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

doi:10.16183/j.cnki.jsjtu.2023.004

Abstract

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

CLC Number:

TM712

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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References 31

[1]	谭嘉, 李知艺, 杨欢, 等. 基于分布式优化思想的配电网用电负荷多层协同预测方法[J]. 上海交通大学学报, 2021, 55(12): 1544-1553. doi: 10.16183/j.cnki.jsjtu.2021.296
	TAN Jia, LI Zhiyi, YANG Huan, et al. A multi-level collaborative load forecasting method for distribution networks based on distributed optimization[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1544-1553.
[2]	王守相, 宋丽可, 舒欣. 分布式电源与多元负荷高渗透接入的主动配电网自适应过流保护方案[J]. 高电压技术, 2019, 45(6): 1783-1794.
	WANG Shouxiang, SONG Like, SHU Xin. Adaptive overcurrent protection of active distribution network with high penetration of distributed generations and multiple loads[J]. High Voltage Engineering, 2019, 45(6): 1783-1794.
[3]	唐金锐, 尹项根, 张哲, 等. 配电网故障自动定位技术研究综述[J]. 电力自动化设备, 2013, 33(5): 7-13.
	TANG Jinrui, YIN Xianggen, ZHANG Zhe, et al. Survey of fault location technology for distribution networks[J]. Electric Power Automation Equipment, 2013, 33(5): 7-13.
[4]	邓丰, 梅龙军, 唐欣, 等. 基于时频域行波全景波形的配电网故障选线方法[J]. 电工技术学报, 2021, 36(13): 2861-2870.
	DENG Feng, MEI Longjun, TANG Xin, et al. Faulty line selection method of distribution network based on time-frequency traveling wave panoramic waveform[J]. Transactions of China Electrotechnical Society, 2021, 36(13): 2861-2870.
[5]	SUN K M, CHEN Q, ZHAO P. Automatic faulted feeder section location and isolation method for power distribution systems considering the change of topology[J]. Energies, 2017, 10(8): 1081.
[6]	郑涛, 马龙, 李博文. 基于馈线终端装置信息畸变校正的有源配电网故障区段定位[J]. 电网技术, 2021, 45(10): 3926-3935.
	ZHENG Tao, MA Long, LI Bowen. Fault section location of active distribution network based on feeder terminal unit information distortion correction[J]. Power System Technology, 2021, 45(10): 3926-3935.
[7]	王飞, 孙莹. 配电网故障定位的改进矩阵算法[J]. 电力系统自动化, 2003, 27(24): 45-46.
	WANG Fei, SUN Ying. An improved matrix algorithm for fault location in distribution network of power systems[J]. Automation of Electric Power Systems, 2003, 27(24): 45-46.
[8]	郭利爽, 李凤婷, 赵新利, 等. 基于子网络划分的含DG配电网故障区段定位[J]. 电力系统保护与控制, 2020, 48(7): 76-84.
	GUO Lishuang, LI Fengting, ZHAO Xinli, et al. Fault section location for distribution network with DG based on sub-network partition[J]. Power System Protection & Control, 2020, 48(7): 76-84.
[9]	郭壮志, 陈涛, 徐其兴, 等. 配电网故障区段定位的互补松弛约束新模型与算法[J]. 电力自动化设备, 2020, 40(5): 129-137.
	GUO Zhuangzhi, CHEN Tao, XU Qixing, et al. Novel fault section location model for distribution network with complementary relaxation constraints and its algorithm[J]. Electric Power Automation Equipment, 2020, 40(5): 129-137.
[10]	孔培, 刘建锋, 周健, 等. 基于整数线性规划的配电网故障定位容错算法[J]. 电力系统保护与控制, 2020, 48(24): 27-35.
	KONG Pei, LIU Jianfeng, ZHOU Jian, et al. Fault-tolerant algorithm for fault location in distribution network based on integer linear programming[J]. Power System Protection & Control, 2020, 48(24): 27-35.
[11]	张健磊, 高湛军, 陈明, 等. 考虑复故障的有源配电网故障定位方法[J]. 电工技术学报, 2021, 36(11): 2265-2276.
	ZHANG Jianlei, GAO Zhanjun, CHEN Ming, et al. Fault location method for active distribution networks considering combination faults[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2265-2276.
[12]	XIONG G J, YUAN X F, MOHAMED A W, et al. Improved binary gaining-sharing knowledge-based algorithm with mutation for fault section location in distribution networks[J]. Journal of Computational Design & Engineering, 2022, 9(2): 393-405.
[13]	吉兴全, 张朔, 张玉敏, 等. 基于IELM算法的配电网故障区段定位[J]. 电力系统自动化, 2021, 45(22): 157-166.
	JI Xingquan, ZHANG Shuo, ZHANG Yumin, et al. Fault section location for distribution network based on improved electromagnetism-like mechanism algorithm[J]. Automation of Electric Power Systems, 2021, 45(22): 157-166.
[14]	郑聪, 周海峰, 郑东强, 等. 基于改进多元宇宙算法的主动配电网故障定位方法研究[J]. 电力系统保护与控制, 2023, 51(2): 169-179.
	ZHENG Cong, ZHOU Haifeng, ZHENG Dongqiang, et al. An active distribution network fault location method based on improved multi-universe algorithm[J]. Power System Protection & Control, 2023, 51(2): 169-179.
