Search Results (72)

The increasing integration of renewable energy sources, particularly photovoltaic (PV) systems, into power grids presents challenges in maintaining frequency stability due to the absence of traditional mechanical inertia. This paper proposes a hybrid control strategy combining Particle Swarm Optimization (PSO) and Reinforcement Learning (RL) to provide Adaptive Virtual Inertia Control for frequency stability in multi-microgrid PV systems. The proposed system dynamically adjusts virtual inertia and damping parameters in response to real-time grid conditions and frequency deviations. The PSO algorithm optimizes the base inertia and damping parameters offline, while the RL algorithm fine-tunes these parameters online by learning from the system’s performance. The adaptive control mechanism effectively mitigates frequency fluctuations and enhances grid synchronization, ensuring stable operation even under varying power generation and load conditions. The hybrid PSO–RL controller demonstrates a superior performance, maintaining a frequency close to nominal (50.02 Hz), with the fastest settling time (0.10 s), minimal RoCoF (0.2 Hz/s), and effectively zero steady-state error. Simulation results demonstrate the effectiveness of the hybrid control approach, showing fast and accurate frequency regulation with minimal power quality degradation. The system’s ability to adapt in real time provides a promising solution for next-generation smart grids that rely on renewable energy sources. Full article

Hybrid AC/DC microgrids (HADCMGs), which integrate renewable energy sources and battery storage systems, often face significant stability challenges due to their inherently low inertia and highly variable power inputs. To address these issues, this paper proposes a novel, robust composite controller based on backstepping fast terminal sliding mode control (BFTSMC). This controller is further enhanced with a virtual capacitor to emulate synthetic inertia and with a fractional power-based reaching law, which ensures smooth and finite-time convergence. Moreover, the proposed control strategy ensures the effective coordination of power sharing between AC and DC sub-grids through bidirectional converters, thereby maintaining system stability during rapid fluctuations in load or generation. To achieve optimal control performance under diverse and dynamic operating conditions, the controller gains are adaptively tuned using the marine predators algorithm (MPA), a nature-inspired metaheuristic optimization technique. Furthermore, the stability of the closed-loop system is rigorously established through control Lyapunov function analysis. Extensive simulation results conducted in the MATLAB/Simulink environment demonstrate that the proposed controller significantly outperforms conventional methods by eliminating steady-state error, reducing the settling time by up to 93.9%, and minimizing overshoot and undershoot. In addition, real-time performance is validated via processor-in-the-loop (PIL) testing, thereby confirming the controller’s practical feasibility and effectiveness in enhancing the resilience and efficiency of HADCMG operations. Full article

With the large-scale integration of renewable energy through power electronic inverters,
modern power systems are gradually transitioning to low-inertia systems. Grid-forming
inverters are prone to power overshoot and frequency deviation when facing external
disturbances, threatening system stability. Existing methods face two main challenges in
dealing with complex disturbances: neural-network-based approaches have high computational
burdens and long response times, while traditional linear algorithms lack sufficient
precision in adjustment, leading to inadequate system response accuracy and stability. This
paper proposes an innovative coordinated adaptive control strategy for virtual inertia and
damping. The strategy utilizes a Radial Basis Function neural network for the adaptive
regulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm.
This approach provides refined inertia regulation while maintaining computational
efficiency, optimizing the rate of change in frequency and frequency deviation. Simulation
results demonstrate that the proposed control strategy significantly outperforms traditional
methods in improving system performance. In the active power reference variation
scenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by
66.7%, and the system recovery time is shortened. In the load variation scenario, frequency
overshoot is reduced by approximately 3.6%, and the maximum frequency deviation is
reduced by approximately 26.9%. In the composite disturbance scenario, the frequency
peak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by
35%, and the power response improves by 23.3%. These results indicate that the proposed
method offers significant advantages in enhancing system dynamic response, frequency
stability, and power overshoot suppression, demonstrating its substantial potential for
practical applications. Full article

In this paper, an image-based visual control scheme is proposed for a quadrotor aerial vehicle with unknown mass and moment of inertia. In order to reduce the impacts of underactuation in quadrotor dynamics, a virtual image plane is introduced and appropriate image moment features are defined to decouple the image features from the movement of the vehicle. Subsequently, based on the quadrotor dynamics, a backstepping method is used to construct the torque controller, ensuring that the control system has superior dynamic performance. Furthermore, an adaptive control scheme is then designed to enable online estimation of dynamic parameters. Finally, stability is formally verified through constructive Lyapunov methods, and performance test results validate the efficacy and robustness of the proposed control scheme. It can be verified through performance tests that the quadrotor successfully positions itself at the desired position under uncertain dynamic parameters, and the attitude angles converge to the expected values. Full article

