Open AccessReactive Power Optimization of Power Grid based on TTGA Hybrid Algorithm
Author(s) -
Li Sun,
Feng Jing,
Fangmiao Sun,
Hongyan Guo,
Dengyu Xiong,
Hanfu Feng,
Lu Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1659/1/012036
Subject(s) - tabu search , hill climbing , algorithm , mathematical optimization , genetic algorithm , convergence (economics) , local optimum , computer science , grid , population , mathematics , geometry , demography , sociology , economics , economic growth
A new hybrid algorithm named TTGA hybrid algorithm which means genctic and tabu hybrid algorithm (TGA) with optimized tent mapping is proposed in this paper, through the research of tabu search(TS), genetic algorithm (GA)and chaotic algorithm(COA)[1].Based on evolutionary group generated by GA, auxiliary group formed by optimized tent mapping are ledinto group through specific selection mechanism so that the group become more diverse and effective. And in the meanwhile the tabu list is used to add a memory so that a similarity judgment mechanism is set based on the same individuals among group which provides a basis for using the TS operator to realize local fine search, so the tabu search plays the role of avoiding roundabout. In the new algorithm the introduction of TS ensures the capacity of hill climbing and refined search while the diversity of population and the effectiveness of evolution is assured by the regularity and ergodicity of chaotic. In this paper GA, IGA and TGA and TTGA is used in reactive power optimization of power grid, the results show that TTGA has the best performance on convergence and overall search.