Reliability based generator maintenance scheduling using hybrid evolutionary approach

With the growth of electrical energy demand, providing reliable energy without interruption has become very important nowadays. Maintenance scheduling of generating units is one of the crucial factors in delivering reliable electrical energy to the vital industrial and urban loads. As number of generating units and constraints over their operation is increasing, there is growing need for developing new methods for planning optimal outage of generating units for maintenance. This paper presents a hybrid evolutionary algorithm to tackle the reliability based generator maintenance scheduling problem. Uncertainties in the generating units and the load variations are included so that a more realistic scheduling is obtained. Maintenance scheduling problem is a large scale constrained optimization problem with a large number of variables which needs novel methods to cope with it. A new local search method which is derived from Extremal Optimization (EO) and Genetic Algorithm (GA) is presented to tackle the problem. The proposed method can be used as a local optimizer to further improve the potential solutions in the GA. The proposed method, Hill Climbing Technique (HCT), GA and their hybrid approaches are applied to the IEEE Reliability Test System (RTS) and the obtained results are discussed.

► We develop a hybrid evolutionary algorithm for solving generator maintenance scheduling problem.
► Extremal optimization method has a higher performance among other local search methods.
► Applying local search methods in the mutation stage of GA has the highest impact on the final result.