本文選題：時空相關性 + RST模型　； 參考：《華北電力大學》2017年碩士論文

【摘要】：近年來我國電力的需求穩(wěn)步增長,而環(huán)境問題愈發(fā)嚴峻,人們對于清潔能源的需求越來越大,風電作為一種清潔能源發(fā)展迅猛。風電場并網(wǎng)接入電力系統(tǒng)之后,其不確定性對于電力系統(tǒng)的安全穩(wěn)定運行存在影響。本文提出了考慮時空相關性的風速預測方法,建立風電場模型,并應用概率性方法對含有風電的電力系統(tǒng)進行了暫態(tài)穩(wěn)定不確定性分析。大型風電場或風電場群的風速之間存在時間相關性,同時也存在空間相關性。考慮風速的時空相關性,建立RST模型對風速進行短期預測。和傳統(tǒng)的考慮風速時空相關性方法不同,RST模型假設風速服從截斷的正態(tài)分布,并將風速的時空相關性體現(xiàn)在風速分布參數(shù)的建模中。由歷史風速數(shù)據(jù)求出分布參數(shù),建立RST模型,對風速進行短期的預測,并將預測的風速轉換為風機的輸出功率以40%恒功率,60%恒阻抗負荷模型的形式接入到系統(tǒng)中。在用概率性方法對系統(tǒng)進行暫態(tài)穩(wěn)定分析時,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)定的相關參數(shù)值(發(fā)電機相對功角、節(jié)點電壓和系統(tǒng)相對頻率),并根據(jù)其統(tǒng)計信息對系統(tǒng)進行暫態(tài)穩(wěn)定分析。Blind Kriging模型為一黑箱代理模型,以預測的風速作為模型輸入,對應的發(fā)電機相對功角、節(jié)點電壓和系統(tǒng)相對頻率作為輸出,建立Blind Kriging代理模型。通過IEEE39節(jié)點測試系統(tǒng)算例,與蒙特卡洛方法和Kriging法的運行結果比較分析,證明Blind Kriging代理模型在進行電力系統(tǒng)暫態(tài)不確定分析時具有可靠性和有效性。通過云南電網(wǎng)實際系統(tǒng)的算例,與蒙特卡洛方法運行結果比較分析,證明Blind Kriging代理模型在解決實際問題時具有實用性。同時兩算例表明,風電的不確定性對系統(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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM712

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本文編號：2012408