风电爬坡事件的非精确条件概率预测

Imprecise conditional probability prediction of wind power ramp events

风电爬坡事件(wind power ramp events,WPRE)易破坏电力系统的有功功率平衡,劣化频率稳定性及电能质量,威胁电网的安全稳定运行。由此,提出一种基于信度网络(credal network,CN)的WPRE非精确条件概率预测方法,对WPRE各状态发生概率的区间范围进行预测。运用贪婪搜索算法挖掘WPRE与多个气象变量之间的相依性关系,并搭建CN结构以抽象表达;在超参数设置方面对非精确狄利克雷模型(imprecise Dirichlet model,IDM)进行了拓展,使用拓展后的IDM对变量间的条件相依性关系进行不确定性量化,完成CN的参数估计;基于建立的CN模型,在获取气象预测信息的条件下,结合CN概率推断算法对多状态WPRE的分布进行非精确概率推断;采用宁夏某风电场的实测数据对本方法进行测试,验证了该方法在观测样本不充足的预测情景下优异的预测性能。

Wind power ramp events(WPRE)could easily destroy the active power balance of the power system,as well as did harm to the frequency stability and power quality,which threatened the safe and stable operation of power grid.A novel imprecise conditional probability prediction approach was proposed based on the credal network(CN),which could provide the interval range of the occurrence probability of each WPRE state.The approach excavated the dependence relationships between WPRE and meteorological variables using the greedy search algorithm,and constructed a CN structure to express the relationships in an abstract way.The proposed approach extended the imprecise Dirichlet model(IDM)on hyperparameter settings to quantify the uncertain conditional dependences among the variables,thus to realize the parameter estimation of the CN.Based on the constructed CN model,a CN probability inference algorithm was employed to estimate the imprecise probability distribution of the multi-state WPRE.The case study with wind-farm operating measurements in Ningxia demonstrated that the proposed approach had excellent performance even under the prediction scenarios with insufficient samples.