[15]	杨国华, 冯骥, 柳萱, 等. 基于改进秃鹰搜索算法的含分布式电源配电网分区故障定位[J]. 电力系统保护与控制, 2022, 50(18): 1-9.
	YANG Guohua, FENG Ji, LIU Xuan, et al. Fault location of a distribution network hierarchical model with a distribution generator based on IBES[J]. Power System Protection & Control, 2022, 50(18): 1-9.
[16]	王宝楠, 水恒华, 王苏敏, 等. 量子退火理论及其应用综述[J]. 中国科学: 物理学力学天文学, 2021, 51(8): 5-17.
	WANG Baonan, SHUI Henghua, WANG Sumin, et al. Theories and applications of quantum annealing: A literature survey[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 2021, 51(8): 5-17.
[17]	高锋阳, 李昭君, 袁成, 等. 量子计算和免疫优化算法相结合的有源配电网故障定位[J]. 高电压技术, 2021, 47(2): 396-406.
	GAO Fengyang, LI Zhaojun, YUAN Cheng, et al. Fault location for active distribution network based on quantum computing and immune optimization algorithm[J]. High Voltage Engineering, 2021, 47(2): 396-406.
[18]	张雅婷, 郭亮, 郭达, 等. 改进量子遗传算法在含分布式电源配电网中的应用[J]. 电测与仪表, 2023, 60(11): 130-135.
	ZHANG Yating, GUO Liang, GUO Da, et al. Application of improved quantum genetic algorithm in distribution network with distributed generation[J]. Electrical Measurement & Instrumentation, 2023, 60(11): 130-135.
[19]	DAS M, ROY A, MAITY S, et al. A quantum-inspired ant colony optimization for solving a sustainable four-dimensional traveling salesman problem under type-2 fuzzy variable[J]. Advanced Engineering Informatics, 2023, 55: 101816.
[20]	LI J J, XU B W, YANG Y S, et al. Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones[J]. Natural Computing, 2020, 19(4): 673-682.
[21]	何小锋, 马良. 求解0-1背包问题的量子蚁群算法[J]. 计算机工程与应用, 2011, 47(16): 3.
	HE Xiaofeng, MA Liang. Quantum-inspired ant algorithm for solving 0-1 knapsack problem[J]. Computer Engineering & Applications, 2011, 47(16): 3.
[22]	LIU M, ZHANG F, MA Y L, et al. Evacuation path optimization based on quantum ant colony algorithm[J]. Advanced Engineering Informatics, 2016, 30(3): 259-267.
[23]	周晓晔, 马小云, 朱梅琳. 机器人-人工拣选环境下混流装配线齐套物料配送优化[J]. 计算机集成制造系统, 2024, 30(4): 1527-1536.
	ZHOU Xiaoye, MA Xiaoyun, ZHU Meilin. Research on optimization of kitting material distribution of mixed-model assembly line under robot-operator picking environment[J]. Computer Intergiated Manufacturing Systems, 2024, 30(4): 1527-1536.
[24]	李絮, 刘争艳, 谭拂晓. 求解TSP的新量子蚁群算法[J]. 计算机工程与应用, 2011, 47(32): 42-44.
	LI Xu, LIU Zhengyan, TAN Fuxiao. Novel quantum ant colony algorithm for TSP[J]. Computer Engineering & Applications, 2011, 47(32): 42-44.
[25]	HAN K H, KIM J H. Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub/spl epsi//gate, and two-phase scheme[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(2): 156-169.
[26]	杜群, 甄成刚, 郝悍勇. 基于量子蚁群的快速碰撞检测算法研究[J]. 计算机仿真, 2019, 36(12): 209-213.
	DU Qun, ZHEN Chenggang, HAO Hanyong. Fast collision detection algorithm based on quantum ant colony[J]. Computer Simulation, 2019, 36(12): 209-213.
[27]	ZHAO Y T, WANG J, XIE X L. Continuous ant colony algorithm based on entity and its convergence[C]// 2008 Second International Symposium on Intelligent Information Technology Application. Shanghai, China: IEEE, 2008: 80-84.
[28]	王秋杰, 金涛, 谭洪, 等. 基于分层模型和智能校验算法的配电网故障定位技术[J]. 电工技术学报, 2018, 33(22): 5327-5337.
	WANG Qiujie, JIN Tao, TAN Hong, et al. The technology on fault location of distribution network based on hierarchical model and intelligent checking algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(22): 5327-5337.
[29]	张颖, 周韧, 钟凯. 改进蚁群算法在复杂配电网故障区段定位中的应用[J]. 电网技术, 2011, 35(1): 224-228.
	ZHANG Ying, ZHOU Ren, ZHONG Kai. Application of improved ant colony algorithm in fault-section location of complex distribution network[J]. Power System Technology, 2011, 35(1): 224-228.
[30]	赵乔, 王增平, 董文娜, 等. 基于免疫二进制粒子群优化算法的配电网故障定位方法研究[J]. 电力系统保护与控制, 2020, 48(20): 83-89.
	ZHAO Qiao, WANG Zengping, DONG Wenna, et al. Research on fault location in a distribution network based on an immune binary particle swarm algorithm[J]. Power System Protection & Control, 2020, 48(20): 83-89.
[31]	邱彬, 罗添元, 宁博, 等. 基于BAS-IGA的含分布式电源配电网故障定位[J]. 电力系统及其自动化学报, 2021, 33(2): 8-14.
	QIU Bin, LUO Tianyuan, NING Bo, et al. Fault location of distribution network with distribution generations based on BAS-IGA[J]. Proceedings of the CSU-EPSA, 2021, 33(2): 8-14.

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