Search Results (790)

Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm. Full article

With the increasing demands of the grid on power electronic converters, active neutral-point-clamped (ANPC) converters have been widely adopted due to their flexible modulation strategies and wide-range power regulation capabilities. To address grid-integration testing requirements for ANPC converters, this paper comparatively studies three electromagnetic transient (EMT) modeling approaches: switch-state prediction method (SPM), associated discrete circuit (ADC), and time-averaged method (TAM). Steady-state and transient simulations reveal that the SPM model achieves the highest accuracy (error ≤ 0.018%), while the TAM-based switching function model optimizes the efficiency–accuracy trade-off with 6.4× speedup versus traditional methods and acceptable error (≤2.62%). Consequently, the TAM model is implemented in a real-time hardware-in-the-loop (HIL) platform. Validation under symmetrical/asymmetrical grid faults confirms both the model’s efficacy and the controller’s robust fault ride-through capability. Full article

Traditional fault phase selection methods are not easily adaptable to large-scale wind farms. This paper proposes a fault phase selection method based on transient voltage S-transform energy relative entropy (SERE) to realize adaptive reclosing of the circuit breaker on the wind farm outgoing line. According to the characteristics of the three-phase transient voltage fault component, the three-phase transient voltage after a fault is collected, and the fault component is calculated. The SERE of the three-phase voltage is calculated. The maximum and minimum entropy are determined, and the criterion is constructed to identify the fault phase. The simulation results show that the proposed method can identify the fault phase accurately and quickly. The method is less affected by fault location, fault type, initial fault phase angle, and wind speed. In particular, the method can also accurately identify the fault phase under a remote high-resistance fault. The results compared with those of the traditional methods show that the proposed method demonstrates its superiority under different transitions and wind speeds. Full article

Virtual synchronous generators (VSGs) simulate the operating characteristics of conventional synchronous generators to provide inertia, voltage and frequency support for new-type power systems dominated by power electronics. However, in the event of grid faults, VSGs inevitably experience transient angle instability, which leads to great challenges to the safe and stable operation of the power system. To address the problem of transient instability so that VSGs can continue to support the power system during a grid fault, this paper firstly analyzes the adverse effect of a reactive power control (RPC) loop on the transient stability of the system and proposes a method for adding the variation in the power angle into RPC to increase the voltage reference of a VSG during grid faults, which can solve the transient instability problem under both equilibrium point existence and nonexistence by increasing the active power output of the VSG. The effect of the additional coefficient on the transient characteristics of the system is then analyzed using a small-signal model, and it is found that this method also enhances the frequency stability of the system. Finally, the feasibility of the proposed method and the correctness of the theoretical analysis are confirmed by a simulation platform. Full article

Distributed photovoltaic grid-connected converters adopting droop control can provide dual support for voltage and frequency in the distribution system. However, under fault conditions, droop-controlled inverters will face the problem of transient synchronization instability, and their transient characteristics are significantly affected by fault conditions, control parameter configurations, and other factors. Nevertheless, at present, the transient operation boundaries of droop inverters, considering key sensitive parameters, are unclear, and the transient stability control mechanism is lacking, which poses a threat to the safe and stable operation of distributed photovoltaic systems. To this end, this paper fully considers the influences of control parameters and fault severity and conducts a multidimensional quantitative characterization of the transient stability boundaries of droop-controlled inverters. Furthermore, a stability enhancement control structure for droop-controlled inverters, based on power angle deviation feedforward, is proposed, and an adaptive configuration method for feedforward coefficients is put forward to ensure the safe and stable operation of droop inverters at different fault sag depths. Finally, the accuracy of the theoretical analysis and the proposed control structure is verified through simulations and experiments. Full article

In this paper, a nonlinear power transfer matrix model is presented for power control of Permanent Magnet Synchronous Generator (PMSG) wind turbines, incorporating the DC link dynamics to account for transient stability, thereby clarifying the technical aspect and purpose. The rising penetration of wind turbines (WTs) into the power grid necessitates that they remain connected during and after faults to ensure system reliability. During voltage dips, the stator and grid-side converter (GSC) of a permanent magnet synchronous generator (PMSG) system are directly impacted by the sudden voltage changes. These disturbances can induce large transient voltages and currents in the stator, which in turn may lead to uncontrolled current flow in the rotor circuit and stress the converter components. Moreover, Low Voltage Ride-Through (LVRT) is a critical requirement for grid connection to Wind Energy Conversion Systems (WECS). It ensures that WTs remain connected and operational during short periods of grid voltage dips (faults), instead of disconnecting immediately. This capability is essential for maintaining grid stability. However, in this paper, the authors propose an LVRT scheme for a grid-connected PMSG-based WECS. A sequence of attempts was performed to validate the effectiveness of the proposed control scheme under fault conditions and to improve its overall performance. Full article

