Tri-objective generator maintenance scheduling model based on sequential strategy

Abstract

A multi-objective modeling approach is required in the context of generator maintenance scheduling (GMS) for power generation systems. Most multi-objective modeling approaches in practice are modeled using a periodic system approach that caters for a fixed maintenance window. This approach is not suitable for different types of generating units and cannot extend the generator lifespan. To address this issue, this study proposes a tri-objective GMS model with three conflicting objectives based on the sequential system approach that accounts for operating hours and start-up times. The GMS model’s objectives are to minimize the total operation cost, maximize system reliability and minimize violation. The main difference between the proposed tri-objective GMS model and other multi-objective GMS models, is that the proposed model uses a sequential strategy based on operating hours and start-up times. In addition, the proposed model has considered the most important criteria in scheduling the generator maintenance, and this reflects the real-life requirements in electrical power systems. A multi-objective graph model is also developed to generate the maintenance units scheduling and used in developing the proposed Pareto ant colony system (PACS) algorithm. A PACS algorithm is proposed to implement the model and obtain solution for GMS. The performance of the proposed model was evaluated using the IEEE RTS 26, 32, and 36-unit systems dataset. The performance metrics used comprise the GMS model objectives. The experimental results showed that the obtained solution from the proposed tri-objective GMS model was a robust solution by considering the different initial operational hours of the units.

Oriented Crossover in Genetic Algorithms for Computer Networks Optimization

“…Shatha Abdulhadi Muthana and Ku Ruhana Ku-Mahamud [18] To determine the optimal choice for scheduling generator maintenance, various multi-objective optimization methods were examined. One optimization method, Non-dominated Sorting Genetic Algorithm, exemplifies the importance of utilizing GA and optimization in a variety of scientific fields.…”

“…An experimental evaluation is conducted on the proposed system (OC), UC, NC , and QC. The fitness comparisons are illustrated in Figures (16)(17)(18)(19), which depict the comparison between the initial population fitness and populations that have undergone optimization using UC, NC, QC and OC. Fig.…”