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类型 基础研究 预答辩日期 2017-11-05
开始(开题)日期 2013-05-14 论文结束日期 2017-03-14
地点 动力楼102 论文选题来源 973、863项目     论文字数 6.14 (万字)
题目 风电功率控制系统的研究及风电空间相关性预测的探讨
主题词 风电预测,功率控制,空间相关性,符号化,一体化监控
摘要 风力发电具有波动性大、随机性强和抗扰性差等特点,高渗透风力发电并网改变了传统电网运行控制的边界条件,给电网安全稳定运行带来巨大冲击。高精度的功率预测和合理准确的功率控制是应对上述问题的两大关键技术手段,其中,高精度的功率预测是合理准确实施功率控制的重要基础。 为提升预测精度、改善功率控制性能,课题组提出了如下思路: (1)针对预测误差对风电功率控制的影响,考虑不同风电场的误差分布特性及大型风电场内风电机组的功率变化趋势,引入风险,实现风电功率精细化控制; (2)利用大数据思维,抽取风电功率动态特征,通过统计分析实现不同风电动态与特征的因果关系,对不同规律的风电动态分别建立预测模型,提高功率预测方法的适用性; (3)由于风速的时空函数往往具有较强的时变性,故很难控制预测的最大误差,特别是遇到上述个别的奇异点或奇异面时,预测甚至可能完全失效。将空间相关预测作为时序外推预测方法的补充,实现风电时空预报的协调,利用全局信息和趋势特征弥补已有方法依据局部信息和断面特征的不全面性,提升预测方法的鲁棒性。 本文为实现上述思路,开展了如下工作: (1)提出基于功率预测信息的风电有功风险控制方法,通过统计分析风电功率预测误差分布特性,评估风电预测功率的置信区间。以此为基础,分析不同风电场可能产生正误差和负误差的概率,考虑不同误差特性可能引起的备用、切负荷、机组停运和弃风等成本,以控制误差引起的系统风险成本最小,建立风电有功风险控制目标函数,通过优化实现有功功率分配。 (2)以信息一体化、平台一体化和调控一体化为原则,设计了基于多源数据的风电场功率控制系统架构。首先,建立统一的信息模型将风电场及其升压站的相关信息有机整合;其次,构造合理的软件架构实现风电场监控、功率预测、有功控制和无功控制等功能的一体化;最后,将基于功率预测的风电有功控制方法针对风电场端应用做适当修改,并开发相应的功能模块。基于多源数据的风电场功率控制系统已在甘肃酒泉风电基地某风电场实现示范应用。 (3)分析了已有空间相关风速预测方法在研究思路和预测建模方法等方面的局限性,探讨利用空间相关特征实施风速预测的可行性和适用条件,设计“离线分类优化模型,在线确认空间相关性并匹配预测模型”的风电空间相关性预测框架。 (4)为了提高风速时间序列形态分类预测的有效性与强壮性,提出单元窗口的变化特征及观测窗口的趋势特征的概念,采用单元窗口特征符号及趋势特征符号串结合的风速时间序列两层符号化思路,实现风速波动特征的粗粒度描述。 (5)针对利用空间相关性的风速预测,提出一种“离线分类优化模型,在线确认空间相关性并匹配预测模型”的方法。根据影响空间相关性的风动态特征和外部条件特征等将风速时间序列划分为不同类型的子集,针对不同子集分别离线建立预测模型并对参数进行优化;在线应用时,抽取当前时刻临近时段内相关变量的特征,通过特征匹配识别和调用相应的预测模型,以参考地点近期窗口的时序相关性预测结果,实现目标点的风速预测。
英文题目 Research on Wind Power Control System and Exploration of Wind Power ForecastingBasedon Sptial Correlation
英文主题词 wind power forecasting,power control,spatial correlation,symbolic,integrated supervisory and control
英文摘要 Wind power is fluctuant and random. It is easy to be influenced by system disturbance. Wind power with high penetration is interconnected into grid will change the boundary condition for traditional power system operation and control, and it will bring huge challenge for stable power system operation. Accurate power forecasting and reasonable power control are two key ways to solve above problems. While, accurate power forecasting is an important basis of reasonable power control. To improve forecasting precision and power control performance, our research team proposes the following roadmap: (1) For the influences of forecasting error on wind power control, probability distribution characteristics of forecasting error for different wind farms and he features of wind power variation for different wind generators are analysed and considered for wind power control. Also, risk is introduced to decrease the impacts of wind power control on stable system operation. It can make wind power control more accurate and efficient. (2) The idea of big data should be used. By data mining, dynamic features of wind power variation are extracted. Then the causality between different features and wind power variation is analysed to establish different power forecasting models, which are suitable for different regular patterns of wind power variation. It can improve the applicability of power forecasting. (3) Because time-space function of wind speed is time variant, the maximum forecasting error is difficulty to be controlled. Especially if there are some singularities or singular surfaces, the forecasting result will be unusable. Spatial-correlation wind power forecasting can be used for the supplement to traditional sequential wind power forecasting. The time-space coordination should be considered for wind power forecasting. Global information and trend features are employed, and it can make up the shortage of traditional method only considering local information and section features. Then the robustness of wind power forecasting will be promoted. Following jobs are done based on above thoughts: (1) A forecasting power