Hybrid power supply systems (HPSSs) offer superior performance in aspects such as dynamic response and power density compared to single-power-source power supply systems. Nevertheless, when a single-point failure occurs in an HPSS, it can easily lead to system instability. This type of failure represents a significant disturbance to the HPSS, and the small-signal stability analysis method struggles to comprehensively account for such operating conditions. To address this issue, this research proposes a large-signal stability analysis method for HPSSs during faults based on the estimation of the largest region of attraction (ROA). First, full-order models of the system after failures of different power sources are established. Then, combined with Lyapunov stability theory, Takagi-Sugeno (T-S) fuzzy models, and linear matrix inequalities (LMIs), the largest ROA of the faulty system is estimated. Based on the ROA estimation results, the stability of the faulty system and methods for stability improvement are analyzed. Finally, the effectiveness of the proposed large-signal stability analysis method is verified through experiments.

A battery swapping station (BSS) is an enabling facility for battery swapping electric vehicles (EVs). To ensure the high quality of service (QoS) provided for EV customers while providing new batteries, the capacities of batteries and chargers in a BSS should be optimized. To achieve that, an EV battery swapping demand prediction model that specially considers the influences of different seasons, the output of which is the key data for capacity sizing, is firstly developed based on Monte Carlo algorithm. Then, an optimal capacity sizing model targeted at both minimizing the construction and operation cost of the BSS and maximizing the grid-shifting ability is proposed under a proposed optimal battery swapping and charging algorithm. The optimal capacity sizing for the batteries and chargers is finally obtained using the NSGA-II algorithm to solve the developed model with all operation constraints. Case studies based on the real data provided by BSS operation companies in China are done to verify the validity of the proposed method. The results show that the cost of the BSS can be reduced while peak-shifting can be enabled with the proposed capacity sizing and battery charging/discharging algorithm.

To supply indispensable transient inertia and damping support for power systems, particularly weak grid scenarios, grid-forming (GFM) generation exhibits superior performance compared with traditional grid-following (GFL) interfaces. Nevertheless, conventional GFL/GFM mode transition schemes suffer from abrupt switching behaviors or slow dynamic responses, which easily induce relay maloperation and even large-scale system instability. To tackle these drawbacks, this paper presents a seamless operating mode switching strategy for inverter-based power generation units. By coordinately optimizing the output states of phase-locked loop (PLL) and multi-loop current controllers, severe transient voltage and current surges during mode transition are effectively suppressed. A 2 MW grid-connected energy storage system is developed to validate the proposed control algorithm. The results demonstrate the feasibility and effectiveness of the proposed seamless switching strategy under grid-connected energy storage system scenarios.

The participation of battery swapping station (BSS) clusters in grid regulation is significantly constrained by the spatio-temporal uncertainty and climate sensitivity of electric vehicle (EV) demand. To address these issues, this paper proposes an aggregated scheduling method that integrates demand forecasting and rolling optimization. First, a demand forecasting model is established by considering seasonal climate and users’ range anxiety. On this basis, a “day-ahead bidding and intra-day tracking” two-stage scheduling framework is constructed. In the day-ahead stage, the optimal bidding power of the cluster is determined for minimizing the overall cluster cost. In the intra-day stage, taking the bidding power as the tracking index, the demand distribution scheme and station charging/discharging strategies are synergistically optimized to minimize the operational costs. Furthermore, for real-time EV swapping requests, suitable BSS nodes are recommended based on the distribution scheme. To address the stochasticity of user rejection, rolling optimization is applied for real-time adjustments, ensuring reliable grid response and service quality. Finally, a case study using real operational data verifies the effectiveness of the proposed model.

Vienna rectifiers can be treated as promising rectification units for transportation electrification systems because of their high efficiency and reliability. However, commonly used modulation techniques for Vienna rectifiers, such as space vector pulse width modulation, heavily depend on the generator's parameters, including winding inductance and rotor position, leading to reduced control performance under complex operating conditions. This article proposes a novel control strategy for the three-phase four-wire Vienna rectifier connected to a permanent magnet synchronous generator to address this issue. By adjusting the input impedance of the rectifier to be purely resistive, automatic power factor correction and current quality enhancement are achieved. The effectiveness of the proposed method is validated through experiments performed by a 500 W prototype.

