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Neural network extended state-observer for energy system monitoring

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  • Zhu, Yunlong
  • Dong, Zhe
  • Cheng, Zhonghua
  • Huang, Xiaojin
  • Dong, Yujie
  • Zhang, Zuoyi

Abstract

Due to the complexity of industrial energy systems such as the thermal power plants, renewable plants and batteries, energy system monitoring is gaining more and more attentions. Energy system monitoring methods give the estimations of internal process variables or parameters that can be adopted as the possible indicators for health conditions, which not only founds the basis for diagnosis and prognostics but also strengthens the situation awareness of system operators. In this paper, a neural network extended state-observer (NN-ESO) is newly proposed for general energy systems with measurements of key process variables, where the neural network adopted is the multi-layer perception (MLP). The advanced characteristics of this NN-ESO lies in the following three aspects: (1) No prior knowledge about the process dynamics is used in the observer design. (2) The globally bounded estimations of the exterior disturbances and their changing rates in the measurement channels can be provided. (3) The MLP in the NN-ESO is trained and deployed online without any offline training samples. By adopting the estimations of the exterior disturbances and their varying rates as the health indicators, the NN-ESO is applied for the operation monitoring of a modular high temperature gas-cooled reactor which is a typical small modular nuclear reactor. Simulation results show not only the satisfactory estimation performance but also the feasibility of applying NN-ESO for system monitoring.

Suggested Citation

  • Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222026226
    DOI: 10.1016/j.energy.2022.125736
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    as
    1. Dong, Zhe & Pan, Yifei & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2018. "Dynamical modeling and simulation of the six-modular high temperature gas-cooled reactor plant HTR-PM600," Energy, Elsevier, vol. 155(C), pages 971-991.
    2. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    5. Zheng Liu & Yuan Qiu & Chunshan Yang & Jianbo Ji & Zhenhua Zhao & Carlos Aguilar-Ibanez, 2021. "A State of Charge Estimation Method for Lithium-Ion Battery Using PID Compensator-Based Adaptive Extended Kalman Filter," Complexity, Hindawi, vol. 2021, pages 1-14, February.
    6. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    7. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.
    8. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
    9. Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
    10. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    11. Che, Yunhong & Deng, Zhongwei & Li, Penghua & Tang, Xiaolin & Khosravinia, Kavian & Lin, Xianke & Hu, Xiaosong, 2022. "State of health prognostics for series battery packs: A universal deep learning method," Energy, Elsevier, vol. 238(PB).
    12. Nastro, Francesco & Sorrentino, Marco & Trifirò, Alena, 2022. "A machine learning approach based on neural networks for energy diagnosis of telecommunication sites," Energy, Elsevier, vol. 245(C).
    13. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    14. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    15. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    16. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
    17. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    18. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
    19. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    20. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    21. Triki-Lahiani, Asma & Bennani-Ben Abdelghani, Afef & Slama-Belkhodja, Ilhem, 2018. "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2680-2692.
    22. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    23. Dong, Zhe & Li, Bowen & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2022. "Power-pressure coordinated control of modular high temperature gas-cooled reactors," Energy, Elsevier, vol. 252(C).
    24. Sturm, J. & Ennifar, H. & Erhard, S.V. & Rheinfeld, A. & Kosch, S. & Jossen, A., 2018. "State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter," Applied Energy, Elsevier, vol. 223(C), pages 103-123.
    25. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    26. Han, Xiaojuan & Wang, Zuran & Wei, Zixuan, 2021. "A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion," Applied Energy, Elsevier, vol. 302(C).
    27. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    28. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    29. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    30. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    31. Ni, Zichuan & Xiu, Xianchao & Yang, Ying, 2022. "Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis," Energy, Elsevier, vol. 254(PC).
    32. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    33. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
    34. Dong, Zhe & Li, Bowen & Li, Junyi & Huang, Xiaojin & Zhang, Zuoyi, 2022. "Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    35. Wang, Huaizhi & Ruan, Jiaqi & Ma, Zhengwei & Zhou, Bin & Fu, Xueqian & Cao, Guangzhong, 2019. "Deep learning aided interval state prediction for improving cyber security in energy internet," Energy, Elsevier, vol. 174(C), pages 1292-1304.
    36. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
    37. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    38. Yuan, Hao & Dai, Haifeng & Wei, Xuezhe & Ming, Pingwen, 2020. "A novel model-based internal state observer of a fuel cell system for electric vehicles using improved Kalman filter approach," Applied Energy, Elsevier, vol. 268(C).
    39. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
    40. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    41. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    42. Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
    43. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    44. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    45. Xi, Zhimin & Wang, Rui & Fu, Yuhong & Mi, Chris, 2022. "Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons," Applied Energy, Elsevier, vol. 305(C).
    46. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    47. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    48. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    49. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    50. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    51. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    52. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    53. Sorrentino, Marco & Acconcia, Matteo & Panagrosso, Davide & Trifirò, Alena, 2016. "Model-based energy monitoring and diagnosis of telecommunication cooling systems," Energy, Elsevier, vol. 116(P1), pages 761-772.
    54. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
    55. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    56. Duong, Van-Huan & Bastawrous, Hany Ayad & See, Khay Wai, 2017. "Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation," Applied Energy, Elsevier, vol. 204(C), pages 560-571.
    57. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    58. Yang, Xiaolong & Chen, Yongji & Li, Bin & Luo, Dong, 2020. "Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model," Energy, Elsevier, vol. 191(C).
    59. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    60. Chen, Leiming & Xu, Zhaoping & Liu, Shuangshuang & Liu, Liang, 2022. "Dynamic modeling of a free-piston engine based on combustion parameters prediction," Energy, Elsevier, vol. 249(C).
    61. Satyavada, Harish & Baldi, Simone, 2018. "Monitoring energy efficiency of condensing boilers via hybrid first-principle modelling and estimation," Energy, Elsevier, vol. 142(C), pages 121-129.
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