Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM
Modern distribution networks with high penetration of distributed energy resources (DERs) are undergoing continuous expansion in scale. However, the increasing complexity of network structure and the high installation cost of measurement equipment introduce operational challenges including state var...
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Elsevier
2025-09-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000953 |
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| author | Xiaodan Yu Ruijia Jiang Xiaolong Jin Hongjie Jia Yunfei Mu Wei Wei Wanxin Tang |
| author_facet | Xiaodan Yu Ruijia Jiang Xiaolong Jin Hongjie Jia Yunfei Mu Wei Wei Wanxin Tang |
| author_sort | Xiaodan Yu |
| collection | DOAJ |
| description | Modern distribution networks with high penetration of distributed energy resources (DERs) are undergoing continuous expansion in scale. However, the increasing complexity of network structure and the high installation cost of measurement equipment introduce operational challenges including state variability and measurement data incompleteness. Substantial data loss significantly compromises fault detection accuracy and network performance, creating obstacles for distributed energy management and posing critical challenges to distribution network state estimation. To address these issues, this paper proposes a hybrid state estimation framework (MC-VMD-ARIMA-LSTM) that integrates alternating-minimization matrix completion (MC) with variational mode decomposition (VMD), autoregressive integrated moving average (ARIMA) modeling, and long short-term memory (LSTM) neural networks for enhanced power flow analysis in low-observability distribution networks. The methodology features a dual-timescale approach: (1) At individual time intervals, an alternating-minimization matrix completion model is formulated, incorporating linearized power flow constraints; (2) For multi-timescale analysis, the measurement dataset undergoes VMD-based decomposition, with subsequent specialized processing where ARIMA handles low-frequency components and LSTM manage high-frequency residuals. The results of state estimation are obtained through systematic component reconstruction. Comprehensive evaluations using IEEE 33-bus distribution network and actual distribution system measurement datasets demonstrate the framework's effectiveness in both multi-timescale data assimilation and state estimation accuracy under limited observability conditions. |
| format | Article |
| id | doaj-art-47e8ffb67b5b4702b3de0890adb02676 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-47e8ffb67b5b4702b3de0890adb026762025-08-20T03:56:17ZengElsevierEnergy and AI2666-54682025-09-012110056310.1016/j.egyai.2025.100563Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTMXiaodan Yu0Ruijia Jiang1Xiaolong Jin2Hongjie Jia3Yunfei Mu4Wei Wei5Wanxin Tang6State key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR ChinaState key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR ChinaState key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR China; National Industry-Education Platform for Energy Storage (Tianjin University), Tianjin University, Tianjin, 300072, PR China; Corresponding author.State key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR China; National Industry-Education Platform for Energy Storage (Tianjin University), Tianjin University, Tianjin, 300072, PR ChinaState key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR China; National Industry-Education Platform for Energy Storage (Tianjin University), Tianjin University, Tianjin, 300072, PR ChinaState key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR ChinaState key laboratory of Intelligent Power Distribution Equipment and System (Tianjin University), Tianjin 300072, PR China; Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, PR China; Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin University, Tianjin 300072, PR China; National Industry-Education Platform for Energy Storage (Tianjin University), Tianjin University, Tianjin, 300072, PR ChinaModern distribution networks with high penetration of distributed energy resources (DERs) are undergoing continuous expansion in scale. However, the increasing complexity of network structure and the high installation cost of measurement equipment introduce operational challenges including state variability and measurement data incompleteness. Substantial data loss significantly compromises fault detection accuracy and network performance, creating obstacles for distributed energy management and posing critical challenges to distribution network state estimation. To address these issues, this paper proposes a hybrid state estimation framework (MC-VMD-ARIMA-LSTM) that integrates alternating-minimization matrix completion (MC) with variational mode decomposition (VMD), autoregressive integrated moving average (ARIMA) modeling, and long short-term memory (LSTM) neural networks for enhanced power flow analysis in low-observability distribution networks. The methodology features a dual-timescale approach: (1) At individual time intervals, an alternating-minimization matrix completion model is formulated, incorporating linearized power flow constraints; (2) For multi-timescale analysis, the measurement dataset undergoes VMD-based decomposition, with subsequent specialized processing where ARIMA handles low-frequency components and LSTM manage high-frequency residuals. The results of state estimation are obtained through systematic component reconstruction. Comprehensive evaluations using IEEE 33-bus distribution network and actual distribution system measurement datasets demonstrate the framework's effectiveness in both multi-timescale data assimilation and state estimation accuracy under limited observability conditions.http://www.sciencedirect.com/science/article/pii/S2666546825000953Distribution networkMatrix completionState estimateVariational mode decompositionLong short-term memory neural network |
| spellingShingle | Xiaodan Yu Ruijia Jiang Xiaolong Jin Hongjie Jia Yunfei Mu Wei Wei Wanxin Tang Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM Energy and AI Distribution network Matrix completion State estimate Variational mode decomposition Long short-term memory neural network |
| title | Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM |
| title_full | Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM |
| title_fullStr | Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM |
| title_full_unstemmed | Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM |
| title_short | Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM |
| title_sort | method for compensating multi time measurement data of distribution network based on alternating minimization matrix completion combined with vmd arima lstm |
| topic | Distribution network Matrix completion State estimate Variational mode decomposition Long short-term memory neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000953 |
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