本文選題：分布式電源 + 極限學(xué)習(xí)機(jī)�。� 參考：《長沙理工大學(xué)》2014年碩士論文

【摘要】：隨著社會(huì)經(jīng)濟(jì)的發(fā)展,能源與環(huán)境問題的重要性日益凸顯,而分布式發(fā)電由于其具有低碳環(huán)保,投資小,發(fā)電方式靈活等優(yōu)點(diǎn)受到廣泛認(rèn)可和應(yīng)用。但是大量風(fēng)電、光伏發(fā)電等分布式電源不斷接入電網(wǎng)對電網(wǎng)的安全可靠、經(jīng)濟(jì)運(yùn)行帶來了更多挑戰(zhàn)。因此,如何合理高效地規(guī)劃分布式電源接入系統(tǒng)就變的尤為重要。文章闡述了分布式發(fā)電的發(fā)展及研究現(xiàn)狀,詳細(xì)介紹了幾種主要分布式電源的并網(wǎng)情況,并從網(wǎng)絡(luò)損耗、電能質(zhì)量、可靠性、潮流分布等方面對其并網(wǎng)后給電網(wǎng)造成的影響進(jìn)行了詳細(xì)分析。通過對現(xiàn)有關(guān)于分布式電源優(yōu)化配置問題求解方法分析表明,傳統(tǒng)算法普遍存在速度較慢、容易陷入局部最優(yōu)等問題,因此提出采用基于極限學(xué)習(xí)機(jī)改進(jìn)遺傳算法來求解此問題；該算法利用了一種新型的單隱層前饋神經(jīng)網(wǎng)絡(luò)算法一極限學(xué)習(xí)機(jī)(Extreme Learning Machine, ELM),來對基本遺傳算法進(jìn)行改進(jìn)；同時(shí)也利用其優(yōu)良的非線性映射能力來模擬前后兩代種群的進(jìn)化過程,并與傳統(tǒng)遺傳算法相結(jié)合；通過合理的參數(shù)設(shè)定,進(jìn)而達(dá)到提高算法的全局搜索能力與收斂速度目的。為了綜合兼顧環(huán)境效益和經(jīng)濟(jì)效益,建立了以投資運(yùn)行成本最小、網(wǎng)絡(luò)損耗費(fèi)用最小、環(huán)境效益最大的經(jīng)濟(jì)優(yōu)化配置模型；針對傳統(tǒng)分布式電源規(guī)劃中所缺少的對候選安裝節(jié)點(diǎn)選擇問題,提出了采用計(jì)算各節(jié)點(diǎn)視在二次精確矩值大小,并進(jìn)行排序,選擇最優(yōu)候選安裝節(jié)點(diǎn)的方法。通過對某地實(shí)際35節(jié)點(diǎn)系統(tǒng)進(jìn)行仿真,算例分析表明：文中所采用的極限學(xué)習(xí)機(jī)改進(jìn)遺傳算法在求解分布式電源優(yōu)化配置問題時(shí),計(jì)算精度、收斂速度和尋優(yōu)能力均優(yōu)于傳統(tǒng)遺傳算法,可以得到更合理可靠的配網(wǎng)優(yōu)化配置方案；同時(shí)也驗(yàn)證了采用計(jì)算各節(jié)點(diǎn)視在二次精確矩值的方法可以極大的減少變量的維數(shù),降低計(jì)算量,提高了算法效率。
[Abstract]:With the development of social economy, the importance of energy and environment is becoming more and more important. Distributed generation has been widely recognized and applied for its advantages of low carbon environmental protection, low investment, flexible power generation and so on. However, a large number of wind power, photovoltaic generation and other distributed sources connected to the grid are safe and reliable, and economic operation brings more challenges. Therefore, it is very important to plan distributed power access system reasonably and efficiently. This paper describes the development and research status of distributed power generation, and introduces in detail the grid-connected situation of several main distributed power sources, including network loss, power quality, reliability, etc. The influence of power flow distribution on power grid is analyzed in detail. Based on the analysis of the existing methods for solving the problem of optimal configuration of distributed power supply, it is shown that the traditional algorithms generally have some problems, such as slow speed, easy to fall into local optimum, etc. Therefore, an improved genetic algorithm based on extreme learning machine is proposed to solve this problem, which uses a new single hidden layer feedforward neural network algorithm, extreme Learning Machine (ELMU), to improve the basic genetic algorithm. At the same time, it also uses its excellent nonlinear mapping ability to simulate the evolution process of the two generations of population before and after, and combines with the traditional genetic algorithm. Through reasonable parameter setting, the global search ability and convergence speed of the algorithm can be improved. In order to give consideration to both environmental benefit and economic benefit, an economic optimal allocation model with minimum operating cost of investment, minimum cost of network loss and maximum environmental benefit is established. In order to solve the problem of selecting candidate installation nodes, which is missing in traditional distributed power generation planning, this paper proposes a method to select the optimal candidate installation nodes by calculating the second accurate moment value of each node and ranking them. Through the simulation of the actual 35 bus system in a certain place, the example shows that the improved genetic algorithm of the ultimate learning machine used in this paper is accurate in solving the problem of optimal configuration of distributed power supply. The convergence speed and optimization ability are superior to the traditional genetic algorithm, and a more reasonable and reliable optimal allocation scheme of distribution network can be obtained. At the same time, it is verified that the method of calculating the apparent accurate moment value of each node can greatly reduce the dimension of variables. The computational complexity is reduced and the efficiency of the algorithm is improved.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM73;TP18

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 單龍飛;含分布式電源的配電網(wǎng)短路計(jì)算[D];鄭州大學(xué);2013年

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本文編號(hào)：2010720