Search Results (130)

The rapid penetration of converter-interfaced renewable generation has reduced effective inertia in islanded microgrids, making frequency regulation increasingly sensitive to renewable intermittency, generation outages, and storage stress. This paper proposes CMSA-OVSG–EMCS, a coordinated dual-layer control architecture for islanded PV–diesel microgrids with battery–supercapacitor hybrid energy storage. Its main novelty lies in coupling an adaptive grid-forming CMSA-OVSG layer, which updates virtual inertia and damping online according to disturbance severity, with a supervisory EMCS that coordinates multi-time-scale HESS power sharing through the common DC link. In contrast to OVSG approaches that rely on offline tuning of fixed controller parameters, the proposed framework uses physics-constrained multi-scenario optimization to jointly account for frequency response, DC-link regulation, converter operating limits, and battery stress. Nonlinear simulations under load variations, renewable intermittency, PV disconnection, and diesel-generator outage show that the proposed method consistently delivers the strongest transient performance among the tested controllers. In the worst-case diesel-generator outage scenario, it reduces the maximum ROCOF by 51.5% and the battery-stress index by 27.5% relative to the strongest benchmark controller. Full article

Virtual synchronous generator (VSG) control is widely used to improve the frequency stability of low-inertia microgrids. However, most existing adaptive VSG strategies tune the virtual inertia and damping coefficient mainly according to local frequency deviations of the energy storage converter, while the effect of supplementary wind turbine frequency support on the admissible VSG parameter range is rarely considered. To address this limitation, this paper proposes a wind–storage coordinated frequency control strategy that combines an energy storage fuzzy VSG with active-power-frequency droop support from a doubly fed induction generator (DFIG). The scientific contribution of this study is that the DFIG droop support term is incorporated into a reduced-order wind–storage small-signal model, and an admissible scheduling region for the virtual inertia and damping coefficient is constructed according to prescribed damping ratio and natural angular frequency constraints. This region is used to constrain the online fuzzy parameter scheduling of the energy storage VSG. In addition, bell-shaped membership functions are introduced to obtain smoother parameter variation and are compared with triangular membership functions under the same operating conditions. MATLAB/Simulink simulations are conducted under grid-connected/islanded transition, load switching, and renewable-power fluctuation conditions. Compared with the benchmark strategies, the proposed method reduces the maximum and average frequency deviations to 0.181 Hz and 0.016 Hz, respectively. The maximum discharge power, RMS power, and cumulative energy throughput of the energy storage system are reduced to 236.853 kW, 155.822 kW, and 5.751 kWh, respectively. These results indicate that the proposed coordinated strategy improves frequency regulation while reducing the transient regulation burden of the energy storage system within the investigated operating conditions. Full article

With the increasing integration of large-scale renewable energy sources (RESs), modern power grids are evolving into low-inertia and weak-damping networks, creating challenges for transient power-angle stability. Conventional grid-following (GFL) control strategies present a lagging synchronization response under weak grid conditions due to their reliance on phase-locked loops (PLLs). Although grid-forming (GFM) control via virtual synchronous generators (VSGs) provides standalone voltage source properties, conventional fixed-parameter VSGs exhibit an inherent design trade-off between first-swing angular suppression and post-fault oscillation damping during severe short-circuit disturbances. To resolve these vulnerabilities, this paper proposes an adaptive VSG control strategy designed to improve the transient power-angle response of the investigated system. By establishing a parametric judgment framework based on real-time frequency deviations and acceleration rates, the virtual inertia $J\left(t\right)$ and damping coefficient $D\left(t\right)$ are adaptively adjusted within predefined limits. Furthermore, a current-limiting mechanism is incorporated into the parameter adaptation loops to prevent converter overcurrent tripping. To evaluate the proposed method, an aggregated grid-connected benchmark system comprising a 5 MVA PMSG-based wind farm, a conventional synchronous generator, and an external grid is implemented in MATLAB/Simulink. The simulation results under continuous ambient operational fluctuations and severe three-phase short-circuit faults demonstrate that the proposed strategy reduces the first-swing power-angle peak by approximately 31% compared with the conventional fixed-parameter VSG, and the response settles within approximately 0.4 s in the investigated fault case. These results indicate the simulation-based feasibility of the proposed bounded adaptive strategy. Further real-time, hardware-in-the-loop, and experimental validation is required before practical implementation. Full article