To address the issue of high computational complexity in online Model Predictive Control (MPC) for three-phase inverters with LC filters, this paper develops a deep neural network control method based on an event-triggered mechanism. First, training data is generated using MPC, and the control approach is learned through a deep neural network to reduce online computational complexity. Building on this, an event-triggered mechanism is introduced to adaptively determine the timing of control updates based on changes in system state, thereby further reducing the frequency of control updates. A MATLAB/Simulink-based three-phase inverter simulation model is established to evaluate the developed control method, and its performance is compared with conventional MPC and DNN approaches. Simulation studies show that the proposed strategy effectively reduces the number of control updates and computational load while maintaining good output voltage quality and system stability, thereby verifying the feasibility and effectiveness of the designed approach.

Hybrid power supply systems (HPSS), which include fuel cells (FC), lithium-ion batteries (LB), and supercapacitors (SC), are commonly used to power onboard electric loads in transportation electrification systems. To improve reliability, hybrid virtual impedance droop (HVID) control is typically employed to enable dynamic power sharing among different sources. However, when one of the main sources, such as the FC or battery, disconnects from the HPSS, maintaining both DC-bus voltage quality and the ability to share power dynamically becomes challenging. During faults, the load may lose power, potentially causing severe damage to the system. This article proposes a simple fault-tolerant control strategy for the FC-LB-SC HPSS. The strategy allows the SC unit to quickly provide power for the excessive load during LB’s outage by adding an adjustable virtual resistor in parallel with the SC unit's virtual capacitor. This ensures that the DC bus voltage quality and dynamic power-sharing capability are preserved even if one of the main power sources fails. The operational principle of this strategy is examined in detail, and its effectiveness is confirmed through experiments with a 5-kW HPSS prototype.

The doubly fed induction generator (DFIG)-based wind turbine (WT) may lose stability during transient events, especially the low-voltage ride-through (LVRT) process, threatening the stable operation of the power grid. To address this issue, this article analyzes the mechanism of synchronization instability from the perspective of the system voltage vector balance. The necessary condition for the existence of an equilibrium point is derived. Then, the rotor’s active current feasible region (ACFR) is constructed by considering the grid code requirements, the rotor side converter’s capacity, and the equilibrium point existence condition altogether. Analysis of the ACFR reveals that inappropriate rotor current settings during LVRT will lead to instability due to the absence of an equilibrium point. On this basis, an optimized current control strategy to improve the synchronization stability of DFIG-based WTs during LVRT is proposed. The proposed strategy is verified by experiments performed by a grid-connected 2-MW DFIG-based WT.

Hybrid power supply systems (HPSSs), which leverage the advantages of each source, are becoming increasingly common in transportation electrification systems. To allocate the wide-frequency variation load power among the power sources in a reliable manner, the hybrid virtual impedance droop (HVID) control strategy, which is implemented in a decentralized way, is typically adopted for HPSSs. However, due to the presence of unknown and time-varying line impedances, the HPSS with conventional HVID control may experience DC-bus voltage drops in steady states and compromised power performances in both dynamic and static conditions. To address these challenges, this paper proposes an enhanced HVID control strategy designed to adaptively eliminate the adverse effects of unpredictable line impedances without the need for complex real-time impedance measurement or observation. By integrating a DC-bus drop voltage compensation loop with a newly developed output power-sharing compensation loop, the proposed strategy can effectively capture line impedance information and adaptively offset the power allocation mismatch caused by impedance through a closed-loop mechanism. The operational principle of the proposed control strategy is detailed, followed by the construction of a 4-kW test rig comprising two fuel cells and two supercapacitor units in the lab. Experimental results verify the effectiveness of the proposed strategy, demonstrating that under heavy load conditions, the steady-state current deviation between identical fuel cells is reduced to within 3% of that in the conventional strategy, and the DC-bus voltage drop is controlled within 1%.

Renewable energy resources (RESs) are typically integrated into the utility grid through grid-connected inverters (GCIs). Such a system may lose synchronization during grid faults, especially under weak grid conditions, resulting in instability problems or even triggering large-scale power outage accidents. This paper analyzes the influences of the system parameters, including the phase-locked loop (PLL) parameters and the grid parameters, on the GCI’s synchronous stability. The results reveal that the PLL exhibits a negative damping effect during the transient process, which is bad for stabilizing the GCI in transients. An improved PLL, which is achieved by adding a feedback low-pass filter into the conventional PLL, is then proposed to address this issue, making the system more stable in transients. Analyzing results show that the improved PLL can enhance the synchronous stability of GCI during transients without changing its steady-state performance. The simulation and experimental results performed by a 2-MW GCI integrated into a weak grid verify the correctness of the theoretical analysis and the effectiveness of the proposed method.

