To address the inadequacy of conventional emergency generator tripping (EGT) in adapting to the topological changes in power systems, this paper proposes a novel knowledgeguided transfer reinforcement learning framework. For training efficiency optimization, we architect a knowledge-embedded action guidance module that incorporates invalid action masking and critical action weighting. This innovative approach transforms raw EGT action into optimized control commands, ensuring physical rationality while reducing ineffective exploration and improving decision-making efficiency. To enhance model adaptability to topology variations, we propose a knowledge-informed spatiotemporal transformer (KISTT) driven proximal policy optimization (PPO) architecture. The framework integrates the KISTT module as the PPO’s feature extraction layer, which effectively captures the spatiotemporal correlations by incorporating expert knowledge, enabling accurate perception of topological changes in EGT scenarios. Regarding model degradation to overload operations and topological variations, we propose a hybrid model transfer framework that facilitates adaptive parameter transfer from source to target domain, thereby significantly enhancing convergence speed and control performance in the target domain. Simulation results on the IEEE 39-bus system and Northeast China Power Grid demonstrate that the proposed method outperforms existing approaches in control performance, model adaptability and transferability, providing an innovative solution for EGT in dynamic grid environments.

Modern power systems face growing uncertainties and structural variations, challenging conventional emergency voltage control in terms of efficiency, scalability, and adaptability. To address these limitations, this paper proposes a scale-adaptive physics-guided transfer reinforcement learning method designed for automation-ready emergency voltage control. Training efficiency is improved by embedding an expert strategy knowledge base into imitation learning, guiding the deep reinforcement learning (DRL) agent rapidly to converge toward superior control strategies. Scalability is achieved through a hierarchical graph pooling–ensemble graph attention network (HAGPool-GAT), which produces fixed-length, scale-independent features that eliminate the necessity for retraining from scratch when deploying the model across power grids of varying scales. To enhance adaptability across heterogeneous grids, a hybrid transfer learning mechanism integrates experience transfer, model transfer, and imitation transfer. This mechanism dynamically selects strategies for intra- and cross-grid scenarios, mitigating degradation under system variations. This integration of physics-informed modeling, advanced graph representation, and hybrid transfer learning represents a novel automation paradigm for emergency voltage control. The proposed method is validated on IEEE 39-bus, IEEE 57-bus, and the Northwest China power grid, demonstrating superior efficiency, robust scalability, and reliable transferability, with clear potential application in large and evolving power systems.

Text-to-video retrieval (T2VR) aims to identify the most semantically relevant video based on a text query. Text queries typically involve diverse visual elements and events in video, making it non-trivial to learn a robust video feature representation for different queries. An abundance of spatial information make the model overwhelmed by redundancy and noisy and struggle to focus on linchpin visual elements. Additionally, without effective guidance, models grapple with connecting temporal information across different frames. In this paper, we introduce a Spatial-Temporal Pooling (STP) method to cohesively capture and unify the inherent spatio-temporal context within videos. For spatial information, STP leverages spatial tags such as entities, scenes, and text as attention prompts, steering the model toward salient visual elements while mitigating the impact of redundancies and noise. For temporal information, STP adopts video narratives summarized in captions as temporal prompts to enhance the model’s perception of events. Experimental results show that our approach has achieved improvements on the MSRVTT(1.9%), MSVD(1.2%), and VATEX(0.7%) datasets compared to the SOTA methods.

Abstract Synthetic aperture radar tomography (TomoSAR) has shown significant potential for the 3D Reconstruction of buildings, especially in critical areas such as topographic mapping, urban planning, and disaster monitoring. In practical applications, the constraints of observation trajectories frequently lead to the acquisition of a limited dataset of sparse SAR images, presenting challenges for TomoSAR 3D Reconstruction and affecting its signal‐to‐noise ratio and elevation resolution performance. The study introduces a cascade adversarial strategy based on the Conditional Generative Adversarial Network (CGAN), optimised explicitly for sparse observation trajectories. In the preliminary phase of the CGAN, the U‐Net architecture was employed to capture more global information and enhance image detail recovery capability, which is subsequently utilised in the cascade refinement network. The ResNet34 residual network in the advanced network stage was adopted to bolster feature extraction and image generation capabilities further. Based on experimental validation performed on the curated TomoSAR 3D super‐resolution dataset tailored for buildings, the findings reveal that the methodology yields a notable enhancement in image quality and accuracy compared to other techniques.

Emergency control is essential for ensuring transient stability in power systems after faults. This study addresses the limitations in existing methods by proposing a knowledge-generative pretrained transformer (GPT)-guided generalizable reinforcement learning (RL) approach for intelligent emergency generator tripping. This approach incorporates general electrical principles and knowledge-GPT to assist deep reinforcement learning (DRL). The general electrical principles involve identifying severely disturbed generators and selecting appropriate control actions through dynamic probability. The knowledge-GPT model extracts insights from an expert strategy knowledge base, reshaping the DRL reward structure by comparing the DRL strategy with the knowledge-GPT outputs. This paradigm is designed to leverage electrical laws and domain expertise to guide the DRL training process, thereby enhancing both training efficiency and electrical consistency. To enhance generalization capability under topological changes, message passing neural networks (NNs) are integrated into the DRL architecture, effectively simulating power flow dynamics in transmission lines. The proposed method is validated through simulations on the IEEE 39-bus system and the Northeast power grid of China, demonstrating superior control effectiveness and adaptability compared to existing approaches, offering a more robust solution for emergency control in complex power systems.

The complexity and uncertainty in power systems leads to a great challenge for controlling the power grid using traditional manual adjustment methods. Reinforcement learning is a promising data-driven paradigm to address control issues in power grids. This article presents a novel human–machine collaborative (HMC) framework for line flow control. We formulate the collaboration between humans and machines as an extended Markov decision process (MDP) and introduce a human–machine collaborative reinforcement learning (HMC-RL) approach, which comprises a routing module, a machine dispatching module and an HMC dispatching module. The routing module determines whether the power system should be operated by the machine or through human–machine collaboration. The machine dispatching module predicts a machine dispatching action for regulating line flow, while the HMC module predicts an HMC dispatching action with human assistance. Experimental results conducted on the IEEE 39-bus and IEEE 118-bus systems demonstrate that our HMC-RL approach can significantly improve the performance of regulation compared to the machine dispatching policy. Specifically, HMC-RL achieves a 40.03% performance improvement on the IEEE 118-bus system, with 25.8% of human participation.

Analyzing network robustness under various circumstances is generally regarded as a challenging problem. Robustness against failure is one of the essential properties of large-scale dynamic network systems such as power grids, transportation systems, communication systems, and computer networks. Due to the network diversity and complexity, many topological features have been proposed to capture specific system properties. For power grids, a popular process for improving a network’s structural robustness is via the topology design. However, most of existing methods focus on localized network metrics, such as node connectivity and edge connectivity, which do not encompass a global perspective of cascading propagation in a power grid. In this paper, we use an informative global metric algebraic connectivity because it is sensitive to the connectedness in a broader spectrum of graphs. Our process involves decreasing the average propagation in a power grid by minimizing the increase in its algebraic connectivity. We propose a topology-based greedy strategy to optimize the robustness of the power grid. To evaluate the network robustness, we calculate the average propagation using MATCASC to simulate cascading line outages in power grids. Experimental results illustrate that our proposed method outperforms existing techniques.