本文選題：傳輸線斷路 + 相量測量單元��； 參考：《復旦大學》2014年碩士論文

【摘要】：近年來,國內外電力系統(tǒng)發(fā)生了多次由連鎖故障導致的大規(guī)模停電事故。這些由微小擾動引發(fā)的電力系統(tǒng)連鎖故障會導致電網大而積崩潰的災難性后果。此類事故的頻繁發(fā)生,引發(fā)了許多關于級聯(lián)故障的研究。預防級聯(lián)故障事故的關鍵之一就在于快速準確地定位初始故障的發(fā)生,包括單傳輸線故障和多傳輸線故障。因此,在大規(guī)模智能電網中,迅速并準確地定位電力線斷路故障非常重要。由于相量測量單元(Phasor Measurement Unit, PMU)的廣泛使用,使得通過PMU來解決傳輸線斷路故障定位問題成為可能。直接利用PMU檢測傳輸斷線斷路故障成為一個新興的研究方向。本論文將斷路定位問題轉化為圖上特定邊的尋找問題開展研究,基于斷路前與斷路后兩次由PMU上測量到的實時電壓相角信息,通過構建優(yōu)化模型來完成電網中的斷路故障定位。首先,從直流潮流計算原理出發(fā),借助于圖理論,把電網等效于一個圖模型。在此纂礎上以簡化的直流潮流計算方程為基礎,通過電網拓撲的加權拉普拉斯矩陣,進行系統(tǒng)建模。同時,結合傳輸線斷路故障的稀疏性本質,從稀疏信號處理的角度研究傳統(tǒng)的傳輸線斷路故障定位問題,提出了一種利用全局PMU相角信息檢測傳輸線斷路故障的算法。其次,針對PMU的成本考慮,目前PMU在電網中還僅限于局部樞紐節(jié)點及關鍵輸電斷面進行配置。本論文提出只利用PMU的局部觀測來定位斷路故障的算法,即根據PMU的布置與否,將電網中節(jié)點分為可量測的內部系統(tǒng)和不可量測的外部系統(tǒng),僅通過可量測的內部系統(tǒng)節(jié)點的電壓相角來估計全網(包括外部系統(tǒng))斷路的傳輸線斷路故障。通過將加權拉普拉斯矩陣進行分塊,得到一個僅使用部分觀測進行全局線路檢測的方程,結合壓縮感知的正交匹配追蹤算法,進而實現(xiàn)對外部故障線路的實時準確定位。IEEE 118節(jié)點模型的仿真結果表明該算法在未提高計算復雜度的前提下,僅使用部分觀測的數據就達到了較為滿意的估計準確率。最后,考慮到多條斷路情況下直接求解引入的高復雜度及低準確率問題,本論文給出了基于隨機采樣的傳輸線斷路故障定位模型,避免了采用貪婪算法直接求解帶來的誤差累積。算法從最大化似然概率角度引入概率模型,假設每一條傳輸線都服從伯努利的先驗分布,進而利用隨機樣本對此概率進行有效性迭代。IEEE 118節(jié)點模型的仿真結果表明所提出的方法在不需要多次迭代的前提下,對于多斷路情況定位性能有顯著提升。
[Abstract]:In recent years, power systems at home and abroad have occurred a number of cascading failures caused by large-scale power outages. These cascading faults of power system caused by small disturbances can lead to the catastrophic result of power grid collapse. The frequent occurrence of such accidents has led to a lot of research on cascading faults. One of the keys to prevent cascading faults is to locate the initial faults quickly and accurately, including single transmission line faults and multiple transmission line failures. Therefore, it is very important to locate power line fault quickly and accurately in large scale smart grid. Due to the wide use of Phasor Measurement Unit (PMU), it is possible to solve the problem of fault location by PMU. It is a new research direction to use PMU directly to detect transmission-break fault. In this paper, the problem of location of open circuit is transformed into the problem of finding specific edges on the graph. Based on the information of the phase angle of real time voltage measured by PMU before and after the break, the optimal model is constructed to locate the fault in the power network. Firstly, based on the principle of DC power flow calculation, the grid is equivalent to a graph model by means of graph theory. Based on the simplified DC power flow calculation equation, the system is modeled by the weighted Laplace matrix of power network topology. At the same time, according to the sparse nature of transmission line fault, the traditional problem of transmission line fault location is studied from the point of view of sparse signal processing, and an algorithm is proposed to detect transmission line fault by using global PMU phase angle information. Secondly, considering the cost of PMU, PMU is only limited to local hub nodes and key transmission sections. In this paper, only local observation of PMU is used to locate open circuit fault. According to the layout of PMU, nodes in power network are divided into measurable internal system and unmeasurable external system. The transmission-line fault of the whole network (including external system) is estimated only by measuring the voltage phase angle of the internal system node. By dividing the weighted Laplace matrix into blocks, an equation for global line detection using only partial observations is obtained, combined with a compressed perceptual orthogonal matching tracking algorithm. The simulation results of the IEEE118-bus model for external fault lines show that the proposed algorithm achieves satisfactory estimation accuracy by using only some of the observed data without increasing the computational complexity. Finally, considering the problem of high complexity and low accuracy caused by direct solution in the case of multiple open circuits, this paper presents a transmission line fault location model based on random sampling. The error accumulation caused by the greedy algorithm is avoided. The algorithm introduces a probabilistic model from the point of view of maximum likelihood probability, assuming that every transmission line follows the prior distribution of Bernoulli. The simulation results of the IEEE118-bus model using random samples show that the proposed method can significantly improve the localization performance of multi-break situations without multiple iterations.
【學位授予單位】：復旦大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM75

【參考文獻】

相關博士學位論文 前1條

1 陳曉剛;電網廣域安全監(jiān)測系統(tǒng)若干關鍵技術問題研究[D];浙江大學;2008年

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本文編號：2114107