The application of power semiconductor devices has increased the risk of partial discharge (PD) under high dv/dt voltage. However, high-resolution sensing systems are essential for accurately detecting PD under the special conditions. A builtin high-resolution fluorescent fiber sensor system is proposed for high dv/dt PD measurement. Considering the propagation discharge light, the generation, transmission, and photoelectric conversion of fluorescence, a mathematical model is established to characterize the relationship between fiber layout and the detected light intensity. The accuracy of the model is verified and the fiber probe parameters are optimized through PD measurements. On the basis, accurate PD detection and feature analysis are conducted under high frequency and high dv/dt pulse voltages. By comparing with conventional ultrahigh-frequency (UHF) and high-frequency current transformer (HFCT) sensors, the fluorescent fiber system shows superior performance sensitivity, resolution, and anti-interference abilities. Specifically, it offers high detection sensitivity, achieving nanosecond optical pulse oscillation-free acquisition with a 1.5-ns half-peak width the pulse output, much lower than 50 ns of HFCT. It exhibits a discharge detection rate comparable to UHF and the highest signal-to-noise ratio (SNR) output while avoiding electromagnetic interference (EMI). This research presents an effective approach for PD detection under high dv/dt voltage and has great potential for industrial applications.

To meet the cross-timescale response requirements of new electrified loads, lithium batteries and supercapacitors are being integrated into onboard electrical power systems via DC/DC converters. Despite the rapid response and energy recovery capabilities of these storage units, the onboard hybrid power systems remain prone to significant power quality degradation and instability under transient conditions such as wide-range load transients, partial source failures, and pulsed loads with a high peak-to-average ratio. To address these issues, this article proposes a composite controller that integrates a fixed-time sliding mode disturbance observer (FTSMDO) with a prescribed performance controller (PPC). The FTSMDO is designed to estimate lumped disturbances, upon which a PPC is developed using the backstepping method. This approach enhances the system’s capability for rapid voltage recovery under various transient events and ensures large-signal stability. Finally, the effectiveness of the proposed composite control strategy is validated through both simulation and experimental results.

Introduction: The high penetration of doubly fed induction generator (DFIG)-based wind farms substantially reduces the equivalent inertia of power systems, rendering the accurate evaluation of their inertia support capability critical for future grid frequency stability. However, continuous wind speed fluctuations pose a major challenge to practical parameter identification.Materials and methods: This paper proposes a fast identification method for DFIG wind farms integrating wind speed fluctuation decoupling and multi-timescale analysis. The proposed scheme consists of three steps: first, an adaptive Savitzky–Golay filter considering the rate of change in frequency performs zero-phase-shift filtering on wide-area measurement data. Second, the inherent frequency regulation characteristics of DFIGs are employed to decouple power response components caused by wind speed variations. Third, multi-timescale decoupling is applied to suppress steady-state drift.Results: The simulation results show that under load disturbance, the proposed method achieves a root-mean-square error of only 0.2017 MW, representing a 55.55% reduction compared with conventional methods. Moreover, its single computation time is merely 8 ms.Conclusions: The results validate that the proposed approach can rapidly and accurately characterize the inertial response of wind farms, offering a reliable and efficient tool for fast inertia assessment to ensure power system stability.

Hydrogen energy stands as a promising clean energy source, and its central component, the hydrogen production converter, has accordingly garnered significant attention. However, these converters operate under demanding conditions, making critical power devices like insulated gate bipolar transistors (IGBT) susceptible to accelerated aging and failure, which severely compromises overall system stability. To address these challenges, this paper focuses on fault prediction for IGBT within hydrogen production converters and develops a prediction-based proactive fault-tolerant control strategy. Initially, IGBT aging experimental data undergoes preprocessing and variational mode decomposition (VMD) based feature extraction. Subsequently, a long short-term memory (LSTM) neural network is constructed, with its hyperparameters optimized using the snake optimization (SO) algorithm to enable accurate prediction of IGBT failure probability. When the predicted failure probability reaches a predefined threshold, the system automatically isolates the fault source and maintains stable circuit operation through a proactive fault-tolerant control strategy. Finally, the effectiveness of the proposed fault prediction and fault-tolerant control methods is validated through comprehensive simulation.

