Linyun Xiong

Hybrid systems comprising grid-following voltage-source converters (GFL-VSCs) and grid-forming voltage-source converters (GFM-VSCs) are increasingly adopted for renewable-energy integration. However, the influence of installed-capacity allocation on their coupled transient synchronization stability remains insufficiently characterized. This paper develops a capacity-ratio-dependent nonlinear transient model under a fixed total installed capacity. A synchronous-machine-like analytical framework is established to elucidate how the capacity ratio modifies GFL current injection, GFM power support, and current-limiting behavior, as well as the electrical coupling between the two converters. The critical clearing time (CCT) is employed as a quantitative measure of transient stability, and the reduced-order model is validated against a detailed MATLAB/Simulink model under three-phase and two-phase-to-ground faults. In addition, the sensitivities to key system and control parameters are investigated. The results demonstrate that transient stability varies non-monotonically with the capacity ratio. A low GFL capacity limits its fault-current contribution and alters the coupling dynamics, whereas a low GFM capacity weakens voltage-forming support and reduces the available margin before current limitation becomes dominant. The proposed analysis provides a quantitative basis for capacity allocation and stability-oriented design of hybrid converter-based power systems.

As a growing portion of inverters in alternating current (AC) micorgrids (MGs) are utilizing the grid-forming (GFM) control due to its superior active grid support capabilities, the hybrid operation of GFM and grid-following (GFL) converters becomes an imperative scenario, making it more complex to achieve coordination among heterogeneous inverters for grid support. This paper investigates the synergetic operation of GFM and GFL inverters for the support of grid frequency and bus voltages. To address the contradiction between the frequency control and voltage regulation missions, the general roles of GFM and GFL are assigned based on a local node‘s power balance profile. Moreover, to simplify the control system design and achieve unified regulation of two indices, a fixed-time containment control (FTCC) scheme is proposed, which chooses two leader distributed generators (DGs) to form a convex hull shaped by the set points of the control objectives and leads the follower DGs for power sharing. In addition, the fixed-time convergence of the control approach is ensured for accelerating the response speed. Hardware-in-the-loop (HIL) experiments are performed to evaluate the feasibility and performance of the proposed method. The experimental results fully validate the effectiveness and superiority in frequency/voltage regulations and robustness performance.

This study presents a novel disturbance observerbased distributed predefined-time fault-tolerant controller (DOBDPTFTC) to achieve precise frequency and voltage regulation, as well as accurate power sharing, in islanded AC MGs subject to actuator fault and saturation constraints. The actuator fault can result in poor performance of the controller, thus degrading the dynamics of the MGs. Predefined-time observers are designed to estimate the disturbances that are caused by the actuator fault within a predefined time. Furthermore, owing to the practical physical limitations of the islanded AC MGs, the output of the actuator will suffer from saturation. The controller saturation is taken into account and a saturation compensation system is designed to remedy the effect of the controller saturation. Besides, the average voltages of all DGs are obtained by a distributed predefined-time observer (DPTO). Then, based on the observations of the disturbances, the DOBDPTFTCs are proposed to guarantee frequency/voltage regulation together with power sharing in a predefined time, which is determined by a tunable parameter. The islanded AC MG with actuator fault and saturation is verified to be predefined-time stable under the proposed DOBDPTFTCs through the Lyapunov theorem. Simulation analysis and hardware-in-the-loop experiments are performed to comprehensively assess the performance of the DOBDPTFTCs in islanded AC MGs.

This study develops an adaptive neural network-based predefined-time nonlinear sliding mode control (ANNBPTSMC) to achieve frequency regulation in heterogeneous power systems supported by grid-forming (GFM) energy storage. Neural network is taken to tackle the nonlinear disturbance resulting from uncertainty of grid-following (GFL) wind turbines and randomness of the load. Consequently, the proposed ANNBPTSMC scheme achieves the inherent advantages of sliding mode control (SMC), including strong robustness and rapid transient performance. Meanwhile, it exhibits enhanced capability in addressing complex nonlinear disturbances. In addition, the designed ANNBPTSMC ensures that the frequency of the heterogeneous power system reaches stability region within a predefined time. Notably, the convergence time is independent of the system’s initial states and is explicitly determined by a tunable parameter. The predefined-time frequency stabilization is rigorously verified through Lyapunov-based analytical methods. Simulations conducted in MATLAB/Simulink demonstrate that the proposed control strategy significantly outperforms existing methods in enhancing the dynamic response of heterogeneous power systems under varying operational conditions.

Voltage source converter-based–high voltage direct current (VSC-HVDC) systems exhibit strong nonlinear characteristics that dominate their dynamic behavior under large disturbances, making large-signal stability assessment essential for secure operation. This paper proposes a large-signal stability analysis framework for VSC-HVDC systems. The framework combines a unified Takagi–Sugeno (T–S) fuzzy model with a model-free predictive control (MFPC) scheme to enlarge the estimated domain of attraction (DOA) and bring it closer to the true stability region. The global nonlinear dynamics are captured by integrating local linear sub-models corresponding to different operating regions into a single T–S fuzzy representation. A Lyapunov function is then constructed, and associated linear matrix inequality (LMI) conditions are derived to certify large-signal stability and estimate the DOA. To further reduce the conservatism of the LMI-based iterative search, we embed a genetic-algorithm-based optimizer into the model-free predictive controller. The optimizer guides the improved LMI iteration paths and enhances the DOA estimation. Simulation studies in MATLAB 2023b/Simulink on a benchmark VSC-HVDC system confirm the feasibility of the proposed approach and show a less conservative DOA estimate compared with conventional methods.

