To improve the accuracy and portability of fault location in secondary circuits of smart substations, a fault location method for secondary circuits based on graph neural networks is proposed. First, a fault analysis of the secondary circuits in smart substations is conducted, and a graph database model of the secondary circuits is constructed, revealing the connection relationships between the secondary devices. Then, the fault location problem in secondary circuits is defined as a graph classification problem, and a fault graph generation model is proposed based on a representation method for fault information in secondary circuits. Finally, a fault location model for secondary circuits based on graph neural networks is constructed, and a performance optimization method based on binary cross-entropy is proposed to achieve accurate fault location in substation secondary circuits. Using the secondary circuits of a 220 kV smart substation as a reference, different case studies of secondary circuit faults are generated by altering the network topology, connection relationships, and network configurations through the fault graph generation model. Experiments comparing the proposed location method with other models show that this method has higher location accuracy and robustness.

With the continuously increasing proportion of renewable energy integration, the structure of power grid networks has become increasingly complex. Under extreme weather conditions such as typhoons and hail, faults like line breaks or information disruptions can occur in the power grid, imposing significant burdens and risks on the economic and reliable operation of the power system. However, existing methods still focus on the allocation of human repair teams, with insufficient utilization of flexible resources within the system, resulting in low efficiency in restoring power supply to the power system. To address this challenge, this paper proposes a resilience enhancement strategy for the power system under typhoon scenarios. It leverages active resources on the grid side and fully exploits the flexibility of both the supply and demand sides to enhance the resilience of the power system. Firstly, this paper aims at the economic operation of the power system, taking into account the physical and operational constraints of both the supply and demand sides, including power flow constraints, mobile energy storage system (MESS) transfer constraints, and phase-shifting transformer (PST) regulation constraints. Meanwhile, an improved grasshopper optimization algorithm is introduced to achieve efficient and rapid problem-solving. Finally, the effectiveness and feasibility of the proposed method are demonstrated through validation using an improved IEEE-33 bus test system. Through analysis, the total system load loss was reduced by 75.6%, with the maximum load loss during the typhoon decreasing by 72.4%. The approach enables real-time response to the dynamic impacts of typhoons, swiftly stabilizes load fluctuations, and significantly enhances the resilience of the power system.

In practical power system operation scenarios, extreme natural weather conditions and fluctuations at both the supply and demand sides pose significant challenges to the stable operation and the formulation of operational decision-making for power systems. Particularly in extreme scenarios involving faults, it may lead to power supply–demand imbalances and instability in the power system. To address this issue, this paper proposes a decision-making approach for the secure and stable operation of power systems using a multi-scenario-based optimization model. Initially, a joint scenario set is generated using historical operational data to accurately depict multiple complex scenarios. Building on this, a multi-scenario-based optimization model is constructed, with responses facilitated by flexible adjustment resources within the system. Considering the non-convex and nonlinear characteristics of the model, an improved Harris Hawks Optimization (HHO) algorithm is employed to search for the global optimal solution. Finally, a modified IEEE-33 bus test system is utilized to demonstrate the feasibility and effectiveness of the proposed method.