The increasing incorporation of renewable energy into power grids has significantly reduced system inertia and damping, posing challenges to frequency stability and power quality. To address this issue, an adaptive virtual synchronous generator (VSG) control strategy is proposed, which dynamically adjusts virtual inertia and damping in response to real-time frequency variations. Virtual inertia is modulated by an exponential function according to the frequency variation rate, while damping is regulated via a hyperbolic tangent function, enabling minor support during small disturbances and robust compensation during severe events. Control parameters are optimized using an enhanced particle swarm optimization (PSO) algorithm based on a composite performance index that accounts for frequency deviation, overshoot, settling time, and power tracking error. Simulation results in MATLAB/Simulink under step changes, load fluctuations, and single-phase faults demonstrate that the proposed method reduces the frequency deviation by over 26.15% compared to fixed-parameter and threshold-based adaptive VSG methods, effectively suppresses power overshoot, and eliminates secondary oscillations. The proposed approach significantly enhances grid transient stability and demonstrates strong potential for application in power systems with high levels of renewable energy integration. Full article

In islanded microgrids with high-proportion renewable energy, the disconnection from the main grid leads to the characteristics of low inertia, weak damping, and high impedance ratio, which exacerbate the safety risks of frequency and voltage. To balance the requirements of system operation economy and frequency–voltage safety, a coordinated optimization scheduling method for frequency and voltage in islanded microgrids considering the active support of battery energy storage (BES) is proposed. First, to prevent the state of charge (SOC) of BES from exceeding the frequency regulation range due to rapid frequency adjustment, a BES frequency regulation strategy with an adaptive virtual droop control coefficient is adopted. The frequency regulation capability of BES is evaluated based on the capacity constraints of grid-connected converters, and a joint frequency and voltage regulation strategy for BES is proposed. Second, an average system frequency model and an alternating current power flow model for islanded microgrids are established. The influence of steady-state voltage fluctuations on active power frequency regulation is analyzed, and dynamic frequency safety constraints and node voltage safety constraints are constructed and incorporated into the optimization scheduling model. An optimization scheduling method for islanded microgrids that balances system operation costs and frequency–voltage safety is proposed. Finally, the IEEE 33-node system in islanded mode is used as a simulation case. Through comparative analysis of different optimization strategies, the effectiveness of the proposed method is verified. Full article

This paper addresses the challenge of poor dynamic performance in Modular Multilevel Converter-based High-Voltage Direct Current (MMC-HVDC) systems within weak power grids when conventional control strategies are applied. To enhance system performance, a novel grid-connected power control method integrating Virtual Synchronous Generators (VSGs) and Passivity-Based Control (PBC) is proposed. The passivity characteristics of the MMC and the roles of virtual inertia and damping in VSG control are thoroughly examined. Based on the passivity property of the MMC, PBC is implemented in the current inner loop, while VSG control, leveraging its unique working characteristics, is incorporated into the power outer loop. To further optimize performance, adaptive virtual inertia and damping compensation mechanisms, utilizing sigmoid functions, are introduced within the VSG framework. The synergistic operation of PBC and adaptive VSGs significantly improves the dynamic response and robustness of the MMC-HVDC system. The effectiveness and feasibility of the proposed method are validated through simulation experiments in MATLAB/Simulink, conducted under power variations, grid voltage variations, and load changes. Full article

In regions where electric vehicles (EVs) are widely adopted and charging stations (CSs) are being built in large numbers, CSs are becoming a critical load-side resource for low-inertia power systems. In this paper, a physics-data fusion enhanced frequency control strategy for multiple CSs is proposed. Firstly, the power grid frequency control architecture is improved, where CSs as multi-agent (MA) can participate in frequency response (FR). Besides, a physics-driven adaptive inertia for CS virtual synchronous generators (VSGs) is proposed to improve system dynamic FR characteristics. Building upon this, the physics-data fusion concept is introduced, wherein the MA-soft-actor-critic (MA-SAC) algorithm dynamically adjusts coordination coefficients with the consideration of CSs’ FR capabilities. To validate the proposed strategy, comparative case studies are conducted on the IEEE 39-node system. The simulation results demonstrate that compared to a single physics-driven method, the proposed control strategy exhibits enhanced adaptability and improved FR characteristics across various scenarios. Under intact MA communication conditions, the proposed strategy reduces the frequency disturbance index to 49.872% and the CS response power oscillation index to 79.542%; Even with MA communication impairments, the strategy maintains significant improvements, reducing these indexes to 48.897% and 86.733% respectively. Full article

