基于LS-SVM和IMF能量矩的配電網(wǎng)故障區(qū)段定位方法
發(fā)布時間：2018-11-12 14:12
【摘要】：目前我國正處于建設(shè)智能電網(wǎng)的關(guān)鍵階段,而配電網(wǎng)保護智能化正是其重要的研究方向。由于配電網(wǎng)接地故障時故障電流小,電流信號容易受到外界環(huán)境的干擾,因此故障特征量變得難以檢測,而且接地故障時往往伴隨著非故障相電壓的升高,使得饋線絕緣薄弱處易于擊穿,故障更有可能發(fā)展為多相、多點短路,擴大事故范圍。因此有必要找到一種能在各種復雜信號中識別出故障特征量,并快速定位故障區(qū)段的方法。論文首先討論了現(xiàn)有的小電流故障定位的理論,從主動式和被動式兩方面介紹了目前不同定位方法的優(yōu)點和缺點,并在被動式保護方法中著重論述了經(jīng)驗模態(tài)分解(EMD)和支持向量機在電力系統(tǒng)上的應用;提出采用集合經(jīng)驗模態(tài)分解(EEMD)算法對信號進行處理,它除了繼承小波分解等算法的優(yōu)點外,還解決了EMD信號分解時容易產(chǎn)生的模態(tài)混疊現(xiàn)象,并通過三組對比算例,就不同算法在信號提取方面的性能進行了對比;簡要介紹了最小二乘支持向量機(LS-SVM)在特征信號分類方面的特點,并與BP神經(jīng)網(wǎng)絡(luò)和支持向量機就分類的快速性和準確性上進行了比較;提出了基于本征模態(tài)函數(shù)(IMF)能量矩和LS-SVM的故障區(qū)段定位方法,利用EEMD分解電流信號得到IMF,進而將IMF與時間積分獲得能量矩,最后將能量矩作為特征向量輸入到LS-SVM,訓練得到故障區(qū)段定位模型并用于未知故障的定位;然后基于數(shù)字化采樣技術(shù),將新的故障區(qū)段定位方法應用到智能電網(wǎng)中,定位采用低功率互感器測量電流,并結(jié)合IEEE1588時鐘同步原理與電力云,使得故障區(qū)段定位精度有了進一步提高;最后采用MATLAB GUI平臺編寫了圖形用戶界面,實現(xiàn)了與用戶間的交互式操作。將不同的信號提取與分類算法在信號處理性能方面分別進行比較,通過對比算例表明EEMD更能反映原信號各自分量的特點,而LS-SVM的分類準確率更高,速度更快。然后結(jié)合這兩種方法提出了新的故障區(qū)段定位理論,通過10kV的配電網(wǎng)故障仿真,表明基于IMF能量矩的LS-SVM故障定位新方法能有效定位不同區(qū)段配電網(wǎng)接地故障,具備在不同接地電阻下的區(qū)段定位的能力。
[Abstract]:At present, China is in the key stage of smart grid construction, and intelligent distribution network protection is an important research direction. Because the fault current is small and the current signal is easily disturbed by the external environment, the fault characteristic change is difficult to detect, and the non-fault phase voltage increases when the grounding fault occurs. The fault is more likely to develop into multi-phase, multi-point short circuit and extend the range of accidents. Therefore, it is necessary to find a method that can identify the fault characteristic quantity in various complex signals and locate the fault section quickly. This paper first discusses the existing theory of small current fault location, and introduces the advantages and disadvantages of different localization methods from the active and passive aspects. In the passive protection method, the application of empirical mode decomposition (EMD) and support vector machine (SVM) in power system is discussed. A set empirical mode decomposition (EEMD) algorithm is proposed to process signals, which not only inherits the advantages of wavelet decomposition, but also resolves the phenomenon of modal aliasing which is easy to occur in the decomposition of EMD signals. The performance of different algorithms in signal extraction is compared. This paper briefly introduces the features of least squares support vector machine (LS-SVM) in feature signal classification, and compares with BP neural network and support vector machine on the rapidity and accuracy of classification. A fault zone location method based on intrinsic mode function (IMF) energy moment and LS-SVM is proposed. The IMF, is obtained by decomposing the current signal by EEMD, and then the energy moment is obtained by integrating IMF with time. Finally, the energy moment is input into the LS-SVM, as the eigenvector to obtain the location model of the fault section and be used to locate the unknown fault. Then, based on the digital sampling technology, the new fault section location method is applied to smart grid. The low power transformer is used to measure the current, and combined with the principle of IEEE1588 clock synchronization and the power cloud. The location accuracy of the fault section has been further improved; Finally, the graphical user interface is written on MATLAB GUI platform, which realizes the interactive operation with users. The different signal extraction and classification algorithms are compared in signal processing performance. The results show that EEMD can better reflect the characteristics of each component of the original signal, while the classification accuracy of LS-SVM is higher and the speed is faster. Then, combining these two methods, a new fault section location theory is proposed. Through the fault simulation of 10kV distribution network, it is shown that the new method of LS-SVM fault location based on IMF energy moment can effectively locate the grounding fault of distribution network in different sections. Ability to position sections at different ground resistances.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM862
[Abstract]:At present, China is in the key stage of smart grid construction, and intelligent distribution network protection is an important research direction. Because the fault current is small and the current signal is easily disturbed by the external environment, the fault characteristic change is difficult to detect, and the non-fault phase voltage increases when the grounding fault occurs. The fault is more likely to develop into multi-phase, multi-point short circuit and extend the range of accidents. Therefore, it is necessary to find a method that can identify the fault characteristic quantity in various complex signals and locate the fault section quickly. This paper first discusses the existing theory of small current fault location, and introduces the advantages and disadvantages of different localization methods from the active and passive aspects. In the passive protection method, the application of empirical mode decomposition (EMD) and support vector machine (SVM) in power system is discussed. A set empirical mode decomposition (EEMD) algorithm is proposed to process signals, which not only inherits the advantages of wavelet decomposition, but also resolves the phenomenon of modal aliasing which is easy to occur in the decomposition of EMD signals. The performance of different algorithms in signal extraction is compared. This paper briefly introduces the features of least squares support vector machine (LS-SVM) in feature signal classification, and compares with BP neural network and support vector machine on the rapidity and accuracy of classification. A fault zone location method based on intrinsic mode function (IMF) energy moment and LS-SVM is proposed. The IMF, is obtained by decomposing the current signal by EEMD, and then the energy moment is obtained by integrating IMF with time. Finally, the energy moment is input into the LS-SVM, as the eigenvector to obtain the location model of the fault section and be used to locate the unknown fault. Then, based on the digital sampling technology, the new fault section location method is applied to smart grid. The low power transformer is used to measure the current, and combined with the principle of IEEE1588 clock synchronization and the power cloud. The location accuracy of the fault section has been further improved; Finally, the graphical user interface is written on MATLAB GUI platform, which realizes the interactive operation with users. The different signal extraction and classification algorithms are compared in signal processing performance. The results show that EEMD can better reflect the characteristics of each component of the original signal, while the classification accuracy of LS-SVM is higher and the speed is faster. Then, combining these two methods, a new fault section location theory is proposed. Through the fault simulation of 10kV distribution network, it is shown that the new method of LS-SVM fault location based on IMF energy moment can effectively locate the grounding fault of distribution network in different sections. Ability to position sections at different ground resistances.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM862
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