Interest in Generator Maintenance Scheduling (GMS) has increased due to the advent of demand-related expansion in size for modern power systems. Timely maintenance plays a significant role in minimizing failures and helps in averting cost incurred as a result of production shutdowns. The GMS problem is a complex and nonlinear optimization problem that specifies the schedule for carrying out planned preventive maintenance on power generation units. There is no clear concept to GMS model types and choosing the appropriate maintenance scheduling type. Thus, this paper presented a comprehensive review on GMS models in electrical power systems that covers the maintenance strategies, main elements of GMS models, and optimization methods used in solving GMS models. The list of references comprised related works from the years 2000 until 2020, which were classified into three based on the objectives. A new type of objective function for the GMS models was among the suggestions provided. A numerical example which focuses on a multi-objective GMS model and a proposed multi-objective Pareto ant colony system algorithm are also presented. The results of this review will not only enable researchers to gain a good overview of the existing GMS models for electrical power systems but also provide a source of references in choosing an appropriate maintenance scheduling strategy that is suitable with the type of generating unit and existing operating conditions.
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.
In any metaheuristic, the parameter values strongly affect the efficiency of an algorithm’s search. This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. For optimal maintenance scheduling with low cost, high reliability, and low violation, the parameter values of the PACS algorithm were tuned using the Taguchi and Gray Relational Analysis (Taguchi-GRA) method through search-based approach. The new parameter values were tested on two systems. i.e., 26- and 36-unit systems for window with operational hours [3000-5000]. The gray relational grade (GRG) performance metric and the Friedman test were used to evaluate the algorithm’s performance. The Taguchi-GRA method that produced the new values for the algorithm’s parameters was shown to be able to provide a better multi-objective generator maintenance scheduling (GMS) solution. These values can be benchmarked in solving multi-objective GMS problems using the multi-objective PACS algorithm and its variants.
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