中文摘要：

为解决应用传统模糊C均值（fuzzy C-means，FCM）算法进行电力负荷模式提取时存在的对初始聚类中心敏感、聚类数目不易确定、算法稳定性较差等问题，从负荷曲线形态出发，提出一种基于云模型和模糊聚类的电力负荷模式提取方法。该方法首先针对电力负荷数据的时间特性，对云变换方法进行了维度扩展，使其能够应用于具有时间特征的二维数据处理，将电力用户典型日负荷的频率分布分解为若干个正态云组的叠加，以各云模型中最能代表各定性概念的期望向量集合作为初始聚类中心；然后，基于云模型确定的初始聚类中心和聚类数目，应用FCM算法进行电力负荷模式提取和用户分类。最后，以某电网实际负荷数据进行算例分析，结果证明了该算法的实用性和有效性。

英文摘要：

In allusion to such defects as sensitive to initial clustering center, not convenient to determine clustering number and poor stability of the algorithm during utilizing traditional fuzzy C-Means （FCM） algorithm to extract power load patterns, starting from the morphological feature of load curve and based on cloud model and fuzzy clustering a method to extract the power load pattern is proposed. Firstly, according to the time characteristic of power load data the dimension extension for the cloud transformation is performed to make it enable to be applied in the processing of two-dimensional data with time characteristic, and the frequency distribution of typical daily load curves of power consumers is decomposed into the superposition of several normal cloud groups by multiple cloud transform and the expected vector set in all cloud models, which can mostly represent ihe qualitative concepts, is taken as the initial clustering center; secondly, based on the initial clustering center and the number of clustering determined by the cloud model the FCM algorithm is utilized to extract power load pattems and classify power consumers; finally, the case analysis based on actual data of a certain power grid is carried out and analysis results show that the proposed method is practicable and effective.