引入聚类分析的光伏出力建模及其在可靠性评估中的应用

对光伏系统出力的建模是评估其容量价值及进行规划的前提,太阳辐照强度的随机性和波动性是其建模的难点。文中引入聚类分析理论,从数据挖掘的角度对光伏出力进行了建模,并将其用于含光伏发电的系统可靠性评估。首先,对传统模糊C-均值(FCM)聚类算法进行了改进,解决了其对迭代初值敏感的问题。其次,利用改进的FCM算法对历史辐照数据进行了聚类分析,确定了聚类数和聚类中心,建立了太阳辐照的时序聚类模型。最后,基于所述模型和蒙特卡洛模拟(MCS)法提出了含光伏电站的发电系统可靠性评估方法。对IEEE-RTS测试系统的测试结果验证了所述模型和方法的有效性和高效性。

Photovoltaic Output Modeling by Introducing Clustering Analysis and Its Application in Reliability Evaluation

Modeling of photovoltaic(PV)system output is the premise of evaluation of its capacity value and planning. For such modeling, how to deal with stochastic and fluctuant nature of solar irradiation is the key point. Based on the theory of cluster analysis, the output model of PV system is established from the data mining perspective and the model is applied in the reliability evaluation of power system incorporating PV generation. Firstly, the conventional fuzzy C-means(FCM)algorithm is improved to solve the problem of initial iterative value sensitivity. Secondly, the improved FCM algorithm is used to make a cluster analysis on historical solar irradiation data. The number of clusters and the clustering centers are determined. Then, based on the membership matrix and Monte-Carlo simulation(MCS)method, the simulation method of solar irradiation is proposed. Finally, through the IEEE-RTS system, the method proposed is used to evaluate the generation system reliability. The simulation results have demonstrated that the proposed model and method are effective and highly efficient.