory analysis may provide another possible way for transformer online monitoring and operating-condition evaluation in practical engineering applications.
Rui Zhu· Applied and Computational En...· 0 citations
Transformer degradation is often accompanied by changes in electrical characteristics. Such changes may originate from insulation defects, winding abnormalities, or local dielectric damage. Detecting these variations at an early stage remains a challenge for condition monitoring. In this work, primary-side and secondary-side power-frequency voltages were analyzed through U₁–U₂ Lissajous trajectories. Ellipse parameters, including the major axis, minor axis, and inclination angle, were extracted from the trajectories and used for comparison under different operating conditions. A transformer equivalent circuit was established, and impedance variations were introduced to represent abnormal states. Trajectory geometry changed continuously as the equivalent parameters varied. Among the extracted features, the inclination angle showed the largest variation. Noticeable differences were already present when the impedance deviation reached ±3%. The obtained results indicate that voltage trajectories contain information related to internal parameter changes and may be used for condition evaluation during operation. Since only terminal voltage measurements are required, the method can be implemented without additional sensing devices.
Zihao Zhong· Applied and Computational En...· 0 citations
High flux tests are widely employed to diagnose inter-laminar short-circuit faults in generator stator cores. The measurement accuracy and diagnostic reliability primarily depend on the precise assessment of the core flux density via trace windings; however, the physical arrangement of these windings has long been overlooked. While IEEE, IEC, and Chinese GB standards specify different requirements for trace winding placement, their impacts on measurement integrity remain insufficiently quantified. This paper calculates the air leakage flux during testing and proposes a methodology for evaluating flux density measurement errors associated with various winding positions. By establishing an equivalent circuit for core short circuits combined with finite element analysis (FEA), the influence of measurement inaccuracies on the temperature rise at fault points is analyzed. Based on the distribution principles of air leakage flux and FEA results, a more rational trace winding arrangement strategy is proposed. Experimental results on a physical core model demonstrate that by applying the proposed flux density correction, the measurement error can be reduced to less than 1%. Furthermore, the optimized trace winding arrangement effectively reduces the core flux density non-uniformity from 7.1% to 0.7%.