Bad Data Identification Based on Measurement Replace and Standard Residual Detection
Zongwei Zhang · Dianli xitong zidonghua · 2007
According to the “residual pollute” and “residual submerge” existing in multiple bad data identification, a method for identifying multiple bad data is presented. Based on the P-Q decoupled method, first, doing state estimation using a part of measurements, then using the remnant measurements replace the measurements which participate in state estimation one by one, and shadiness data are identified after replacement based on the value of standard residual. Meantime, measurement replace can break the balance existing in “residual submerge”, and make the bad data whose standard residual are eligible because of “residual submerge” appear. Then the shadiness data are checked via state estimation, the good data that effected by “residual pollute” can be resumed and all bad data are identified finally. In addition, some simplified formulas for computing standardization residual are given when a measurement is replaced or reduced, which can improve the computation speed. Lastly, taking one area’s 220 kV power network as background and doing experiment analysis, it is proved that the method is effective and feasible.