To mitigate oscillatory instability caused by pulsed-load impact currents in diesel-generator-based medium-voltage direct current (MVDC) integrated power systems, an adaptive virtual synchronous generator (AVSG)-based model predictive control (MPC) strategy is proposed. A mathematical model of the pulsed load and its power-supply circuit is established based on the MVDC system architecture, and the overall transfer function from the load side to the source side is derived. On this basis, the relationship between the pulsed-load impact currents and diesel-generator speed fluctuation is clarified, and the coupling between the disturbance frequency and the generator’s inherent frequency is revealed. In addition, the active compensation mechanism of a supercapacitor–lithium-battery hybrid energy storage system is analyzed. An AVSG control strategy suitable for MVDC systems is then developed, and adaptive tuning laws for the virtual inertia and damping coefficients are designed. MPC is incorporated into the current inner loop of the hybrid energy storage system to improve the dynamic response. Hardware-in-the-loop results obtained on the RT Box 3 platform verify the effectiveness of the proposed strategy. Full article

The increasing penetration of renewable energy sources has increased the need for advanced control strategies capable of maintaining stability under low-inertia, converter-dominated operating conditions. In grid-connected wind energy conversion systems (WECSs), constant power loads (CPLs) exhibit negative incremental impedance characteristics that can amplify DC-link oscillations and complicate the coordination between the electrical and mechanical subsystems. The main contribution of this work is a Soft Actor–Critic (SAC) reinforcement learning algorithm that tunes the outer proportional-integral gains of the machine-side DC-voltage-squared control loop together with the active damping gain, allowing online adaptation of the controller according to the operating condition and disturbance level, thereby improving energy system sustainability. The proposed control framework includes a two-mass shaft model, virtual inertia control, and DC-link load uncertainty in the form of both resistive loads and CPLs. The system is modeled and evaluated using MATLAB/Simulink, and its performance is compared with that of a conventional fixed-gain controller under AC load disturbances and wind speed variations. It has been found that for a 25% load disturbance, the maximum DC-link voltage deviation is reduced by 1.2% under resistive loading and 6.5% under CPL operation. For a 1 m/s reduction in wind speed, the corresponding reductions are 0.8% and 0.9%, respectively. The proposed controller also provides smoother output power and improved damping of the rotor speed and system frequency responses. Full article

The increasing integration of wind power reduces the equivalent inertia of power systems, leading to lower frequency nadirs and higher rate of change of frequency following disturbances. In Type-4 wind turbine systems, conventional maximum power point tracking (MPPT) may counteract the additional inertial power command during frequency support and cause secondary frequency dips during rotor-speed recovery. To address these issues, this paper proposes a virtual-inertia rate-of-change-of-frequency (VI-RoCoF) frequency-modulated Andronov-Hopf oscillator (AHO)-based adaptive inertia enhancement method together with an adaptive MPPT coordination strategy. The proposed method constructs a frequency-support demand from frequency deviation and VI-filtered RoCoF and embeds it into the instantaneous angular-frequency evolution of the AHO. Different from a conventional linear virtual-inertia controller that directly converts frequency-deviation and RoCoF signals into an algebraic power command, the proposed method realizes the additional support through a bounded limit-cycle frequency-forming process, thereby preserving phase continuity and nonlinear amplitude self-regulation during frequency modulation. Meanwhile, the adaptive MPPT strategy adjusts the power reference in stages to suppress the counteractive effect of conventional MPPT on inertial support and to ensure a smooth transition back to maximum power point tracking. Theoretical analysis shows that the proposed modulation maintains the limit-cycle stability of the AHO under bounded control constraints while improving the equivalent inertia and damping characteristics of the system. Simulation results, including both averaged-model and switching-level SPS simulations, demonstrate that, compared with conventional AHO-based, fixed-inertia AHO-based, and linear VI-RoCoF benchmark schemes without AHO dynamics, the proposed AHO-MPPT coordinated control strategy increases the frequency nadir, reduces the peak RoCoF, improves recovery-stage frequency dynamics, mitigates secondary frequency dips, maintains bounded AHO internal variables, and preserves DC-link voltage stability. Full article

