Search Results (216)

In single-phase PWM rectifiers, due to the inherent time-varying characteristics of the source voltage and current as well as the periodic operation of the converter bridge, the instantaneous input power on the AC side inevitably exhibits a twice-fundamental-frequency pulsation. This phenomenon consequently generates a double-line-frequency (100 Hz) voltage ripple on the DC-link capacitor, which causes an inherent contradiction in conventional voltage outer-loop control between steady-state ripple suppression and dynamic response speed. To address this issue, this paper proposes a control strategy based on an Adaptive Time-Delayed Feedforward Neural Network (Adaptive TD-FNN). The proposed method explicitly introduces the delayed voltage error of half a ripple period into the network state input, thereby achieving time-domain decoupling of the 100 Hz low-frequency disturbance. In addition, a physics-driven training framework is constructed by integrating the rectifier’s discrete difference equation, thereby strengthening the network’s capacity to learn the dynamic characteristics of the system. On this basis, a dynamic adaptive smoothness-weight penalty mechanism is designed to adjust the weighting factor of the current command smoothness constraint in the loss function according to the system operating state. Specifically, the penalty weight is increased under steady-state conditions to suppress command oscillations caused by ripple disturbances, while it is rapidly reduced during load or grid-voltage transients to release the network’s transient optimization capability. Simulation and experimental results show that the proposed Adaptive TD-FNN controller can simultaneously achieve smooth steady-state current command output and fast dynamic voltage regulation without introducing additional complex digital notch-filtering algorithms. Compared with conventional dual-loop control, the proposed strategy reduces the total harmonic distortion (THD) of the grid-side input current from 8.45% to 3.42%, satisfying grid-connected power quality requirements. Meanwhile, under large load transients and grid-voltage disturbance conditions, the DC-link voltage recovery time is about 40 ms, verifying the comprehensive advantages of the proposed method in ripple suppression, dynamic response, and operating-condition adaptability. Full article

On-orbit micro-vibration has emerged as a critical constraint impairing the imaging performance and ultra-high pointing accuracy of space optical payloads. Most existing investigations separately concentrate on disturbance modeling, vibration isolation design, or line-of-sight (LOS) stabilization, leaving the full-link integrated dynamic modeling and analysis severely insufficient. To address this gap, this paper proposes an integrated dynamic modeling methodology for spacecraft equipped with optical payloads, which synergizes disturbance identification, finite element modeling, model order reduction, hybrid active–passive vibration isolation mechanism control, and fast steering mirror (FSM) regulation. The experimental and simulation results demonstrate that the root mean square (RMS) acceleration induced by flywheels and pumps at the mounting interface of the vibration isolation mechanism approximates 4.50 mg. Specifically, the passive vibration isolation scheme attains an attenuation of −16 dB, while the hybrid active–passive strategy achieves a remarkable −30 dB attenuation. Moreover, flywheels generate lower acceleration amplitude but more severe LOS jitter, owing to their time-varying disturbance characteristics and dispersed frequency energy distribution. Additionally, a full-spacecraft micro-vibration ground test incorporating horizontal gravity unloading via suspension is implemented to validate the model. The model-calculated acceleration and pointing angle exhibit excellent consistency with the experimental data, with the relative acceleration error below 7% and the angular error less than 9%. The proposed integrated dynamic model enables accurate prediction of micro-vibration transmission and suppression performance, laying a dependable theoretical foundation for design optimization of high-precision spacecraft systems. Full article

This paper presents a capability-aware frequency control strategy for microgrids comprising a ramp-rate-limited synchronous generator (SG) and a bounded inverter-based resource (IBR). In contrast to conventional droop and virtual inertia methods, the proposed design activates IBR support according to whether the required power-rate exceeds the ramp-rate capability of synchronous generation. A smooth activation mechanism detects when the required power-ramp demand exceeds the SG ramp-rate limit. The IBR is then engaged to supply the excess ramping requirement while providing additional damping through frequency-deviation feedback. A two-timescale model is formulated, where the IBR power-tracking dynamics evolve on a fast boundary-layer timescale. In contrast, the SG regulation loop evolves on a slow electromechanical timescale. Using singular perturbation theory combined with Lyapunov and input-to-state stability (ISS) analysis, local practical stability of the closed-loop system is established for sufficiently fast IBR dynamics. The proposed framework yields a physically interpretable coordination mechanism that exploits the fast response of IBR without introducing artificial inertia or frequency-domain disturbance splitting. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

