Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

Article retrieval

文章检索

首页 >> 文章检索 >> 最新索引

基于混合优化算法的电网故障诊断

来源:电工电气发布时间:2020-11-19 14:19 浏览次数:8
基于混合优化算法的电网故障诊断
 
谢瑞,张兴旺
(南昌工程学院 江西省精密驱动与控制重点实验室,江西 南昌 333000)
 
    摘 要:电网故障过程中保护和断路器动作及告警信息存在不确定性,会使原有电网故障解析模型诊断出现错误。在现有解析模型基础上,通过电网结构、保护配置及断路器的动作规则进行解析,考虑各级保护之间的互相影响,针对可疑母线和线路分别建立目标函数,构建新的解析模型。采用混合优化算法对目标函数进行求解,将模拟植物生长算法(PGSA)与粒子群算法(PSO)结合,初始生长点的选取对于PGSA能否收敛于全局最优解起着决定作用,先通过PSO的高鲁棒性初选优秀的初始生长点,再基于PGSA的高效搜索能力得到最终的全局最优解。算例结果表明,改进的解析模型更加合理,混合优化算法搜索速度与收敛精度大幅度提高。
    关键词:故障诊断;优化模型;模拟植物生长算法;粒子群算法;告警信息
    中图分类号:TM711     文献标识码:A     文章编号:1007-3175(2020)11-0031-05
 
Power Grid Fault Diagnosis Based on Mixed Optimization Algorithm
 
XIE Rui, ZHANG Xing-wang
(Jiangxi Provincial Key Laboratory of Precision Drive and Control, Nanchang Institute of Technology, Nanchang 333000, China)
 
    Abstract: There are uncertainties in the protection and circuit breaker action and alarm information in the process of power grid failure, which will cause errors in the diagnosis of the original grid fault analysis model. On the basis of the existing analytical model, analyze the power grid structure, protection configuration and the action rules of the circuit breaker, consider the mutual influence between all levels of protection, establish objective functions for suspicious buses and lines, and build a new analytical model. A hybrid optimization algorithm is used to solve the objective function, and the simulated plant growth algorithm (PGSA) is combined with the particle swarm algorithm (PSO). The selection of the initial growth point determines whether the PGSA can converge to the global optimal solution. First pass the PSO The high robustness of PGSA initially selects excellent initial growth points, and then obtains the final global optimal solution based on the efficient search ability of PGSA. The results of calculation examples show that the improved analytical model is more reasonable, and the search speed and convergence accuracy of the hybrid optimization algorithm are greatly improved.
    Key words: fault diagnosis; optimization model; simulation plant growth algorithm; particle swarm algorithm; warning information
 
参考文献
[1] 徐彪,尹项根,张哲,等. 电网故障诊断的分阶段解析模型[J]. 电工技术学报,2018,33(17):4113-4122.
[2] ZHANG Yan, ZHANG Yong, WEN Fushuan, et al. A fuzzy Petri net based approach for fault diagnosis in power systems considering temporal constraints[J].International Journal of Electrical Power & Energy Systems,2016,78:215-224.
[3] LI Z, YIN X, ZHE Z, et al. Wide-Area Protection Fault Identification Algorithm Based on Multi-Information Fusion[J]. IEEE Transactions on Power Delivery,2013,28(3):1348-1355.
[4] YAN Z, CHI Y C, WEN F, et al. An Analytic Model for Fault Diagnosis in Power Systems Utilizing Redundancy and Temporal Information of Alarm Messages[J].IEEE Transactions on Power Systems,2016,31(6):4877-4886.
[5] 郭文鑫,文福拴,廖志伟,等. 计及保护和断路器误动与拒动的电力系统故障诊断解析模型[J]. 电力系统自动化,2009,33(24):6-10.
[6] 张岩,张勇,文福拴,等. 融合信息理论的电力系统故障诊断解析模型[J]. 电力自动化设备,2014,34(2):158-164.
[7] 翁汉琍,毛鹏,林湘宁. 一种改进的电网故障诊断优化模型[J]. 电力系统自动化,2007,31(7):66-70.
[8] 文福拴,韩祯祥,田磊,等. 基于遗传算法的电力系统故障诊断的解析模型与方法——第一部分:模型与方法[J]. 电力系统及其自动化学报,1998,10(3):1-7.
[9] 李彤,王春峰,王文波,等. 求解整数规划的一种仿生类全局优化算法——模拟植物生长算法[J].系统工程理论与实践,2005,25(1):76-85.
[10] 胡年平,徐芳敏,谢宁,等. 改进小生境粒子群算法应用于电网故障诊断[J]. 电网与清洁能源,2018,34(2):9-16.
[11] 文福栓,韩祯祥. 基于遗传算法和模拟退火算法电力系统的故障诊断[J]. 中国电机工程学报,1994,14(3):29-35.