Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation
Dynamic state estimation (DSE) accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time. This paper proposes a DSE approach for a doubly-fed induction generator (DFIG) with unknown inputs based on adaptive interpolation and cubature Kalman filte...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10105888/ |
| _version_ | 1827891747824336896 |
|---|---|
| author | Maolin Zhu Hao Liu Junbo Zhao Bendong Tan Tianshu Bi Samson Shenglong Yu |
| author_facet | Maolin Zhu Hao Liu Junbo Zhao Bendong Tan Tianshu Bi Samson Shenglong Yu |
| author_sort | Maolin Zhu |
| collection | DOAJ |
| description | Dynamic state estimation (DSE) accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time. This paper proposes a DSE approach for a doubly-fed induction generator (DFIG) with unknown inputs based on adaptive interpolation and cubature Kalman filter (AICKF-UI). DFIGs adopt different control strategies in normal and fault conditions; thus, the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases. Consequently, the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs, which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter. Furthermore, as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs, a large estimation error may occur or the DSE approach may diverge. To this end, in this paper, a local-truncation-error-guided adaptive interpolation approach is developed. Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can (1) effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient; (2) accurately track the dynamic states and unknown inputs of the DFIG; and (3) deal with various types of system operating conditions such as time-varying wind and different system faults. |
| first_indexed | 2024-03-12T21:29:54Z |
| format | Article |
| id | doaj.art-c2fd6b908c194db792d3680ec789096d |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-03-12T21:29:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-c2fd6b908c194db792d3680ec789096d2023-07-27T23:00:31ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202023-01-011141086109910.35833/MPCE.2023.00004210105888Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive InterpolationMaolin Zhu0Hao Liu1Junbo Zhao2Bendong Tan3Tianshu Bi4Samson Shenglong Yu5North China Electric Power University,State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,Beijing,ChinaNorth China Electric Power University,State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,Beijing,ChinaUniversity of Connecticut,Department of Electrical and Computer Engineering,Storrs,USA,06269University of Connecticut,Department of Electrical and Computer Engineering,Storrs,USA,06269North China Electric Power University,State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,Beijing,ChinaDeakin University,School of Engineering,Waurn Ponds,VIC,Australia,3216Dynamic state estimation (DSE) accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time. This paper proposes a DSE approach for a doubly-fed induction generator (DFIG) with unknown inputs based on adaptive interpolation and cubature Kalman filter (AICKF-UI). DFIGs adopt different control strategies in normal and fault conditions; thus, the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases. Consequently, the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs, which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter. Furthermore, as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs, a large estimation error may occur or the DSE approach may diverge. To this end, in this paper, a local-truncation-error-guided adaptive interpolation approach is developed. Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can (1) effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient; (2) accurately track the dynamic states and unknown inputs of the DFIG; and (3) deal with various types of system operating conditions such as time-varying wind and different system faults.https://ieeexplore.ieee.org/document/10105888/Adaptive interpolationcubature Kalman filteringdoubly-fed induction generator (DFIG)dynamic state estimationunknown input |
| spellingShingle | Maolin Zhu Hao Liu Junbo Zhao Bendong Tan Tianshu Bi Samson Shenglong Yu Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation Journal of Modern Power Systems and Clean Energy Adaptive interpolation cubature Kalman filtering doubly-fed induction generator (DFIG) dynamic state estimation unknown input |
| title | Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation |
| title_full | Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation |
| title_fullStr | Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation |
| title_full_unstemmed | Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation |
| title_short | Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation |
| title_sort | dynamic state estimation for dfig with unknown inputs based on cubature kalman filter and adaptive interpolation |
| topic | Adaptive interpolation cubature Kalman filtering doubly-fed induction generator (DFIG) dynamic state estimation unknown input |
| url | https://ieeexplore.ieee.org/document/10105888/ |
| work_keys_str_mv | AT maolinzhu dynamicstateestimationfordfigwithunknowninputsbasedoncubaturekalmanfilterandadaptiveinterpolation AT haoliu dynamicstateestimationfordfigwithunknowninputsbasedoncubaturekalmanfilterandadaptiveinterpolation AT junbozhao dynamicstateestimationfordfigwithunknowninputsbasedoncubaturekalmanfilterandadaptiveinterpolation AT bendongtan dynamicstateestimationfordfigwithunknowninputsbasedoncubaturekalmanfilterandadaptiveinterpolation AT tianshubi dynamicstateestimationfordfigwithunknowninputsbasedoncubaturekalmanfilterandadaptiveinterpolation AT samsonshenglongyu dynamicstateestimationfordfigwithunknowninputsbasedoncubaturekalmanfilterandadaptiveinterpolation |