In this paper, a Kalman filter and particle filter based dynamic state estimation method are proposed for nonlinear systems with unknown inputs. In the proposed method, the dynamic states of a generation system are estimated in three stages. At the first stage, the biased states are predicted using unscented transform without unknown inputs. At the second stage, the unknown inputs are estimated using a particle filter technique with phasor measurements and the predicted biased states. At the final stage, the unbiased states are estimated using an unscented Kalman filter method with the estimated unknown inputs. The proposed algorithm is implemented in Korean power system model, and is compared with dynamic state estimation performances of other estimation algorithms with unknown inputs.
|Title of host publication||2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2020 Jun|
|Event||29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands|
Duration: 2020 Jun 17 → 2020 Jun 19
|Name||IEEE International Symposium on Industrial Electronics|
|Conference||29th IEEE International Symposium on Industrial Electronics, ISIE 2020|
|Period||20/6/17 → 20/6/19|
Bibliographical noteFunding Information:
This research was supported by Korea Electrotechnology Research Institute (KERI) Primary research program through the National Research Council of Science & Technology (NST) funded by the Ministry of Science and ICT (MSIT) (No. 19-12-N0101-04). This research was also supported by Korea Electric Power Corporation (KEPCO) #R18XA05.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Control and Systems Engineering