Zhenglong Sun

Synchrophasor-based sub-synchronous oscillations (SSOs) parameter identification is effective for monitoring SSOs, while its performance is significantly degraded by measurement noise, posing challenges to reliable parameter identification. This paper proposes a novel technique using multi-channel enhanced synchro-reassigning transform (MESRT) for robust and accurate SSO parameter identification. First, each measurement channel is transformed using the short-time Fourier transform (STFT) to construct a global multi-channel time-frequency (TF) representation. Then, an average modal energy-based strategy is developed to eliminate noise-induced spurious modes in the STFT spectrum. Subsequently, a three-step selection rule is applied to extract TF coefficients representing oscillation modes, from which the time-domain components are reconstructed. Finally, the Hilbert transform (HT) method is employed to identify the oscillation frequency and damping factor of each mode. The proposed MESRT method effectively improves the noise immunity and accuracy of conventional SRT by considering inter-channel correlations and leveraging modal energy to remove spurious modes. Case studies validate that the proposed method performs exceptionally well in terms of accuracy and noise robustness, demonstrating superior performance compared to existing methods.

To address the frequency stability challenges arising from the high penetration of renewable energy, this study proposes a coordinated frequency regulation strategy for wind-power–hydrogen coupled systems, considering the Equivalent State of Charge (ESOC). While wind-power–hydrogen integration offers significant regulation potential, frequent ESOC excursions toward operational limits may lead to power interruptions and increased durability-related stress on hydrogen units. To resolve this, a refined mathematical model comprising wind turbines, electrolyzers, and fuel cells is first established to characterize system dynamics. The proposed method adopts an ESOC-based partitioning control logic: within normal ESOC ranges, the hydrogen storage system provides rapid frequency support via virtual inertia control; when ESOC reaches operational thresholds, the hydrogen unit seamlessly transitions out of service to prolong its lifespan, while the wind turbine dynamically compensates for the power deficit through adaptive droop control. Compared with other methods, the strategy proposed in this paper, implemented via DIgSILENT/PowerFactory simulations, improves the frequency nadir by 0.02 Hz during load increases and reduces the frequency peak by 0.04 Hz during load shedding. Under stochastic disturbances, the absolute steady-state frequency error is maintained below 0.02 Hz, while frequency deviations are strictly confined within ±0.5 Hz. These improvements significantly enhance both grid resilience and the operational safety of hydrogen units.

The increasing integration of wind power poses a challenge to power system transient stability. Conventional low‐voltage ride‐through (LVRT) control is hindered by passive triggering and delays, while synchronous generator tripping (SGT) incurs economic costs. This study proposes a coordinated strategy that enhances transient stability by combining active LVRT control of Wind Turbine Generators (WTGs) with SGT. The stability mechanism of active LVRT is analysed, and a model of an active LVRT doubly‐fed induction generator (DFIG) is developed. An intelligent decision‐making platform, employing the Proximal Policy Optimisation (PPO) deep reinforcement learning algorithm, dynamically optimises active LVRT parameters—start‐stop timing, duration, and participating WTG count—based on real‐time system states. Furthermore, a disturbance severity‐graded coordinated control strategy dynamically selects the appropriate control response level between active LVRT and SGT. Simulations confirm that the active LVRT strategy significantly improves transient stability. Its coordination with SGT optimises stability enhancement, demonstrating strong adaptability and robustness across diverse fault scenarios. This research presents a viable approach for ensuring the secure and stable operation of power grids with high penetration of renewable energy.

With the increasing popularity of low-cost, clean, and environmentally friendly new energy sources, the proportion of grid-connected new energy units has increased significantly. However, since these units are frequency decoupled from the grid through a power electronic interface, they are unable to provide inertia support during active power perturbations, which leads to a decrease in system inertia and reduced frequency stability. In this study, the urgent need to accurately assess inertia is addressed by developing an energy-function-based inertia identification technique that eliminates the effect of damping terms. By integrating vibration mechanics, the proposed method calculates the inertia value after a perturbation using port measurements (active power, voltage phase, and frequency). Simulation results of the Western System Coordinating Council (WSCC) 9-bus system show that the inertia estimation error of the method is less than 1%, which is superior to conventional methods such as rate-of-change-of-frequency (RoCoF) and least squares methods. Notably, the technique accurately evaluates the inertia of synchronous generators and doubly fed induction generators (DFIGs) under virtual inertia control, providing a robust inertia evaluation framework for low-inertia power systems with high renewable energy penetration. This research deepens the understanding of inertial dynamics and contributes to practical applications in grid stability analysis and control strategy optimalization.

With the evolution of modern power systems, the proportion of renewable energy generation in the grid continues to grow. At the same time, grid operation modes have become increasingly complex and dynamic, leading to heightened uncertainty in disturbance faults. Moreover, power electronic equipment exhibits relatively low-level immunity to disturbances. The issue of frequency stability in power systems is becoming increasingly severe. These factors make the pre-programmed control strategies based on strategy tables, which are widely used as the second line of defense for frequency stability in power systems, prone to mismatches. When a power disturbance occurs, it is crucial to adopt an appropriate emergency load-shedding strategy based on the characteristics of unbalanced power distribution and the network’s frequency profile. In this paper, for a simplified multi-zone equivalent system, the coupling relationship between different load-shedding locations and the system’s frequency response after a disturbance is analyzed. This analysis integrates the power distribution characteristics after the disturbance, a system frequency response (SFR) model, and the frequency distribution law in the network. It is demonstrated that under identical load-shedding amounts and action times, implementing load shedding closer in electrical distance to the disturbance location is more beneficial for stabilizing system frequency. A convolutional neural network (CNN) is employed to localize system faults, and combined with research on the emergency load-shedding amounts based on SFR model parameter identification, a rapid disturbance location-based emergency load-shedding strategy is proposed. This strategy enables prompt and accurate load-shedding actions to enhance the security and stability of the power system. Finally, the effectiveness of the proposed approach is validated using the CEPRI-LF standard arithmetic system.