Search Results (73)

The operational reliability of aero-engines is critically dependent on the health of rolling element bearings, while incipient fault diagnosis remains particularly challenging under small-sample conditions. Although multiscale entropy methods are widely used for complexity analysis, conventional coarse-graining strategies suffer from severe information loss and unstable estimation when data are extremely limited. To address this, the primary objective of this study is to develop a robust diagnostic framework that ensures feature consistency and classification stability even with minimal training samples. Specifically, this paper proposes an integrated approach combining Refined Time-shifted Multiscale Rating Entropy (RTSMRaE) with an Animated Oat Optimization (AOO)-optimized Extreme Learning Machine (ELM). By introducing a refined time-shift operator and a dual-weight fusion mechanism, RTSMRaE effectively preserves transient impulsive features across multiple scales while suppressing stochastic fluctuations. Meanwhile, the AOO algorithm is employed to optimize the input weights and hidden biases of the ELM, alleviating performance instability caused by random initialization and improving generalization capability. Experimental validation on both laboratory-scale and real-world aviation bearing datasets demonstrates that the proposed RTSMRaE-AOO-ELM framework achieves a diagnostic accuracy of 99.47% with a standard deviation of ±0.48% using only five training samples per class. These results indicate that the proposed method offers superior diagnostic robustness and computational efficiency, providing a promising solution for intelligent condition monitoring in data-scarce industrial environments. Full article

Local renewable energy microgrids in remote regions are typically characterized by high renewable energy penetration and weak grid-interconnection channels. These features lead to insufficient inertia and poor stability in both the microgrid and the AC main grid, with a failure to meet the power supply demands of microgrid loads. Conventional grid-forming converters or flexible interconnection devices have limited optional capabilities, making it challenging to comprehensively address these issues. This paper proposes a grid-forming energy-storage-based flexible interconnection system (GFM-ESFIS) which integrates the flexible interconnection converters with energy-storage units to fully meet the stability and power supply reliability requirements of the microgrid–main grid interconnection system in remote regions. Key steady-state and transient control strategies are analyzed and designed for the GFM-ESFIS. Simulations based on MATLAB/Simulink 2024a and hardware-in-the-loop experiments based on RT-LAB verify the effectiveness of the proposed system and control strategies. Compared with conventional schemes, the proposed system can operate flexibly in series or parallel modes, realizing multiple capabilities including dual-terminal grid-forming support, fault ride-through control, power flow regulation, operation mode transition, and black start. It holds significant application value in reducing grid investment costs and improving the power supply reliability of microgrids in remote regions. Full article

Effective and intelligent fault diagnosis is essential for ensuring the operational safety and reliability of gearbox systems. In practical engineering environments, however, weak fault-related features are often obscured by strong background noise, pronounced nonstationarity, and time-varying operating conditions, which significantly degrade the performance of conventional feature extraction techniques. To address these challenges, this paper proposes an adaptive feature extraction approach that integrates the complementary advantages of variational mode decomposition (VMD), Teager energy operator (TEO), and multi-scale permutation entropy (MPE) to enhance the characterization of weak and transient fault signatures. Vibration signals associated with different fault conditions are first adaptively decomposed into a series of intrinsic mode functions (IMFs) using VMD, enabling the effective separation of fault-sensitive components and enrichment of fault-related information. Subsequently, an enhanced multi-scale permutation entropy (EMPE) method is developed to emphasize transient impulsive characteristics and capture fault-induced complexity variations across multiple temporal scales. By jointly exploiting instantaneous energy modulation and multi-scale dynamical complexity analysis, the proposed approach exhibits improved sensitivity to weak fault signatures and enhanced robustness against variable operating conditions. The effectiveness and generalization capabilities of the proposed framework are validated using three experimental datasets involving gearboxes and rolling bearings under diverse operating conditions. Comparative results demonstrate that the proposed method outperforms conventional entropy-based approaches in terms of fault feature separability and diagnostic performance, highlighting its potential for practical condition monitoring and fault diagnosis of rotating machinery. Full article