As renewable energy sources become more prevalent, maintaining the sustainable and reliable operation of power systems has become as a major challenge. Modern grid-connected converter systems are particularly prone to losing synchronization when encountering large disturbances, which is a huge threat to the stability of the power grid, which is mainly based on renewable energy. This paper studies the impact of filter inductors on the synchronous stability of grid-connected converter systems, hoping to help us better connect renewable energy to the power grid. First, the existing synchronization stability model of grid-following (GFL) converters is extended by incorporating filter inductance modeling. Based on this model, a mathematical relationship between the phase-locked loop’s (PLL) frequency deviation and the filter inductance at the moment of fault is established, and a predictive method for GFL frequency deviation considering filter inductance is proposed. Furthermore, the impact of filter inductance on synchronization stability is systematically investigated through two key indicators: power angle overshoot and critical fault voltage, revealing the variation trends of transient stability under different operating conditions. Finally, the analytical results are validated through Matlab/Simulink simulations, providing theoretical guidance for the design of sustainable and robust renewable energy grid integration strategies. Full article

Insulation degradation in photovoltaic (PV) cables can cause electrical faults and fire hazards, thereby compromising system reliability and safety. Early detection of precursor signals is crucial for preventive maintenance. However, conventional diagnostic techniques are limited to static assessments and fail to capture early-stage electrical anomalies in real-time. This study investigates the time-series behavior of voltage, current, and temperature in PV cables under thermal stress conditions. Experiments were conducted using TFR-CV cables installed in a vertically stacked and tight-contact configuration. A gas torch was applied for localized heating to induce insulation degradation. A grid-connected testbed with six series-connected PV modules was constructed. Each module was instrumented with PV-M sensors, temperature sensors, and an infrared camera. Data were acquired at 1 Hz intervals. Results showed that cable surface temperature exceeded 280 °C during degradation. The output voltage exhibited transient surges of up to +13.3% and drops of −68%, while the output current decreased by over 20%, particularly in the PV-M3 module. These anomalies, such as thermal imbalance, voltage spikes/dips, and current drops, were closely associated with critical degradation points and are interpreted as precursor signals. This work confirms the feasibility of identifying fire-related precursors through real-time monitoring of PV cable electrical characteristics. The observed correlation between electrical responses and thermal expansion behaviors suggests a strong link to the stages of insulation degradation. Future work will focus on quantifying the relationship between degradation and electrical behavior under controlled environmental conditions. Full article

Electrochemical energy storage technology, primarily lithium-ion batteries, has been widely applied in large-scale energy storage systems. However, differences in assembly structures, manufacturing processes, and operating environments introduce parameter inconsistencies among cells within a pack, producing complex, high-volume datasets with redundant and fragmented charge–discharge records that hinder efficient and accurate system monitoring. To address this challenge, we propose a hybrid ASW-UKF-TRF framework for the classification and compression of battery data collected from energy storage power stations. First, an adaptive sliding-window Unscented Kalman Filter (ASW-UKF) performs online data cleaning, imputation, and smoothing to ensure temporal consistency and recover missing/corrupted samples. Second, a temporally aware TRF segments the time series and applies an importance-weighted, multi-level compression that formally prioritizes diagnostically relevant features while compressing low-information segments. The novelty of this work lies in combining deployment-oriented engineering robustness with methodological innovation: the ASW-UKF provides context-aware, online consistency restoration, while the TRF compression formalizes diagnostic value in its retention objective. This hybrid design preserves transient fault signatures that are frequently removed by conventional smoothing or generic compressors, while also bounding computational overhead to enable online deployment. Experiments on real operational station data demonstrate classification accuracy above 95% and an overall data volume reduction in more than 60%, indicating that the proposed pipeline achieves substantial gains in monitoring reliability and storage efficiency compared to standard denoising-plus-generic-compression baselines. The result is a practical, scalable workflow that bridges algorithmic advances and engineering requirements for large-scale battery energy storage monitoring. Full article

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (${BER}_{sys}$). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and ${BER}_{sys}$ at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article

Induction motors (IM) play essential tasks in distinct production sectors because of their low cost and robustness. Considering that most of the energy demand in industry is allocated for powering up IM, recent research has focused on detecting and predicting faults to avoid severe disturbances. Broken rotor bars (BRB) in IM cause a significant deficit of energy, above all in those applications where constant changes in speed are required, increasing the probability of a catastrophic failure. Variable speed drives (VSD) introduce harmonic components to the power supply current controlling the IM rotating speed, which make it difficult to identify BRB. Therefore, in this work, an innovative methodology is proposed for detecting BRB in VSD-fed IM with a wide rotating-speed bandwidth during their start-up transient. The introduced procedure performs a statistical analysis for computing the mean, median, mode, variance, skewness, and kurtosis, to identify slight changes on the acquired current signal. These values are fed into an artificial neural network (ANN), which carries out the IM operational condition classification as healthy (HLT) or with BRB. Experimentally obtained results corroborate the effectiveness of the proposed approach to detecting BRB even for dynamically varying rotating speed, reaching a high accuracy of 99%, similar to recently reported techniques. Full article

With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate. 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

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. 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