本文選題：雙饋風(fēng)力發(fā)電場　切入點：電力系統(tǒng)無功優(yōu)化　出處：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：能源是一個國家發(fā)展的基本推動力,面對當(dāng)今社會化石能源日漸短缺,空氣質(zhì)量逐年下降,霧霾問題日益嚴(yán)重的現(xiàn)狀,各國都將發(fā)展重點放在了新能源上。并網(wǎng)發(fā)電的新能源主要包括太陽能、風(fēng)能、水能、核能、生物能源等。其中風(fēng)能因其存在范圍廣且可開發(fā)利用程度高,成為新能源發(fā)展的主要發(fā)力點。截止2010年年底,我國風(fēng)電累計裝機(jī)容量已經(jīng)躍居為世界第一,風(fēng)力發(fā)電展現(xiàn)出良好的發(fā)展前景與應(yīng)用前景。伴隨著風(fēng)力發(fā)電研究發(fā)展的不斷深入,風(fēng)力發(fā)電將為國民經(jīng)濟(jì)發(fā)展提供更多優(yōu)質(zhì)的電能。我國風(fēng)力發(fā)電場采用較多的是雙饋異步風(fēng)力發(fā)電機(jī),因此論文選取其作為研究對象,重點分析了雙饋異步風(fēng)力發(fā)電機(jī)的基本類型和出力特征,并用數(shù)學(xué)方法分析建立了雙饋異步風(fēng)力發(fā)電機(jī)的穩(wěn)態(tài)數(shù)學(xué)模型以及動態(tài)數(shù)學(xué)模型。在此基礎(chǔ)上,論文闡述了雙饋異步風(fēng)力發(fā)電機(jī)的有功功率,電場電壓以及其無功功率之間的數(shù)學(xué)關(guān)系,并建立了含雙饋異步風(fēng)電場的電力系統(tǒng)的潮流計算模型。其次,論文綜合了粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)以及和聲搜索算法(Harmony Search Algorithm,HSA)的優(yōu)點,即將和聲搜索算法能夠有效跳出局部最優(yōu)的特性與粒子群算法收斂具有方向的特性結(jié)合。將和聲庫中的元素視作粒子群中的粒子,每次迭代時,首先利用粒子群算法對和聲記憶庫中的記憶元素進(jìn)行一次尋優(yōu),然后將尋優(yōu)后的和聲記憶庫元素帶入改進(jìn)和聲搜索算法。論文選取了四個常用的多目標(biāo)收斂測試函數(shù)對所提算法的尋優(yōu)性能進(jìn)行了檢測,并與常見的智能算法進(jìn)行了收斂對比測試,測試結(jié)果說明了論文所改進(jìn)的算法具有較好的收斂精度以及收斂速度。最后,論文針對含雙饋型風(fēng)力發(fā)電機(jī)的風(fēng)電場接入電力系統(tǒng)時的無功補(bǔ)償問題,建立了有功網(wǎng)損最小以及節(jié)點電壓偏差最小的雙目標(biāo)無功優(yōu)化模型,采用上文改進(jìn)的粒子群優(yōu)化和聲搜索算法,對含風(fēng)電場的電力系統(tǒng)進(jìn)行無功優(yōu)化研究。將論文提出的改進(jìn)粒子群優(yōu)化和聲搜索算法應(yīng)用于含風(fēng)電場的IEEE30節(jié)點電力系統(tǒng)進(jìn)行無功優(yōu)化,并與粒子群算法以及非支配排序遺傳算法(NSGA-Ⅱ)算法的無功優(yōu)化結(jié)果進(jìn)行比較。實驗結(jié)果表明,論文所改進(jìn)的粒子群優(yōu)化和聲搜索算法具有較好的收斂精度以及較快的收斂速度,在使用該算法對電力系統(tǒng)進(jìn)行無功優(yōu)化之后,系統(tǒng)內(nèi)各節(jié)點電壓幅值得到了不同程度的改善,降低了有功網(wǎng)損值,提高了含風(fēng)電場電力系統(tǒng)的電能質(zhì)量以及電力系統(tǒng)對風(fēng)電的接納能力,有利于促進(jìn)風(fēng)力發(fā)電的進(jìn)一步發(fā)展。
[Abstract]:Energy is the basic driving force of the development of a country, in the face of the growing shortage of fossil energy in today's society, declining air quality, present situation of increasingly serious haze problems, all countries will focus on the development of new energy power generation. New energy including solar energy, wind energy, hydropower, nuclear energy, bio energy, wind energy. Because of the wide range and extent of development and utilization, has become the main force of the new energy development. By the end of 2010, China's total wind power installed capacity has been ranked as the world's first wind power, show the development prospects and good application prospects. With the development of wind power research deepening, wind power will to provide more high-quality electricity for the national economic development. China's wind power field is used more doubly fed asynchronous wind generator, so this paper chooses it as the research object, analyzes the The basic types of doubly fed asynchronous wind generator and output characteristics, and the establishment of the mathematical model of doubly fed asynchronous wind generator and dynamic mathematical model by mathematical analysis method. On this basis, this paper expounds the active power of DFIG, the mathematical relation between electric voltage and reactive power, and the establishment of the power system with doubly fed induction wind power flow calculation model. Secondly, the particle swarm optimization algorithm (Particle Swarm Optimization, PSO) and harmony search algorithm (Harmony Search Algorithm, HSA) advantages, will search characteristics and acoustic characteristics and particle swarm optimization algorithm can effectively jump out of local optimum with direction the combination of the elements in the library. And as the particles, each iteration, using particle swarm optimization algorithm for the harmony memory memory element In a search, the harmony memory elements will then be optimized into improved harmony search algorithm. This paper selects four common multi-objective function convergence test to test the performance of optimization algorithm, and intelligent algorithms and the common convergence comparison test, the test results show the improved algorithm has better convergence accuracy and convergence speed. Finally, reactive power compensation of wind farm power system according to the content of double fed wind generator is established, the minimum network loss and node voltage deviation of minimum double target reactive power optimization model, using the improved particle swarm optimization and above search algorithm of power system containing wind farms for reactive power optimization. The proposed improved particle swarm optimization and search algorithm is applied to wind power system IEEE30 node Reactive power optimization, and the particle swarm algorithm and non dominated sorting genetic algorithm (NSGA-) algorithm for reactive power optimization results were compared. The experimental results show that the improved particle swarm optimization and search algorithm has better convergence precision and fast convergence speed, after the use of the algorithm in power system without reactive power optimization, the system of the node voltage has been improved to some extent, reduce the power loss, improve the ability to accept the power system with wind electric power quality and power system of wind power, to promote the further development of wind power generation.

【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614;TM714.3

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