Radiation-induced transient faults pose a growing challenge for safety-critical embedded systems, yet traditional radiation testing and large-scale statistical fault injection (SFI) remain costly and impractical during early design stages. This paper presents a predictive approach for early reliability assessment that replaces handcrafted feature engineering with automatically learned vector representations of source code and execution traces. We derive multiple embeddings for traces and source code, and use them as inputs to a family of regression models, including ensemble methods and linear baselines, to build predictive models for reliability. Experimental evaluation shows that embedding-based models outperform prior approaches, reducing the mean absolute percentage error (MAPE) from 6.24% to 2.14% for correct executions (unACE), from 20.95% to 10.40% for Hangs, and from 49.09% to 37.69% for silent data corruptions (SDC) after excluding benchmarks with SDC below 1%. These results show that source code and trace embeddings can serve as effective estimators for expensive fault injection campaigns, enabling early-stage reliability assessment in radiation-exposed embedded systems without requiring any manual feature engineering. This capability provides a practical foundation for supporting design-space exploration during early development phases. Full article

The high rate of photovoltaic integration poses significant challenges in terms of violations of voltage limits in power grids. Additionally, the operational behavior of PV systems under fault conditions requires thorough investigation in receiving-end grids. This paper analyzes the dynamic coupling characteristics between reactive power and transient voltage in a receiving-end grid with high PV penetration and multiple HVDC infeeds, considering typical AC and DC fault scenarios. Voltage adaptability issues in PV generation systems are also examined. Through an enhanced sensitivity analysis method, the suppression capabilities of transient voltage peaks are quantified in the control parameters of low-voltage ride-through (LVRT) and high-voltage ride-through (HVRT) photovoltaic inverters. On this basis, a hierarchical optimization strategy for PV inverter control parameters is proposed to mitigate post-fault transient voltage peaks and improve the transient voltage response both during and after faults. The feasibility of the proposed method has been verified through simulation on a revised 10-generator 39-bus power system. Following optimization, the transient voltage peak is reduced from 1.263 to 1.098. This validation offers support for the reliable grid connection of the Henan Power Grid. In the events of the N-2 fault at 500 kV and Tian-zhong HVDC monopolar block fault, the post-fault voltage at each node remains below 1.1 p.u. This serves as evidence of a significant enhancement in transient voltage stability within the Henan Power Grid, demonstrating effective improvements in power supply reliability and operational performance. Full article

The capacity of traditional distribution networks is limited. After large-scale distributed power sources are connected, it is difficult to consume them at the same voltage level, which can lead to transformer reverse overloading and voltage limit violations. Although the unified power flow controller (UPFC) excels in flexible power flow regulation and power quality optimization, existing research on it is mostly focused on the transmission grid, focusing on device topology, power flow control, etc. Fault protection is also targeted at high-voltage and ultra-high-voltage domains and only covers a single overvoltage or overcurrent fault. Research on the protection of the unified power flow controller in a distribution network (D-UPFC) remains scarce. A key challenge is the absence of fault protection schemes that are compatible with the unified power flow controller in a distribution network, which cannot meet the requirements of the distribution network for monitoring and protecting multiple fault types, rapid response, and equipment economy. This paper first designs a protection device centered on the distribution thyristor bypass switch (D-TBS), completes the thyristor selection and transient energy extraction, optimizes the overvoltage protection loop parameter, then builds a three-level coordinated protection architecture, and, finally, verifies through functional and system tests. The results show that the thyristor control unit trigger is reliable and the total overvoltage response delay is 1.08 μs. In the case of a three-phase short-circuit fault in a 600 kVA/10 kV system, the distribution thyristor bypass switch can rapidly reduce the secondary voltage of the series transformer, suppress transient overcurrent, achieve isolation protection of the main equipment, provide a reliable guarantee for the engineering application of the distribution network unified power flow controller, and expand its distribution network application scenarios. Full article

As the global energy system accelerates its transition towards high penetration of renewable energy and high penetration of power electronic devices, regional power grids have undergone profound changes in their structural forms and component composition compared to traditional power grids. Conventional dynamic equivalencing methods struggle to balance modeling accuracy and computational efficiency simultaneously. To address this challenge, this paper focuses on the dynamic equivalencing of regional power grids and proposes a dynamic equivalencing scheme considering multiple feature constraints. First, based on the structural characteristics and the evolution of dynamic attributes of regional power grids, three key constraint conditions are identified: network topology, spatial characteristics of frequency response, and nodal residual voltage levels. Secondly, a comprehensive equivalencing scheme integrating multiple constraints is designed, which specifically includes delineating the retained region through multi-objective optimization, optimizing the internal system based on coherent aggregation and the current sinks reduction (CSR) method, and constructing a grey-box external equivalent model composed of synchronous generators and composite loads to accurately fit the electrical characteristics of the external power grid. Finally, the proposed methodology is validated on a Back-to-Back VSC-HVDC-connected regional power grid in Eastern Guangdong, China. Results demonstrate that the equivalent system reproduces the original power-flow profile and short-circuit capacity with negligible deviation, while its transient signatures under both AC and DC faults exhibit high consistency with those of the reference system. Full article

