本文選題：電網(wǎng)故障診斷　切入點：復(fù)雜故障　出處：《華北電力大學(xué)(北京)》2017年碩士論文

【摘要】：隨著全球能源互聯(lián)網(wǎng)的提出,各級電網(wǎng)之間的互聯(lián)性增強,尤其是間歇式新能源和微電網(wǎng)的接入,電網(wǎng)自身的動態(tài)行為日趨復(fù)雜,發(fā)生拒動等復(fù)雜故障的可能性仍舊存在,準(zhǔn)確、快速的實現(xiàn)電網(wǎng)故障診斷成為更為緊迫的現(xiàn)實要求。本文以診斷電網(wǎng)復(fù)雜故障為目的,針對現(xiàn)有專家系統(tǒng)缺乏學(xué)習(xí)能力的不足,利用支持向量機(jī)(SVM)模型基于歷史故障經(jīng)驗對復(fù)雜故障進(jìn)行診斷,其主要工作如下:研究統(tǒng)計學(xué)習(xí)理論的內(nèi)在特點,深入理解VC維和結(jié)構(gòu)風(fēng)險最小化原則,闡述支持向量機(jī)模型的數(shù)學(xué)原理,在Visual studio 2010平臺使用C++語言分別編寫線性和非線性支持向量機(jī)模型的實現(xiàn)算法,為支持向量機(jī)在電網(wǎng)故障診斷的應(yīng)用打下理論基礎(chǔ)。根據(jù)電網(wǎng)故障診斷的需求與特點,提出基于支持向量機(jī)的方法對復(fù)雜故障進(jìn)行診斷。通過對歷史故障案例的訓(xùn)練學(xué)習(xí),獲取這些復(fù)雜故障案例中的“隱性”診斷知識,利用這些經(jīng)驗規(guī)律對現(xiàn)有故障進(jìn)行診斷,并不斷用新的故障事件對SVM模型進(jìn)行再訓(xùn)練。為使建立的支持向量機(jī)模型具有廣泛的通用性,以數(shù)據(jù)采集與監(jiān)視控制系統(tǒng)(SCADA)系統(tǒng)采集的保護(hù)動作和斷路器跳閘信息為基礎(chǔ),針對母線、線路和變壓器等三類元件分別設(shè)置其支持向量機(jī)模型的輸入特征量,并利用遺傳算法對懲罰因子和徑向基核函數(shù)參數(shù)進(jìn)行尋優(yōu)。在課題組已開發(fā)的電網(wǎng)故障診斷系統(tǒng)的基礎(chǔ)上,增設(shè)利用SVM算法的復(fù)雜故障診斷模塊,并對該模塊五個組成部分的功能設(shè)計進(jìn)行了逐一論述,提高了電網(wǎng)故障診斷系統(tǒng)針對復(fù)雜故障的診斷效率,最后通過三種典型的復(fù)雜故障案例測試驗證了模塊實用性與有效性。
[Abstract]:With the development of the global energy Internet, the interconnection between all levels of power grids is enhanced, especially the interconnect of intermittent new energy sources and microgrids, the dynamic behavior of the grid itself is becoming more and more complex, and the possibility of complex faults such as rejection still exists. Accurate and rapid realization of power network fault diagnosis becomes a more urgent practical requirement. This paper aims to diagnose complex fault of power grid, aiming at the lack of learning ability of the existing expert system. Support vector machine (SVM) model is used to diagnose complex faults based on historical fault experience. The main work is as follows: the inherent characteristics of statistical learning theory are studied, and the principle of VC and structural risk minimization is deeply understood. The mathematical principle of support vector machine (SVM) model is expounded. The algorithms of linear and nonlinear SVM models are programmed in C language on Visual studio 2010 platform. This paper lays a theoretical foundation for the application of support vector machine in power network fault diagnosis. According to the requirements and characteristics of power network fault diagnosis, a method based on support vector machine is proposed to diagnose complex faults. Acquiring the "implicit" diagnosis knowledge in these complex fault cases, and using these empirical rules to diagnose existing faults, In order to make the established support vector machine model universal, it is based on the protection action and breaker tripping information collected by the data acquisition and monitoring control system (SCADAA) system. For three kinds of components, such as busbar, line and transformer, the input characteristic quantity of support vector machine model is set up separately. The genetic algorithm is used to optimize the parameters of the penalty factor and the radial basis function. On the basis of the fault diagnosis system developed by the research group, the complex fault diagnosis module using SVM algorithm is added. The function design of the five components of the module is discussed one by one to improve the efficiency of the fault diagnosis system for complex faults. Finally, the practicability and effectiveness of the module are verified by three typical complex fault cases.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM711

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