| 65 | 0 | 575 |
| 下载次数 | 被引频次 | 阅读次数 |
为提高故障诊断准确度与诊断容错性，提出基于加权队列的配电网智能分布式故障自动化诊断算法。馈线终端单元采集各开关的故障电流信息后，根据加权公平队列算法进行故障电流向调度中心服务器的调度与传输；通过设计基于带宽分配的改进加权公平队列算法，构建配电网智能分布式故障诊断评价函数，引入量子粒子群算法，自动优化评价函数求解过程，最终实现配电网智能分布式故障的自动化诊断。试验结果表明：当学习系数为4时，量子粒子群算法具有最优性能；所提算法可诊断配电网中的多点故障，并确定故障区段；算法具有良好的故障诊断容错性，仅在故障位置附近多开关故障信息丢失时，诊断效果才受到一定影响；同时，该算法的信息调度能力较好，延迟时间较短。
Abstract：To enhance the accuracy and fault tolerance of fault diagnosis, an intelligent distributed fault automatic diagnosis algorithm for distribution networks based on weighted queues is proposed. After the feeder terminal unit collects the fault current information of each switch, the fault current is scheduled and transmitted to the dispatching center server according to the weighted fair queue algorithm. By designing an improved weighted fair queue algorithm based on bandwidth allocation, a distribution network intelligent distributed fault diagnosis evaluation function is constructed. The quantum particle swarm optimization algorithm is introduced to automatically optimize the solution process of the evaluation function, ultimately achieving the automatic diagnosis of intelligent distributed faults in distribution networks. The test results show that when the learning coefficient is 4, the quantum particle swarm optimization algorithm has the best performance. The proposed algorithm can diagnose multiple faults in the distribution network and determine the fault section. The algorithm has good fault diagnosis fault tolerance, and the diagnosis effect is only affected when the fault information of multiple switches near the fault location is lost. At the same time, the algorithm has good information scheduling capability and short delay time.
[1]PONUKUMATI B K, BEHERA A K, SUBHADARSHINI L, et al. Unbalanced distribution network cross-country fault diagnosis method with emphasis on high-impedance fault syndrome[J]. Engineering, Technology&Applied Science Research, 2024, 14(2):13517-13522.
[2]SINHAP, PAUL K, CHATTERJEE S, et al. Crosscountry high impedance fault diagnosis scheme for unbalanced distribution network employing detrended crosscorrelation[J]. IET Generation, Transmission&Distribution, 2024, 18(24):4192-4208.
[3]管恩齐，何晋，骆通，等．基于改进人工鱼群算法的有源配电网的故障区间定位[J]．电力电容器与无功补偿，2022, 43(1):102-110.GUAN E Q, HE J, LUO T, et al. Fault section location of active distribution network based on improved artificial fish swarm algorithm[J]. Power Capacitor&Reactive Power Compensation, 2022, 43(1):102-110.
[4]张文轩，李京，陈平，等．基于配电自动化终端的含DG配电网故障定位优化算法[J]．水电能源科学，2021, 39(7):192-196.ZHANG W X,LI J,CHEN P, et al. Optimal algorithm for fault location of distribution network containing DG based on distribution automation terminal[J]. Water Resources and Power, 2021, 39(7):192-196.
[5]李海锋，张正刚，梁远升，等．基于工频变化量的含逆变型分布式电源不平衡配电网故障分析方法[J]．广东电力，2023, 36(5):27-38.LI H F, ZHANG Z G, LIANG Y S, et al. Fault analysis method based on power frequency variation for unbalanced distribution network with IIDGs[J]. Guangdong Electric Power, 2023, 36(5):27-38.
[6]李斌，刘文帅，费泽松．面向空天地异构网络的边缘计算部分任务卸载策略[J]．电子与信息学报，2022,44(9):3091-3098.LI B, LIU W S, FEI Z S. Partial computation offloading for mobile edge computing in space-air-ground integrated network[J]. Journal of Electronics&Information Technology, 2022, 44(9):3091-3098.
[7]杨珺，孔文康，孙秋野．智能算法在含分布式电源配电网故障恢复的应用综述[J]．控制与决策，2019, 34(9):1809-1818.YANG J, KONG W K, SUN Q Y. Application of intelligent algorithms to service restoration of distribution network with distributed generations[J]. Control and Decision, 2019, 34(9):1809-1818.
[8]刘小红，张人龙，单汨源．基于云模型的量子混合粒子群算法及其应用[J]．统计与决策，2021, 37(3):54-58.LIU X H, ZHANG R L, SHAN M Y. Quantum hybrid particle swarm optimization based on cloud model and its application[J]. Statistics and Decision, 2021, 37(3):54-58.
[9]苏力，薛峰．基于改进PSO-PID算法的液压机压下系统控制优化[J]．自动化与仪表，2022, 37(6):14-17.SU L, XUE F. Optimal control of rolling mill hydraulic down system based on improved PSO-PID algorithm[J].Automation&Instrumentation, 2022, 37(6):14-17.
[10]周俊宇，唐鹤，区允杰．基于电网故障告警的风险智能预判分析[J]．微型电脑应用，2021, 37(9):197-200.ZHOU J Y, TANG H, OU Y J. Intelligent prediction analysis for risk based on power grid fault alarm[J]. Microcomputer Applications, 2021, 37(9):197-200.
基本信息:
中图分类号:TP277;TM73
引用信息:
[1]席佳伟,胡静,蒋浩,等.配电网智能分布式故障自动化诊断算法的设计[J].大连交通大学学报().
2026-04-11
2026-04-11
2026-04-11