Youhan Deng

Wind–photovoltaic (PV) modular-multilevel-converter-based high-voltage direct-current (MMC-HVDC) collection systems have limited inherent inertia and are vulnerable to frequency disturbances caused by renewable power fluctuations. Moreover, frequency deterioration and recovery impose different and partially conflicting requirements on virtual inertia and damping. To address this problem, an asymmetric Gaussian fuzzy coordination method is proposed for the virtual synchronous generator (VSG)-controlled sending-end MMC. A low-frequency model is first established to characterize the effects of wind–photovoltaic power variations on the collection-bus frequency. The frequency deviation and rate of change of frequency (RoCoF) are then used to identify the frequency-evolution state and generate coupled adjustments of virtual inertia and damping. Nonuniform and asymmetric Gaussian membership functions enhance sensitivity near the nominal operating point and distinguish the regulation requirements during deterioration and recovery. Simulations under three representative disturbance cases show that the proposed method reduces the maximum frequency deviation by 19.4–28.6% and shortens the recovery time by 17.5–24.4% compared with fixed-parameter control. The alternating-current voltage and current and direct-current-link voltage remain within acceptable ranges. Furthermore, 20 randomized tests demonstrate consistent frequency performance without current or voltage-limit violations.

Low-switching-frequency operation is essential for high-power modular multilevel converters (MMCs), but it makes the simultaneous achievement of low harmonics, fast transient response, and reliable internal control more difficult. Conventional lookup-table selective harmonic elimination PWM (SHEPWM) preserves steady-state harmonic optimization, yet its fundamental-period pattern update limits transient flexibility; predictive pulse-pattern methods improve current or flux tracking, but do not directly coordinate the MMC execution chain from arm-level pulse displacement to physical submodule gating. This paper proposes a coordinated switching-event control scheme for SHEPWM-based MMCs. The offline harmonic-optimized pattern is used as the steady-state backbone, while selected time-stamped events are corrected online through a virtual-flux formulation. The same events are then processed by causal pulse-edge compensation, paired upper/lower-arm displacement for circulating-current suppression, and threshold-based asynchronous submodule scheduling. Simulations of startup, active-power steps, and power-flow reversal show that more than 90% of the virtual-flux error is compensated within approximately 5 ms. Device-level co-simulation reduces current THD from 1.5881% to 0.702%, and paired-event control reduces arm-current THD from 21.600% to 4.9266%. At rated steady state, the method achieves 1.25% grid-current THD with a 50 Hz average submodule switching frequency, supporting low-loss MMC operation without abandoning SHEPWM harmonic optimization.

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process.

Hydro-wind-solar hybrid power systems have become a mainstream configuration for new-type power systems. However, the high proportion of power-electronics-interfaced generation alters system inertia and damping characteristics, leading to complex high-dimensional frequency dynamics and severe stability challenges. This paper investigates the frequency response mechanism and Hopf bifurcation characteristics of the aggregated frequency response model for hydro-wind-solar hybrid power systems. First, primary frequency response models for hydropower, wind power, and photovoltaic (PV) generation are established under a small-signal analysis framework. On this basis, a tenth-order nonlinear dynamic model of the integrated system is constructed by considering hydraulic nonlinearities, virtual inertia control of wind power, and reserve-based frequency regulation of PV systems. Then, Hopf bifurcation theory is applied to analyze stability and oscillatory instability mechanisms of the high-dimensional system. The bifurcation conditions are derived via high-dimensional Jacobian matrix analysis and Routh-Hurwitz criterion, with supplementary normal form calculation and first Lyapunov coefficient derivation to identify the supercritical/subcritical nature of the bifurcation. Finally, numerical simulations under both small and large disturbances validate the theoretical analysis. The results demonstrate that variations in key control parameters may induce Hopf bifurcation, leading the high-dimensional system from a stable equilibrium to sustained low-frequency oscillations. The findings provide insights and practical guidance for stable operation and parameter tuning of hydro-wind-solar hybrid power systems.

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery.

This study aims to reduce reverse power and improve frequency regulation performance in hydropower systems. To achieve this objective, a refined hydropower plant (HPP) simulation model is developed and coupled with a battery energy storage system (BESS), implementing an Integrated Adaptive Virtual Droop Control (IAVDC) strategy. The refined HPP model achieves a simulation accuracy of 98.5%, representing a 26.2% improvement over conventional simplified models. With the BESS integrated under the IAVDC strategy, reverse power is completely eliminated, and frequency regulation time is substantially shortened. The results demonstrate that the joint HPP-BESS frequency regulation effectively mitigates the adverse impact of water hammer, while the proposed IAVDC strategy enhances system responsiveness and reduces frequency recovery time, thereby improving the quality of primary frequency control.

As renewable energy sources are becoming more widely integrated into the modern power system, the uncertainties within this system are becoming increasingly prominent. It is crucial to accurately describe the uncertainties in renewable energy output for the effective planning, scheduling, and control of power systems. For this purpose, the aim of this paper is to introduce a method for generating short-term output scenarios for renewable energy sources based on an improved Wasserstein Generative Adversarial Nets-Gradient Penalty. First, a Deep Neural Network structure inspired by the Transformer algorithm is developed to capture the temporal characteristics of renewable energy outputs. Then, combined with the advantage of the data generation of the Wasserstein Generative Adversarial Nets-Gradient Penalty, the Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty is proposed to generate short-term renewable energy output scenarios. Finally, experimental validation is conducted on open-source wind and photovoltaic datasets from the U.S. National Renewable Energy Laboratory, where the performance of the proposed model in generating renewable energy output scenarios across various aspects (i.e., individual sample representation, expectation and variance, probability density function, cumulative distribution function, power spectral density, autocorrelation coefficient, and pinball loss) is assessed. The results show that our method outperforms the Wasserstein Generative Adversarial Nets-Gradient Penalty, Variational Autoencoder, Copula function, and Latin Hypercube Sampling models in the abovementioned evaluation indicators, providing a more precise probability distribution representation of realistic short-term renewable energy outputs.