To enhance the resistance to commutation failures of high-voltage direct current (HVDC) transmission systems under interphase short-circuit faults, an improved commutation failure prevention (CFPREV) control strategy is developed and validated. Initially, the adaptability of conventional CFPREV under interphase short-circuit faults is analyzed, and the time-varying mismatch between its three-phase criterion and the actual fault-phase voltage drop magnitude is quantified, thereby revealing the limitations of existing CFPREV control in handling interphase faults. Then, an innovative phase-by-phase real-time voltage drop calculation algorithm is proposed, which requires no integration and can be realized using only two adjacent sampling points. The calculated phase voltage indices remain stable during normal operation and respond rapidly after faults, ensuring the fast activation of advance firing control. On this basis, a real-time prediction algorithm for commutation voltage zero-crossing offset is further developed, and an improved CFPREV control strategy is designed. Subsequently, two real-time algorithms are proposed: a per-phase voltage drop magnitude calculation algorithm and a commuta-tion voltage zero-crossing shift prediction algorithm. Based on these, an improved control strategy is designed. Finally, simulation results using the PSCAD V46/EMTDC platform con-firm that the proposed strategy significantly improves commutation failure mitigation under interphase short-circuit faults compared to conventional CFPREV.

Commutation failure (CF) is one of the most prevalent events in line-commutated converter-based high-voltage direct current (LCC–HVDC) systems. The frequent occurrence of CF poses a significant threat to the safe and stable operation of power grids. The commutation failure prevention control (CFPREV) is the main method to prevent the initial CF, which relies on the detection of a drop in AC voltage. However, its slow fault detection hinders the rapid response of post-fault control, thereby affecting the effectiveness of CF suppression. Therefore, this paper proposes a fast fault detection method based on Bayesian theory. This algorithm can calculate the conditional probability of each variable in a given dataset, effectively mitigating the impact of noise and errors in data to yield precise and dependable results. By processing the collected continuous data and calculating the probability of the existence of a fault point, it determines whether a fault occurs. Based on this method, an improved prevention strategy is proposed, which can effectively enhance the CF resilience of LCC–HVDC systems under AC faults. Finally, using the power systems computer-aided design (PSCAD) platform, the accuracy of the fault probability detection algorithm was verified based on actual engineering data. The effectiveness of the proposed strategy was further validated under three typical fault scenarios, leading to significant improvements: a 64.12% reduction in detection time for three-phase grounding faults, a 69.88% decrease for single line-to-ground faults, and a 36.84% improvement in phase-to-phase fault detection. Additionally, the overall performance of the strategy was thoroughly assessed through extensive simulations covering various fault cases within a selected range of typical faults. The simulations demonstrated the superiority of the proposed strategy in CF mitigation, with a significant reduction in incidents from 89 to 34 out of 150 tested scenarios. This highlights the robustness and reliability of the proposed strategy.

In order to accurately grasp the operational state of DC protection devices in converter stations, a DC protection device state assessment method based on the variable weight theory and correlation degree analysis is proposed. Constructing condition assessment indicators for DC protection device of converter stations containing overhaul in-formation, operation information and defect information, when the actual value of the indicator exceeds the specified range of values, the DC protection device is directly judged to be in ‘alarm’ status; when the actual value of the indicator is within the specified range of values, Analytic Hierarchy Process (AHP) and variable weight theory are combined to adjust variable weights of assessment indicators in real time. At the same time, the correlation between the indicators and each state level is calculated, and the correlation of the indicators and their corresponding weights are weighted and summed to obtain the comprehensive correlation of each state level of the DC protection device, and the correlation of each state level of the DC protection device is calculated, using the principle of maximum correlation, the DC protection device status is obtained. Example analyses show that the method is simple and easy to implement and can accurately assess the operational state of the DC protection device in converter stations.

The UHV dry-type bushing plays a critical role in power transmission by enabling electrical connection, electrical insulation, and mechanical support, making it a core component for ensuring the safe and stable operation of UHV direct current (DC) transmission projects. Epoxy resin, serving as the fundamental insulating material for the bushing core, undergoes significant residual strain during high-temperature curing due to chemical shrinkage and thermal strain, which directly affects the molding quality and service reliability of the component. This paper investigates the curing process of a large-thickness epoxy material, which is on the same scale as a UHV bushing. An in situ monitoring system combining fiber Bragg grating (FBG) sensors and thermocouples, together with COMSOL Multiphysics simulations, is employed to systematically study the evolution of the temperature field and residual strain throughout the entire curing process, considering the demolding effect. The results show that during the curing stage, the internal temperature distribution is non-uniform, with a maximum temperature difference of 65 °C between the center and the edge. The residual strain is dominated by chemical shrinkage (accounting for 73.25%) and exhibits a pronounced radial gradient. Mold constraint and demolding cause abrupt changes in the strain. The developed thermo-chemo-mechanical coupled model shows good agreement between simulations and experimental measurements. Thermal cycling relaxes the residual stress, achieving a reduction of 3.89–5.77%. This study provides support for process optimization and defect prevention in large-scale epoxy insulation components.