阀厅系统是换流站的核心, 为保证地震下电力系统的稳定运行, 需分析阀厅系统可靠度。传统的可靠度分析通常假设各设备响应状态彼此独立, 忽视了设备间的失效相关性。该文首先提出了一种考虑设备失效相关性的可靠度分析框架, 建立了某±800 kV换流站阀厅系统的有限元模型, 通过数值仿真获得各设备在不同地震动输入下的地震响应; 其次, 基于该响应, 利用Gaussian Copula函数建立了各设备地震响应的相关性结构; 最后, 在考虑设备失效相关性的基础上, 分析了阀厅系统可靠度。研究结果表明: 假设设备状态完全独立, 会低估阀厅系统在地震状况下的可靠度, 高估换流站的经济损失。本文研究结果可为换流站阀厅系统在地震状况下的性能评估提供参考。

Objective: As a crucial component of power systems, the valve hall plays a vital role in ensuring the safe and stable operation of converter stations, as well as the reliable transmission of electricity. However, earthquakes pose a significant threat to the integrity and functionality of these systems. Under seismic loading, due to the uniformity of the ground motion input and the mechanical and functional coupling between components, the dynamic responses of the equipment are correlated. This interdependence challenges the conventional assumption of independent component failures, highlighting the need for more advanced reliability modeling. Methods: To demonstrate the importance of considering response correlation between components, this study first analyzes the reliability of series and parallel systems under two extreme conditions: complete independence and complete correlation. The findings show that the correlation between equipment responses has a significant effect on system reliability, especially when the number of components increases or the failure probability varies widely. To quantify the correlations within the valve hall system, finite element simulations were conducted on both high-voltage and low-voltage valve halls of a ±800 kV converter station. The seismic responses of individual equipment were obtained under various ground motion inputs. Based on these data, a Gaussian copula-based method was employed to model the joint behavior of equipment responses. This method captures their statistical dependencies without assuming a predefined joint distribution. The analysis process mainly consists of marginal distribution modeling of the responses, transformation to uniform and normal distributions, determination of the correlation matrix, generation of independent normal samples, singular value decomposition of the correlation matrix, standard normal transformation, inverse mapping to the uniform domain, and generation of correlated samples. This approach enables the construction of a realistic joint distribution of equipment responses while preserving their marginal characteristics. Using the correlated samples generated through the Gaussian copula method, the system-level reliability of the valve hall was evaluated while accounting for dependent failure behavior. Results: The Gaussian copula method effectively modeled the correlation structure of variables using the correlation matrix, enabling accurate modeling of the correlation structure of the response states of the whole system. The assumption of complete independence of component states underestimated the actual system reliability. The degree of underestimation varied significantly with the peak ground acceleration (PGA), with the most pronounced effect observed at PGA=0.400g. Furthermore, neglecting the correlation between component failures resulted in an overestimation of the economic losses of the converter station, and the magnitude of this overestimation increased with the system's design life. Conclusions: The proposed reliability assessment framework incorporates equipment response correlations, yielding more accurate and realistic assessments of valve hall system performance under seismic conditions. This method also overcomes the limitations of traditional approaches based on independent assumptions, which are commonly adopted in large-scale system reliability analysis due to their computational simplicity. The proposed method is flexible and extensible, with broad application prospects in the reliability and risk assessment of complex infrastructure systems subjected to extreme events.