【摘要】：目前我國(guó)大電網(wǎng)采用的是由預(yù)防控制、緊急控制和恢復(fù)控制組成的三層安全防御體系。預(yù)防控制策略的搜索和決策必須建立在暫態(tài)穩(wěn)定評(píng)估的基礎(chǔ)之上。目前對(duì)于暫態(tài)穩(wěn)定的評(píng)估只能通過(guò)枚舉事故，并針對(duì)每個(gè)事故逐個(gè)求解微分方程組，觀察輸出變量的變化關(guān)系來(lái)進(jìn)行判斷。對(duì)于運(yùn)行方式變化與穩(wěn)定水平變化之間的關(guān)聯(lián)關(guān)系尚未建立起任何規(guī)則或模型。這就導(dǎo)致目前的預(yù)防控制多數(shù)依靠運(yùn)行人員的經(jīng)驗(yàn)，參考大量預(yù)想工況的離線穩(wěn)定仿真結(jié)果，以試探的方式調(diào)整運(yùn)行方式安排。隨著新能源發(fā)電的加入，系統(tǒng)結(jié)構(gòu)和運(yùn)行工況日益復(fù)雜，大電網(wǎng)的不可預(yù)測(cè)性不斷增強(qiáng)，單純依靠人的經(jīng)驗(yàn)進(jìn)行判斷和決策已經(jīng)難以駕馭大電網(wǎng)的安全運(yùn)行。 調(diào)度中心每天針對(duì)運(yùn)行方式的變化不斷進(jìn)行著在線和離線穩(wěn)定評(píng)估。這些評(píng)估結(jié)果實(shí)際上已經(jīng)為研究運(yùn)行方式與穩(wěn)定水平的關(guān)聯(lián)關(guān)系提供了大量的樣本。廣域相位量測(cè)信息的加入也使得電網(wǎng)狀態(tài)的可觀測(cè)性不斷增強(qiáng)。應(yīng)用數(shù)據(jù)挖掘和模式識(shí)別技術(shù)，結(jié)合穩(wěn)定機(jī)理分析，研究和建立電網(wǎng)拓?fù)浜瓦\(yùn)行方式與電網(wǎng)失穩(wěn)模式和穩(wěn)定水平的關(guān)聯(lián)關(guān)系，將為大電網(wǎng)的智能預(yù)防控制決策提供有效的支撐，也是電網(wǎng)調(diào)度智能化和電網(wǎng)的大數(shù)據(jù)挖據(jù)的重要應(yīng)用。 主導(dǎo)失穩(wěn)機(jī)群的運(yùn)動(dòng)是決定暫態(tài)穩(wěn)定水平的主要因素，本文提出了一種主導(dǎo)失穩(wěn)機(jī)群辨識(shí)算法，通過(guò)電網(wǎng)運(yùn)行方式信息和電網(wǎng)拓?fù)洌Y(jié)合故障時(shí)刻的短路電壓，實(shí)現(xiàn)對(duì)主導(dǎo)失穩(wěn)機(jī)群的辨識(shí)。在此基礎(chǔ)上，構(gòu)建了基于拓?fù)浜瓦\(yùn)行方式信息的在線暫態(tài)穩(wěn)定評(píng)估模型和算法，，通過(guò)一系列穩(wěn)態(tài)電氣特征量可以有效預(yù)測(cè)當(dāng)前運(yùn)行點(diǎn)的暫態(tài)穩(wěn)定水平。所提算法的有效性在IEEE10機(jī)39節(jié)點(diǎn)系統(tǒng)和南方電網(wǎng)系統(tǒng)上得到了檢驗(yàn)。
[Abstract]:At present, a three-layer security defense system is adopted in China's large power grid, which consists of preventive control, emergency control and restoration control. The search and decision of preventive control strategy must be based on transient stability assessment. At present, the evaluation of transient stability can only be judged by enumerating the accidents, solving the differential equations one by one for each accident, and observing the variation of the output variables. No rules or models have been established for the relation between the change of operation mode and the change of stable level. This leads to the majority of current prevention and control rely on the experience of the operators, referring to a large number of expected conditions of off-line stability simulation results, and adjust the operation mode arrangements in a trial manner. With the addition of new energy generation, the system structure and operation conditions are becoming more and more complex, and the unpredictability of large power grid is increasing. It is difficult to control the safe operation of large power grid simply by relying on human experience to judge and make decisions. The dispatching center continuously evaluates the stability of operation mode online and offline every day. These results have actually provided a large number of samples for the study of the relationship between operation patterns and stable levels. With the addition of wide area phase measurement information, the observability of power grid state is enhanced. Applying data mining and pattern recognition technology, combining with stability mechanism analysis, the paper studies and establishes the relationship between power network topology and operation mode and power network instability mode and stability level, which will provide an effective support for intelligent preventive control decision of large power grid. It is also an important application of intelligent dispatching and big data of power grid. The movement of dominant unstable cluster is the main factor to determine the level of transient stability. In this paper, a dominant unstable cluster identification algorithm is proposed, which combines the short-circuit voltage at fault time through the operation mode information and topology of power network. The identification of dominant unstable cluster is realized. On this basis, an on-line transient stability evaluation model and algorithm based on topology and operation mode information are constructed. A series of steady state electrical characteristics can effectively predict the transient stability level of the current operating point. The validity of the proposed algorithm is tested on IEEE10 39 bus system and Southern Power Grid system.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM712