With a large number of constant power loads (CPLs) connected to the DC microgrid (MG), the injected negative impedance drastically deteriorates the stability of the DCMG. To address this issue, a stabilization strategy for the DCMG with CPLs fed by the four-switch Buck-Boost (FSBB) converters is proposed in this paper. Due to the adopted DC machine technique, a virtual capacitor is paralleled to the output port of the FSBB converter to support the bus voltage in the presences of CPL power changes. The stability of the system is analyzed based on the Middlebrook impedance stability criterion, and the effectiveness of the proposed control strategy is verified by MATLAB/Simulink simulation.

This paper presents a decentralized power allocation strategy relying on the droop control for hybrid energy storage systems (HESSs) in DC microgrids (DC MG), which are composed of batteries and supercapacitors (SCs). The proposed approach employs virtual impedance injection to enable dynamic power allocation between storage units. Then, a sliding mode control (SMC) technique is developed. This technique is essential for the HESS as it significantly strengthens the large-signal stability characteristics, especially when the system encounters constant power loads (CPLs). Finally, simulations are conducted on the system under study. These simulations have successfully validated the efficacy of the proposed strategy.

Renewable energy resources (RES) are typically integrated into the utility grid through grid-following inverters (GFLIs). Such a system may lose synchronization during grid faults, especially under weak grid conditions, resulting in instability problems or even triggering large-scale power outage accidents. This paper analyzes the influences of the system parameters, including the phase-locked loop (PLL) parameters and the grid parameters, on the GFLI’s synchronous stability. The results reveal that the PLL exhibits a negative damping effect during the transient process, which is bad for stabilizing the GFLI in transients. An improved PLL, which is achieved by adding a feedback low-pass filter into the conventional PLL, is then proposed to address this issue, making the system more stable in transients. Analyzing results show that the improved PLL can enhance the synchronous stability of GFLI during transients without changing its steady-state performance. The simulation and experimental results performed by a 2-MW GFLI integrated into a weak grid verify the correctness of the theoretical analysis and the effectiveness of the proposed method.

In dc microgrids (MGs), droop control is commonly used for load current sharing. However, conventional droop-controlled dc MGs have two drawbacks: dc bus voltage deviation and degraded current-sharing performance. Even worse, due to mismatches between the actual and desired output impedances of distributed generators (DGs), their transient performance tends to deteriorate, causing significant overshoot or undershoot in their output currents. This issue can adversely affect the dc MGs' operation, yet it is frequently overlooked. To address these issues, this article proposes an enhanced droop controller that improves the traditional design by incorporating voltage compensation and both transient and static current-sharing compensation loops. It eliminates the impact of line resistances on steady-state current sharing accuracy without requiring prior knowledge of them, enhances the transient performance of DGs, and reduces dc bus voltage variation. Moreover, it exhibits strong robustness and mitigates the impact of parameter variations in DGs on the transient current-sharing performance. A 1.35-kW test rig was designed to verify the effectiveness of the proposed controller. The experimental results show that, compared to the existing methods, it reduces DGs' overshoot or undershoot by at least 68% and decreases the dc bus voltage drop and surge by at least 18.6%.

Hybrid power supply systems (HPSSs), which integrate the dynamic properties of different power sources, are a promising solution for transportation electrification systems. However, extreme cases such as large variations of load like large step-changing load and high peak-to-average ratio pulsed power load are highly susceptible to power supply system destabilization, which is beyond the scope of small-signal analysis. In this article, a comprehensive large-signal stability analysis for HPSSs considering variation in virtual impedance droop parameters and proportional and integral (PI) regulators under extreme load switching conditions is conducted based on the region of attraction estimation (ROA). On this basis, the impact of system parameters and load power on the large-signal stability is elaborated. The stability of the system is greatly improved by the adoption of active capacitors to absorb the pulsed power. The effectiveness of the proposed large-signal stability analysis method and the correctness of the analyzing results are verified through experiment results.

Virtual synchronous generator (VSG) technology introduces synthetic rotational inertia and damping into inverter-based systems, thereby enhancing regulation performance under grid-connected operation. However, the output characteristics of VSGs are strongly influenced by virtual inertia and damping. This paper develops a self-tuning inertia–damping coordination mechanism for VSGs. The coupling between virtual inertia and damping with respect to grid power quality is systematically investigated, and a power-angle dynamic response model for synchronous generators (SGs) under extreme operating conditions is established. Building on these results, an improved adaptive control strategy for the VSG’s virtual inertia and damping is proposed. The proposed strategy detects changes in frequency and load power, enabling adaptive tuning of virtual inertia and damping in response to system variations, thereby reducing frequency overshoot while accelerating the dynamic response. The effectiveness of the proposed strategy is validated by hardware-in-the-loop real-time simulations.