/10-3 /10-3 /10-3 1 0.8020 0.5545 0.5512 2 1.0026 0.6975 0.6975 3 0.9163 0.6308 0.6324 4 0.9441 0.6200 0.6205 5 0.8511 0.5644 0.5645 6 0.8806 0.6300 0.6267 7 0.9507 0.6835 0.6801 8 1.0514 0.6665 0.6612 9 0.8672 0.6520 0.6555 10 0.9535 0.6490 0.6501 11 0.9144 0.5795 0.5880 12 0.8738 0.5961 0.5961 13 0.8441 0.5660 0.5643 14 0.8863 0.5205 0.5201 From last table, data fusion based on time-series data correlation mining has better effective estimation than other traditional algorithm in stability or disturbance period. Summary And Conclusion Hybrid measurement data estimation improves the state estimation which only use SCADA data with WAMS data. However, there is not an effective scheme to solve the problem of state estimation based on WAMS/SACDA data. The scheme of time-series data correlation fusion is proposed in this paper: Pearson correlation coefficient function is introduced in this paper. WAMS/SCADA data correlation estimation is done by determining upper and lower limit and derivation of correlation coefficient. In the case of time difference of time-series curves, optimizing data fusion by building and solving model function. Calculating the matrix of time-series data weight to complete an effective scheme based on time-series correlation by analyzing measurement data. The results are verified by IEEE 118 nodes system. The stability has improved a lot than other schemes.
/10-3 /10-3 /10-3 1 0.8020 0.5545 0.5512 2 1.0026 0.6975 0.6975 3 0.9163 0.6308 0.6324 4 0.9441 0.6200 0.6205 5 0.8511 0.5644 0.5645 6 0.8806 0.6300 0.6267 7 0.9507 0.6835 0.6801 8 1.0514 0.6665 0.6612 9 0.8672 0.6520 0.6555 10 0.9535 0.6490 0.6501 11 0.9144 0.5795 0.5880 12 0.8738 0.5961 0.5961 13 0.8441 0.5660 0.5643 14 0.8863 0.5205 0.5201 From last table, data fusion based on time-series data correlation mining has better effective estimation than other traditional algorithm in stability or disturbance period. Summary And Conclusion Hybrid measurement data estimation improves the state estimation which only use SCADA data with WAMS data. However, there is not an effective scheme to solve the problem of state estimation based on WAMS/SACDA data. The scheme of time-series data correlation fusion is proposed in this paper: Pearson correlation coefficient function is introduced in this paper. WAMS/SCADA data correlation estimation is done by determining upper and lower limit and derivation of correlation coefficient. In the case of time difference of time-series curves, optimizing data fusion by building and solving model function. Calculating the matrix of time-series data weight to complete an effective scheme based on time-series correlation by analyzing measurement data. The results are verified by IEEE 118 nodes system. The stability has improved a lot than other schemes.
AIMEE2017-Paper63.docx