Traditional control strategies, such as droop-frequency and PI controllers, often show limited adaptability when the system operates under highly variable renewable generation and load conditions. In order to overcome this limitation, this study proposes the design and simulation of a clustered microgrid supported by reinforcement learning (RL)-based virtual inertia control. Three continuous-control RL algorithms were evaluated: Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC). The SAC agent provided the most robust training performance, reaching stable convergence after approximately 400 episodes and a final reward close to 86 units after 600 episodes. DDPG presented the second-best behavior, whereas TD3 achieved the lowest final reward, approximately 43 units. The proposed Agent SAC-Reinforcement Learning control was tested on a two-area microgrid cluster, which demonstrated greater frequency stability. Results indicate a frequency nadir of 59.82 Hz and ROCOF of 0.1484 Hz/s, with 6.78% nadir deviation improvement and 37.23% ROCOF reduction compared to PID-based strategies. Full article

As global power systems transition toward increasing penetration of renewable energy sources (RESs), such as solar and wind, maintaining frequency stability in converter-dominated low-inertia grids has become a critical challenge. This review examines the role of inertia in power system dynamics, emphasising the consequences of reduced mechanical inertia, the resulting increase in the rate of change of frequency (RoCoF), and the associated stability risks in grids with high inverter-based penetration. Inertial, primary, and secondary frequency response mechanisms are discussed alongside potential cascading failures, protection system triggering, and pathways toward fully renewable grids are assessed. Virtual inertia techniques, including synchronverters, swing-equation-based methods, virtual synchronous generators (VSGs), droop control, Virtual Oscillator Control (VOC), and matching control, are evaluated in terms of benefits, limitations, implementation complexity, and Technology Readiness Levels (TRLs). A key contribution is a multi-criteria evaluation framework that classifies these methods by control adaptability, scalability, and communication requirements, providing system operators with a structured basis for strategy selection. A comparative assessment of Phase-Locked Loop (PLL) synchronisation methods, including SRF-PLL, DDSRF-PLL, FLL-PLL, and Kalman filter-based approaches, is presented under weak-grid, unbalanced, and harmonic-distorted conditions. The integration of virtual inertia with energy storage technologies, such as batteries, supercapacitors, and flywheels, is also discussed, along with its role as an ancillary service within evolving electricity markets and grid codes. Collectively, this study provides a unified reference to advance intelligent, scalable, and deployment-ready frequency control in low-inertia renewable power systems, offering both theoretical insights and practical guidance for future high-RES grid architectures. Full article

Aiming at the problems of the severe active–reactive power coupling, insufficient adaptive inertia–damping regulation, and degraded dynamic performance of virtual synchronous generators (VSGs) under the operating conditions of a weak grid, high resistance-to-reactance ratio, and large power angle, this paper proposes a cooperative control strategy that combines reactive power feedforward decoupling with adaptive damping–inertia regulation. First, a small-signal power model of the VSG is established, and a dynamic relative gain array is employed to quantitatively analyze the effects of the resistance-to-reactance ratio and power angle on power coupling characteristics, revealing that large power angles and high resistance-to-reactance ratios significantly aggravate active–reactive power coupling. Based on this analysis, a reactive-power-oriented feedforward decoupling strategy is designed to suppress the cross-coupling between reactive power and power angle while preserving the intrinsic inertia support characteristics of the active power loop. Eigenvalue migration analysis further demonstrates that the proposed reactive-power-oriented decoupling provides higher damping ratios and larger stability margins than conventional full active–reactive power decoupling. Furthermore, a deep deterministic policy gradient-based adaptive damping–inertia control method is developed by incorporating frequency deviation, power fluctuation, voltage deviation, and coupling degree into the state space, enabling the online coordinated optimization of virtual inertia and damping coefficients. The hardware-in-the-loop experimental results verify that the proposed strategy effectively suppresses active–reactive power coupling, reduces power overshoot and oscillation, enhances frequency support capability and dynamic response speed, and maintains superior stability under weak grid conditions. Sensitivity analysis under grid impedance estimation errors further confirms its strong robustness against parameter uncertainty, while tests under composite disturbance scenarios demonstrate excellent transient performance. The proposed strategy provides an effective solution for improving the grid-connected operation performance and adaptability of VSGs in low-inertia power systems. Full article

