Optimization of Maintenance Planning in Nuclear Power Plant Security Systems Using Cuckoo Optimization Algorithm

Optimizing performance is one of the main goals in science and technology. However, it is a significant challenge, as it is usually constrained by safety requirements and budget limitations. As a result, optimization algorithms utilizing artificial intelligence to enhance the performance of industrial systems have been developed over the last few decades. Due to advances in computing and the proven effectiveness of artificial intelligence methods, there has been a growing application of these methods in the nuclear sector. This has encouraged the publication of studies that cover everything from the design phase to maintenance policies and life extension strategies. A nuclear power plant is typically designed for a 40-year service life, during which the reliability of system components, including main and auxiliary safety systems, is ensured by high safety standards and management policies such as maintenance, inspection, and testing. As the end of this period approaches, plants can choose between decommissioning or extending their service life, often opting for life extension to operate for 40 to 60 years, based on feasibility studies that assess component performance and the need for replacements. Maintenance is crucial but can increase system downtime. Therefore, planning for redundant trains, which allow maintenance while the system continues operating, is fundamental. Backup systems must also maintain high levels of reliability to avoid failures when needed. This work developed an optimization tool using Probabilistic Safety Assessment (PSA) and the Cuckoo Optimization Algorithm (COA). Applied to a simplified model of the High-Pressure Injection System of a Pressurized Water Reactor (PWR), the tool aims to increase component reliability and reduce costs and downtime associated with maintenance. COA was chosen for its optimization efficiency, and the fitness function focused on minimizing unavailability and maintenance costs. The results of this work demonstrate an improvement of up to 60% compared to literature.

Keywords: Reliability, Cuckoo, Artificial intelligence.