2026· IEEE Transactions on Instrumentation and Measurement· Vol 75, pp. 9006416-9006416· 0 citations· 26 references
Abstract
Metal oxide surge arresters (MOAs) are core overvoltage protection devices in power systems. However, conventional single-parameter MOA fault diagnostic methods exhibit low accuracy and limited anti-interference capability in complex operating conditions. In this article, a distributed in situ measurement technique and a lightweight analytical criterion for MOA fault diagnosis are proposed. First, a measurement system is designed based on a distributed parameter equivalent circuit model, which deploys eight embedded sensors within the valve column along the axial direction. This architecture enables synchronous acquisition of the longitudinal leakage current vector and temperature profile. Second, experiments are performed under three typical conditions of normal operation, surface fouling, and valve disk failure to reveal the distribution characteristics of temperature and leakage current. Third, an analytical criterion is proposed, which integrates multiple feature parameters, including leakage current inhomogeneity coefficient, full current, and maximum temperature rise. Last, the K-means clustering method is adopted to determine thresholds for three typical operating conditions. The experimental results show that the proposed criterion achieves an accuracy of 96.67 %, outperforming single-feature and traditional machine learning methods. The research provides a new approach for MOA fault diagnosis.
The Metal-Oxide Arrester (MOA) is a crucial device for ensuring the safety of power grids, but its fault diagnosis faces the challenge of scarce actual fault samples. In this study, a 3D electro-thermal coupled simulation model is developed to reconstruct four operating conditions: internal moisture, aging of the insulating sleeve, wet contamination, and normal operation. Specifically, fault mechanisms are physically modeled by altering the conductivity of the insulating sleeve for aging, mapping conductive layers with varying coverage on the porcelain housing for wet contamination, and attaching geometric water bands to the valve discs for internal moisture. Based on this model, current and temperature signals are obtained to analyze the electro-thermal characteristics under different faults. Subsequently, a CatBoost-based fault diagnosis framework is introduced to construct a fault diagnosis model. By preprocessing data and integrating features such as temperature statistics and geometric quantities, the model’s recognition performance is significantly improved. Comparative experiments against XGBoost, fitcnet, and Support Vector Machine (SVM) demonstrate the superiority of the proposed method. The final model achieves an accuracy of 99.3%, outperforming XGBoost (98.6%), fitcnet (96.5%), and SVM (90.0%). This study provides an effective solution for MOA fault diagnosis under sample-scarce conditions, enhancing the intelligent assessment and maintenance of power grids.
When a short-circuit fault occurs along the transmission line of a modular multilevel converter high-voltage direct-current (MMC-HVDC) grid, the sub-module capacitors discharge, causing the fault current to rapidly rise, posing a threat to the safe operation of the system. Therefore, this paper proposes a novel fault location method based on the Gram Angle Difference Field (GADF). The column corresponding to the maximum differential value in a sliding window is used to identify the fault moment and locate faults in MMC-HVDC transmission lines. In order to effectively distinguish normal fluctuations from fault mutations and avoid false alarms, a dynamic threshold is set based on the statistical characteristics of normal data. This method utilizes the unique feature extraction capability of the GADF matrix, the adaptive mechanism of the dynamic threshold, and the stability of line-mode voltage to achieve fast and accurate fault location. Finally, this method is validated using a simulation model. The results show that the proposed method can accurately locate faults in different conditions.
Xiangyang Liu, Zhong Tang, Hong Qian et al.· Energies· 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
In this paper, a fault diagnosis method based on temperature rise detection is proposed for power converters in switched reluctance motor drive systems used in electrical transportation equipment. First, the total power losses of all power devices are calculated and recorded under different operating conditions in both healthy and faulty states. A finite-element electrothermal model is then established to characterize the relationship between fault-induced power-loss redistribution and variations in the temperature rise of the converter devices. Based on the power-loss analysis, temperature rise is used as a key characteristic, and a corresponding fault diagnosis method is proposed. To account for the influence of operating conditions on the diagnostic criterion, three independent backpropagation neural network (BPNN) models are developed to predict fault-specific temperature-rise thresholds using rotor speed, load torque, and ambient temperature as inputs. During diagnosis, the real-time temperature evolution of the power diodes is compared with the selected thresholds to detect power converter faults. Finally, experimental results demonstrate the validity of the proposed fault diagnosis method.
Xiangsu Wang, Zhijie Zhang, Qing Wang et al.· Machines· 0 citations
Electronic devices and power systems are vulnerable to transient overvoltage caused by lightning strikes, switching operations, and system faults. In DC circuit breaker applications, metal oxide surge arresters (MOSAs), composed of multiple metal oxide varistors (MOVs), play a critical role in absorbing fault energy and limiting overvoltage during interruption processes. However, MOVs may exhibit non-uniform V-I characteristics due to manufacturing tolerances, differences in cooling performance, and progressive aging under repeated surge current and overvoltage stresses. Such non-uniformity can lead to current imbalance within a MOSA bank, resulting in uneven energy absorption and degradation of overall overvoltage protection performance. In this paper, the impact of current imbalance in a MOSA bank is investigated using a PSCAD/EMTDC-based impulse current test-bed model. A virtual MOV impulse current test environment is implemented to analyze current sharing behavior under different MOV degradation conditions. The degradation level of individual MOV units is represented by controlled variations in their V-I characteristics to reproduce non-uniform degradation scenarios. The resulting current imbalance is quantitatively evaluated in terms of transient interruption voltage (TIV) and absorbed energy. The simulation results indicate that progressive MOV degradation exacerbates current imbalance and may degrade the overvoltage protection capability of the MOSA bank.
Jeong-Cheol Lee, I. Kwon, Y. Kwak et al.· Electrical Insulation Confer...· 0 citations
As core connection components in power transmission systems, high-voltage bushings operate under extreme conditions for extended periods, including high voltage, high current, elevated ambient temperature, and intense mechanical loads. Under the combined effects of electrical, thermal, and mechanical stresses, internal discharge and overheating faults in bushings are particularly prominent, posing a serious threat to the safe and stable operation of ultrahigh-voltage AC/ultrahigh-voltage DC (UHVDC) power grids. As grid voltage levels and transmission capacities continue to increase, accurate detection and intelligent assessment of the operating condition of high-voltage bushings have become crucial for ensuring their reliable operation. However, most existing studies focus on individual detection methods but lack systematic comparisons and engineering applicability analyses of different technologies, making it difficult to guide the selection of on-site detection schemes. This article first reviews the typical structures and evolutionary history of high-voltage bushings and summarizes their typical faults and occurrence mechanisms. On this basis, this article reviews recent advances in six categories of fault detection and condition assessment technologies, including electromagnetic, thermal, acoustic, optical, chemical-composition analysis, and intelligent diagnosis methods, with emphasis on the applicable scenarios, technical advantages, and application limitations of each method. Finally, this article discusses in depth the main challenges faced by current technologies, including environmental interference, sample dependency, and difficulties in virtual–physical synchronization, and outlines key future research directions, aiming to provide theoretical guidance for real-time monitoring and intelligent diagnosis of high-voltage bushings.
Guangning Wu, Shengbing Wei, Zhipeng Shi et al.· IEEE Transactions on Instrum...· 0 citations