The wake effect may reduce the received wind speeds by the wind turbines (WTs), leading to degraded performance in power system frequency support. Existing researches only consider the impact of single-directional wake effect on the WT’s frequency support ability, neglecting the scenarios of non-array formed wind farm where the wake effect might be multi-directional. Out of this consideration, this paper proposes an optimal energy storage system sizing (OES) scheme to mitigate the impact of multi-directional wake (MDW) effect on the WT’s frequency support performance. Firstly, we consider the scenario of none-array formed WF layout, and develop the MDW effect model which incorporates 8 characteristic wind directions to assess the reduction of the received wind speed by the WTs. Subsequently, the impact of MDW effect on the WTs’ frequency support margin (FSM) is evaluated based on the grid synchronization characteristic curves. Meanwhile, the WTs are divided into separate clusters based on their FSM via fuzzy K-means algorithm. Afterwards, the OES scheme is proposed, which tries to maximize the coherency level among different WTs in their FSM by optimally allocating ESSs in each cluster. Ultimately, the effectiveness and superiority of the proposed OES scheme is validated via simulations and comparative study.

Due to information exchange on the communication links, the conventional consensus controller could result in the leakage of privacy information. Therefore, this paper proposes a dynamic event-triggered privacy-preserving consensus algorithm (DETPPCA) to regulate frequency/voltage and achieve optimal power allocation (OPA), while preserving the privacy information of distributed generators (DGs) in islanded AC microgrids (MGs). Firstly, a newly dynamic event-triggered mechanism (DETM) is developed to enlarge the triggering interval by an internal adaptive dynamic variable, thereby greatly reducing the amount of data transmitted and the risk of privacy leakage. Moreover, the proposed DETM can avoid the Zeno behavior. Subsequently, based on different decomposition functions, an improved state decomposition method that allows only one of the sub-states to communicate with neighboring DGs is proposed to provide privacy protection for initial states. Ultimately, the DETPPCA is applied to design secondary controllers to replace the traditional active power sharing controller, which can fulfill the objectives of frequency/voltage restoration and optimal economic operation simultaneously, but not an additional tertiary controller. The effectiveness of the designed DETPPCA-based secondary controllers is validated through extensive case studies on the Matlab/Simulink platform.

Addressing the issue of power quality degradation caused by parameter uncertainty and the problem of voltage and frequency deviation due to disturbance in islanded AC microgrids (MGs) in harsh environments, this article proposes a hierarchical robustness strategy combining model-free prediction and fixed-time control (CMFP-FTC). Firstly, an ultra-local model (ULM) of an LC-filter is established, and the unknown disturbances in the ULM are observed using the designed fixed-time sliding model observer to enhance the robustness and dynamic performance of the system. Additionally, the Artstein transform theory is employed to convert the model with communication delay into a delay-free model. Furthermore, a distributed fixed time secondary controller (DFTSC) based on saturation function is designed to avoid the control signal from exceeding the physical limitations of the actuator, and the deviations in frequency and voltage are corrected by DFTSC for accurate power sharing. Finally, the simulation and experimental results demonstrate that the proposed CMFP-FTC strategy exhibits strong robustness and has the ability to rapidly and accurately restore frequency and average voltage to rated values, as well as achieve active and reactive power sharing.

Accurate power load forecasting is crucial for maintaining the equilibrium between power supply and demand and for safeguarding the stability of power systems. Through a comprehensive optimization of both the parameters and structure of the traditional load forecasting model, this study developed a short-term power load prediction model (QGA-RMNN) based on a quantum genetic algorithm to optimize an artificial recurrent memory network. The model utilizes the principle of quantum computing to improve the search mechanism of the genetic algorithm. It also combines the memory characteristics of the recurrent neural network, combining the advantages of the maturity and stability of traditional algorithms, as well as the intelligence and efficiency of advanced algorithms, and optimizes the memory, input, and output units of the LSTM network by using the artificial excitation network, thus improving the prediction accuracy. Then, the hyperparameters of the RMNN are optimized using quantum genetics. After that, the proposed prediction model was rigorously validated using case studies employing load datasets from a microgrid and the Elia grid in Belgium, Europe, and was compared and analyzed against the classical LSTM, GA-RBF, GM-BP, and other algorithms. Compared to existing algorithms, the results show that this model demonstrates significant advantages in predictive performance, offering an effective solution for enhancing the accuracy and stability of load forecasting.

A low inertia heterogeneous power system with a substantial share of renewable energy and extensive use of power electronics suffers from serious frequency stability problems when it is subjected to disturbances. The grid-forming (GFM) converter based on virtual synchronous generator (VSG) control exhibits voltage source characteristics and provides inertia and frequency support. This paper proposes a predefined-time observer-based sliding mode controller (PTOBSMC) for the VSG-based GFM converter to provide frequency support in the heterogeneous power system with large load and grid-following (GFL) renewable energy variation. The model of the heterogeneous power system, which consists of GFM converter, GFL renewable energy, and synchronous generator, is established. The predefined-time observer is proposed to accurately estimate the fluctuations in both the output power of GFL wind turbines and the load power. The observer is specifically engineered to ensure that the estimation error progressively diminishes and ultimately reaches zero within a predefined time. Based on the predefined-time observer and sliding mode control, the PTOBSMC is proposed to regulate the GFM converter to provide frequency support and facilitate the rapid restoration of the frequency of the heterogeneous power system within a predefined time. The Lyapunov function is employed to analyze the predefined-time stability of the heterogeneous power system under the proposed PTOBSMC. Comparative results obtained through Matlab/Simulink demonstrate that the proposed PTOBSMC exhibits remarkable robustness and superior performance for the heterogeneous power system.