A KRR-UKF robust state estimation method for distribution networks
State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfigurati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1295070/full |
| _version_ | 1797629477752143872 |
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| author | Wei Zhang Shaomei Zhang Yongchen Zhang Guang Xu Huizong Mao |
| author_facet | Wei Zhang Shaomei Zhang Yongchen Zhang Guang Xu Huizong Mao |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness. |
| first_indexed | 2024-03-11T10:53:50Z |
| format | Article |
| id | doaj.art-28b81d2dee7a4044b31259d4e58be955 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-11T10:53:50Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-28b81d2dee7a4044b31259d4e58be9552023-11-13T11:45:22ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-11-011110.3389/fenrg.2023.12950701295070A KRR-UKF robust state estimation method for distribution networksWei Zhang0Shaomei Zhang1Yongchen Zhang2Guang Xu3Huizong Mao4Zhuhai XJ Electric Co, Ltd, Zhuhai, ChinaCollege of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaZhuhai XJ Electric Co, Ltd, Zhuhai, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaState estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1295070/fulldeep learningkernel ridge regressionoutlier detection methodstate estimationunscented Kalman filter |
| spellingShingle | Wei Zhang Shaomei Zhang Yongchen Zhang Guang Xu Huizong Mao A KRR-UKF robust state estimation method for distribution networks Frontiers in Energy Research deep learning kernel ridge regression outlier detection method state estimation unscented Kalman filter |
| title | A KRR-UKF robust state estimation method for distribution networks |
| title_full | A KRR-UKF robust state estimation method for distribution networks |
| title_fullStr | A KRR-UKF robust state estimation method for distribution networks |
| title_full_unstemmed | A KRR-UKF robust state estimation method for distribution networks |
| title_short | A KRR-UKF robust state estimation method for distribution networks |
| title_sort | krr ukf robust state estimation method for distribution networks |
| topic | deep learning kernel ridge regression outlier detection method state estimation unscented Kalman filter |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1295070/full |
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