Details

Autor(en) / Beteiligte

• Balaji, G.
• Balamurugan, R.
• Lakshminarasimman, L.

Titel

Mathematical approach assisted differential evolution for generator maintenance scheduling

Ist Teil von

• International journal of electrical power & energy systems, 2016-11, Vol.82, p.508-518

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2016

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •A new approach for generator maintenance scheduling problem is proposed.•The proposed approach hybridizes lambda iteration method with integer coded differential evolution algorithm.•Minimize the generator operating cost subject to system and operational constraints.•Providing proper maintenance schedule for the generating units.•Results are compared with mathematical approach assisted particle swarm optimization. Maintenance scheduling of power generating units is very essential for the economical and reliable operation of a power system. The objective of Generator Maintenance Scheduling (GMS) problem is to find the exact time interval for preventive maintenance of power generating units in order to minimize the operating cost, maximize the system reliability and to extend the life time of the generating units. In this paper, the problem of scheduling of generating units for maintenance is formulated as a mixed integer optimization problem by considering minimizing the operating cost. Since generator maintenance scheduling is a mixed integer problem, differential evolution algorithm is suitably modified to handle the integer variables. The control variables in differential evolution algorithm are integers which denote the starting period of each generating unit for carrying out maintenance work. The lambda iteration method is used to determine the optimal generation schedule of committed generating units. This paper presents a mathematical approach assisted differential evolution (MADE) to solve maintenance scheduling problem in a power system. The performance of the proposed algorithm is validated by considering two test systems. The result obtained by the proposed MADE method is compared with mathematical approach assisted particle swarm optimization. The test results reveal the capability of the proposed MADE algorithm in finding optimal maintenance schedule for the GMS problem.