本文選題：直覺模糊集 + 粒子群算法��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：大停電事故后的恢復(fù)控制問題是現(xiàn)代電力系統(tǒng)安全防御的一個(gè)重要課題。其中,機(jī)組啟動(dòng)是整個(gè)恢復(fù)控制的基礎(chǔ)。機(jī)組啟動(dòng)的關(guān)鍵是如何在恢復(fù)過程中根據(jù)系統(tǒng)的實(shí)際狀況進(jìn)行機(jī)組啟動(dòng)的優(yōu)化。因此,如何在恢復(fù)控制過程中合理選擇被啟動(dòng)機(jī)組是機(jī)組啟動(dòng)優(yōu)化策略研究的核心問題。由于求解機(jī)組啟動(dòng)策略本身屬于優(yōu)化問題,所有文章的主要研究都是建立在如何解決優(yōu)化問題的基礎(chǔ)上,側(cè)重點(diǎn)為粒子群算法研究。所以在這一前提下,文章提出應(yīng)用粒子群算法(PSO)及直覺模糊集理論(IFS)來解決發(fā)電機(jī)組啟動(dòng)最優(yōu)策略問題,并對(duì)發(fā)電機(jī)組的啟動(dòng)問題進(jìn)行了相關(guān)的分析與研究。論文主要工作與成果如下:第一,分析了算法的運(yùn)行機(jī)制及其相關(guān)改進(jìn),并對(duì)算法的收斂性進(jìn)行了相關(guān)的推導(dǎo),研究算法存在的缺陷和及其產(chǎn)生的原因。確定了對(duì)PSO算法改進(jìn)的可能性及改進(jìn)的方向。第二,對(duì)大停電事故后的恢復(fù)控制的核心機(jī)組啟動(dòng)問題進(jìn)行了深入分析,在已有的機(jī)組啟動(dòng)的優(yōu)化策略研究中,通常以運(yùn)行經(jīng)驗(yàn)為基礎(chǔ),在目標(biāo)函數(shù)和求解方法上都存在需要完善的地方。文章在分析與發(fā)電機(jī)組啟動(dòng)策略密切相關(guān)有關(guān)因素后,給出發(fā)電機(jī)組啟動(dòng)目標(biāo)函數(shù)和相關(guān)約束條件,建立了發(fā)電機(jī)組啟動(dòng)問題的0-1規(guī)劃模型,提出了分時(shí)間周期進(jìn)行啟動(dòng)的辦法來解決含時(shí)間約束條件的機(jī)組啟動(dòng)問題。第三,針對(duì)PSO算法運(yùn)行中種群多樣性難以測(cè)定的問題,分析了算法運(yùn)行過程中粒子的運(yùn)動(dòng)狀態(tài),結(jié)合IFS理論提出了直覺模糊種群熵(IFPE)作為種群多樣性的測(cè)度,并證明了IFPE比其他常用多樣性測(cè)度的優(yōu)越性。第四,充分研究了離散二進(jìn)制粒子群算法(BDPSO)的求解原理,通過實(shí)驗(yàn)手段在分析了可能影響的求解的因素及IFPE在求解過程中的相應(yīng)變化。在此基礎(chǔ)上,提出了兩種基于IFPE的離散粒子群算法(IFDPSO)及其衍生型。將這些算法和原始DPSO進(jìn)行了對(duì)比研究和實(shí)驗(yàn),發(fā)現(xiàn)IFDPSO系列算法更適合解決0-1背包問題。第五,在研究發(fā)電機(jī)組啟動(dòng)特點(diǎn)及可能影響發(fā)電機(jī)組啟動(dòng)因素的基礎(chǔ)上,編寫基于IFDPSO的機(jī)組啟動(dòng)策略決策支持系統(tǒng),并在此平臺(tái)上驗(yàn)證了發(fā)電機(jī)組最有啟動(dòng)策略,并與常規(guī)啟動(dòng)策略、不同初始功率、不同優(yōu)化時(shí)間段的啟動(dòng)策略對(duì)比分析,從而驗(yàn)證算法的有效性。
[Abstract]:The problem of recovery control after blackout is an important issue in modern power system security defense.Among them, the unit start-up is the basis of the whole recovery control.The key of unit start-up is how to optimize the unit start-up according to the actual condition of the system during the recovery process.Therefore, how to select the start-up unit reasonably in the recovery control process is the core problem in the research of the start-up optimization strategy of the unit.Because solving the start-up strategy of generating unit is an optimization problem, the main research of all papers is based on how to solve the optimization problem, with emphasis on particle swarm optimization algorithm.In this paper, PSO (particle swarm optimization) and IFS (intuitionistic fuzzy set theory) are proposed to solve the problem of optimal starting strategy of generator set, and the related analysis and research on the start-up problem of generator set are also given in this paper.The main work and achievements are as follows: first, the mechanism of the algorithm and its related improvements are analyzed, and the convergence of the algorithm is derived, the defects of the algorithm and its causes are studied.The possibility and direction of improving PSO algorithm are determined.Secondly, the start-up problem of the core unit after blackout is analyzed deeply. In the research of the optimization strategy of the existing unit start-up, it is usually based on the operation experience.Both objective function and solution method need to be improved.Based on the analysis of the factors closely related to the start-up strategy of the generator set, the objective function and the relevant constraints are given, and the 0-1 programming model of the start-up problem of the generator unit is established.In this paper, a method of time cycle start-up is proposed to solve the problem of unit start-up with time constraints.Thirdly, aiming at the problem that population diversity is difficult to measure in the operation of PSO algorithm, the motion state of particles is analyzed, and the intuitionistic fuzzy population entropy is proposed as the measure of population diversity combined with IFS theory.It is proved that IFPE is superior to other commonly used diversity measures.Fourthly, the principle of discrete binary Particle Swarm Optimization (Dbinary Particle Swarm Optimization) algorithm is fully studied. The possible factors affecting the solution and the corresponding variation of IFPE in the process of solution are analyzed by means of experiments.On this basis, two discrete particle swarm optimization algorithms based on IFPE and their derivation are proposed.By comparing these algorithms with the original DPSO, it is found that the IFDPSO series algorithms are more suitable to solve the 0-1 knapsack problem.Fifthly, on the basis of studying the characteristics of generator set startup and the factors that may influence the start of generator set, the decision support system of unit start-up strategy based on IFDPSO is developed, and the most effective startup strategy of generator set is verified on this platform.The algorithm is compared with conventional startup strategy, different initial power, and different optimization time period to verify the effectiveness of the algorithm.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM31
，

本文編號(hào)：1763252