基于解析模型的输电网故障诊断技术的研究

发布时间：2018-01-04 14:31

本文关键词：基于解析模型的输电网故障诊断技术的研究　出处：《东北大学》2014年硕士论文　论文类型：学位论文

【摘要】：电力系统故障诊断是调度员处理电网事故的重要辅助工具,可以起到缩短事故处理时间、防止事故扩大和提高电力系统自动化水平的作用。在现代工业中,电力系统故障诊断是指利用保护和断路器的动作信息来推断可能的故障位置,识别故障元件和误动作的保护与断路器,并对保护和断路器的动作情况做出评价其中故障元件的识别是故障诊断实现的关键。本文中,首先,对电力系统故障诊断的基本概念和研究背景作了简要介绍,对国内外现有电力系统故障诊断基本理论和常用方法作了分析和比较。阐明了电力系统故障诊断的目标与功能,并介绍了故障分类和故障信息源。其次,基于继电保护动作规则等常规知识,应用优化技术,将电力系统故障诊断问题转化为0-1整数规划问题,建立了解析法的数学模型,并详细探讨了目标函数自动形成方法、故障区域自动识别策略和优化问题求解算法。然后,针对传统故障模型进行了优化,提出两种更完善的故障诊断解析方法,分别是基于混合法的电网故障诊断方法,和基于广域测量系统的故障解析模型。前者可以降低目标变量的维数,使得求解更快捷,后者将当下最流行的广域测量系统中的电气量信息与之结合,预测了电力系统故障诊断未来的发展趋势。提出两种新型群体智能仿生方法,改进粒子群算法和人工鱼群算法均是基于群体搜索的新型优化方法,具有并行处理特征,鲁棒性好,易于实现,且计算效率较高。本文介绍了改进粒子群算法和人工鱼群算法的基本原理,详细论述了两种算法的流程和实现步骤,分析了算法中参数对算法本身的影响,比较了两种算法的特点。在此基础上,应用两种算法求解故障诊断优化问题,给出了算法的具体实现步骤,讨论了算法参数选择等注意事项。最后,在给出的故障诊断测试系统上,对故障情形进行了仿真。与遗传算法等传统优化算法比较,采用改进粒子群算法和人工鱼群算法求解故障诊断优化问题稳定性高、收敛特性好、运行速度快。最后对论文中所作的研究进行简要总结,并指出了这一领域有待进一步深入研究的问题。
[Abstract]:Fault diagnosis of power system is an important tool for the dispatcher to handle the power grid accidents, to shorten the processing time of the accident, prevent accident and to improve the level of automation of power system. In modern industry, power system fault diagnosis refers to the use of circuit breakers and protection action information to infer the possible location of the fault protection and circuit breaker to identify the faulty elements and false action, and action of protective relays and circuit breakers to make evaluation of the fault component is the key to realize fault diagnosis. In this paper, firstly, the basic concepts and research background of power system fault diagnosis is briefly introduced, the existing domestic and foreign power system fault diagnosis theory and the commonly used methods are analyzed and compared. The goal and function of the power system fault diagnosis, and introduces the fault classification and fault information source. Secondly, based on the Relay protection rules of conventional knowledge, application of optimization technology, the power system fault diagnosis problem into 0-1 integer programming problem, establishes a mathematical model of the analytical method, and discusses the objective function of automatic forming method, strategy and optimization algorithm of automatic identification fault region. Then, according to the traditional fault model was optimized. Put forward two kinds of improved fault diagnosis analytic method, namely power system fault diagnosis method based on hybrid method, and fault analysis model based on wide area measurement system. The former can reduce the dimension of the target variable, which makes the calculation more efficient, the electrical information of wide area measurement system in the most popular with the combination forecast the development trend of future power system fault diagnosis. This paper proposes two new bionic swarm intelligence method, improved particle swarm optimization algorithm and artificial fish swarm algorithm is A new optimization method based on population search, parallel processing features, good robustness, easy to implement, and high computational efficiency. This paper introduces the basic principle of improved PSO and artificial fish swarm algorithm, discusses two kinds of algorithm flow and implementation steps, analyzed the influence of the parameters in the algorithm on the algorithm itself. Comparison of the characteristics of two algorithms. On this basis, the application of two kinds of fault diagnosis algorithm for solving the optimization problem, the specific implementation steps of the method is given and discussed the algorithm parameter selection considerations. Finally, the fault diagnosis testing system is given, the fault is simulated. Compared with the traditional genetic algorithm. The optimization algorithm, the improved particle swarm algorithm and artificial fish swarm algorithm for solving the optimization problem of fault diagnosis of high stability, good convergence, fast running speed. At the end of this thesis the research done by Jane It is necessary to sum up and point out the problems that need to be further studied in this field.

【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM727

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