Maintenance Scheduling Strategy for MMCs Within an MVDC System Using Sensitivity Analysis

The rapid deployment of renewable energy sources is highlighting the critical need for availability and efficiency in power systems, particularly within medium-voltage direct-current (MVDC) systems. Modular multilevel converters (MMCs) are pivotal in these systems, serving as key components for the efficient distribution and management of electricity. Consequently, MMCs must be managed as critical assets, necessitating maintenance strategies that minimize both maintenance and outage costs while ensuring high system reliability. Previous studies have primarily focused on maintenance methodologies targeting single MMC stations, assessing them based on availability. However, such approaches often overlook the systemic ripple effects of failures and the associated economic losses across the entire system. This study proposes a novel algorithm designed to determine the maintenance priority among multiple MMC converters within an MVDC system. The proposed algorithm optimizes the maintenance timing and priority by performing a sensitivity analysis of the system reliability indicators, from both the consumer and operator perspectives. Contrary to the traditional methods, this approach comprehensively considers the broader impacts on the system reliability and economic efficiency, providing a more holistic maintenance strategy. The effectiveness of the proposed algorithm is extensively compared and analyzed through detailed case studies, which benchmark it against the existing reliability-centered maintenance methodologies. The results indicate that the proposed algorithm is economically viable within the evaluation period while also enhancing the overall system availability by mitigating potential risks associated with the MMC failures. This approach facilitates more informed decision-making by incorporating sensitivity analysis and addressing the systemic ripple effects, thereby contributing to the optimization of system performance and cost-effectiveness.