基于模糊等价矩阵聚类分析的不良数据辨识OA北大核心CSCDCSTPCD
Clustering method of fuzzy equivalence matrix to bad-data detection and identification
采用模糊数学的方法来辨识电力系统实时运行数据中的不良数据.利用基于模糊等价矩阵的聚类分析方法,以标准残差和相邻采样时刻的量测量差值作为特征值,通过寻找最佳阈值,对量测项目进行动态聚类,根据个别已知的良数据和“数以类聚”的原则,得到全良数据的分类,进而辨识出不良数据.最后分别对传统算例模型和某地区电网实时数据进行仿真分析,表明该方法能快速准确的辨识出不良数据,有效避免残差污染和残差淹没现象,更适合实际电网的计算要求.
Fuzzy mathematics method is used to identify the bad-data in power system real-time data. By use of clustering analysis method based on fuzzy equivalence matrix, and regarding the normalized residual difference and the difference value between adjacent sampling times data as the eigen values, measured items are clustered dynamically by searching the best threshold value. According to individual given good-data and 'like attracts like' principle, the good-dat…查看全部>>
蒋德珑;王克文;王祥东
郑州大学电气工程学院,河南郑州45000171781部队,河南洛阳471100郑州大学电气工程学院,河南郑州450001
信息技术与安全科学
电力系统不良数据辨识模糊等价矩阵聚类分析传递闭包
power systembad-data identificationfuzzy equivalence matrixcluster analysistransitive closure
《电力系统保护与控制》 2011 (21)
1-6,11,7
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