کنفرانس بین المللی فناوری و مدیریت انرژی

صفحه اصلی / هشتمین کنفرانس بین المللی فناوری و مدیریت انرژی

Comparing Three Separate Discrete Algorithms for Generation Maintenance Optimization

نویسندگان :

Sina Soltani¹ Masoud Jokar Kouhanjani²

1- Department of Automation and Instrument Engineering Neyriz Ghadir Steel Complex Shiraz, Iran 2- Design and Supervision Expertise Shiraz Electric Distribution Company Shiraz, Iran

کلمات کلیدی :

GMS،Power system،Preventive maintenance scheduling،Discrete optimization algorithms،Energy in industries

چکیده :

In general, the electric power system consists of three parts: generation, transmission, and distribution, all of which require maintenance to improve the reliability and energy efficiency of the power system. Nowadays, the entrance of distributed generation into distribution networks has been significantly developed. Most of the generation maintenance scheduling (GMS) packages focus on planning preventive maintenance for units over a one- or two-year period to reduce total operating costs accompanied by meeting system energy needs. In advanced power systems, the inclusion of systems such as budget constraints, fuel, demand, and crew for electricity have increased dramatically, as the system size has. Therefore, they have caused more generators and less reserve margin, complicating the generator maintenance scheduling problem. This paper presents a budget model and a fixed security constraint model for preventive maintenance scheduling problems. To have a more optimal program, a multi-objective function (economical cost and reliability) is involved and solved. For a more pragmatic and complete study, a new manpower constraint, as well as relationship constraints to solve the multi-objective function, is considered and proposed for the maintenance scheduling problem. This function is simulated by 3 optimization methods and the matters of population size and iterations are expressed and compared in GMS problem. An experimental system containing 21 generators is noticed for simulation and the accuracy of the results in our case study indicates the capability of DPSO optimization algorithm for scheduling maintenance and other economic and crew constraints.