Fault detection and control in integrated energy system using machine learning

Abstract Integrated Energy System (IES), which covers electricity/gas/heat and other different energy sources, is an integral source of energy and Fault Detection in dynamic processing. Some key Challenges, such as collaborative planning, tracking optimization, threat review, state assessment, situational awareness, and general demand-side management, are addressed and discussed in this paper. Further, the Integrated Energy System using Machine Learning Technology (IES-ML) has a significant practical and strategic significance for related study and practice in China's energy system development proposed in this research. The Regional Internet Research (RIR) and Development In Energy (DIE) focus on fault detection in china's Energy-based district heating system. In comparison to the conventional power delivery system, IES-ML is used to enhance the economy efficiently. Besides, the protection, reliability, stability, and strength of multi-energy coupling have been validated. RIR and AIE are often used to minimize environmental demand from the District heating energy system. The experimental result shows that IES-ML achieves the highest accuracy of 98.67% and performance in fault detection and control in IES.

[1]  Dirk Müller,et al.  Real-world application of machine-learning-based fault detection trained with experimental data , 2020 .

[2]  Ehab Mahmoud Mohamed,et al.  A Trust-Based Energy-Efficient and Reliable Communication Scheme (Trust-Based ERCS) for Remote Patient Monitoring in Wireless Body Area Networks , 2020, IEEE Access.

[3]  Jaume Salom,et al.  Evaluation of energy flexibility of low-energy residential buildings connected to district heating , 2020, Energy and Buildings.

[4]  Chi Zhang,et al.  Vibrational Triboelectric Nanogenerator-Based Multinode Self-Powered Sensor Network for Machine Fault Detection , 2020, IEEE/ASME Transactions on Mechatronics.

[5]  Guojin Qin,et al.  Investigating an assessment model of system oil leakage considering failure dependence , 2020, Environmental Science and Pollution Research.

[6]  Yu Zhang,et al.  Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals , 2019, IEEE Transactions on Industry Applications.

[7]  James E. Braun,et al.  Development, implementation, and evaluation of a fault detection and diagnostics system based on integrated virtual sensors and fault impact models , 2020 .

[8]  H. Ravn,et al.  Modelling of renewable gas and renewable liquid fuels in future integrated energy systems , 2020, Applied Energy.

[9]  J. Jokisalo,et al.  Demand response potential of district heating and ventilation in an educational office building , 2019, Science and Technology for the Built Environment.

[10]  Natasa Nord,et al.  Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach , 2020, Eng. Appl. Artif. Intell..

[11]  Ali Sulaiman Alsagri,et al.  A new generation of district heating system with neighborhood-scale heat pumps and advanced pipes, a solution for future renewable-based energy systems , 2020 .

[12]  Yuyun Zeng,et al.  Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model , 2019, Renewable Energy.

[13]  Gerasimos Theotokatos,et al.  Machine learning and data-driven fault detection for ship systems operations , 2020 .

[14]  Qingping Li,et al.  Fundamental characteristics of gas hydrate-bearing sediments in the Shenhu area, South China Sea , 2020, Frontiers in Energy.

[15]  Yongping Yang,et al.  Thermodynamic and economic evaluation of a novel heat supply design using low-pressure feedwater in a cogeneration plant , 2020 .

[16]  A. Zarrella,et al.  Increasing the energy flexibility of existing district heating networks through flow rate variations , 2020 .

[17]  Amir Mosavi,et al.  FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks , 2019 .

[18]  Weihao Hu,et al.  Imbalance fault detection based on the integrated analysis strategy for variable-speed wind turbines , 2020 .

[19]  Andrea Borghesi,et al.  A machine learning approach to online fault classification in HPC systems , 2020, Future Gener. Comput. Syst..