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources—such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems—has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system’s transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources. Full article

This study proposes an adaptive fault-tolerant control strategy for parametric strict-feedback systems subject to actuator faults and unknown dead-zone nonlinearities, a combination that presents significant challenges for controller design. First, a novel prescribed-performance fault-tolerant control framework is developed by incorporating a funnel function, a barrier Lyapunov function, and a bounded estimation mechanism to address the issue of multiple constrained nonlinear disturbances. Second, the proposed strategy offers two key improvements: (1) adequate compensation for the coupled effects of actuator faults and dead-zone nonlinearities, and (2) guaranteed globally prescribed transient performance, making the settling time and tracking accuracy independent of initial conditions and design parameters. Lastly, simulation results verify the approach’s effectiveness, showing rapid convergence within 0.8 s and a tracking error bounded by $\pm 0.05$, thus surpassing traditional methods. Full article

This study addresses the issues related to the inaccurate assessment of transient voltage stability margins and the limited participation of resources involved in regulation during high-voltage direct current (HVDC) receiving-end grid faults under high-penetration new energy integration. This paper proposes a method for transient voltage emergency control at the HVDC receiving-end grid, utilizing a multi-resource approach based on transient voltage similarity partitioning with a multiple-two-element notation criterion. First, the transient voltage stability margin and the new energy transient off-grid margin index, based on the multiple-two-element notation criterion, are introduced. Second, a grid partitioning scheme is employed, which clusters nodes based on the similarity of their transient voltage features, and the impact of multiple resources on the transient voltage stability of the HVDC receiving-end system is analyzed using trajectory sensitivity. On this basis, a multi-resource optimization model for transient voltage emergencies is established with the aim of minimizing the control cost, considering the transient voltage stability, off-grid new energy, and other safety evaluation constraints, in order to coordinate multiple resources participating in transient voltage control until the stability requirements are met. Finally, the validity of the proposed control scheme is verified using the improved frequency stability benchmark test system (Chinese Society for Electrical Engineering—Frequency Stability, CSEE-FS). The research results demonstrate that the scheme proposed in this study can be utilized to accurately assess the transient voltage stability and off-grid potential of renewable energy units following failure at the HVDC receiving-end system. Additionally, it can reasonably partition the grid based on transient operating conditions while fully exploiting the potential of multiple resources within the faulted partition to control transient voltage emergencies in the grid. Full article

This paper investigates the attitude control problems of spacecraft subject to external disturbances and compound actuator faults, including both additive and multiplicative components. To address these problems, an improved learning observer (ILO) is proposed. Compared to traditional learning observers (TLOs), the improved learning observer incorporates the previous-step state estimation error as an iterative term. Based on the observer’s outputs, a robust adaptive fault-tolerant attitude control scheme is developed using the backstepping method, under a prescribed performance bound (PPB). This control framework guarantees that the attitude tracking error adheres to prescribed transient performance specifications, such as bounded overshoot and accelerated convergence. Unlike conventional control schemes, the proposed approach ensures that system trajectories remain strictly within the desired bound throughout the transient process. A comprehensive Lyapunov-based analysis rigorously demonstrates the global uniform ultimate boundedness of all closed-loop signals. Numerical simulations substantiate the efficacy of the proposed approach, highlighting the enhanced disturbance estimation capability of the ILO in comparison to the TLO, as well as the superior transient tracking performance of the PPB-based control strategy relative to existing methods. Full article