Flywheel energy storage systems (FESSs) offer millisecond-level response speed, making them highly suitable for providing system inertia/frequency support in emergency grid scenarios. However, the FESSs often have limited energy capacity due to their high capacity cost, which necessitates a comprehensive consideration between remaining stored energy and sustained support capability. Thus, this paper proposes a virtual synchronous generator (VSG) control strategy based on a multi-time-step model predictive control (MPC) that considering flywheel’s state of charge (SOC), which provides both emergency frequency support and autonomous flywheel energy recovery within a single integrated framework. First, a multi-time-step MPC with the objective function aiming for both fast frequency response and smooth power output is introduced to compensate the reference power generated by the VSG strategy. Second, an SOC-adaptive frequency weight function is designed and incorporated into the objective function to balance the frequency deviation and the inertia/frequency support duration. Furthermore, an SOC self-recovery strategy is developed, allowing the flywheel to autonomously adjust its SOC to the desired range when the FESS is not participating in frequency regulation. Finally, the proposed strategy is verified through comprehensive simulations on various scenarios, demonstrating that it can efficiently and rapidly meet the frequency regulation demands when the SOC is sufficient, as well as achieve the balances between the frequency regulation performance and the support continuity when the SOC is insufficient. Full article

Frequency instability caused by reduced system inertia in inverter-dominated islanded microgrids represents a critical challenge in renewable-integrated power systems. Conventional fixed-parameter controllers exhibit limited adaptability to uncertain and time-varying low-inertia conditions. This paper proposes an LSTM–MPC + VIC framework that embeds a Long Short-Term Memory (LSTM) surrogate predictor directly within a Model Predictive Control (MPC) optimisation loop, coordinated with a Virtual Inertia Controller (VIC) for immediate transient support. The LSTM provides data-driven frequency predictions without requiring precise analytical system modelling, while the VIC supplies reactive inertial damping within the same control cycle. The proposed controller is evaluated against Proportional–Integral–Derivative (PID), PSO-optimised PID, and standard MPC baselines on a 50 Hz islanded microgrid. Results demonstrate the lowest maximum frequency deviation of 0.009748 Hz, fastest settling time of 36.34 s, and minimum integral absolute error of 0.12283 Hz·s among all controllers. A Lyapunov-based Input-to-State Stability (ISS) analysis, incorporating the load disturbance term via Young’s inequality, confirms an ISS ultimate bound of 0.057866 Hz and an effective decay rate of 1.2952 s⁻¹. Robustness is further validated through multi-scenario testing, parametric sensitivity analysis, component ablation, and computational feasibility assessment, confirming suitability for real-time deployment in low-inertia microgrid systems. Full article

Two-stage grid-forming (GFM) photovoltaic inverters leverage the dynamic characteristics of the DC-bus capacitor to emulate the inertia of synchronous generators. However, under conditions of drastic solar irradiance fluctuations or sudden increases in grid load, fixed virtual inertia parameters struggle to simultaneously ensure both effective grid frequency support and stable DC-bus voltage, often leading to DC voltage dips or even system disconnection. To address this issue, this paper proposes an adaptive virtual inertia control strategy that takes into account the dynamic resources on the DC side. First, based on the dynamics of the DC-bus voltage, the synchronous equations for the inverter are derived, and a quantitative mapping relationship between the control parameters and the virtual inertia, as well as the damping coefficient, is established. Second, an inertia control law that adaptively adjusts according to the photovoltaic output power is designed. When solar irradiance is abundant, the virtual inertia is increased to provide sufficient frequency support, and vice versa. At the same time, a damping coordination mechanism with power-difference feedforward is introduced, which enhances the system’s dynamic response under complex operating conditions. Verification using the PLECS RT-Box hardware-in-the-loop (HIL) experimental platform demonstrates that, compared to the conventional fixed-parameter control, the proposed strategy effectively suppresses DC-bus voltage dips during sudden changes in solar irradiance, thereby avoiding undervoltage protection trips. Under load-transient conditions, the strategy dynamically adjusts the inertia response based on the photovoltaic output status, achieving a balance between transient system stability and grid friendliness. Full article

