大规模风电并网对电力系统概率稳定性影响及有效控制措施研究

项目名称： 大规模风电并网对电力系统概率稳定性影响及有效控制措施研究

项目编号： No.51277049

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 电工技术

项目作者： 李生虎

作者单位： 合肥工业大学

项目金额： 75万元

中文摘要： 大规模风电并网降低了电力系统稳定性。将风电简化为随机功率，忽略动态控制特性，不能准确反映风电系统暂态运行风险。不同运行场景、故障模式和时间尺度下，风电系统控制策略、等值模型、对应稳定问题不同。风电机组不能采用转速同调方法聚合。风电系统概率小扰动稳定性，随初始运行点变化。大扰动后机组停运和紧急控制，将改变状态变量维数，增加概率稳定分析难度。 计及初始运行点、故障模式、控制策略和拓扑结构不确定性以及时间尺度不同，采用数学模型聚合和振荡模式降阶方法，建立风电场概率动态等值模型。研究风电系统小扰动概率稳定性对运行点的依赖性，量化衰减特性和振荡特性对结构参数和控制参数的灵敏度。建立多参数能量函数和随机微分方程算法，判定风电系统概率暂态稳定性。研究风电系统预防控制和紧急控制的自适应整定算法。建立风电系统概率稳定评估算法和指标体系。将失稳风险分解至风电机组、同步机组和电网，提出改善风电系统概率稳定性的措

中文关键词： 风电系统；稳定分析与控制；低频振荡；潮流建模；概率仿真

英文摘要： Power system stability level is decreased by bulk wind power integration. By assuming the wind farms as stochastic power and ignoring their dynamic control characteristics, the operational transient risk of wind power system can not be exactly quantified. With different initial states, failure modes, and time scales, the control measures, equivalence models, and corresponding stability problems are different. The wind turbine generators can not be aggregated with the coherency method for the rotor speed. The probabilistic small-disturbance stability are dependent on the initial operation point. Unexpected outage and emergency control change the number of the state variables, and add difficulty to the probabilistic stability evaluation. Including the uncertainty of initial states, failure modes, control strategies, topology constructions, and time scales, the probabilistic dynamic equivalence model is proposed for wind farms based on mathematical model aggregation and oscillation mode reduction. The dependence of probabilistic small-disturbance stability of on the initial operation points is studied. The sensitivities of the damping and oscillating characteristics to the configuration parameters and control parameters are quantified. The probabilistic transient stability algorithm is described by multi-variable

英文关键词： wind power system；stability analysis and control；low-frequency oscillation；power flow modeling；probabilistic simulation