This study investigates the coordinated impact of a synchronous condenser (SC), battery energy storage system (BESS), and static synchronous compensator (STATCOM) on enhancing voltage and frequency stability in a modified IEEE 9-bus power system under severe disturbances. The aim is to quantify the individual and combined contributions of these technologies during both fault ride-through (FRT) and load-increment events. The methodology includes dynamic modelling of all three devices in DIgSILENT PowerFactory. The SC is represented as a synchronous machine with inertia and AVR-based voltage control; the BESS employs converter-based active power and frequency-droop control; and the STATCOM provides fast reactive power injection through a dual-loop voltage regulator. Key indicators include nadir (minimum frequency), Rate of Change of Frequency (RoCoF), steady-state deviation, voltage sag depth, and recovery characteristics. Results indicate distinct roles for each device. The SC increases inertia and improves damping, but it also introduces small, well-damped oscillations. The BESS significantly enhances frequency stability by mitigating nadir, reducing RoCoF, and accelerating recovery, with negligible effect on voltage regulation. The STATCOM substantially reduces voltage sag and speeds up voltage recovery, but it does not influence frequency behaviour. When combined, the hybrid SC–BESS–STATCOM system demonstrates strong complementarity: the SC supports inertia, the BESS stabilizes active-power imbalance, and the STATCOM ensures fast reactive-power compensation. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

As a clean heating solution, electric heating loads (EHLs) have become a critical flexible load resource on the demand side in recent years. To enhance the power grid’s frequency regulation capability and mitigate the impacts of both EHLs and high-penetration renewable energy on the power grid, a symmetry-guided distributed control strategy for active distribution networks (ADNs) considering demand response (DR) of EHLs is proposed from the perspective of source–load bilateral coordination. Based on the symmetry of information interaction and control structure between distributed generators (DGs) and EHLs, a thermodynamic dynamic model of EHLs and a source–load coordinated response control framework are established. An improved consensus-based distributed control algorithm and a temperature queue sorting-based distributed response strategy are designed to maintain symmetrical power allocation and symmetrical response coordination between DGs and EHLs, achieving rapid and stable source–load coordination. Finally, comprehensive simulations verify the effectiveness of the proposed strategy. The results show that the proposed strategy improved the convergence speed by 27.5%, achieved fast and effective control of DGs and EHLs, maintained the steady-state frequency above 49.95 Hz under various interferences, effectively eliminated frequency deviation caused by source–load interference, and significantly improved the stability and frequency support capability of ADNs. Full article

Direct power control (DPC) is widely recognized for its simplicity and fast dynamic response; however, conventional implementations based on hysteresis comparators suffer from critical limitations, including variable switching frequency and pronounced active power oscillations, which hinder their applicability in renewable and hybrid energy systems. To address these challenges, this study proposes a fractional-order predictive DPC strategy incorporating a fractional-order proportional–integral (FOPI) regulator to enhance dynamic performance and robustness. The proposed method is systematically evaluated against both a conventional proportional–integral-based DPC (PI-DPC) and existing fractional-order DPC approaches under identical operating conditions using MATLAB simulations. The results demonstrate that the proposed controller achieves a stabilized switching frequency while significantly improving DC-link voltage performance. Specifically, the proposed method reduces voltage ripples to 0.027 V compared to 0.094 V and 0.104 V for PI-DPC and FOPI-FOPI-DPC with space vector modulation (SVM), corresponding to improvements of 71.27% and 74.03%, respectively. The overshoot is also reduced to 0.75%, outperforming PI-DPC (1.25%) and FOPI-FOPI-DPC-SVM (1%), with improvements of 40% and 25%. In terms of dynamic response, the proposed approach achieves a fast response time of 0.06 s, representing a 40% improvement over PI-DPC, while maintaining comparable performance with other fractional-order methods. Additionally, the steady-state error is reduced to 0.04 V, achieving improvements of 60% and 50% compared to PI-DPC and FOPI-FOPI-DPC-SVM, respectively. Although the settling time shows marginal variation, the overall system exhibits enhanced stability and robustness. These outcomes highlight the effectiveness of integrating fractional-order control with predictive strategies, offering a robust and practically viable solution for real-world hybrid power systems that integrate renewable generation and energy storage. Full article

