基于量子萤火虫算法的配电网故障恢复策略

doi:10.11930/j.issn.1004-9649.202309079

摘要/Abstract

摘要：

针对原有配电网故障恢复策略在处理高渗透率分布式光伏并网时效率低、易陷入局部收敛等问题，提出了一种基于量子萤火虫算法的配电网故障恢复策略。首先，以最小化失电负荷量为主和最小化网络损耗为从，构建主从双目标优化目标函数，并计及约束条件建立含分布式光伏并网的配电网故障恢复模型。其次，为克服标准萤火虫算法的不足，采用量子编码和量子旋转门的方式提高萤火虫算法的寻优速度和全局搜索能力，缩短故障恢复模型求解时间。然后，采用量子萤火虫算法求解故障恢复模型，通过控制配电网系统开关的通断，在保证重要负荷恢复供电的基础上尽可能多地恢复失电负荷，同时减少网络损耗。最后，在IEEE 33节点系统上进行算例分析以验证所述方法的可行性和优越性。

关键词: 配电网, 故障恢复, 分布式光伏, 量子旋转门, 量子萤火虫算法

Abstract:

Existing distribution network fault recovery strategy tends to be inefficient and locally convergent for high penetrated distributed photovoltaic grid connection. Hence in this paper, a distribution network fault recovery strategy is proposed based on quantum firefly algorithm . The objective function is constructed in terms of a master-slave dual objective optimization to minimize both the load loss and network losses, and corresponding fault recovery model with distributed photovoltaic grid connection is established by taking into account of the constraints. To overcome the drawbacks of the standard firefly algorithm, quantum encoding and quantum rotation gates were applied to enhance the optimization efficiency and global search capability of the firefly algorithm such that the total solution time can be greatly shortened. Next from the fault recovery model solved after proposed algorithm, by controlling the on-off status of the circuit breakers in the distribution network system, the lost load can be restored as much as possible to ensure the restoration of power supply to critical loads, while minimizing network losses. Finally, case studies were conducted on the IEEE 33 bus system to verify the feasibility and advantages of the proposed method.

Key words: distribution network, fault restoration, distributed photovoltaic, quantum rotation gate, quantum firefly algorithm

李娟, 汪家铭, 许苏迪, 姜云龙, 杨雄. 基于量子萤火虫算法的配电网故障恢复策略[J]. 中国电力, 2023, 56(12): 191-198.

Juan LI, Jiaming WANG, Sudi XU, Yunlong JIANG, Xiong YANG. Fault Recovery Strategy of Distribution Networks Based on Quantum Firefly Algorithm[J]. Electric Power, 2023, 56(12): 191-198.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202309079

https://www.electricpower.com.cn/CN/Y2023/V56/I12/191

图/表 9

图 1 基于量子萤火虫算法的配电网故障恢复模型求解流程

Fig.1 Solution process of distribution network fault recovery model based on quantum firefly algorithm

图 2 IEEE 33节点配网系统网络拓扑结构

Fig.2 Topology diagram of IEEE 33 bus distribution network system

表 1 节点处负荷重要程度划分和权重系数取值

Table 1 Importance classification of node load and weight coefficient values

负荷等级	负荷节点集合	权重系数w
一级	3、20、21、23、17、31	1.0
二级	4、5、12、13、15、19、22、24、 25、26、30、32	0.6
三级	剩余节点	0.1

表 2 方案一配电网系统故障前后结果对比

Table 2 Comparison of results before and after distribution network system failure of Scheme 1

时期	总失电负荷量/kW	重要负荷失电量/kW	恢复供电的负荷总功率/kW	失电负荷节点	恢复供电节点	闭合开关	断开开关
故障恢复前	860	480	0	27、28、29、30、 31、32、33	—	—	26—27
故障恢复后	0	0	860	—	27、28、29、30、 31、32、33	8—21、12—22、25—29、 9—15、18—33	7—8、9—10、14—15、 32—33、26—27

图 3 各算法的收敛特性曲线（无分布式光伏）

Fig.3 Convergence curve of QFA and PSO algorithm (without distributed PV)

图 4 分布式光伏日有功出力曲线

Fig.4 Daily curve of distributed photovoltaic active output

表 3 方案二配电网系统故障前后结果对比

Table 3 Comparison of results before and after distribution network system failure of Scheme 2

时期	总失电负荷量/ kW	重要负荷失电量/ kW	恢复供电的负荷总功率/kW	失电负荷节点	恢复供电节点	闭合开关	断开开关	孤岛情况
故障恢复前	1015	180	0	7、8、9、10、11、12、13、14、15、16、18	—	—	6—7	孤岛（节点17）
故障恢复后	0	0	1015	—	7、8、9、10、11、12、 13、14、15、16、18	8—21、12—22、 9—15、18—33	9—10、14—15、 16—17、6—7	—

表 4 算法性能对比

Table 4 Comparison of algorithm performance

算法	失电负荷/kW	平均迭代次数	运行时间/s
QFA	0	12.3	11.74
PSO	0	17.4	20.06
文献[15]所提算法	0	14.2	12.68

图 5 各算法的收敛特性曲线（含分布式光伏）

Fig.5 Convergence curve of each algorithm (with distributed PV)

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