With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.

The safe approach distance detection of large construction equipment in substations is important to ensure the safety and stability of the power system, as well as to prevent equipment damage, power outages and other accidents. The current method is unable to intelligently distinguish construction equipment from power equipment and realize real-time safety approach distance detection. Therefore, this paper constructs a safety approach distance detection system for large-scale construction equipment in substations based on stereo vision and target detection, and realizes real-time high-precision safe approach distance detection between large-scale construction equipment and electric power equipment. Firstly, the system distinguishes construction equipment from power equipment using a GhostNet-based substation construction target detection model. Secondly, the system obtains spatial information regarding the operation scene using a lightweight stereo matching model based on channel attention, then calculates the spatial surface center of the target based on the spatial information and detection results, and finally calculates the safety approach distance between construction equipment and power equipment. Compared with MobileNetv3-YOLOv4, the map and the recall rate of the proposed method are improved by 13.1% and 29.0%; compared with the AnyNet stereo matching method, the proposed method decreases the end point error and 3 pixels error by 34.2% and 25.8%. The actual data show that the detection speed of the proposed method is 19.35 frames per second, and the mean absolute error is 0.942 m and the mean relative error is 3.802%. This method can accurately measure the safe approach distance in real time in real scenarios to guarantee the safety of personnel and equipment.

Transmission lines are essential carriers for the transmission of electrical energy, and their safe operation is fundamental to ensuring reliable power supply. However, the environment of transmission line corridors is complex and variable, posing numerous risks of external damage. With the development of smart power systems, information technologies such as video monitoring provide effective means for monitoring and managing external damage to transmission lines. Nonetheless, the current technology for identifying external damage and safe distances for transmission lines is weak, leading to frequent false alarms and low efficiency in practical applications. To address this issue, this paper systematically reviews and summarizes the progress of distance recognition technology in power systems. First, it outlines the spatial distance perception performance requirements for models in transmission line corridor scenarios and summarizes the spatial distance data characteristics of these corridors. Second, the paper categorizes existing common safety distance recognition methods from the perspectives of active and passive ranging, analyzing their advantages and disadvantages in the context of transmission line corridors. Finally, this paper summarizes the types and performance characteristics of current deep learning-based distance recognition methods and proposes a potential application framework for these methods in transmission line corridor scenarios. It also discusses the future application prospects and challenges faced by this framework.

With the rapid development of power electronic equipment, the automation and intelligence level of active distribution networks (ADNs) continues to improve. Against this background, soft open points (SOPs) are gradually replacing traditional segmented switches and interconnection switches. The voltage support capability and fast response characteristics of SOPs can shorten power outage time and expand load recovery range. However, the widespread integration of distributed renewable energy and new power electronic devices has made the fault characteristics of ADNs more complex, significantly increasing the computational complexity of ADN reliability assessment. At present, there are few studies that comprehensively consider ADNs with multiple devices. Therefore, this paper proposes a reliability evaluation method for ADNs that considers multiple devices. Firstly, the impact of circuit breakers, SOPs, and segmented switches on the load recovery process is analyzed. Secondly, an improved virtual fault flow model based on the action mechanisms of circuit breakers, SOPs, and segmented switches is established. The virtual fault flow is represented by logical variables to simulate post fault network reconstruction strategies that include circuit breaker tripping, SOP power supply recovery, and segmented switch isolation actions. Then, with the goal of minimizing the system average outage time after network reconstruction, a generalized mixed integer linear programming (MILP) model is given. Finally, taking the IEEE 33-node testing system as a case, the effectiveness and feasibility of the proposed method are demonstrated.

With the increasing demand for electrical reliability, power grid companies often need to operate urban underground cable systems at flexible ratings during planned or emergency events, but the risk of failure caused by such loading operations has not yet been quantified. Based on this, this paper proposes a reliability assessment method for underground cables based on dual sequence Monte Carlo simulation, which can consider the cumulative risk impact caused by electrical faults and insulation aging of underground cables during emergency events. Firstly, the integration of the electrical and thermal design characteristics of the integrated underground cable system within the overall network reliability framework was introduced. A dual sequence Monte Carlo simulation loop was used to evaluate the performance of the cable and network while considering the thermal aging effects of emergency loads on the cable. Then, the proposed method is applied to improve the IEEE 14 bus network by introducing cable design risk factor α and aging risk acceptance factor β to achieve flexibility and operational risk under long-term and short-term high-load emergency rating scenarios. Finally, the research results of the IEEE 14 bus network show that the expected unserved energy of scenario 1 is 30.15 MWh/year, which is 59% lower than the basic scenario of 73.34 MWh/year. The expected network failure frequency remains almost unchanged. Scenario 2 and scenario 3 show a decrease in the gain of network performance due to emergency-rated operation after using α and β, demonstrating the accuracy of the proposed method in cable reliability assessment.

Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate status evaluation and proactive risk management. This paper proposes TransQwen, a domain-adapted LLM tailored for transformer fault diagnosis and maintenance strategy generation. Built upon the Qwen-7B-Chat architecture, TransQwen is fine-tuned on a domain-specific corpus encompassing transformer fault cases aligned with technical standards and operational procedures. It integrates DoRA for efficient parameter adaptation and RoPE to enhance positional encoding during training. The model is evaluated in three core tasks: fault type classification, fault severity grading, and strategy generation. The results show significant improvements—over 10 percentage point gains in standard conditions and up to 30 percentage points in F1 score under extreme low-sample settings (e.g., 100 samples), demonstrating robust generalization. In the maintenance strategy generation experiment, all the evaluation results of the TransQwen model reached the optimal. Through a knowledge-driven approach, the model can perform question-and-answer tasks involving professional knowledge in the power vertical field, and customize and generate accurate maintenance strategies for specific fault scenarios.