摘要
提出基于电力系统运行数据集的网损优化评估方法。对电网运行数据进行改进PNN算法聚类,通过运行数据集获得电网典型运行方式及其出现的概率。并分析发电机不同数据属性对电力系统运行的影响,优化电网有功损耗评估。并与K-means算法和改近K-means算法做比较。仿真结果表明,由于所提出的方法充分考虑发电机数据属性、系统运行特性,该方法能有效降低电网有功损耗评估误差。
A method of power loss optimization evaluation based on power system operation data set is proposed.The power network operation data is clustered by improved PNN algorithm,and the typical operation mode and its occurrence probability are obtained from the operation data set.The influence of different generator data attributes on power system operation is analyzed,and the active power loss assessment of pow⁃er grid is optimized.And compared with K-means Algorithm and improved K-means algorithm.The simulation results show that the pro⁃posed method can effectively reduce the estimation error of active power loss due to the consideration of generator data attributes and sys⁃tem operation characteristics.
作者
张军
宣铁锋
吴磊
ZHANG Jun;XUAN Tie-feng;WU Lei(School of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090)
出处
《现代计算机》
2021年第7期20-23,共4页
Modern Computer
基金
国家自然科学基金项目(No.61273190)
上海市电站自动化技术重点实验室资助项目(No.13DZ2273800)。
关键词
聚类算法
有功网损评估
运行数据集
典型运行方式
软件模拟
K-MEANS
Clustering Algorithm
Assessment of Active Power Loss
Run Data Set
Typical Mode of Operation
Software Simulation
K-means
