A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data
Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart met...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/7978263 |
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| _version_ | 1832563462809583616 |
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| author | Felix Ghislain Yem Souhe Alexandre Teplaira Boum Pierre Ele Camille Franklin Mbey Vinny Junior Foba Kakeu |
| author_facet | Felix Ghislain Yem Souhe Alexandre Teplaira Boum Pierre Ele Camille Franklin Mbey Vinny Junior Foba Kakeu |
| author_sort | Felix Ghislain Yem Souhe |
| collection | DOAJ |
| description | Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method. |
| format | Article |
| id | doaj-art-e97056d3f56d412483bf6ac95212560f |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-e97056d3f56d412483bf6ac95212560f2025-02-03T01:20:12ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/7978263A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter DataFelix Ghislain Yem Souhe0Alexandre Teplaira Boum1Pierre Ele2Camille Franklin Mbey3Vinny Junior Foba Kakeu4Technology and Applied Science LaboratoryDepartment of Electrical EngineeringTechnology and Applied Science LaboratoryDepartment of Electrical EngineeringDepartment of Electrical EngineeringSmart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.http://dx.doi.org/10.1155/2022/7978263 |
| spellingShingle | Felix Ghislain Yem Souhe Alexandre Teplaira Boum Pierre Ele Camille Franklin Mbey Vinny Junior Foba Kakeu A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data Applied Computational Intelligence and Soft Computing |
| title | A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data |
| title_full | A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data |
| title_fullStr | A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data |
| title_full_unstemmed | A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data |
| title_short | A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data |
| title_sort | novel smart method for state estimation in a smart grid using smart meter data |
| url | http://dx.doi.org/10.1155/2022/7978263 |
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