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙明,汪秉宏,蔣品群,周濤;復(fù)雜網(wǎng)絡(luò)上動(dòng)力系統(tǒng)同步的研究進(jìn)展[J];物理學(xué)進(jìn)展;2005年03期

2 鄭勁,王明俊,施永鐸;電力系統(tǒng)在線穩(wěn)定分析的模糊模式識(shí)別法研究[J];中國(guó)電機(jī)工程學(xué)報(bào);1990年03期

3 顧雪平,張文朝;基于Tabu搜索技術(shù)的暫態(tài)穩(wěn)定分類神經(jīng)網(wǎng)絡(luò)的輸入特征選擇[J];中國(guó)電機(jī)工程學(xué)報(bào);2002年07期

4 薛巍,舒繼武,嚴(yán)劍峰,王心豐,韓英鐸;基于集群機(jī)的大規(guī)模電力系統(tǒng)暫態(tài)過(guò)程并行仿真[J];中國(guó)電機(jī)工程學(xué)報(bào);2003年08期

5 馬騫,楊以涵,劉文穎,齊鄭,郭金智;多輸入特征融合的組合支持向量機(jī)電力系統(tǒng)暫態(tài)穩(wěn)定評(píng)估[J];中國(guó)電機(jī)工程學(xué)報(bào);2005年06期

6 王康;孫宏斌;張伯明;吳文傳;蔣維勇;;基于二維組合屬性決策樹的暫態(tài)穩(wěn)定評(píng)估[J];中國(guó)電機(jī)工程學(xué)報(bào);2009年S1期

7 葉圣永;王曉茹;劉志剛;錢清泉;;基于支持向量機(jī)的暫態(tài)穩(wěn)定評(píng)估雙階段特征選擇[J];中國(guó)電機(jī)工程學(xué)報(bào);2010年31期

8 葉圣永;王曉茹;劉志剛;錢清泉;;基于受擾嚴(yán)重機(jī)組特征及機(jī)器學(xué)習(xí)方法的電力系統(tǒng)暫態(tài)穩(wěn)定評(píng)估[J];中國(guó)電機(jī)工程學(xué)報(bào);2011年01期

9 江涵;江全元;;一種可變步長(zhǎng)的暫態(tài)穩(wěn)定自適應(yīng)修正牛頓組合算法[J];中國(guó)電機(jī)工程學(xué)報(bào);2011年34期

10 李揚(yáng);顧雪平;;基于改進(jìn)最大相關(guān)最小冗余判據(jù)的暫態(tài)穩(wěn)定評(píng)估特征選擇[J];中國(guó)電機(jī)工程學(xué)報(bào);2013年34期

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