Search Results (3,009)

Fault diagnosis methods based on deep learning have been widely applied to bearing fault diagnosis. However, current methods usually diagnose on the same individual device, which cannot guarantee reliability in real industrial scenarios, especially for new individual devices. This article explores a practical cross-individual scenario and proposes a Kolmogorov–Arnold enhanced convolutional transformer (KACFormer) model to improve both general feature representation and cross-individual capabilities. Specifically, the Kolmogorov–Arnold representation theorem is embedded into convolution and multi-head attention mechanisms to develop novel Kolmogorov–Arnold enhanced convolution (KAConv) and Kolmogorov–Arnold enhanced attention (KAA). The adaptive activation function enhances its nonlinear modeling ability. Comprehensive experiments are performed on two public datasets, demonstrating the superior generalization of the proposed KACFormer model with a higher accuracy of 95.73% and 91.58% compared to existing advanced models. Full article

Circulating current control is a critical part of the Modular Multilevel Converter (MMC) control system. Existing control methods rely on arm current information obtained from complex current measurement devices. However, these devices are susceptible to failures, which can lead to distorted arm currents, increased peak arm current values, and higher losses. In extreme cases, this can result in system instability. This paper first analyzes four typical arm current measurement faults, i.e., constant gain faults, amplitude deviation faults, phase shift faults, and stuck faults. Then, a Kalman Filter (KF)-based fault detection method is proposed, which allows for the simultaneous monitoring status of all six arm current measurements. Moreover, to facilitate fault detection, the Moving Root Mean Square (MRMS) value of the observation residual is defined, which effectively detects faults while suppressing noise. The entire fault detection process takes less than 20 ms. Finally, the feasibility and effectiveness of the proposed method are validated through MATLAB/Simulink simulations and experimental results. Full article

In view of the complex operating environments of wind farms and the characteristics of multi-branch mixed collector lines, in order to improve the accuracy of single-phase grounding fault location, the convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (attention) were combined to construct a single-phase grounding fault location strategy for the CNN–BiLSTM–attention hybrid model. Using a zero-sequence current as the fault information identification method, through the deep fusion of the CNN–BiLSTM–attention hybrid model, the single-phase grounding faults in the collector lines of the wind farm can be located. The simulation modeling was carried out using the MATLAB R2022b software, and the effectiveness of the hybrid model in the single-phase grounding fault location of multi-branch mixed collector lines was studied and verified. The research results show that, compared with the random forest algorithm, decision tree algorithm, CNN, and LSTM neural network, the proposed method significantly improved the location accuracy and is more suitable for the fault distance measurement requirements of collector lines in the complex environments of wind farms. The research conclusions provide technical support and a reference for the actual operation and maintenance of wind farms. Full article

This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt active filter (SAF) mechanism within the GFM control structure to achieve a real-time suppression of harmonic distortions from the inverter and grid currents. In parallel, a virtual impedance-based dynamic current limiting strategy is incorporated to constrain fault current magnitudes, ensuring the protection of power electronic components and maintaining system stability. The SAF operates in a current-injection mode aligned with harmonic components, derived via instantaneous reference frame transformations and selective harmonic extraction. The virtual impedance control (VIC) dynamically modulates the inverter’s output impedance profile based on grid conditions, enabling adaptive response during fault transients to limit overcurrent stress. A detailed analysis is performed for the coordinated control of the grid-forming inverter. Supported by simulations and analytical methods, the approach ensures system stability while addressing overcurrent limitations and active harmonic filtering under nonlinear load conditions. This establishes a viable solution for the next-generation inverter-dominated power systems where reliability, power quality, and fault resilience are paramount. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

This paper analyzes the transient voltage stability of the dual-source DC power system. The system’s equivalent model is first established. Subsequently, the effect mechanisms of line parameters and voltage-source rectifiers’ current control inner loops on the system’s transient voltage instability are investigated. It indicates that these factors reduce the power supply capacity of the source, increasing the risk of transient instability in the system. Then, considering the influence of fault depths, the influence of different large disturbances on the transient voltage stability is investigated. Furthermore, the critical cutting voltage and critical cutting time for DC power systems are determined and then validated on the MATLAB R2023b/Simulink platform. Finally, based on the mixed potential function theory, the impact of system parameter variations on stability boundaries is analyzed quantitatively. Simulation verification is conducted on the MATLAB R2023b/Simulink platform, and experimental verification is conducted on the RT-LAB Hardware-in-the-Loop platform. The results of the quantitative analysis and experiments corroborate the conclusions drawn from the mechanistic analysis, underscoring the critical role of line parameters and converter control parameters in the system’s transient voltage stability. Full article

With the large-scale integration of new power systems and distributed generators (DGs), cable fault detection and localization face numerous challenges, where artificial intelligence (AI) techniques demonstrate significant advantages. This review first outlines the causes of cable faults and traditional methods for fault detection and localization. Subsequently, it comprehensively analyzes the applications of both conventional machine learning and deep learning approaches in this field, elaborating on their application scenarios, strengths, defects, and successful case studies, providing valuable references for researchers and professionals. Additionally, the paper discusses the strengths and limitations of current AI techniques, along with the impacts introduced by DG integration. Finally, it highlights future development trends and potential research directions for advancing AI-based solutions in cable fault detection and localization. Full article

