基于量子蚁群算法的配电网故障区段快速定位技术

doi:10.16183/j.cnki.jsjtu.2023.004

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (5): 693-708.doi: 10.16183/j.cnki.jsjtu.2023.004

• 新型电力系统与综合能源 • 上一篇下一篇

基于量子蚁群算法的配电网故障区段快速定位技术

毕忠勤¹, 余晓婉¹, 王宝楠¹^,²(), 黄文焘², 张丹³, 董真⁴

1.上海电力大学计算机科学与技术学院,上海 201306
2.上海交通大学电力传输与功率变换控制教育部重点实验室,上海 200240
3.上海海事大学物流科学与工程研究院,上海 201306
4.国网上海市电力公司电力科学研究院,上海 200437

收稿日期:2023-01-04 修回日期:2023-04-04 接受日期:2023-05-08 出版日期:2024-05-28 发布日期:2024-06-17
通讯作者: 王宝楠,讲师;E-mail:wbn_shu0099@163.com.
作者简介:毕忠勤(1977-),教授,从事云计算、数据处理和智能电网中的质量控制等研究.
基金资助:
电力传输与功率变换控制教育部重点实验室开放课题(2022AA02)

Fast Fault Location Technology for Distribution Network Based on Quantum Ant Colony Algorithm

BI Zhongqin¹, YU Xiaowan¹, WANG Baonan¹^,²(), HUANG Wentao², ZHANG Dan³, DONG Zhen⁴

1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201306, China
2. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
3. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
4. State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China

Received:2023-01-04 Revised:2023-04-04 Accepted:2023-05-08 Online:2024-05-28 Published:2024-06-17

摘要/Abstract

摘要：

分布式电源并入配电网已成为新型电力系统重要特征之一,分布式电源的接入与发电的不确定性使配电网潮流复杂多变,对配电网故障快速定位提出更高的技术要求.然而,现有智能优化算法在解决配电网故障区段定位问题时会出现收敛速度慢、易陷入局部最优等问题.针对这些挑战与问题,提出一种基于量子蚁群算法(QACA)的配电网故障区段快速定位技术.首先,根据状态逼近思想和最小故障集理论构建配电网故障定位的数学模型;其次,针对馈线终端单元上传信息缺失情况提出信息自修正方法,并提出分级定位模型来缩短定位时间;然后,提出3种改进技术对QACA进行针对性改进,改进量子旋转门更新机制,以函数控制形式动态调整旋转角大小,同时引入精英策略加快算法收敛速度.最后,在关键参数确定后验证了改进技术、信息自修正法、分级定位模型的有效性.将所提算法与7种不同算法进行对比,结果表明:改进的QACA可有效完成故障区段定位,具有良好的收敛速度、准确率以及容错性能.

关键词: 配电网故障区段定位, 量子蚁群算法, 信息自修正法, 分级定位模型

Abstract:

Integration of distributed generations into distribution networks has become one of the important features of new power systems. The integration of distributed generation and the uncertainty of power generation make the power flow in distribution networks complex and variable, which poses higher technical requirements for rapid fault location in distribution networks. However, existing intelligent optimization algorithms may encounter problems such as slow convergence speed and susceptibility to local optimization when solving the problem of fault section location in distribution networks. To address these challenges and problems, a rapid fault section location technology based on quantum ant colony algorithm (QACA) is proposed. First, a location mathematical model is constructed based on the state approximation idea and the minimum fault set theory. Then, an information self-correction method is proposed for the missing information uploaded by feeder terminal unit, and a hierarchical location model is proposed to shorten the location time. Afterwards, three improvement techniques are proposed to improve the QACA. The update mechanism of the quantum rotary gate is improved, the rotation angle is dynamically adjusted in the form of function control, and the elite strategy is introduced to accelerate the convergence speed of the algorithm. Finally, after the key parameters are determined, the effectiveness of the improved technique, the information self-correction method, and the hierarchical positioning model is verified. A comparison with 7 different algorithms indicates that the improved QACA can effectively locate the fault section, and has a fast convergence speed, great accuracy, and fault tolerance.

Key words: location of fault section in distribution network, quantum ant colony algorithm (QACA), information self-correction method, hierarchical location model network

中图分类号:

TM712

毕忠勤, 余晓婉, 王宝楠, 黄文焘, 张丹, 董真. 基于量子蚁群算法的配电网故障区段快速定位技术[J]. 上海交通大学学报, 2024, 58(5): 693-708.

BI Zhongqin, YU Xiaowan, WANG Baonan, HUANG Wentao, ZHANG Dan, DONG Zhen. Fast Fault Location Technology for Distribution Network Based on Quantum Ant Colony Algorithm[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 693-708.

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x_x	c_x	F(x)> F(c)	Δθ_x	s(α_x, β_x)
x_x	c_x	F(x)> F(c)	Δθ_x	α_xβ_x>0	α_xβ_x<0	α_x=0	β_x=0
0	0	否	0	-1	+1	±1	±1
0	0	是	0	-1	+1	±1	±1
0	1	否	0.06π	-1	+1	±1	±1
0	1	是	0.06π	+1	-1	±1	±1
1	0	否	0.06π	+1	-1	±1	±1
1	0	是	0.06π	-1	+1	±1	±1
1	1	否	0	+1	-1	±1	±1
1	1	是	0	+1	-1	±1	±1