and risk based wind power control method is proposed. Firstly, confidence interval of forecasting wind power is evaluated by statistic analysis on probability distribution of wind power forecasting error. Based on this, the probability of positive and negative error of each wind farm can be calculated. Because forecasting error may cause the cost for reserve, load shedding, unit outage and wind power curtailment, an objective function of wind power control to minimum the risk cost caused by error. Then by optimizing, power control command can be distributed reasonably. (2) An architecture of wind power control system based on data fusion is designed. The principle for the architecture is information integration, platform integration and control integration. Firstly, unified information model is established to combine the information of all equipments in wind farm and substation. Secondly, software structure is designed to combine the functions including wind farm supervisor control and data acquisition (SCADA), wind power forecasting, active power control and reactive power control. Then wind power control method based on forecastingpower is adjusted for application in wind farm. Finally, the wind power control system based on data fusion is developed and has been applied at a wind farm in Jiuquan wind power base in Gansu Province. (3) The limits of existing sptial-correlation based wind power forecasting methods are analysed from the point of the view of the research thought and models. Then the feasibility and applicable conditions of wind speed forecasting using the spatial-correlation features are discussed. And a new idea and framework of sptial-correlation based wind power forecasting is proposed. It establishes different forecasting models according to different spatial-correlation features off-line by analysing historical data and background information. And appropriate model will be selected on-line by feature matching. (4) To improve the effectiveness and robustness of shape classification for wind speed time series, unit window feature and trend feature are defined. Then a two-layer symbolic idea considering section and trend features is proposed to simply describe the feature of wind fluctuation. (5) For sptial-correlation based wind speed forecasting, a new wind speed forecasting method is proposed, which establishes models for different shapes off-line and selects appropriate model by feature matching on-line. Firstly, according to dynamic features of wind and external conditions, wind speed time series are classified into different patterns. Secondly, different forecasting models are established and corresponding parameters are optimized for different patterns off-line. Then features of wind speed variation in recent time are extracted. By feature matching, appropriate model will be used for on-line application, and the forecasting result at reference location in recent time window is used to forecasting wind speed at objective location.
学术讨论
主办单位时间地点报告人报告主题
中国电力科学研究院 2013.5.16 中国电科院新能源所B509会议室 陈宁 超短期风电预测预报技术
中国电力科学研究院 2013.12.18 中国电科院新能源所B509会议室 陈宁 风电建模技术
甘肃省电力公司风电技术中心 2014.2.27 甘肃省电力公司风电技术中心会议室 乔颖 基于复合数据源的风电场功率预测技术
中国电力科学研究院 2014.6.12 中国电科院新能源所B509会议室 陈宁 风电场智能控制技术
甘肃省电力公司风电技术中心 2014.11.13 甘肃省电力公司风电技术中心会议室 李征 电网友好型风电场的概念及指标体系
中国电力科学研究院 2014.12.17 中国电科院新能源所B509会议室 陈宁 风电场一体化监控技术
中国电力科学研究院 2015.3.19 中国电科院新能源所B509会议室 赵亮 大规模光伏电站群有功功率控制
中国电力科学研究院 2015.9.17 中国电科院新能源所B509会议室 葛路明 大规模光伏电站群无功功率控制
     
学术会议
会议名称时间地点本人报告本人报告题目
中国电机工程学会年会 2015.11.17 武汉 新能源发电模型统一化研究
IEEE CYBER 2016.6.19 成都 Research on State Estimation of Power System with Large-Scale Photovoltaic Plant
     
代表作
论文名称
风速时间序列的符号化描述
 
答辩委员会组成信息
姓名职称导师类别工作单位是否主席备注
袁越 正高 教授 博导 河海大学
邹云 正高 教授 博导 南京理工大学
高丙团 正高 博导 东南大学
汤奕 副高 副教授 博导 东南大学
朱凌志 正高 研究员级高工 硕导 中国电力科学研究院
      
答辩秘书信息
姓名职称工作单位备注
杨燕 其他 讲师 东南大学