The development of solar technologies and the widespread adoption of photovoltaic (PV) panels have significantly transformed the global energy landscape. PV panels have evolved from niche applications to become a primary source of electricity generation, driven by their environmental benefits and declining costs. However, the performance and operational lifespan of PV systems are often compromised by various faults, which can lead to efficiency losses and increased maintenance costs. Consequently, effective and timely fault detection methods have become a critical focus of current research in the field. This work proposes an innovative video-based method for the dimensional evaluation and detection of malfunctions in solar panels, utilizing processing techniques applied to aerial images captured by unmanned aerial vehicles (drones). The method is based on a novel mini-pattern matching algorithm designed to identify specific defect features despite challenging environmental conditions such as strong gradients of non-uniform lighting, partial shading effects, or the presence of accidental deposits that obscure panel surfaces. The proposed approach aims to enhance the accuracy and reliability of fault detection, enabling more efficient monitoring and maintenance of PV installations. Full article

In this paper, a detailed review of electric vehicle (EV) charging station architectures is first presented, and then an optimal architecture suitable for a large MW-scale EV fast-charging station (EVFS) with multiple fast chargers is proposed and evaluated. The study examines various EVFS architectures, including those currently deployed in commercial sites. Most EVFS implementations use either a common AC-bus or a common DC-bus configuration, with DC-bus architectures being slightly more predominant. The paper analyzes the EV charging and battery energy storage system (BESS) requirements for future large-scale EVFSs and identifies key implementation challenges associated with the full adoption of the common DC-bus approach. To overcome these limitations, a novel multi-zone EVFS architecture is proposed that employs an optimal combination of isolated and non-isolated DC-DC converter topologies while maintaining galvanic isolation for EVs. The system efficiency and total power converter capacity requirements of the proposed architecture are evaluated and compared with those of other EVFS models. A major feature of the proposed design is its multi-zone division and zonal isolation capabilities, which are not present in conventional EVFS architectures. These advantages are demonstrated through a scaled-up model consisting of 156 EV fast chargers. The analysis highlights the superior performance of the proposed multi-zone EVFS architecture in terms of efficiency, total power converter requirements, fault tolerance, and reduced grid impacts, making it the best solution for reliable and scalable MW-scale commercial EVFS systems of the future. Full article

Transmission lines are constantly exposed to changes in climatic conditions and aging which affect the parameters and change the characteristics of the three-terminal circuit over time. In this paper we propose a fault location algorithm for three-terminal transmission lines to solve this problem. The algorithm utilizes the positive components of the voltage and current signals measured synchronously from the terminals. In this work no prior knowledge of the line parameters was required when calculating the fault location and the use of fault classification algorithms was not necessary. In addition, the proposed method determines the parameters of the line segment and fault location based on a solid mathematical basis and has been verified through simulation results using SIMULINK/MATLAB R2018a software. The fault location results demonstrate the high accuracy and efficiency of the algorithm. Moreover, this method can estimate the characteristic impedance and propagation constants of the transmission lines and determine the location of the fault, which is not affected by different fault parameters including fault location, and fault resistance. Full article

During the manufacturing process of oil-immersed current transformers, metallic particles may become embedded in the insulation wrapping, and the resulting electric field distortion is one of the primary causes of failure. Historically, the shape of metallic particles has often been simplified to a standard sphere, whereas in practice, these particles are predominantly irregular. In this study, ellipsoidal and flaky particles were selected to represent smooth and angular surfaces, respectively. Using COMSOL Multiphysics^® (version 6.2) software, a three-dimensional simulation model of an oil-immersed inverted current transformer was developed, and the influence of defect position and size on electric field characteristics was analyzed. The results indicate that both types of defects cause electric field distortion, with longer particles exerting a greater influence on the electric field distribution. Under the voltage of a 220 kV system, elliptical particles (9 mm half shaft) lead to the maximum electric field intensity of main insulation of up to 45.1 × 10⁶ V/m, while the maximum field strength of flaky particles (length 30 mm) is 28.9 × 10⁶ V/m. Additionally, the closer the particles are to the inner side of the main insulation, the more significant their influence on the electric field distribution becomes. The findings provide a foundation for fault analysis and propagation studies related to the main insulation of current transformers. Full article

Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

Large steam turbine generators are increasingly vulnerable to damage from shaft voltages and bearing currents due to the widespread adoption of modern power electronic excitation systems and more flexible operating regimes. Earthing brushes provide a critical path for discharging these shaft currents and voltages, but their effectiveness depends on the timely detection of brush degradation or faults. Conventional monitoring of shaft voltage and current is often rudimentary, typically limited to peak readings, making it challenging to identify specific fault conditions before mechanical damage occurs. This study addresses this gap by systematically analyzing shaft voltage and current signals under various controlled earthing brush fault conditions (floating brushes, worn brushes, and oil/dust contamination) in several large turbine generators. Experimental site tests identified distinct electrical signatures associated with each fault type, demonstrating that online shaft voltage and current measurements can reliably detect and classify earthing brush faults. These include unique RMS, DC, and harmonic patterns in both voltage and current signals, enabling accurate fault classification. These findings highlight the potential for more proactive maintenance and condition-based monitoring, which can reduce unplanned outages and improve the reliability and safety of power generation systems. Full article

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. The primary control employs droop-based regulation, and a distributed secondary layer enables proportional power sharing via peer-to-peer communication. A modified IEEE 14-bus test system is implemented in PSCAD to validate the proposed strategy under scenarios including load disturbances, reactive demand variations, and plug-and-play operations. Compared to conventional droop-based control, the proposed framework reduces frequency nadir by up to 0.3 Hz and voltage deviation by 1.1%, achieving optimised sharing indices. Results demonstrate that consensus-based coordination enhances dynamic stability and power-sharing fairness and supports the flexible integration of heterogeneous assets without requiring centralised control. Full article