SCKF-LSTM-Based Trajectory Tracking for Electricity-Gas Integrated Energy System
A novel approach of tracking the dynamic trajectories for electricity-gas interconnected networks is developed in the studies, leveraging a Kalman filter-based structure. To capture the accurate system trajectories, the Holt's exponential smoothing techniques and nonlinear dynamic equations of...
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| Vydáno v: | IEEE transactions on industrial informatics Ročník 21; číslo 6; s. 4296 - 4305 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | A novel approach of tracking the dynamic trajectories for electricity-gas interconnected networks is developed in the studies, leveraging a Kalman filter-based structure. To capture the accurate system trajectories, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish the power and gas system equations, respectively. Addressing the numerical challenges posed by the strongly nonlinear system, a square-root cubature Kalman technique-based tracking solution is adopted. For the effectiveness in time series prediction, the mass flow rates forecasting task of gas loads is undertaken by employing a long short-term memory network at each computation step. Consequently, a combined method for tracking the dynamic trajectories of comprehensive energy systems by combining these two algorithms is constructed. The IEEE 39-bus network as well as the GasLib-40 node gas network is integrated by gas turbine units to form the multienergy network, and two indexes are introduced for a numerical analysis of the tracking performances. The outcomes demonstrate that the suggested approach significantly improves tracking accuracy when contrasted with the reference measurements. |
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| AbstractList | A novel approach of tracking the dynamic trajectories for electricity–gas interconnected networks is developed in the studies, leveraging a Kalman filter-based structure. To capture the accurate system trajectories, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish the power and gas system equations, respectively. Addressing the numerical challenges posed by the strongly nonlinear system, a square-root cubature Kalman technique-based tracking solution is adopted. For the effectiveness in time series prediction, the mass flow rates forecasting task of gas loads is undertaken by employing a long short-term memory network at each computation step. Consequently, a combined method for tracking the dynamic trajectories of comprehensive energy systems by combining these two algorithms is constructed. The IEEE 39-bus network as well as the GasLib-40 node gas network is integrated by gas turbine units to form the multienergy network, and two indexes are introduced for a numerical analysis of the tracking performances. The outcomes demonstrate that the suggested approach significantly improves tracking accuracy when contrasted with the reference measurements. |
| Author | Li, Yang Chen, Liang Gu, Songlin Cai, Jun Yan, Ying |
| Author_xml | – sequence: 1 givenname: Liang orcidid: 0000-0002-2412-9066 surname: Chen fullname: Chen, Liang email: ch.lg@nuist.edu.cn organization: School of Automation, Nanjing University of Information Science and Technology, Nanjing, China – sequence: 2 givenname: Yang orcidid: 0000-0002-6515-4567 surname: Li fullname: Li, Yang email: liyang@neepu.edu.cn organization: School of Electrical Engineering, Northeast Electric Power University, Jilin, China – sequence: 3 givenname: Jun orcidid: 0000-0002-4574-1692 surname: Cai fullname: Cai, Jun email: j.cai@nuist.edu.cn organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing, China – sequence: 4 givenname: Songlin surname: Gu fullname: Gu, Songlin email: gsl0516@163.com organization: State Grid Economic and Technological Research Institute Company Ltd, Beijing, China – sequence: 5 givenname: Ying orcidid: 0000-0002-3609-0496 surname: Yan fullname: Yan, Ying email: ying.yan@nuist.edu.cn organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing, China |
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| Cites_doi | 10.1016/j.energy.2021.121917 10.1016/j.jenvman.2024.120131 10.1109/TSG.2018.2870327 10.1109/TII.2017.2730846 10.3390/rs14184494 10.1016/j.automatica.2023.111148 10.1109/TIA.2022.3161607 10.1109/TIM.2023.3338666 10.1016/j.jfranklin.2023.07.022 10.23919/PCMP.2023.000142 10.1109/TAC.2009.2019800 10.1016/j.energy.2022.125083 10.1016/j.apenergy.2023.120866 10.1016/j.arcontrol.2023.100909 10.1016/j.est.2023.108920 10.1016/j.egyr.2023.06.027 10.1016/j.enconman.2022.115447 10.1109/TSG.2018.2847223 10.17775/CSEEJPES.2019.00400 10.1016/j.energy.2022.124068 10.1109/EI256261.2022.10117101 10.1016/j.epsr.2023.109557 10.1016/j.energy.2023.127007 10.1109/TII.2023.3242580 10.1016/j.isatra.2023.02.010 10.1016/j.est.2023.108245 10.1109/tii.2023.3275712 10.1016/j.egyr.2023.05.046 10.3390/data2040040 10.1109/TSTE.2017.2717600 10.1016/j.apenergy.2020.114789 10.1016/j.energy.2020.117664 10.1109/JSEN.2022.3159662 10.1109/TII.2021.3109095 |
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| SubjectTerms | Algorithms Current measurement Dynamical systems Electricity Gas pipelines Gas turbines Integrated energy system (IES) Integrated energy systems Kalman filters Long short term memory long short-term memory (LSTM) Mass flow rate Mathematical models Nonlinear dynamics Nonlinear systems Numerical analysis Pipelines Power system dynamics Predictive models Smoothing methods square-root cubature Kalman filter (SCKF) Tracking Trajectory tracking Vectors |
| Title | SCKF-LSTM-Based Trajectory Tracking for Electricity-Gas Integrated Energy System |
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