基于粒子滤波和词计算的电网故障检测和定位研究

发布时间：2018-11-06 08:14

【摘要】：科学技术的发展和国民对电力需求的日益增长使得电力系统的规模逐渐增大,电网的结构也越来越复杂,电网出现故障的几率也随之提高,引发的后果更加严重,如果不能及时的检测出故障并且确定故障位置,将会给国民经济带来巨大损失。因此,采取有效的算法对故障进行检测和定位,为调度中心的决策提供及时有效的辅助措施,对于保证供电可靠性,避免不必要的损失有着重要意义。广域测量系统所用的采集装置是同步相量测量单元,由于其上传的数据为实时数据,速度快并且可精确到微秒级,所以其投入和发展对于电网的研究起到了至关重要的作用,但目前PMU测点较少,而SCADA系统覆盖面广,上传信息较全面,所以本文首先运用粒子滤波方法通过PMU上传的信息进行故障检测,若检测出故障后,则进一步运用词计算理论通过SCADA中的获得的信息进行故障定位。首先,研究粒子滤波的应用原理,通过总结得出其运用过程中有两方面缺陷:重要性函数选取困难和粒子容易丧失多样性。从这两方面对粒子滤波进行改进,通过PMU测得的数据运用粒子滤波算法对发电机的功角进行实时监测,用残差法进行故障的检测,当出现残差较大时,判断故障发生。其次,介绍词计算理论的基本原理以及其优势,针对其实现过程的三个步骤中的核心步骤,利用三角模糊数实现模糊约束的表达,并用格值自动机理论完成模糊约束的传播,形成一种模糊的格值自动机推理方法,并总结推理步骤,在检测出电网发生故障后,利用SCADA中的开关信息进一步对故障位置进行确定。最后,在前面研究的基础上,提出基于粒子滤波和词计算理论的电网故障检测和定位的方法,总结方法步骤并给出算法流程图,通过算例分析,验证本文所提出的方法的可行性和有效性。
[Abstract]:With the development of science and technology and the increasing demand for power, the scale of power system is gradually increasing, the structure of power network is becoming more and more complex, the probability of power network failure is also increasing, and the consequences are more serious. If failure can not be detected in time and fault location can be determined, it will bring huge losses to the national economy. Therefore, it is of great significance to adopt effective algorithms to detect and locate faults and to provide timely and effective auxiliary measures for decision making of dispatching center, which can ensure the reliability of power supply and avoid unnecessary losses. The acquisition device used in the wide area measurement system is the synchronous phasor measurement unit. Because the data uploaded by the system is real-time, fast and accurate to the microsecond level, its input and development have played an important role in the research of power grid. But at present, there are few PMU measurement points, and the SCADA system covers a wide area and uploads more comprehensive information. Therefore, this paper firstly uses particle filter method to detect the fault through the information uploaded by PMU, if the fault is detected, Then further use word computing theory to locate fault through the information obtained in SCADA. Firstly, the application principle of particle filter is studied, and two defects in its application are concluded: the difficulty of selecting importance function and the loss of diversity of particles. From these two aspects, the particle filter is improved, the data measured by PMU is used to monitor the power angle of generator in real time, and the residual method is used to detect the fault. When the residual error is large, the fault occurs. Secondly, the basic principle and advantages of word computing theory are introduced. According to the core steps of the three steps in the realization process, the expression of fuzzy constraints is realized by triangular fuzzy numbers, and the propagation of fuzzy constraints is accomplished by lattice valued automata theory. A fuzzy lattice-valued automata reasoning method is formed, and the reasoning steps are summarized. After detecting the fault in the power network, the fault location is further determined by using the switch information in SCADA. Finally, on the basis of the previous research, the paper puts forward the method of fault detection and location based on particle filter and word computing theory, summarizes the method steps and gives the flow chart of the algorithm. The feasibility and effectiveness of the proposed method are verified.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM711;TM732

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