This paper proposes a voltage-sensitivity estimation and droop-allocation framework for virtual power plant (VPP)-based frequency regulation in partially observable distribution feeders. In practical distribution systems, active-power adjustments by distributed energy resources (DERs) for frequency regulation may cause voltage excursions, while full real-time feeder information is often unavailable. To address this issue, an anchor-least-squares (Anchor-LS)-aided sensitivity-estimation method is developed using only point-of-common-coupling (PCC) voltage measurements and feeder-network information. Unlike state-estimation-based, data-driven, or optimization-heavy approaches that typically require wider measurement coverage, large training datasets, or repeated centralized computation, the proposed framework is designed for fast VPP-based frequency regulation under partial observability using only limited PCC measurements and feeder information. The proposed method reconstructs an approximate operating point and derives an operating-point-sensitive PCC voltage-magnitude-sensitivity matrix based on a coupled Z-bus formulation. Based on the estimated sensitivity, a voltage-constrained asymmetric droop-allocation framework is developed for under-frequency and over-frequency events, together with a practical iterative droop-adjustment method that mitigates PCC voltage violations without relying on a full optimization-based dispatch model. The proposed framework is validated through two case studies. In Monte Carlo simulations on the IEEE 33-bus feeder, the proposed sensitivity model reduced the mean RMSE by about 117 times compared with the common-path resistance method and by about 30 times compared with the conventional Z-bus method. In simulations on a practical 115-bus Korean distribution feeder, the proposed method achieved acceptable droop capacities comparable to those of a centralized LP baseline while reducing the mean computation time by about 3.2 times for both under-frequency and over-frequency events. These results confirm the practical usefulness of the proposed framework for fast VPP-based frequency regulation in real distribution networks under partial observability. Full article

With the increasing penetration of renewable energy, power systems require fast and reliable frequency regulation resources. Vehicle-to-grid (V2G) aggregation can provide fast response capability. However, it relies heavily on communication networks and is vulnerable to communication degradation and false data injection attacks (FDIAs). To address this challenge, this paper proposes a detection-free resilient control method for V2G-based frequency regulation. Rather than relying on explicit attack detection or compensation, the proposed method achieves decision-level adaptation from closed-loop system feedback through dynamic selection and switching of aggregator subsets. In this way, unreliable or compromised aggregators are implicitly avoided, improving system robustness under uncertain communication and cyber conditions. To further enhance robustness, a diffusion-based adversarial reinforcement learning framework is developed. A conditional diffusion model is used to generate diverse capacity scenarios with spatial heterogeneity. Adversarial training formulates the interaction between the attacker and the defender as a zero-sum game. This enables the learning of robust selection–switching policies under worst-case disturbances. Simulation results on the IEEE 39-bus system show that the proposed method improves frequency regulation performance under communication degradation and FDIA. The RMS frequency deviation is reduced from 0.13426 Hz to 0.09174 Hz compared with the no-defense case. Full article

Current-source DC links and their associated power converters require continuous conduction mode (CCM), necessitating specialized switching device configurations. These topologies have gained significant attention due to the increasing adoption of current-mode power distribution systems. The operation of a current-source DC-DC converter relies on temporary magnetic energy storage, typically regulated using established switch-mode power conversion techniques. For a stable current step up or step down the use of the tapped inductor concept can provide an ultimate stable solution for current adjustment and the proposed concept is now developed on a step-down current source DC-DC power converter for the first time to reveal in the power electronics field. The use of tapping concept is similar to a coupled inductor and this allows flexible current modification. In this article, this concept is extended to a family of Tapped inductor current-based DC-DC together with soft-switching to reduce the loss of the switching devices. The key advantage is that it can offer a wide range of current conversions with high efficiency. The theoretical and experimental analysis of the proposed converter family is presented. An experimental prototype of the converter was built and tested, operating with a switching frequency of 100 kHz and accommodating input currents ranging from 1 A to 10 A. The converter achieved current conversion ratios of 0.8, 0.67 and 0.57 times the input current, with an output power range of 1 W to 314 W. The maximum efficiency of 88% was achieved at an output power of 314 W. The high efficiency and wide current conversion range of this current-based converter make it suitable for a variety of applications such as current driving LED systems, photovoltaic (PV) system current source control, and constant current fast charging systems for electric vehicles (EVs). Full article

