基于电子搜索算法的电力系统无功优化

doi:10.16180/j.cnki.issn1007-7820.2019.01.0012

摘要/Abstract

摘要：

针对电力系统无功优化过程中,粒子群算法收敛慢以及计算结果容易陷入局部最优的问题,文中利用电子搜索算法代替粒子群算法,以提高计算的收敛速度并使优化计算更容易得到最优解。以网损期望最小为目标,建立了考虑电容器无功补偿和电压器变比的配电网无功优化模型。利用IEEE14节点系统进行模拟计算,通过结果验证了电子搜索算法在无功优化中的效果。通过比较了粒子群算法和电子搜索算法的结果,证明了电子搜索算法在收敛速度以及优化效果上优于粒子群算法。

关键词: 无功优化, 粒子群算法, 网损, 变压器变比, 电子搜索算法, 最优解

Abstract:

Aiming at the slow convergence of PSO and the problem that the result of PSO was easy to fall into local solution in the reactive power optimization process of power system, the electro-search algorithm was used instead of PSO to improve the convergence speed and make the optimization calculation easier to get the optimal solution. In order to minimize the expected loss, a reactive power optimization model for distribution network was established, which considered the capacitor reactive power compensation and the voltage transformer ratio. The simulation results of IEEE 14 bus system verified the effectiveness of the electro-search algorithm in reactive power optimization. Finally, the results of particle swarm optimization and electro-search algorithm were compared. The results showed that the electro-search algorithm was superior to particle swarm optimization in convergence speed and optimization effect.

Key words: reactive power optimization, particle swarm optimization, network loss, transformer ratio, electro-search algorithm, optimal solution

中图分类号:

TP312

黄泰相,陈波,管鑫,程璐瑶. 基于电子搜索算法的电力系统无功优化[J]. 电子科技, 2019, 32(1): 58-61.

HUANG Taixiang,CHEN Bo,GUAN Xin,CHENG Luyao. Reactive Power Optimization of Power System Based on Electronic Search Algorithm[J]. Electronic Science and Technology, 2019, 32(1): 58-61.

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	P_G/MW	Q_c_max/Mvar	Q_c_min/Mvar	V_max/p.u.	V_min/p.u.
1	232.38	50	-40	1.06	0.94
2	40	50	-40	0.16	0.94

算法	平均值/MW	最大值/MW	最小值/MW	运行时间/s
粒子群算法	15.43	15.64	15.28	1067.358
电子搜索算法	15.04	15.12	14.87	1075.829

节点	粒子群算法/p.u.	电子搜索算法/p.u.
1	1.060 0	1.020 0
2	1.042 5	1.035 0
3	1.010 0	1.030 5
4	1.019 0	1.013 7
5	1.020 0	0.990 0
6	1.060 0	1.008 6
7	1.052 0	0.994 4
8	1.022 4	1.024 5
9	1.056 0	1.035 8
10	1.050 0	1.032 6
11	1.047 0	1.021 9
12	1.041 0	1.010 0
13	0.980 0	1.019 0
14	1.001 0	1.030 8