本文選題：配電網(wǎng)網(wǎng)架優(yōu)化 + 蟻群算法　； 參考：《華北電力大學(xué)》2014年碩士論文

【摘要】：配電網(wǎng)網(wǎng)架優(yōu)化是配電網(wǎng)規(guī)劃中的重要組成部分，是保證電網(wǎng)穩(wěn)定運(yùn)行、電網(wǎng)安全可靠性的重要前提。因此對配電網(wǎng)網(wǎng)架進(jìn)行科學(xué)合理的優(yōu)化對配電網(wǎng)規(guī)劃工作具有重要的指導(dǎo)意義。 本文首先對配電網(wǎng)網(wǎng)架優(yōu)化問題進(jìn)行詳細(xì)描述，并選取基于費(fèi)用最小的模型作為優(yōu)化問題的目標(biāo)函數(shù)；其次，對蟻群優(yōu)化算法與遺傳優(yōu)化算法分別做出改進(jìn)，并分別應(yīng)用于配電網(wǎng)網(wǎng)架優(yōu)化工作中去驗(yàn)證算法的可行性和有效性；最后，將兩種改進(jìn)的算法相互結(jié)合，并提出基于融合算法的配電網(wǎng)網(wǎng)架優(yōu)化模型，將其應(yīng)用于配電網(wǎng)網(wǎng)架優(yōu)化問題中去，驗(yàn)證算法的有效性,為呼和浩特地區(qū)的配電網(wǎng)網(wǎng)架優(yōu)化工作進(jìn)行科學(xué)的規(guī)劃與指導(dǎo)，為國內(nèi)類似配電網(wǎng)網(wǎng)架優(yōu)化項(xiàng)目提供合理的參考依據(jù)。本文的主要研究內(nèi)容與成果如下： （1）對城市配電網(wǎng)網(wǎng)架優(yōu)化問題進(jìn)行深入研究，針對配電網(wǎng)規(guī)劃特點(diǎn)，從投資費(fèi)用、折舊維護(hù)費(fèi)用和線路損耗費(fèi)用三個(gè)方面建立配電網(wǎng)網(wǎng)架優(yōu)化的目標(biāo)函數(shù)，并設(shè)置相應(yīng)的約束條件。 （2）對蟻群算法進(jìn)行簡單描述，根據(jù)其特點(diǎn)對蟻群算法做出改進(jìn)。在轉(zhuǎn)移概率的改進(jìn)中，本文實(shí)現(xiàn)了參數(shù)、與最大迭代次數(shù)N max的聯(lián)動性；在信息素?fù)]發(fā)因子的改進(jìn)中，提出一種基于自適應(yīng)的揮發(fā)因子；進(jìn)而明確改進(jìn)蟻群算法的配電網(wǎng)網(wǎng)架優(yōu)化步驟后，將其應(yīng)用于呼和浩特地區(qū)的配電網(wǎng)網(wǎng)架優(yōu)化中，驗(yàn)證該方法的可行性和有效性。 （3）在對遺傳算法的改進(jìn)中，首先對基本遺傳算法作基本介紹；其次，根據(jù)遺傳算法的特點(diǎn)，從編碼方式、交叉概率、變異概率三個(gè)方面進(jìn)行改進(jìn)說明；最后將改進(jìn)后的遺傳算法應(yīng)用于呼和浩特地區(qū)的配電網(wǎng)網(wǎng)架優(yōu)化中去，驗(yàn)證方法的有效性和可行性，并說明該方法對配電網(wǎng)網(wǎng)架優(yōu)化工作的指導(dǎo)意義和實(shí)際應(yīng)用價(jià)值。 （4）單個(gè)算法由于其本身缺陷所致，在解決配電網(wǎng)網(wǎng)架優(yōu)化問題時(shí)容易出現(xiàn)一系列問題，，如遺傳算法出現(xiàn)冗余迭代，蟻群算法初始解匱乏等；為能夠得到更加精確地網(wǎng)架優(yōu)化解，本文將改進(jìn)后的蟻群算法和改進(jìn)后的遺傳算法相互融合，讓二者優(yōu)勢互補(bǔ)，避免各自缺陷。最后，將該方法應(yīng)用于呼市地區(qū)配電網(wǎng)網(wǎng)架優(yōu)化問題中去，驗(yàn)證該方法在配電網(wǎng)網(wǎng)架優(yōu)化中的優(yōu)越性和實(shí)用性。 （5）將融合算法、改進(jìn)蟻群算法和改進(jìn)遺傳算法分別在配電網(wǎng)網(wǎng)架優(yōu)化應(yīng)用中的結(jié)果進(jìn)行對比；結(jié)論表明：融合算法與單個(gè)算法的優(yōu)化性能相比，融合算法的優(yōu)化效果更好，優(yōu)化精度更高；該方法為今后城市配電網(wǎng)網(wǎng)架優(yōu)化工作提供新的思路。
[Abstract]:The optimization of distribution network frame is an important part of distribution network planning. It is an important prerequisite for ensuring the stable operation of the power grid and the safety and reliability of the power grid. Therefore, the scientific and rational optimization of the distribution network frame has an important guiding significance for the distribution network planning.
In this paper, the optimization problem of the distribution network is described in detail, and the model based on the minimum cost is selected as the objective function of the optimization problem. Secondly, the improvement of the ant colony optimization algorithm and the genetic optimization algorithm is made respectively, and the feasibility and effectiveness of the algorithm are verified respectively in the distribution network grid optimization work. Finally, the feasibility and effectiveness of the algorithm are verified. The two improved algorithms are combined, and the optimization model of distribution network grid based on the fusion algorithm is put forward. It is applied to the optimization problem of distribution network frame to verify the effectiveness of the algorithm. It provides scientific planning and guidance for the optimization work of distribution network frame in Hohhot area, and provides the network frame optimization project similar to the distribution network in China. The main contents and achievements of this paper are as follows:
(1) in-depth study on the optimization of urban distribution network frame. Aiming at the characteristics of distribution network planning, the objective function of the optimization of distribution network frame is set up from three aspects of investment cost, depreciation maintenance cost and line loss cost, and the corresponding constraints are set up.
(2) a simple description of the ant colony algorithm is made to improve the ant colony algorithm based on its characteristics. In the improvement of the transfer probability, the linkage of the parameters and the maximum iteration number of N Max is realized. In the improvement of the pheromone volatilization factor, a self-adaptive volatile factor is proposed, and then the distribution network network of the ant colony algorithm is improved. After optimization steps, it is applied to the optimization of distribution network in Hohhot area to verify the feasibility and effectiveness of the method.
(3) in the improvement of genetic algorithm, the basic genetic algorithm is introduced first; secondly, according to the characteristics of the genetic algorithm, it is improved from the encoding mode, cross probability and mutation probability in three aspects. Finally, the improved genetic algorithm is applied to the distribution network optimization of distribution network in Hohhot area. The effectiveness and feasibility of the method are illustrated, and the guiding significance and practical application value of the method to the optimization of distribution network frame are explained.
(4) a single algorithm, due to its own defects, is prone to a series of problems in solving the problem of grid grid optimization in distribution network, such as the redundant iteration of the genetic algorithm, the shortage of the initial solution of ant colony algorithm, and so on. In order to get more accurate solution of the network frame, the improved ant colony algorithm and the improved genetic algorithm are fused together. The advantages of the two are complementary to avoid each defect. Finally, this method is applied to the optimization problem of the distribution network frame in huhhhun area, and the superiority and practicability of the method in the distribution network grid optimization are verified.
(5) the results of the fusion algorithm, the improved ant colony algorithm and the improved genetic algorithm in the distribution network truss optimization application are compared. The conclusion shows that the fusion algorithm is better than the optimization performance of the single algorithm, and the optimization effect is better and the optimization precision is higher. This method provides the optimization work of the urban distribution network frame in the future. A new idea.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM727.2;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 曹蘊(yùn);李科陽;姚煜;;基于改進(jìn)混沌遺傳算法的配電網(wǎng)架優(yōu)化[J];電力科學(xué)與工程;2009年05期

