Simulated Annealing, Differential Evolution and Directed Search Methods for Generator Maintenance Scheduling

Abstract

Generator maintenance scheduling presents many engineering issues that provide power system personnel with a variety of challenges, and one can hardly afford to neglect these engineering issues in the future. Additionally, there is vital need for further development of the repair planning task complexity in order to take into account the vast majority of power flow constraints. At present, the question still remains as to which approach is the simplest and most effective, as well as appropriate for further application in the power flow-oriented statement of the repair planning problem. This research compared directed search, differential evolution, and very fast simulated annealing methods based on a number of numerical calculations and made conclusions about their prospective utilization in terms of a more complicated mathematical formulation of the repair planning task. A comparison of results shows that the effectiveness of directed search methods should not be underestimated, and that the pure differential evolution and very fast simulated annealing approaches are not essentially reliable for repair planning. The experimental results demonstrate the perspectivity of unifying single-procedure methods in order to net out risk associated with specific features of these approaches.

Formation of power equipment repair schedules using indicators of planned reliability

“…As for the methods for solving the problem, a sufficient number of them have been developed. We can highlight the following organizations in which work was carried out on setting the problem to be solved and methods for solving it: ESI SB RAS [5][6][7], MPEI [8,9], UrFU [10][11][12], etc.…”

A Hybrid Differential Evolution for Non-Smooth Optimization Problems

“…The joint use of regular and random processes in the algorithm contributes to finding the global extremum of the function. The algorithm is used to solve optimization problems in the energy sector, related, for example, to the optimal distribution of power units and ensuring reliable power supply to consumers by minimizing the non-admission of electricity during the repair of network equipment [8,9,10]. In material science, DE algorithm is used for structure prediction of materials [11].…”