Problem-specific genetic algorithm for power transmission system planning |
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| Affiliation: | 1. Institute of Radiochemistry and Radioecology, University of Pannonia, 10 Egyetem Str., H-8200 Veszprém, Hungary;2. Social Organization for Radioecological Cleanliness, Hungary;3. NORM Hungary Kft., Hungary;1. Guilan Regional Electric Company, Rasht, Iran;2. Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran;1. Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, United States;2. Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, United States;1. Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran |
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| Abstract: | A hypothesis is proposed that the only way for Turing computers to solve efficiently NP complete problems and NP hard problems is to use randomization techniques. By comparison of the popular randomization techniques, it is concluded that genetic algorithm may be the best choice until now. To overcome the disadvantages of conventional genetic algorithms, problem-specific genetic algorithm is suggested. Then, a problem-specific genetic algorithm that is developed specially for power transmission system planning problems is proposed. The algorithm searches global optimum from local optimums instead of from all feasible solutions, while the local optimums are found by a more efficient linear iterative minimum-cost-flow algorithm. Furthermore, the network flow model for power transmission system planning is improved so that the capacities and locations of transmission lines, substations and power plants can be optimized simultaneously. Results from a comparative study have proved the reasonableness and efficiency of the proposed model and algorithm. |
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