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This paper addresses the challenges of broadband impedance identification in wind farms connected to the power grid, where broadband oscillations can compromise grid stability. Traditional impedance modeling approaches, including white-box and black/grey-box methods, face limitations in real-world applications, particularly when dealing with commercial new energy units with unknown control structures. To overcome these challenges, a novel real-time impedance identification method is proposed for PMSGs(Permanent Magnet Synchronous Generators). The method, called ASSA (Attention-based Shared and Specific Architecture), utilizes a multi-task neural network model combined with an attention mechanism to improve the accuracy of impedance fitting across different frequency bands. A broadband impedance dataset is constructed offline under various operating conditions, incorporating uncertainties like wind speed. The proposed approach offers an efficient solution for impedance identification, enhancing the stability and reliability of grid-connected renewable energy systems.

In large wind farms, uneven voltage distribution caused by feeder impedance and turbine spacing may pose safety hazards and reduce operational efficiency. This paper proposes a two-layer voltage coordination optimal control method for wind farms that balances both grid-connection point voltage levels and turbine-end voltage safety. The outer layer tracks voltage commands issued by the AVC master station at the point of common coupling (PCC), while the inner layer establishes a global optimization model considering generator terminal voltage safety. The second-order cone relaxation method converts nonlinear constraints into solvable convex forms. Through a two-layer iterative solution, it achieves optimal allocation of active and reactive power between wind turbines and static var compensators (SVGs) within the field, thereby enhancing the active power output at the wind farm port and increasing the system’s reactive power margin. Simulation results demonstrate that compared to conventional unified power factor control, the proposed method effectively enhances terminal voltage security, increases wind farm power generation, and boosts system reactive power reserve capacity while stably tracking PCC voltage commands.

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (SVDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between SVDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids.

This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function, enabling high-accuracy equivalent modeling with limited data and overcoming the typical “black-box” constraints and large data requirements of traditional data-driven approaches. To enhance the model’s real-time adaptability, we introduce an online update mechanism leveraging Bayesian Neural Networks (BNNs) combined with a clustering-guided strategy. This mechanism estimates uncertainty in the neural network weights in real-time, accurately identifies error sources, and performs local fine-tuning on clustered data. This improves the model’s ability to track real-time errors and addresses the challenge of parameter-specific adjustments. Finally, the data-driven model is integrated into the CloudPSS platform, and its multi-scenario modeling accuracy is validated across various typical cases, demonstrating the robustness of the proposed approach.

This paper develops the impedance models of grid-connected converters under the virtual synchronous generator (VSG) strategy with a cascaded inner control loop and analyzes the system stability of VSG controlled converters with different kinds of weak grid. Different from existing small-signal models with high dimensions, a single-in-single-out (SISO) impedance model with simple mathematical expression is obtained in this paper, which is applied to identify the influence of the cascaded control loop on impedance characteristics and system stability. It is found that the impedance characteristics of VSG controlled converters can become capacitive below the fundamental frequency, and it is mainly caused by the voltage controller in the cascaded control loop of the VSG strategy. Impedance-based stability analysis shows that the capacitive impedance characteristics can benefit the compatibility of converters operated with the series-compensated weak grid, but may deteriorate the system stability with the inductive weak grid, which can be avoided by increasing the proportional coefficients of the cascaded voltage and current controllers or applying a larger virtual resistor to reduce the negative resistance in the capacitive frequency range. Experiments based on the control-hardware-in-loop (CHIL) platform were carried out to verify the developed analytical models and possible system instable cases.

The sequence domain impedance modeling of wind turbines (WTs) has been widely used in the stability analysis between WTs and weak grids with high line impedance. An aggregated impedance model of the wind farm is required in the system-level analysis. However, directly aggregating WT small-signal impedance models will lead to an inaccurate aggregated impedance model due to the mismatch of reference frame definitions among different WT subsystems, which may lead to inaccuracy in the stability analysis. In this paper, we analyze the impacts of the reference frame mismatch between a local small-signal impedance model and a global one on the accuracy of aggregated impedance and the accuracy of impedance-based stability analysis. The results revealed that the impact is related to the power distribution of the studied network. It was found that that the influence of mismatch on stability analysis became subtle when subsystems were balanced loaded. Considering that balanced loading is a common configuration of the practical application, direct impedance aggregation by local small-signal models can be applied due to its acceptable accuracy.

Immunotherapy targeting PD‐L1 is still ineffective for a wide variety of tumors with high unpredictability. Deploying combined immunotherapy with alternative targeting is practical to overcome this therapeutic resistance. Here, the deficiency of serine‐threonine kinase STK24 is observed in tumor cells causing substantial attenuation of tumor growth in murine syngeneic models, a process relying on cytotoxic CD8+ T and NK cells. Mechanistically, STK24 in tumor cells associates with and directly phosphorylates AKT at Thr21, which promotes AKT activation and subsequent PD‐L1 induction. Deletion or inhibition of STK24, by contrast, blocks IFN‐γ‐mediated PD‐L1 expression. Various murine models indicate that in vivo silencing of STK24 can significantly enhance the efficacy of the anti‐PD‐1 blockade strategy. Elevated STK24 levels are observed in patient specimens in multiple tumor types and inversely correlated with intratumoral infiltration of cytotoxic CD8+ T cells and with patient survival. The study collectively identifies STK24 as a critical modulator of antitumor immunity, which engages in AKT and PD‐L1/PD‐1 signaling and is a promising target for combined immunotherapy. Immunotherapy targeting PD‐L1 is ineffective in many tumors and with inherent unpredictability. Substantial efforts are being channeled toward identifying novel targets to enhance immunotherapy efficacy through combination treatments. Here, STK24 is identified as a promoter of tumorigenesis in a non‐cancer cell‐autonomous manner. STK24 phosphorylates AKT at a previously unrecognized Thr21, augments PD‐L1 expression, and facilitates the evasion against antitumor immunity.

Hematopoietic stem cells (HSCs) are important for the hematopoietic system because they can self-renew to increase their number and differentiate into all the blood cells. At a steady state, most of the HSCs remain in quiescence to preserve their capacities and protect themselves from damage and exhaustive stress. However, when there are some emergencies, HSCs are activated to start their self-renewal and differentiation. The mTOR signaling pathway has been shown as an important signaling pathway that can regulate the differentiation, self-renewal, and quiescence of HSCs, and many types of molecules can regulate HSCs’ these three potentials by influencing the mTOR signaling pathway. Here we review how mTOR signaling pathway regulates HSCs three potentials, and introduce some molecules that can work as the regulator of HSCs’ these potentials through the mTOR signaling. Finally, we outline the clinical significance of studying the regulation of HSCs three potentials through the mTOR signaling pathway and make some predictions.