Square-root cubature Kalman filter based power system dynamic state estimation
The state estimator’s real-time state information plays a vital role in monitoring and control of the power system. As a result, one of the primary requirements of the estimator is to provide accurate state estimates with good convergence under varied system operating conditions. In this context, dy...
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| Vydané v: | Sustainable Energy, Grids and Networks Ročník 31; s. 100712 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.09.2022
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| ISSN: | 2352-4677, 2352-4677 |
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| Abstract | The state estimator’s real-time state information plays a vital role in monitoring and control of the power system. As a result, one of the primary requirements of the estimator is to provide accurate state estimates with good convergence under varied system operating conditions. In this context, dynamic state estimation (DSE) consistently tracks the power system dynamics and delivers real-time system state evolution. In literature, this dynamic state estimation (SE) problem is generally solved using an extended Kalman filter (EKF). The EKF approach neglects the non-linear mathematical functions of network equations by linearly approximating the measurement equations. This leads to degradation of SE accuracy during a sudden change in load conditions. In this paper, a new derivative-free method based on square-root cubature Kalman filter (SCKF) is proposed to overcome the limitations of the EKF technique and achieves improved performance. The proposed SCKF technique has been tested on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems under various operating conditions. The effectiveness of the SCKF technique has been compared with the traditional EKF and cubature Kalman filter (CKF) techniques using different performance indices. The obtained results demonstrate the versatility of the proposed SCKF technique. |
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| AbstractList | The state estimator’s real-time state information plays a vital role in monitoring and control of the power system. As a result, one of the primary requirements of the estimator is to provide accurate state estimates with good convergence under varied system operating conditions. In this context, dynamic state estimation (DSE) consistently tracks the power system dynamics and delivers real-time system state evolution. In literature, this dynamic state estimation (SE) problem is generally solved using an extended Kalman filter (EKF). The EKF approach neglects the non-linear mathematical functions of network equations by linearly approximating the measurement equations. This leads to degradation of SE accuracy during a sudden change in load conditions. In this paper, a new derivative-free method based on square-root cubature Kalman filter (SCKF) is proposed to overcome the limitations of the EKF technique and achieves improved performance. The proposed SCKF technique has been tested on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems under various operating conditions. The effectiveness of the SCKF technique has been compared with the traditional EKF and cubature Kalman filter (CKF) techniques using different performance indices. The obtained results demonstrate the versatility of the proposed SCKF technique. |
| ArticleNumber | 100712 |
| Author | Shiva, Chandan Kumar Chandel, Ashwani Kumar Basetti, Vedik |
| Author_xml | – sequence: 1 givenname: Vedik surname: Basetti fullname: Basetti, Vedik email: b.vedik@gmail.com organization: Electrical and Electronics Engineering Department, SR University, Warangal, T.S., 506371, India – sequence: 2 givenname: Ashwani Kumar surname: Chandel fullname: Chandel, Ashwani Kumar organization: Electrical Engineering Department, National Institute of Technology-Hamirpur, H.P., 177005, India – sequence: 3 givenname: Chandan Kumar surname: Shiva fullname: Shiva, Chandan Kumar organization: Electrical and Electronics Engineering Department, SR University, Warangal, T.S., 506371, India |
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| Keywords | Dynamic state estimation Brown’s double exponential smoothing Square-root cubature Kalman filter Power systems |
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