Single-phase ground (SPG) faults pose significant challenges in three-phase four-wire distribution systems due to their complex transient characteristics and the presence of multiple influencing factors. To solve the aforementioned issues, a comprehensive fault identification framework is proposed, which uses the TabNet deep learning architecture with hyperparameters optimized by Optuna. Firstly, a 10 kV simulation model is developed in Simulink to generate a diverse fault dataset. For each simulated fault, voltage and current signals from eight channels (L1–L4 voltage and current) are collected. Secondly, multi-domain features are extracted from each channel across time, frequency, waveform, and wavelet perspectives. Then, an attention-based fusion mechanism is employed to capture cross-channel dependencies, followed by L2-norm-based feature selection to enhance generalization. Finally, the optimized TabNet model effectively classifies 24 fault categories, achieving an accuracy of 97.33%, and outperforms baseline methods including Temporal Convolutional Network (TCN), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Capsule Network with Sparse Filtering (CNSF), and Dual-Branch CNN in terms of accuracy, macro-F1 score, and kappa coefficient. It also exhibits strong stability and fast convergence during training. These results demonstrate the robustness and interpretability of the proposed method for SPG fault detection. Full article

To analyze and enhance the transient stability of a hydro-wind-PV VSC-HVDC transmission system, this paper establishes a transient stability analytical model and proposes strategies for stability improvement. Based on the dynamic interaction mechanisms of multiple types of power sources, an analytical model integrating GFM converters, GFL converters, and SGs is first developed. The EAC is employed to investigate how the factors such as current-limiting thresholds and fault locations influence transient stability. Subsequently, a parameter tuning method based on optimal phase angle calculation and delayed control of current-limiting modes is proposed. Theoretical analysis and PSCAD simulations demonstrate that various factors affect transient stability by influencing the PLL of converters and the electromagnetic power of synchronous machines. The energy transfer path during transient processes is related to fault locations, parameter settings of current-limiting modes in converters, and the operational states of equipment. The proposed strategy significantly improves the transient synchronization stability of multi-source coupled systems. The research findings reveal the transient stability mechanisms of hydro-wind-PV VSC-HVDC transmission systems, and the proposed stability enhancement method combines theoretical innovation with engineering practicality, providing valuable insights for the planning and design of such scenarios. Full article

This paper presents a comprehensive register transfer-level (RTL) fault injection study targeting the program counter (PC) of the LEON3 processor, a SPARC V8-compliant core widely used in safety-critical and radiation-prone embedded applications. Using the enhanced NETFI+ framework, over four million faults, including single-event upsets (SEUs), multiple-bit upsets (MBUs), and single-event transients (SETs), were systematically injected into the PC across all pipeline stages. The analysis reveals that early stages, particularly Fetch (FE), Decode (DE), Register Access (RA), and Execute (EX), are highly sensitive to SEU and MBU faults. The propagation of errors detected in the two early stages of the pipeline (FE and DE) is classified with an important percentage of halt execution and timeout traps. Intermediate stages, such as RA and EX, exhibited a higher incidence of silent data corruption and halt execution, while the Memory (ME) and Exception (XC) stages demonstrated greater resilience through fault masking. SET faults were mostly transient and masked, though they occasionally resulted in control flow anomalies. In addition to error classification, detailed trap and exception analysis was performed to characterize fault-induced failure mechanisms. The findings underscore the need for pipeline-stage-specific hardening strategies and highlight the value of simulation-based fault injection for early design validation in safety-critical embedded processors. Full article

The large-scale integration of renewable energy and the high penetration of power electronic devices have led to a significant reduction in system inertia and short-circuit capacity. This is particularly manifested in the form of insufficient multiple renewable energy stations short-circuit ratio (MRSCR) and transient overvoltage issues following severe disturbances such as AC and DC faults, which greatly limit the power transfer capability of large renewable energy bases. To effectively mitigate these challenges, this paper proposes an optimal synchronous condenser deployment method tailored for large-scale renewable energy bases. The proposed mathematical model supports a hybrid centralized and distributed configuration of synchronous condensers with various capacities and manufacturers while considering practical engineering constraints such as short-circuit ratio, transient overvoltage, and available bays in renewable energy stations. A practical decomposition and iterative computation strategy is introduced to reduce the computational burden of transient stability simulations. Case studies based on a real-world system verify the effectiveness of the proposed method in determining the optimal configuration of synchronous condensers. The results demonstrate significant improvements in grid strength (MRSCR) and suppression of transient overvoltages, thereby enhancing the stability and transfer capability of renewable energy bases in weak-grid environments. Full article