With the increasing penetration of renewable energy, power systems are facing stronger frequency fluctuations, which make fast and flexible frequency support increasingly important. Although vehicle-to-grid (V2G) technology provides a promising source of distributed regulation capacity, many existing studies do not explicitly consider EV owners’ participation, which may lead to a mismatch between theoretical regulation potential and practically available V2G support. To address this issue, this paper proposes a distributed Grid–Aggregator–EV frequency-regulation (FR) framework that incorporates EV participation factor into the control design. A three-layer architecture and a dynamic participation-aware model are established to describe the coordination of distributed V2G resources, and a Hamiltonian-based robust control law is developed under V2G power constraints. An integral reinforcement learning scheme is then adopted to realize the optimal regulation policy online, where the controller does not require explicit online knowledge of the system drift matrix, while preserving the physical control structure. In this way, the proposed method explicitly links the EV participation factor, dispatchable V2G regulation capacity, and coordinated FR, thereby improving robustness, adaptability, and practical relevance. Simulation studies on the IEEE 14-bus and IEEE 39-bus systems, together with an evening-period, time-varying participation case, demonstrate that the proposed method provides more effective frequency-deviation suppression, better overall regulation performance, and stable operation under dynamic EV participation. Full article

The increasing integration of wind power reduces system inertia and weakens the frequency regulation capability of power systems. To address the problems of unclear task allocation, repeated compensation, and insufficient consideration of wind turbine energy limits in wind–thermal coordinated frequency regulation, this paper proposes a multi-band collaborative frequency regulation strategy with dynamic energy-constrained control. The proposed method decomposes the frequency deviation into high- and low-frequency components, enabling wind turbines to provide fast support and thermal units to undertake sustained regulation. A residual-based cross-band feedback mechanism is introduced to reduce repeated compensation, while dynamic segmented thresholds and releasable power constraints are used to adaptively adjust wind turbine support according to available energy. Simulations on a modified IEEE 3-machine 9-bus system show that, compared with the conventional strategy, the proposed method reduces the secondary frequency drop from 0.028 Hz to 0.015 Hz and shortens the recovery time from 20.7 s to 16.6 s. The results indicate that the proposed method can provide a practical reference for coordinated primary frequency regulation in wind–thermal power systems. Full article

With the increasing penetration of wind power, the participation of wind farms in primary frequency regulation has become important for maintaining power system frequency stability. However, virtual inertia and droop control, while providing frequency support, can increase structural fatigue loads in wind turbines and shorten their service life. To address this issue, this study proposes a coordinated optimization method for wind farm primary frequency control parameters based on fatigue load prediction. First, damage equivalent load (DEL) data under different power disturbances, wind speeds, and control parameter settings are generated through OpenFAST–Simulink co-simulation. Then, a multilayer perceptron (MLP) neural network is developed to establish the mapping from power disturbance, wind speed, and control parameters to turbine DEL. Based on the trained model, an optimization framework is constructed to minimize the total DEL of the wind farm, improve the uniformity of DEL distribution among turbines, and satisfy grid frequency support constraints. Simulation results show that the proposed method effectively reduces the overall fatigue load of the wind farm while ensuring system frequency security and improving load distribution uniformity among turbines. Full article

This paper proposes a co-design methodology for the power and control stages of a bidirectional battery charger/discharger based on a boost converter topology. The approach ensures safe operation by limiting the battery current derivative, preventing abrupt transients that could degrade battery lifespan. The control strategy combines a cascade structure with an inner sliding mode current controller (for robustness and fast response) and an outer adaptive PI voltage loop (to regulate the DC-link voltage under varying load conditions). Additionally, the design constrains the switching frequency to reduce power losses. Experimental validation on a prototype converter demonstrates the effectiveness of the co-design framework, showing precise current/voltage regulation, adherence to switching frequency limits, and compliance with battery charging/discharging requirements. The results highlight the methodology’s potential to enhance efficiency and reliability in energy storage systems. The dynamic restrictions, overshoot lower than 5%, settling time shorter than 5 ms, and a battery current limitation less than 50 A/ms were always met with SMC and, in some cases, with the PI controller, but the results with SMC were always better: lower overshoot, shorter settling time, and greater restriction on the derivative of the battery current. In addition, the SMC system was 2.5–5.0% more efficient than the PI controller. Full article