Robust State Estimation of Active Distribution Networks with Multi-source Measurements
The volatile and intermittent nature of distributed generators (DGs) in active distribution networks (ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units (D-PMUs) enhances the monitoring level. The trade-offs of computational performance and...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9919796/ |
| _version_ | 1797679522987900928 |
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| author | Zhelin Liu Peng Li Chengshan Wang Hao Yu Haoran Ji Wei Xi Jianzhong Wu |
| author_facet | Zhelin Liu Peng Li Chengshan Wang Hao Yu Haoran Ji Wei Xi Jianzhong Wu |
| author_sort | Zhelin Liu |
| collection | DOAJ |
| description | The volatile and intermittent nature of distributed generators (DGs) in active distribution networks (ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units (D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming (SOCP) based robust state estimation (RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems. |
| first_indexed | 2024-03-11T23:16:00Z |
| format | Article |
| id | doaj.art-d406eb56dccb4ea38a3ab40cb8145a20 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-03-11T23:16:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-d406eb56dccb4ea38a3ab40cb8145a202023-09-20T23:00:30ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202023-01-011151540155210.35833/MPCE.2022.0002009919796Robust State Estimation of Active Distribution Networks with Multi-source MeasurementsZhelin Liu0Peng Li1Chengshan Wang2Hao Yu3Haoran Ji4Wei Xi5Jianzhong Wu6Tianjin University,Key Laboratory of Smart Grid of Ministry of Education,Tianjin,China,300072Tianjin University,Key Laboratory of Smart Grid of Ministry of Education,Tianjin,China,300072Tianjin University,Key Laboratory of Smart Grid of Ministry of Education,Tianjin,China,300072Tianjin University,Key Laboratory of Smart Grid of Ministry of Education,Tianjin,China,300072Tianjin University,Key Laboratory of Smart Grid of Ministry of Education,Tianjin,China,300072Digital Grid Research Institute of China Southern Power Grid,Guangzhou,China,510670School of Engineering, Cardiff University,Institute of Energy,Cardiff,UK,CF24 3AAThe volatile and intermittent nature of distributed generators (DGs) in active distribution networks (ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units (D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming (SOCP) based robust state estimation (RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.https://ieeexplore.ieee.org/document/9919796/Active distribution network ADNrobust state estimation (RSE)second-order cone programming (SOCP)multi-source measurementbad data identification |
| spellingShingle | Zhelin Liu Peng Li Chengshan Wang Hao Yu Haoran Ji Wei Xi Jianzhong Wu Robust State Estimation of Active Distribution Networks with Multi-source Measurements Journal of Modern Power Systems and Clean Energy Active distribution network ADN robust state estimation (RSE) second-order cone programming (SOCP) multi-source measurement bad data identification |
| title | Robust State Estimation of Active Distribution Networks with Multi-source Measurements |
| title_full | Robust State Estimation of Active Distribution Networks with Multi-source Measurements |
| title_fullStr | Robust State Estimation of Active Distribution Networks with Multi-source Measurements |
| title_full_unstemmed | Robust State Estimation of Active Distribution Networks with Multi-source Measurements |
| title_short | Robust State Estimation of Active Distribution Networks with Multi-source Measurements |
| title_sort | robust state estimation of active distribution networks with multi source measurements |
| topic | Active distribution network ADN robust state estimation (RSE) second-order cone programming (SOCP) multi-source measurement bad data identification |
| url | https://ieeexplore.ieee.org/document/9919796/ |
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