【摘要】：隨著電力網(wǎng)結(jié)構(gòu)日趨稠密復(fù)雜以及電網(wǎng)智能化程度的日益加深,電網(wǎng)調(diào)度系統(tǒng)要求更高效且更易于操作的電網(wǎng)無功優(yōu)化計(jì)算方法。因此,如何進(jìn)一步增強(qiáng)電網(wǎng)的調(diào)控能力,提高無功優(yōu)化的效率,成為電力系統(tǒng)研究者關(guān)注的問題。電壓無功優(yōu)化控制作為電力網(wǎng)調(diào)度的基本任務(wù),是通過調(diào)度系統(tǒng)自動(dòng)化地采集場(chǎng)站節(jié)點(diǎn)信息來進(jìn)行實(shí)時(shí)數(shù)據(jù)地綜合分析與控制,最終達(dá)到使電壓的合格指數(shù)更高,電容器投切更加科學(xué)合理以及電網(wǎng)網(wǎng)損更低的目標(biāo)�，F(xiàn)有的無功優(yōu)化模型有:單一目標(biāo)模型、多重目標(biāo)動(dòng)態(tài)優(yōu)化模型等;優(yōu)化算法包括線性規(guī)劃法、簡(jiǎn)化梯度法等傳統(tǒng)算法以及遺傳算法、模擬退火算法等智能算法。在實(shí)際應(yīng)用中,規(guī)劃類算法雖然有嚴(yán)格的理論支持,但較難處理具有大量離散變量和約束條件的問題,并且復(fù)雜無功優(yōu)化模型的實(shí)時(shí)控制達(dá)不到要求。而對(duì)于單一智能算法在優(yōu)化計(jì)算時(shí)來說,算法易早熟,易陷入整個(gè)解空間內(nèi)局部最優(yōu)的陷阱,并且存在計(jì)算時(shí)間長,后期尋優(yōu)速度慢等缺陷。因此,針對(duì)無功優(yōu)化在實(shí)際應(yīng)用中遇到的難點(diǎn),本文在綜合分析了屬于無功優(yōu)化范疇內(nèi)的科研狀況之后,研究設(shè)計(jì)出變慣性權(quán)值和加速因子的粒子群算法。在改進(jìn)算法的更新公式中慣性權(quán)重w和加速因子c2會(huì)根據(jù)粒子與最優(yōu)粒子之間的距離來更改w和c2的值,比如當(dāng)粒子靠近群體最優(yōu)粒子時(shí),增大慣性權(quán)重w,減小加速因子c2。同時(shí),將算法與遺傳算法、模擬退火算法和蟻群算法進(jìn)行比較,試驗(yàn)對(duì)比發(fā)現(xiàn)算法在降低電網(wǎng)損耗方面效果較為明顯,且算法優(yōu)化時(shí)間較短,在全局尋優(yōu)的把握較大。最后將改進(jìn)后的算法應(yīng)用到基于struts2框架和oracle數(shù)據(jù)庫開發(fā)出來的四川自貢地區(qū)電網(wǎng)無功優(yōu)化可視化軟件系統(tǒng)中。
[Abstract]:With the increasing density and complexity of power grid structure and the deepening of power network intelligence, power dispatching system requires more efficient and easy to operate reactive power optimization calculation method. Therefore, how to further enhance the power grid control ability and improve the efficiency of reactive power optimization, become the focus of power system researchers. As the basic task of power grid dispatching, the voltage and reactive power optimal control is to collect the node information of the station automatically to analyze and control the real-time data, and finally to make the voltage quality index higher. The target of capacitor switching is more scientific and reasonable, and the power loss is lower. The existing reactive power optimization models include: single objective model, multi-objective dynamic optimization model, optimization algorithm including linear programming, simplified gradient method and other traditional algorithms, genetic algorithm, simulated annealing algorithm and other intelligent algorithms. In practical application, although the programming algorithm has strict theoretical support, it is difficult to deal with the problems with a large number of discrete variables and constraints, and the real-time control of the complex reactive power optimization model can not meet the requirements. For the single intelligent algorithm, it is easy to precocious and fall into the trap of local optimum in the whole solution space, and it has some defects such as long calculation time and slow searching speed. Therefore, aiming at the difficulties encountered in the practical application of reactive power optimization, this paper studies and designs a particle swarm optimization algorithm with variable inertia weight and acceleration factor after synthetically analyzing the scientific research situation in the category of reactive power optimization. In the updating formula of the improved algorithm, the inertial weight w and the acceleration factor c2 will be changed according to the distance between the particle and the optimal particle. For example, when the particle is close to the swarm optimal particle, the inertia weight w will be increased and the acceleration factor c2 will be reduced. At the same time, comparing the algorithm with genetic algorithm, simulated annealing algorithm and ant colony algorithm, it is found that the algorithm is more effective in reducing power loss, and the optimization time is shorter and the global optimization is better. Finally, the improved algorithm is applied to the visual software system of reactive power optimization in Zigong area based on struts2 framework and oracle database.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM714.3

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