2 王春麗;趙書強(qiáng);李勇;;基于改進(jìn)量子遺傳算法的輸電網(wǎng)規(guī)劃方法[J];電力科學(xué)與工程;2010年11期

3 張永伍,余貽鑫,嚴(yán)雪飛,羅鳳章;基于區(qū)間算法和范例學(xué)習(xí)的配電網(wǎng)網(wǎng)架規(guī)劃[J];電力系統(tǒng)自動化;2005年17期

4 章文俊;程浩忠;程正敏;姚茵;谷慶利;;配電網(wǎng)優(yōu)化規(guī)劃研究綜述[J];電力系統(tǒng)及其自動化學(xué)報(bào);2008年05期

5 李可;馬孝義;甘學(xué)濤;符少華;;配電網(wǎng)架結(jié)構(gòu)和饋線截面同步優(yōu)化模型與算法[J];電力系統(tǒng)及其自動化學(xué)報(bào);2010年01期

6 方興,郭志忠;配電網(wǎng)規(guī)劃研究述評[J];電力自動化設(shè)備;2003年05期

7 周愈鵬;覃拓;危秋珍;藍(lán)慧;;基于免疫優(yōu)勢算法的配電網(wǎng)規(guī)劃[J];電氣開關(guān);2011年02期

8 袁曉輝,王乘,張勇傳,袁艷斌;粒子群優(yōu)化算法在電力系統(tǒng)中的應(yīng)用[J];電網(wǎng)技術(shù);2004年19期

9 符楊;徐自力;曹家麟;;混合粒子群優(yōu)化算法在電網(wǎng)規(guī)劃中的應(yīng)用[J];電網(wǎng)技術(shù);2008年15期

10 符楊;孟令合;胡榮;曹家麟;;改進(jìn)多目標(biāo)蟻群算法在電網(wǎng)規(guī)劃中的應(yīng)用[J];電網(wǎng)技術(shù);2009年18期

本文編號：1958566