The traditional secondary control method for virtual synchronous generators suffers from limitations such as slow dynamic response and poor adaptability under varying operating conditions, which significantly affect the reliability and stability of microgrids. To address these issues, this paper proposes an adaptive virtual synchronous generator secondary control method for microgrids based on predefined-time convergence. First, a predefined-time controller is designed, whose convergence time can be preset by the user, thereby resolving the problem of excessively long convergence times for frequency regulation and power sharing. Second, an adaptive inertia damping control method incorporating Gaussian functions is introduced to mitigate frequency fluctuations during disturbances in the microgrid system, effectively suppressing frequency deviations and enhancing microgrid stability. Finally, based on Lyapunov stability theory, the convergence of the proposed control method is rigorously proved, and its feasibility is validated through MATLAB/Simulink simulations. The results demonstrate that the proposed secondary control method reduces the frequency and active power convergence times by 0.98 s and 0.49 s, respectively, compared to traditional virtual synchronous generator secondary control methods. Additionally, it exhibits smaller frequency fluctuation magnitude during disturbances, enabling fast and smooth frequency recovery. Full article

Grid-forming virtual synchronous generator control can improve the frequency-support capability of converter-interfaced systems. However, under successive disturbances and varying operating conditions, fixed inertia and damping settings often struggle to balance inertial response, oscillation suppression, and recovery speed. To address this issue, this paper develops an adaptive parameter coordination strategy for grid-forming virtual synchronous generators by using frequency deviation and rate of change of frequency as dynamic indicators. A piecewise regulation law is established to adjust virtual inertia and damping during different transient stages, while an improved parrot optimization algorithm is introduced for the offline coordinated tuning of the adaptive-law parameters. In the proposed optimizer, SPM-chaotic initialization, adaptive probability adjustment, and Cauchy-Gaussian hybrid mutation are incorporated to improve population diversity, convergence efficiency, and local refinement capability. Simulation results obtained in MATLAB/Simulink under successive disturbance events show that the proposed strategy achieves smaller frequency excursions, weaker secondary oscillations, and shorter settling times than fixed-parameter control and standard PO-based tuning. The results demonstrate that the proposed method can effectively enhance the dynamic support capability and disturbance adaptability of grid-forming virtual synchronous generators under complex operating conditions. Full article

Photovoltaic (PV)-storage systems operating in weak grids are affected by high grid impedance, transient voltage disturbances, and measurement noise, which can degrade frequency regulation, increase converter current stress, and impose high-frequency current fluctuations on the battery. To address these issues, this paper proposes a multi-timescale transient-state sensing and signal-processing framework for grid-forming PV-hybrid storage systems. The proposed framework combines three coordinated functions. First, a frequency-domain HESS power-decoupling mechanism separates high-frequency transient power components and assigns them to the supercapacitor, while the battery mainly handles low-frequency energy variations. Second, a voltage-deviation-driven adaptive virtual inductance is introduced to increase the equivalent output impedance during voltage-sag events and reduce transient inrush current. Third, a noise-resilient frequency sensing strategy based on a filtered frequency derivative and a dead-band for false-trigger suppression is developed to reduce noise-induced false triggering in adaptive inertia and damping control. Comparative simulations indicate that under the tested weak-grid conditions, the proposed method reduces the transient inrush-current peak by 53.2%, decreases the maximum dynamic frequency deviation by approximately 75%, and improves the active-power regulation speed by more than 50%. These results indicate that the proposed sensing-oriented framework can improve transient response while reducing converter and battery current stress in PV-storage systems connected to high-impedance grids. Full article