本文選題：時(shí)空相關(guān)性 + RST模型　； 參考：《華北電力大學(xué)》2017年碩士論文

【摘要】：近年來(lái)我國(guó)電力的需求穩(wěn)步增長(zhǎng),而環(huán)境問(wèn)題愈發(fā)嚴(yán)峻,人們對(duì)于清潔能源的需求越來(lái)越大,風(fēng)電作為一種清潔能源發(fā)展迅猛。風(fēng)電場(chǎng)并網(wǎng)接入電力系統(tǒng)之后,其不確定性對(duì)于電力系統(tǒng)的安全穩(wěn)定運(yùn)行存在影響。本文提出了考慮時(shí)空相關(guān)性的風(fēng)速預(yù)測(cè)方法,建立風(fēng)電場(chǎng)模型,并應(yīng)用概率性方法對(duì)含有風(fēng)電的電力系統(tǒng)進(jìn)行了暫態(tài)穩(wěn)定不確定性分析。大型風(fēng)電場(chǎng)或風(fēng)電場(chǎng)群的風(fēng)速之間存在時(shí)間相關(guān)性,同時(shí)也存在空間相關(guān)性�？紤]風(fēng)速的時(shí)空相關(guān)性,建立RST模型對(duì)風(fēng)速進(jìn)行短期預(yù)測(cè)。和傳統(tǒng)的考慮風(fēng)速時(shí)空相關(guān)性方法不同,RST模型假設(shè)風(fēng)速服從截?cái)嗟恼龖B(tài)分布,并將風(fēng)速的時(shí)空相關(guān)性體現(xiàn)在風(fēng)速分布參數(shù)的建模中。由歷史風(fēng)速數(shù)據(jù)求出分布參數(shù),建立RST模型,對(duì)風(fēng)速進(jìn)行短期的預(yù)測(cè),并將預(yù)測(cè)的風(fēng)速轉(zhuǎn)換為風(fēng)機(jī)的輸出功率以40%恒功率,60%恒阻抗負(fù)荷模型的形式接入到系統(tǒng)中。在用概率性方法對(duì)系統(tǒng)進(jìn)行暫態(tài)穩(wěn)定分析時(shí),代替?zhèn)鹘y(tǒng)的蒙特卡洛法,利用Blind Kriging代理模型快速獲得反映系統(tǒng)暫態(tài)穩(wěn)定的相關(guān)參數(shù)值(發(fā)電機(jī)相對(duì)功角、節(jié)點(diǎn)電壓和系統(tǒng)相對(duì)頻率),并根據(jù)其統(tǒng)計(jì)信息對(duì)系統(tǒng)進(jìn)行暫態(tài)穩(wěn)定分析。Blind Kriging模型為一黑箱代理模型,以預(yù)測(cè)的風(fēng)速作為模型輸入,對(duì)應(yīng)的發(fā)電機(jī)相對(duì)功角、節(jié)點(diǎn)電壓和系統(tǒng)相對(duì)頻率作為輸出,建立Blind Kriging代理模型。通過(guò)IEEE39節(jié)點(diǎn)測(cè)試系統(tǒng)算例,與蒙特卡洛方法和Kriging法的運(yùn)行結(jié)果比較分析,證明Blind Kriging代理模型在進(jìn)行電力系統(tǒng)暫態(tài)不確定分析時(shí)具有可靠性和有效性。通過(guò)云南電網(wǎng)實(shí)際系統(tǒng)的算例,與蒙特卡洛方法運(yùn)行結(jié)果比較分析,證明Blind Kriging代理模型在解決實(shí)際問(wèn)題時(shí)具有實(shí)用性。同時(shí)兩算例表明,風(fēng)電的不確定性對(duì)系統(tǒng)的暫態(tài)穩(wěn)定存在一定影響。
[Abstract]:In recent years, the demand for electricity in China has been increasing steadily, but the environmental problems are becoming more and more serious, and the demand for clean energy is increasing. Wind power as a clean energy is developing rapidly. After the wind farm is connected to the power system, its uncertainty has an impact on the safe and stable operation of the power system. In this paper, a wind speed prediction method considering the temporal and spatial correlation is proposed, and the wind farm model is established, and the transient stability uncertainty analysis of the power system with wind power is carried out by using the probabilistic method. There is a temporal and spatial correlation between the wind speed of large wind farms or wind farm groups. Considering the temporal and spatial correlation of wind speed, a RST model is established to predict the wind speed in the short term. Different from the traditional method of considering the temporal and spatial correlation of wind speed, the RST model assumes that the normal distribution of wind speed is truncated, and the temporal and spatial correlation of wind speed is reflected in the modeling of wind speed distribution parameters. The distribution parameters are obtained from the historical wind speed data, and the RST model is established to predict the wind speed in the short term. The predicted wind speed is converted into the output power of the fan in the form of 40% constant power and 60% constant impedance load model. When using probabilistic method to analyze the transient stability of the system, instead of the traditional Monte Carlo method, the Blind Kriging agent model is used to quickly obtain the relative power angle of the generator to reflect the transient stability of the system. The node voltage and the relative frequency of the system are used to analyze the transient stability of the system according to its statistical information. Blind Kriging model is a black-box agent model. The predicted wind speed is taken as the input of the model and the relative power angle of the generator is obtained. The Blind Kriging agent model is established by using the node voltage and the relative frequency of the system as the output. The simulation results of IEEE 39 bus test system are compared with those of Monte Carlo method and Kriging method. It is proved that Blind Kriging agent model is reliable and effective in transient uncertainty analysis of power system. The practical application of Blind Kriging agent model in solving practical problems is proved by the comparison and analysis of the actual system of Yunnan power grid and the results of operation by Monte Carlo method, and the results show that the Blind Kriging agent model is practical in solving the practical problems. At the same time, two examples show that the uncertainty of wind power has a certain impact on the transient stability of the system.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM712

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