Focused on the contradiction between the steady-state error of active power and the dynamic oscillation caused by the virtual damping characteristics of the virtual synchronous generator (VSG) under disturbances during grid-connected operation, this article proposes an adaptive virtual inertia regulation and compensation method (PFFCVSG_AJ) based on an active power differential feedforward compensation strategy (PFFCVSG). Firstly, this article presents the working and control principles of VSG, analyzing its control mechanisms through a small-signal model. Models for VSG’s active power, reactive power, and virtual impedance components are established, with particular focus on the impact of the damping coefficient on active power regulation. Based on the PFFCVSG, an adaptive virtual inertia adjustment method is introduced to resolve the inherent inertia deficiency in PFFCVSG control, the influence of the moment of inertia on PFFCVSG is theoretically analyzed, and a dynamic adjustment mechanism for moment of inertia is developed based on the rate of change in frequency (RoCoF). Finally, simulation validation using MATLAB/Simulink (MathWorks, R2022b, Natick, MA, USA) demonstrates that the proposed PFFCVSG_AJ strategy effectively eliminates active power steady-state deviation, suppresses active power dynamic oscillation, and mitigates the frequency overshoot issue prevalent in traditional PFFCVSG. Experimental verification is conducted via a TMS320F28378DPTPS-based control platform, confirming the algorithm’s effectiveness under sudden load variations, and that the power quality of the power grid is not affected under the premise of efficient grid connection. Full article

In order to solve the problem of frequency and voltage stability degradation caused by high proportion of renewable energy grid connection, this paper proposes a multi-energy dynamic coordinated control framework, which integrates the inertia damping characteristics of virtual synchronous generator (VSG) and the flexible load regulation capability of virtual synchronous motor (VSM) to build a two-way interactive mechanism. For the first time, a virtual inertia dynamic compensation algorithm based on VSG is proposed. By introducing the frequency change rate adaptive inertia coefficient adjustment mechanism, the system’s active support capability for wind and solar power fluctuations is improved by 32% compared with the traditional fixed inertia strategy; a breakthrough design of the VSM-driven hydrogen production system dynamic matching control strategy is made, and an electrolyzer efficiency-power dual variable coupling model is established to achieve optimal control of hydrogen production efficiency fluctuation rate ≤ ±2.1% within a wide power range of 10–95%; an innovative mixed integer quadratic programming real-time optimization model considering battery SOC safety constraints is constructed, and the wind and solar consumption efficiency is improved by 28.6% compared with the single energy storage mode through energy storage-hydrogen production complementary scheduling. A simulation platform was built based on Simulink to verify the system performance under three conditions: load mutation, source-grid fluctuation, and simultaneous source-load change. The simulation results show that under different working conditions, the fluctuation range of the system frequency can be stabilized within ±0.15Hz, and the voltage deviation is less than 2%; through the coordinated scheduling of the battery and the hydrogen production system, the battery charge state is always maintained in a safe range of 15–85%, and the hydrogen production power regulation rate reaches 1.5 kW/s. The study shows that the proposed control strategy can significantly enhance the inertia response capability of the system, achieve dynamic power balance and power quality optimization under multiple working conditions, and provide a feasible technical path for the high proportion of renewable energy grid connection and efficient preparation of green hydrogen. Full article

The application of fractional calculus in power electronics modeling provides an innovative method for improving inverter performance. This paper presents a three-phase inverter topology with fractional-order LC filter characteristics, analyzes its frequency response, and develops mathematical models in both stationary and rotating reference frames. Based on these models, a dual closed-loop decoupling control strategy for voltage and current is designed to enhance system stability and dynamic performance. In the power control loop, fractional-order virtual synchronous generator control (FOVSG) is employed. Observations show that increasing the fractional-order of the rotor leads to a higher transient frequency variation rate. To address this, an adaptive rotational inertia control scheme is integrated into the FOVSG structure (ADJ-FOVSG), enabling real-time adjustment of inertia to suppress transient frequency fluctuations. Experimental results demonstrate that when the reference active power changes, ADJ-FOVSG effectively suppresses power overshoot. Compared to traditional VSG, ADJ-FOVSG reduces the power regulation time by approximately 34.5% and decreases the peak frequency deviation by approximately 37.2%. Compared to the adaptive rotational inertia control in traditional VSG, ADJ-FOVSG improves regulation time by about 24% and reduces peak frequency deviation by roughly 24.4%. Full article

