Power System Operation Mode Identification Method Based on Improved Clustering Algorithm

Dian Chen, Runzhao Lu, Xi Wang, Yongcan Wang
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Abstract

For power system calculation and analysis, the accuracy and rationality of operation mode selection is the key to determine the calculation quality. With the access of a high proportion of renewable energy, the traditional manual selection method is not applicable. How to automatically extract the typical operation mode form the data set obtained from production simulation is an urgent scientific and technical problem to be solved. This paper firstly carries out the demand analysis of operation mode extraction of high proportion renewable energy power system. Secondly, an automatic mode extraction algorithm based on K-means++ algorithm and improved cluster validity index is proposed. Then this paper designed a mode extraction approach with joint manual processing and automatic algorithm. Finally, based on the practical data of a region power grid in China, the numerical experiments demonstrate the effectiveness and rationality of the proposed algorithm based on the comparison with the manually selected operation mode from two aspects of mode characteristics and security check. The contribution of the algorithm in improving the level of power system planning was proved.
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基于改进聚类算法的电力系统运行模式识别方法
在电力系统计算分析中,运行方式选择的准确性和合理性是决定计算质量的关键。随着可再生能源的高比例接入,传统的人工选择方法已不适用。如何从生产仿真数据集中自动提取出典型的运行模式,是一个迫切需要解决的科学技术问题。本文首先对高比例可再生能源发电系统运行模式提取进行了需求分析。其次,提出了一种基于k -means++算法和改进的聚类有效性指标的自动模式提取算法;然后设计了一种人工处理与自动算法相结合的模式提取方法。最后,以中国某区域电网的实际数据为基础,从模式特征和安全性检查两方面与人工选择运行模式进行了比较,验证了所提算法的有效性和合理性。验证了该算法对提高电力系统规划水平的贡献。
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