ε	单点故障		两点故障		三点故障
ε	平均迭代次数	准确率/%	平均迭代次数	准确率/%	平均迭代次数	准确率/%
0	8.67	98	23.68	96	35.57	80
0.01	4.64	100	14.41	92	18.55	94
0.02	3.20	100	8.20	100	13.24	100
0.04	3.46	100	8.84	100	13.28	100
0.08	5.80	100	16.32	100	17.86	100
0.12	14.60	96	26.48	98	37.93	98

故障类型	FTU上传的节点信息	目标函数	程序输出故障区段	平均迭代次数	准确率/%
单点故障	[111111100000000000 0000000000000000]	F=0.5	x7	2.72	100
两点故障	[111111111000000000 0000000111100000]	F=1	x9、x29	8.2	100
三点故障	[11111110000000000 0000011011000000]	F=1.5	x7、x24、x27	13.32	100

故障区段	信息缺失点	信息畸变点	补全前节点信息	补全后节点信息	是否成功
x6	k7	无	[111111x00000000000000000000000000]	[111111100000000000000000000000000]	是
x15、x30	k2	无	[1x111111111111100 00000000011111000]	[11111111111111100 00000000011111000]	是
x23、x4、x20	k3	无	[11x1000000000000 00110010000000000]	[1111000000000000 00110010000000000]	是
x20	k5	k7	[1100x01000000000 00110000000000000]	[1100101000000000 00110000000000000]	否
x12、x30	k11	k18	[1111111111x100000 1000000011111000]	[11111111111100000 1000000011111000]	是
x22、x5、x24	k9	k14	[11111000x00001000 0111111000000000]	[11111000100001000 0111111000000000]	否

故障序列	故障类型	FTU上传的节点信息	目标函数	程序输出结果
1	单点故障,一点畸变(k11)	[11000000001000000011100000000000]	F=1.5	x21
2	单点故障,两点畸变(k3、k30)	[11011111111111100000000000000100]	F=2.5	x15
3	单点故障,三点畸变(k8、k24、k5)	[11110111000000000000000101110000]	F=4.5	x28
4	两点故障,一点畸变(k8)	[11110001000000000011000000000000]	F=2	x4、x20
5	两点故障,两点畸变(k4、k11)	[11101111110111111100000001111100]	F=3	x18、x30
6	两点故障,三点畸变(k5、k12、k25)	[11110100000100000010000011100000]	F=4	x27、x19

基于量子蚁群算法的配电网故障区段快速定位技术

Fast Fault Location Technology for Distribution Network Based on Quantum Ant Colony Algorithm

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故障序列	IQACA		IACA		ACA
故障序列	平均迭代次数	准确率/%	平均迭代次数	准确率/%	平均迭代次数	准确率/%
1	3.58	100	5.35	98	20.38	58
2	3.44	100	4.74	100	18.3	78
3	2.92	100	5.34	100	17.16	50
4	8.16	100	13.38	100	19.38	72
5	8.84	100	10.18	98	17.21	56
6	8.24	100	10.28	100	22.05	36

FTU上传的节点信息	缺失位置	畸变位置	程序输出故障区段	是否定位正确
[111110000000000000000000]	无	无	x5	是
[000000001111011100000000]	无	无	x12、x16	是
[0x0000000001011111001111]	k2	k12	x24	是
[000100001110011x11110000]	k16	k4	x20、x11	是

算例	故障区段	分布式电源投切情况	缺失位置	畸变位置	目标函数	程序输出故障区段
1	x7	[1, 0]	无	无	F=0.5	x7
2	x4、x31	[0, 1]	无	无	F=1	x4、x31
3	x11、x49、x56	[1, 1]	无	无	F=1.5	x11、x49、x56
4	x55	[1, 0]	k41	k22	F=1.5	x55
5	x29	[0, 1]	k14	k47	F=1.5	x29
6	x4	[1, 1]	k2	k49	F=2.5	x4

故障区段	畸变点	平均迭代次数				正确次数
故障区段	畸变点	BPSO^[30]	IBPSO^[30]	QACA	IQACA	BPSO^[30]	IBPSO^[30]	QACA	IQACA
x22	无	13.1	7.6	4.16	3.44	31	44	50	50
x19、x33	无	13.44	7.94	10.74	8.40	24	39	50	50
x16	k29	12.38	7.82	3.69	3.00	32	43	48	50
x28	k4、k14、k25	13.28	7.56	5.98	3.20	29	37	49	50
x18、x32	k21、k29	13.28	8.5	11.93	8.46	25	41	45	50

算法	准确率/%	最小迭代次数	最大迭代次数	平均迭代次数
GA^[31]	95	27	59	34.00
IGA^[31]	100	13	18	14.40
BAS-IGA^[31]	100	9	12	10.85
QACA	100	1	21	4.97
IQACA	100	1	10	3.23

故障区段	畸变位置	分级定位模型定位维度	分级定位模型			原模型
故障区段	畸变位置	分级定位模型定位维度	第一级定位使用时间/ms	第二级定位使用时间/ms	总定位时间/ms	总定位时间/ms
x7	无	19	19.5	58.9	78.4	114.3
x9、x29	无	27	21.4	85.2	106.6	131.4
x7、x24、x27	无	30	19.7	101.7	121.4	176.6
x21	k11	11	22.3	19.5	41.8	100.0
x15	k3、k30	19	22.4	10.4	87.9	120.7
x28	k8、k24、k5	15	19.5	27.7	47.2	104.2
x20、x4	k8	14	19.7	35.2	54.9	151.5
x18、x30	k11、k4	27	21.4	89.6	111.0	138.6
x27、x19	k5、k12、k25	19	23.8	37.8	61.6	131.5

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