Jul 2026· Applied and Computational Engineering· Vol 243, pp. 64-74· 0 citations
Abstract
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.
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
As a critical component of power systems, the operational status of power transformers directly affects the safety and reliability of the power grid. Addressing the challenge that traditional methods struggle to achieve online monitoring of winding deformation faults, this paper proposes a novel method for online detection of transformer winding deformation based on Lissajous figure trajectories. This method involves collecting voltage and current signals from both the highand low-voltage sides, constructing voltage-squared and instantaneous power characteristics, and generating corresponding Lissajous curves. By establishing a mapping relationship between changes in the mechanical structure of the windings and electrical parameter characteristics, the method enables the qualitative identification and quantitative assessment of axial and radial deformation in the windings. Experimental results demonstrate that this method can detect minor winding deformation (deformation amount = 3%) and exhibits higher sensitivity in detecting radial winding deformation compared to the frequency response analysis method. This study provides a new technical approach for online monitoring of transformer winding deformation and holds significant engineering application value for enhancing the condition monitoring and intelligent operation and maintenance of power grid equipment.
Lai-Hong Wei· European Conference on Elect...· 0 citations
Strong noise in high-voltage transformer environments can easily overwhelm weak fault signals during the switching control of simulated fault resistance networks. This interference hinders the accurate identification of fault types and levels, ultimately compromising the stability and accuracy of the switching process. To address this, we propose an automatic switching control method for simulated fault-resistant transformer networks based on a high-voltage integrated signal enhancement algorithm. By acquiring and enhancing signals from the high-voltage side of the transformer, we extract fault features from three domains — time, frequency, and nonlinearity — to construct a feature vector. An improved extreme learning machine is employed to identify fault types and levels, dynamically calculate fault-resistance parameters, and design a closed-loop automatic switching strategy using an FPGA controller. Experimental results show that, under 12 data sets representing four fault categories, the absolute deviation between the actual and theoretical resistance values is controlled within 0.0005 Ω to 0.0020 Ω. The switching delay is significantly lower than that of the comparison method, and the switching jitter time is substantially reduced.
Wei-Ping Wu, Tie-Gang Yang· Journal of Physics, Conferen...· 0 citations
Internal temperature monitoring is an important approach for assessing the operating condition of power transformers. Temperature-rise characteristics not only indicate faults such as inter-turn short circuits but also indirectly reflect the diffusion and transport behaviors of characteristic dissolved gases within the transformer oil. To enable early warning and localization of inter-turn short circuits in transformer windings, this paper proposes a method based on monitoring the oil temperatures at the inlets and outlets of the transformer cooling system. First, a simulation model of a 110 kV transformer was developed, and coupled thermal–fluid simulations were performed to analyze the oil-temperature distributions in the transformer body and cooling system under normal operating conditions and representative inter-turn short-circuit conditions. Next, a simulation-based fault-localization strategy is proposed to investigate whether the spatial temperature distribution of cooling-system oil can be used to infer the vertical position; faulty phase; and, for certain fault locations, the winding side of an inter-turn fault. Finally, the temperature sensor locations were selected, and the performance of the localization strategy was evaluated. The simulation results demonstrate the feasibility of using the temperature distribution of cooling-system oil for the detection and localization of inter-turn short-circuit faults.
Zi-Fan Zhao, Shan Yu, Shi-Man Lin et al.· Energies· 0 citations
Existing EMTR methods typically adopt passive location schemes that rely on transient signals generated by faults. When faults occur at a low inception angle, the resulting traveling-wave signals are typically weak and suffer from poor detectability. Active pulse injection provides controllable excitation and improves signal identification. The transfer function similarity method achieves high accuracy; its practical application is constrained by the requirement for transient voltage at the fault point. To address these limitations, this paper proposes a fault location method based on active pulse injection and systematically investigates the characteristics of fault voltage in both frequency and time domains. First, the frequency-domain formulation of fault voltage in the reversed-time process is derived, and the applicable scope of the energy metric is evaluated. The waveform features of the reversed-time fault voltage are then analyzed to assess the similarity between the fault voltage and the injected pulse voltage, as well as the applicability of the MCCC metric. An improved IMCCC criterion is developed to quantify the similarity between the fault voltage and the forward-time voltage. Furthermore, a symmetry similarity coefficient (SSC) is defined by leveraging the inherent symmetry property of fault voltage waveforms. The four metrics were validated using reduced-scale experiments and simulation studies. All metrics achieved accurate fault location in simple lines. In complex networks, the energy metric showed significant deviation from the real fault location. The IMCCC and SSC metrics provided higher accuracy than the MCCC metric and maintained reliable fault location for grounding faults up to 300 Ω.
In recent years, high penetrations of inverter-based resources are posing significant challenges to the medium-voltage networks, in which protection schemes based on high fault currents and unidirectional power flow may not perform as expected. This paper proposes a dynamic fault-detection and relay-coordination scheme for a medium-voltage network with high penetration of grid-forming inverter sources. A detailed 33 kV system model comprising six battery energy storage system (BESS) feeders and a four-distributed-load model was built in DIgSILENT Power Factory and tested under various grid-connected and islanded system conditions using the complete short-circuit method. Four simultaneous fault checks, including sequence component analysis, symmetrical voltage variation, superimposed current with voltage restraint, and current waveform analysis, are used to detect the fault in a specific part of the medium-voltage network. After-fault detection, dynamic pickup scaling and relay blocking are coordinated through IEC 61850 GOOSE and DNP3 so only the closest unblocked relay or relay pair trips. The dynamic pickup settings are adjusted considering the ratio of fault levels in the conventional system versus the inverter-based resources-fed medium-voltage system. Simulation results show successful overcurrent coordination retention even when inverter fault current limitation is set at 1.3 p.u. or lower with 10% generation margin. The proposed scheme allows traditional relays with existing infrastructure to function correctly in fully inverter-dominated medium-voltage systems without any synchronous backup. The novelty is the integration of fault confirmation, pickup scaling and a blocking scheme with retention of an independently operating local backup. Compared to fixed grid-connected settings, the proposed scheme recovers islanded-mode pickup values while maintaining primary–backup grading margin for the relay.
Muhammad Abdul Rauf, Munira Batool, I. Madni· Energies· 0 citations
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