论文部分内容阅读
针对多不良数据辨识中存在的残差污染和残差淹没问题,提出一种多不良数据辨识方法,在应用P-Q分解法的基础上,首先选取部分量测进行状态估计,接着用剩余量测逐一替换参与估计计算的量测,并根据替换后各量测标准化残差的大小得到可疑数据,其间量测量的替换可以打破发生残差淹没的平衡,使得由于发生残差淹没而导致标准化残差合格的不良数据凸显出来,之后又通过状态估计对可疑数据进行校核,恢复受到残差污染的量测为正常量测,最终将不良数据辨识出来。此外,还给出替换和减少一维量测后计算标准化残差的简化方法,以提高计算速度。最后以某地区220kV电网为背景进行算例分析,表明该方法的有效性和可行性。
Aiming at the problem of residual pollution and residual submergence in the identification of many bad data, a method of identifying many unhealthy data is proposed. Based on the PQ decomposition method, the state of partial measurement is selected firstly, and then the residual measurement is used one by one Substituting for the measurement involved in the estimation calculation and obtaining suspicious data according to the sizes of the normalized residuals after the replacement. The replacement of the measurement during the measurement can break the balance of the residual flooding, so that the standardized residuals are qualified due to the residual flooding Of the bad data highlights, and then through the state of the suspect data to check, restore residual pollution measured as a normal measurement, the final bad data identified. In addition, a simplified method to calculate and standardize residuals after replacing and reducing one-dimensional measurements is also provided to improve computational speed. Finally, an example analysis is made on the background of a 220kV power grid in an area, which shows the effectiveness and feasibility of the method.