含风电场的电力系统暂态稳定分析

发布时间：2018-06-13 03:06

本文选题：时空相关性 + RST模型　；参考：《华北电力大学》2017年硕士论文

【摘要】：近年来我国电力的需求稳步增长,而环境问题愈发严峻,人们对于清洁能源的需求越来越大,风电作为一种清洁能源发展迅猛。风电场并网接入电力系统之后,其不确定性对于电力系统的安全稳定运行存在影响。本文提出了考虑时空相关性的风速预测方法,建立风电场模型,并应用概率性方法对含有风电的电力系统进行了暂态稳定不确定性分析。大型风电场或风电场群的风速之间存在时间相关性,同时也存在空间相关性。考虑风速的时空相关性,建立RST模型对风速进行短期预测。和传统的考虑风速时空相关性方法不同,RST模型假设风速服从截断的正态分布,并将风速的时空相关性体现在风速分布参数的建模中。由历史风速数据求出分布参数,建立RST模型,对风速进行短期的预测,并将预测的风速转换为风机的输出功率以40%恒功率,60%恒阻抗负荷模型的形式接入到系统中。在用概率性方法对系统进行暂态稳定分析时,代替传统的蒙特卡洛法,利用Blind Kriging代理模型快速获得反映系统暂态稳定的相关参数值(发电机相对功角、节点电压和系统相对频率),并根据其统计信息对系统进行暂态稳定分析。Blind Kriging模型为一黑箱代理模型,以预测的风速作为模型输入,对应的发电机相对功角、节点电压和系统相对频率作为输出,建立Blind Kriging代理模型。通过IEEE39节点测试系统算例,与蒙特卡洛方法和Kriging法的运行结果比较分析,证明Blind Kriging代理模型在进行电力系统暂态不确定分析时具有可靠性和有效性。通过云南电网实际系统的算例,与蒙特卡洛方法运行结果比较分析,证明Blind Kriging代理模型在解决实际问题时具有实用性。同时两算例表明,风电的不确定性对系统的暂态稳定存在一定影响。
[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.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM712

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