摘要
电网同时存在遥测坏数据和参数错误时,由于坏数据会影响参数辨识结果,全网参数辨识和估计方法很难保证结果的准确性。文中提出一种基于增广状态估计的混合不良数据诊断与参数辨识方法,先通过残差平衡度判断不良数据是遥测坏数据还是错误参数,将遥测坏数据直接剔除;然后,通过分区方法将多个潜在的不良参数尽可能分开在不同的局部区域,以减弱不良数据之间的相互影响;最后,采用分区增广状态估计方法修正不良参数。算例结果表明,该方法能有效区分坏数据和错误参数,且分区参数辨识能避免不良数据之间相互影响,从而提高了可疑参数辨识的精度。
With presence of hybrid bad telemetry data and error parameter in power system, the validity of parameter identification and estimation methods of whole network cannot be guaranteed due to the fact that bad data will affect the parameter identification accuracy. It presents a detection and identification approach of bad-data based on augmented state estimation. First of all, the bad data are estimated whether they are bad telemetry data or parameters with error according to the residual balance degree. After deleting bad telemetry data, parameters with errors are kept within a certain area using node partition and then are modified according to augmented state estimation. The example results show that the proposed method can identify the bad telemetry data and parameters with error effectively, and interaction between the bad data can be avoid through parameter partition, so that the estimation accuracy of the suspicious parameters can be improved.
作者
陆东生
马龙鹏
LU Dongsheng;MA Longpeng(State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210024,China;State Grid Jiangsu Electric Power Co.,Ltd.Economic Research Institute,Nanjing 210008,China)
出处
《电力工程技术》
2019年第2期99-104,共6页
Electric Power Engineering Technology
关键词
参数辨识
状态估计
数据诊断
残差平衡度
parameter estimation
state estimation
data detection
residual balance degree
