Robust state estimation in power systems using pre-filtering measurement data
State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2017-03-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_722_e2f566a3eacbd1b0c6e0317ee4358747.pdf |
| _version_ | 1830134918080888832 |
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| author | Mohsen Khosravi Mahdi Banejad Heydar Toosian Shandiz |
| author_facet | Mohsen Khosravi Mahdi Banejad Heydar Toosian Shandiz |
| author_sort | Mohsen Khosravi |
| collection | DOAJ |
| description | State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual calculation. The estimator is equipped with a filter formed in different times according to Principal Component Analysis (PCA) of measurement data. In addition, the proposed estimator employs the dynamic relationships of the system and the prediction property of the Extended Kalman Filter (EKF) to estimate the states of network fast and precisely. Therefore, it makes real-time monitoring of the power network possible. The proposed dynamic model also enables the estimator to estimate the states of a large scale system online. Results of state estimation of the proposed algorithm for an IEEE 9 bus system shows that even with the presence of bad data, the estimator provides a valid and precise estimation of system states and tracks the network with appropriate speed. |
| first_indexed | 2024-12-17T07:22:34Z |
| format | Article |
| id | doaj.art-7669db5860b64bb59dd7038a22f56db4 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-17T07:22:34Z |
| publishDate | 2017-03-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-7669db5860b64bb59dd7038a22f56db42022-12-21T21:58:43ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442017-03-015111112510.22044/jadm.2016.722722Robust state estimation in power systems using pre-filtering measurement dataMohsen Khosravi0Mahdi Banejad1Heydar Toosian Shandiz2Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran.Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran.Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran.State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual calculation. The estimator is equipped with a filter formed in different times according to Principal Component Analysis (PCA) of measurement data. In addition, the proposed estimator employs the dynamic relationships of the system and the prediction property of the Extended Kalman Filter (EKF) to estimate the states of network fast and precisely. Therefore, it makes real-time monitoring of the power network possible. The proposed dynamic model also enables the estimator to estimate the states of a large scale system online. Results of state estimation of the proposed algorithm for an IEEE 9 bus system shows that even with the presence of bad data, the estimator provides a valid and precise estimation of system states and tracks the network with appropriate speed.http://jad.shahroodut.ac.ir/article_722_e2f566a3eacbd1b0c6e0317ee4358747.pdfBad DataEKFPCAPhasor Measurement UnitRobust State Estimation |
| spellingShingle | Mohsen Khosravi Mahdi Banejad Heydar Toosian Shandiz Robust state estimation in power systems using pre-filtering measurement data Journal of Artificial Intelligence and Data Mining Bad Data EKF PCA Phasor Measurement Unit Robust State Estimation |
| title | Robust state estimation in power systems using pre-filtering measurement data |
| title_full | Robust state estimation in power systems using pre-filtering measurement data |
| title_fullStr | Robust state estimation in power systems using pre-filtering measurement data |
| title_full_unstemmed | Robust state estimation in power systems using pre-filtering measurement data |
| title_short | Robust state estimation in power systems using pre-filtering measurement data |
| title_sort | robust state estimation in power systems using pre filtering measurement data |
| topic | Bad Data EKF PCA Phasor Measurement Unit Robust State Estimation |
| url | http://jad.shahroodut.ac.ir/article_722_e2f566a3eacbd1b0c6e0317ee4358747.pdf |
| work_keys_str_mv | AT mohsenkhosravi robuststateestimationinpowersystemsusingprefilteringmeasurementdata AT mahdibanejad robuststateestimationinpowersystemsusingprefilteringmeasurementdata AT heydartoosianshandiz robuststateestimationinpowersystemsusingprefilteringmeasurementdata |