Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 17-23.doi: 10.6040/j.issn.1672-3961.0.2019.123

• Engineering—Special Topic on Artificial Intelligence Application • Previous Articles     Next Articles

Transmission network reconfiguration strategy based on preference multiobjective optimization and genetic algorithm

Runjia SUN1(),Hainan ZHU2,Yutian LIU1   

  1. 1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, Shandong, China
    2. Weifang Power Supply Company, State Grid Shandong Electric Power Company, Weifang 261021, Shandong, China
  • Received:2019-03-28 Online:2019-10-20 Published:2019-10-18
  • Supported by:
    国家重点研发计划项目(2017YFB0902600)

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Abstract:

This paper proposed a transmission network reconfiguration strategy based on preference multiobjective optimization and genetic algorithm, which incorporated the preferences for different objectives to obtain network reconfiguration schemes. Considering the factors about generators, transmission lines and loads, three objectives were proposed to establish a preference multiobjective optimization model. Considering the preference and discreteness of the model, a preference-based nondominated sorting genetic algorithm Ⅱ was designed. To improve the solve efficiency, the preference-based dominance relation, population sacle control technique and repetitive individual filtration technique were proposed to obtain a preference Pareto solution set with a controllable number of solutions. The simulation results demonstrated that the strategy could reasonably leverage the tradeoff among different factors about network reconfiguration, and the proposed algorithm was highly efficient in solving network reconfiguration optimization problems.

Key words: power system restoration, preference multiobjective optimization, genetic algorithm, NSGA-Ⅱ

CLC Number: 

  • TM732

Fig.1

Flowchart of P-NSGA-Ⅱ"

Fig.2

Flowchart of reference point determining"

Fig.3

The backbone networks obtained by different algorithms"

Table 1

Load data"

节点编号 重要负荷比例
3 0.20
4 0.50
7 0.25
8 0.20
13 0.45
15 0.40
16 0.35
18 0.55
20 0.25
21 0.30
23 0.40
24 0.35
25 0.30
26 0.20
27 0.25
28 0.50
29 0.20

Table 2

Generator start-up sequence and objective values"

方案编号 机组启动顺序 f1 f2 f3
1 34, 36, 35, 31, 37, 32, 38, 30 5.642 6 0.123 8 0.336 7
2 34, 36, 35, 31, 37, 32, 38, 30 5.661 5 0.129 6 0.3279
3 34, 36, 35, 38, 37, 32, 31, 30 5.500 0 0.130 0 0.3279

Table 3

Results of network reconfiguration optimization"

方法 f1 f2 f3 In
本研究 5.642 6 0.123 8 0.336 7 0.893 1
MILP[15] 5.756 0 0.109 5 0.323 9 0.892 5
TCSN[16] 5.600 3 0.102 5 0.319 9 0.900 9

Fig.4

The convergence curves of different algorithms"

Fig.5

Solution distribution with different reference points"

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