本文選題：故障定位 + 輸電線路�。� 參考：《西安理工大學(xué)》2017年碩士論文

【摘要】：隨著智能電網(wǎng)工程的提出,特高壓交流輸電網(wǎng)大力發(fā)展。與此同時(shí),電網(wǎng)中安裝的無(wú)功補(bǔ)償裝置、柔性交流輸電裝置(FACTS)以及各種清潔能源的并網(wǎng)使整個(gè)電網(wǎng)動(dòng)態(tài)化,造成故障定位變得更加復(fù)雜化,現(xiàn)有的故障定位方法也受到影響。輸電線路作為電力系統(tǒng)的命脈,同時(shí)又是電力系統(tǒng)故障的多發(fā)部位,研究快速準(zhǔn)確的輸電線路故障定位算法,對(duì)電網(wǎng)的穩(wěn)定高效運(yùn)行意義重大。在此背景下,本文針對(duì)兩種輸電線路(高壓輸電線路和串補(bǔ)輸電線路)進(jìn)行研究,針對(duì)不同的線路提出了相應(yīng)的故障定位算法,具體工作如下:首先,高壓輸電線路發(fā)生短路故障時(shí)會(huì)產(chǎn)生暫態(tài)行波,暫態(tài)行波中包含著豐富的故障信息。針對(duì)暫態(tài)行波的特點(diǎn)和行波波頭的精準(zhǔn)提取問(wèn)題,采用TT變換提取雙端輸電線路行波波頭。在此基礎(chǔ)上,將TT變換應(yīng)用到T型輸電線路中,推導(dǎo)出一種故障分支判別方法,根據(jù)故障判別方法先進(jìn)行故障分支判別之后進(jìn)行故障定位。其次,串補(bǔ)輸電線路因其能提高線路的輸電距離、改善系統(tǒng)的穩(wěn)定性等優(yōu)點(diǎn)而廣泛應(yīng)用,但由于串補(bǔ)電容主保護(hù)MOV是非線性的,原有故障定位算法對(duì)串補(bǔ)線路不再適合。為此,將小波神經(jīng)網(wǎng)絡(luò)應(yīng)用到串補(bǔ)輸電線路故障定位中,該方法首先對(duì)故障零序電流進(jìn)行離散小波變換,提取故障特征向量,然后將故障特征向量和對(duì)應(yīng)的故障距離目標(biāo)值送入BP神經(jīng)網(wǎng)絡(luò)的進(jìn)行訓(xùn)練,訓(xùn)練結(jié)果即為故障距離。仿真結(jié)果驗(yàn)證:在雙端及T型輸電線路的故障定位中,采用TT變換能準(zhǔn)確捕捉行波波頭,實(shí)現(xiàn)故障定位;在串補(bǔ)輸電線路故障定位中,采用小波神經(jīng)網(wǎng)絡(luò)不受串補(bǔ)主保護(hù)MOV的影響,樣本測(cè)試證明該方法能準(zhǔn)確實(shí)現(xiàn)故障定位。此外,這兩種算法均不受線路長(zhǎng)度、故障類型、過(guò)渡電阻的影響,具有很高的定位精度。
[Abstract]:With the development of smart grid project, UHV AC transmission network develops vigorously. At the same time, the installation of reactive power compensation device, flexible AC transmission device (facts) and the connection of various clean energy make the whole power network dynamic, which makes fault location more complicated, and the existing fault location methods are also affected. As the lifeblood of power system, transmission line is the fault location of power system at the same time. It is very important to study the fast and accurate fault location algorithm of transmission line for the stable and efficient operation of power network. In this context, this paper studies two transmission lines (high-voltage transmission line and series-compensated transmission line), and proposes the corresponding fault location algorithm for different transmission lines. The specific work is as follows: first of all, Transient traveling waves occur when short circuit faults occur in high voltage transmission lines, and the transient traveling waves contain abundant fault information. In view of the characteristics of transient traveling wave and accurate extraction of traveling wave head, TT transform is used to extract traveling wave head of dual terminal transmission line. On this basis, the TT transform is applied to T type transmission lines, and a fault branch discrimination method is derived, according to which fault branch identification is first performed and then fault location is carried out. Secondly series compensated transmission lines are widely used because of their advantages such as increasing transmission distance and improving the stability of the system. But because the main protection MOV of series compensation capacitor is nonlinear the original fault location algorithm is no longer suitable for series compensation lines. In this paper, wavelet neural network is applied to fault location of transmission line with series compensation. Firstly, the fault characteristic vector is extracted by discrete wavelet transform to zero sequence current of fault. Then the fault eigenvector and the corresponding target value of fault distance are sent to BP neural network for training. The training result is called fault distance. The simulation results show that the TT transform can accurately capture the traveling wave head and realize the fault location in the fault location of the double terminal and T transmission lines, and in the fault location of the series compensation transmission line, The wavelet neural network is not affected by the series compensation main protection MOV, and the sample test proves that this method can accurately realize the fault location. In addition, the two algorithms are independent of line length, fault type and transition resistance, so they have high positioning accuracy.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM755

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本文編號(hào)：1934816