To address the challenge of virtual synchronous generator (VSG) control technology in simultaneously achieving transient oscillation suppression and steady-state accuracy, as well as the poor anti-interference capability of fixed damping parameters under scenarios such as sudden changes in the short-circuit ratio (SCR), this paper proposes a transient damping optimization VSG control strategy based on a dissipative Hamiltonian neural network (DHNN) adaptive mechanism. Without affecting the original droop characteristics and rotational inertia, a transient damping feedback (TDF) branch is introduced to provide an additional damping ratio for the system to suppress low-frequency oscillations. The TDF control directly acts on the rotor motion equation through active-power low-frequency component feedback, featuring a simple structure without requiring complex computations. A small-signal model was established to quantitatively analyze the oscillation suppression mechanism. Furthermore, the dissipative Hamiltonian neural network (DHNN) was employed to dynamically optimize TDF parameters, ensuring a robust system performance under disturbances. The simulation and experimental results ultimately validated the effectiveness of the proposed control strategy. Full article

The increasing integration of renewable energy sources has posed significant challenges to grid frequency stability. To maximize the advantages of energy storage in primary frequency regulation, this paper proposes a comprehensive control strategy for a hybrid energy storage system (HESS) based on supercapacitor battery. Firstly, considering the characteristics of the HESS and different control strategies, the battery responds to virtual droop control to reduce frequency deviation, while the supercapacitor responds to inertia control to suppress frequency drops and facilitate frequency recovery. Simultaneously, a reasonable dynamic dead zone is configured to prevent frequent actions of the battery and thermal unit while allowing flexible adjustments according to the load condition. Thirdly, an algebraic S-curve-based adaptive droop coefficient incorporating SOC is proposed, while the inertia coefficient additionally considers load type, enhancing adaptability. Furthermore, to better maintain the battery’s SOC, an improved adaptive recovery strategy within the battery dead zone is proposed, considering both SOC recovery requirements and system frequency deviation constraints. Finally, a simulation validation was conducted in MATLAB/Simulink. Compared to the conventional strategy, the proposed control strategy reduces the frequency drop rate by 17.43% under step disturbance. Under compound disturbances, the RMS of frequency deviation decreases by 13.34%, and the RMS of battery SOC decreases by 68.61%. The economic benefit of this strategy is 3.212 times that of the single energy storage scheme. The results indicate that the proposed strategy effectively alleviates sudden frequency disturbances, suppresses frequency fluctuations, and reduces battery output while maintaining the SOC of both the supercapacitor and the battery, thereby extending the battery lifespan and improving economic performance. Full article

This study introduces a computational framework for understanding the symmetry and asymmetry of human movement by integrating Laban Movement Analysis (LMA). By conceptualizing movement refinement as a structured computational process, we model the golf swing as a series of state transitions where perceptual invariants guide biomechanical optimization. The golf club’s motion is analyzed using the instantaneous screw axis (ISA) and inertia tensor revealing how expert golfers dynamically adjust movement by detecting and responding to invariant biomechanical structures. This approach extends Gibson’s ecological theory by proposing that movement execution follows an iterative optimization process analogous to a Turing machine updating its states. Furthermore, we explore the role of symmetry in motor control by aligning Laban’s X-scale with structured computational transitions, demonstrating how movement coordination emerges from dynamically balanced affordance–action couplings. This insight gained from the study suggests that AI-driven sports training and rehabilitation can leverage symmetry-based computational principles to enhance motion learning and real-time adaptation in virtual and physical environments. Full article

As renewable energy penetration increases, maintaining grid frequency stability becomes more challenging due to reduced system inertia. This paper proposes an analytical control strategy that enables distributed energy resources (DERs) to provide inertial and primary frequency support. A reduced second-order model is developed based on aggregation theory to simplify the multi-machine system and facilitate time-domain frequency analysis. Building on this model, we design virtual inertia and damping coefficients for the frequency response, ensuring that it meets acceptable limits for both overshoot and steady-state deviation. To address energy storage constraints, an adaptive strategy is introduced to adjust control parameters dynamically based on the state of charge (SOC). Simulation results validate the accuracy of the aggregation model, showing that it closely approximates the full multi-machine system with minimal error. The proposed method significantly enhances frequency stability under varying load conditions while maintaining efficient SOC utilization. This study provides a practical framework for integrating DERs into grid frequency regulation by combining analytical control design with SOC-aware adaptation. The approach offers a computationally efficient alternative to detailed models, supporting more responsive and stable low-inertia power systems. Full article