| Title |
Dynamic State Estimation Using Limited PMU Data and Linear State Estimation |
| Authors |
이호준(Ho-Jun Lee) ; 유석진(Seok-Jin Yoo) ; 김병호(Byoung-Ho Kim) ; 김홍래(Hongrae Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2025.74.9.1476 |
| Keywords |
Dynamic State Estimation; Optimal PMU Placement; Linear State Estimation; Cubature Kalman Filter |
| Abstract |
PMUs(Phasor Measurement Units) provide observers with high-speed synchronized data, enabling real-time monitoring of power system dynamics. However, due to high installation costs, PMUs are typically installed only at key substations, limiting the practical applicability of traditional DSE(Dynamic State Estimation) methods that assume full PMU deployment. This study proposes a realistic and efficient technique for real-time DSE using a limited number of PMUs. System observability is ensured through graph-theoretic optimal PMU placement, and voltage phasors are reconstructed via LSE(Linear State Estimation). Generator rotor angles and speeds are then estimated using the CKF(Cubature Kalman Filter). The proposed method is validated on WSCC(Western Systems Coordinating Council) 9-bus and IEEE(Institute of Electrical and Electronics Engineers) 39-bus systems, showing accurate and stable performance even under disturbances such as generator and line outages. It also outperforms the UKF(Unscented Kalman Filter) in computational efficiency and numerical stability, making it suitable for practical deployment. |