Research on Supervision System of Power Safety Tools and Equipment Based on Internet of Things Technology

Authors

  • Ping He State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China
  • Zhengyi Zhu State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China
  • Xuyan Wang State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China
  • Can Zhang State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China
  • Wei Yuan State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China
  • Junhua Hao Yijiahe Technology Co., Ltd, Nanjing 210012, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.3847

Keywords:

Power safety, IoT, equipment, electrical system, cloud computing

Abstract

A power-system protection device built using Internet-of-Things (IoT) technologies in an intelligent environment. IoT supports electrical and physical parameters monitoring. One of the characteristics that must be checked is electricity usage from electronic gadgets. It is a complex problem to design energy-efficient IoT methods. IoT gets more complicated because of its vast size, and current wireless sensor network approaches cannot be used directly to IoT. Information gathering on the area is monitored by intelligent cellular terminals, intelligent security tools, and other multi-source sensing equipment. That is the foundation for the combined analysis and evaluation of security risk extensive data by cloud computing and edge computing. The IoT-based Power safety tools management (IoT-PSTM) system has been developed to integrate it into intelligent settings, such as smart homes or smart cities, to safeguard electrical equipment. It is meant to increase power security by quickly disconnecting in failure events such as leaking current. The system allows for real-time monitoring and alerting of events using a sophisticated data-concentration architecture communication interface. The goal is to progress and merge several technologies technically and integrate them into a personal safety system to increase security, preserve their availability, eliminate mistakes, and reduce the time required for scheduled or ad hoc interventions. Real-time data transmission, instant data processing from diverse sources, local intelligence in low-power embedded systems, interaction with many on-site users, sophisticated user interfaces, portability, and wearability are the main difficulties for the research project. This article offers a comprehensive explanation of the design and execution of the proposed system and the test findings. The results denote the higher performance of the suggested IoT-PSTM system with IoT module and enhanced performance of 94.7%.

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Author Biographies

Ping He, State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China

Ping He (1981.01–), who received a bachelor’s degree from Wuhan University of water resources and electric power in 2002, is now the deputy director of the operation and maintenance department of Nanjing power supply company of State Grid. Her research interests include smart grid, power system automation, etc.

Zhengyi Zhu, State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China

Zhengyi Zhu (1987.10–), who received a doctorate from Shandong University in 2018, is now an engineer in the operation and maintenance department of State Grid Nanjing power supply company. His research interests include smart grid, power Internet of things, energy Internet, etc.

Xuyan Wang, State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China

Xuyan Wang (1982.10–), who received a master’s degree from Newcastle University in 2007, is now a senior engineer in the operation and maintenance department of State Grid Nanjing power supply company. His research interests include smart grid, distribution automation, etc.

Can Zhang, State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China

Can Zhang (1989.10–), who obtained a master’s degree from Zhejiang University in 2014, is now an engineer in the operation and maintenance department of State Grid Nanjing power supply company. His research interests include intelligent distribution network, intelligent operation inspection of distribution network, etc.

Wei Yuan, State Grid Nanjing Power Supply Company, Nanjing 210019, Jiangsu, China

Wei Yuan (1991.05–), who obtained a bachelor’s degree from Southeast University in 2014, is now an engineer in the operation and maintenance department of State Grid Nanjing power supply company. His research interests include intelligent distribution network, intelligent operation inspection of distribution network, etc.

References

Zhang, J., Fu, J., Hao, H., Fu, G., Nie, F., and Zhang, W. (2020). Root causes of coal mine accidents: Characteristics of safety culture deficiencies based on accident statistics. Process Safety and Environmental Protection, 136, 78–91.

Utne, I. B., Rokseth, B., Sørensen, A. J., and Vinnem, J. E. (2020). Towards supervisory risk control of autonomous ships. Reliability Engineering & System Safety, 196, 106757.

Manogaran, G., Saravanan, V., and Hsu, C. H. (2021). Information-Centric Content Management Framework for Software-Defined Internet of Vehicles Towards Application Specific Services. IEEE Transactions on Intelligent Transportation Systems.

Qu, J., He, L., Tang, N., and Lee, C. K. (2020). Wireless power transfer using domino-resonator for 110-KV power grid online monitoring equipment. IEEE Transactions on Power Electronics, 35(11), 11380–11390.

Nguyen, T., Liu, B. H., Nguyen, N., Dumba, B., and Chou, J. T. (2021). Smart Grid Vulnerability and Defense Analysis Under Cascading Failure Attacks. IEEE Transactions on Power Delivery, 1–1.

Kuthadi, V. M., Selvaraj, R., Baskar, S., Shakeel, P. M., and Ranjan, A. (2021). Optimized Energy Management Model on Data Distributing Framework of Wireless Sensor Network in IoT System. Wireless Personal Communications, 1–27. https://doi.org/10.1007/s11277-021-08583-0

Zhang, X., Manogaran, G., and Muthu, B. (2021). IoT enabled an integrated system for green energy into smart cities. Sustainable Energy Technologies and Assessments, 46, 101208.

Gao, J., Wang, H., and Shen, H. (2020, May). Smartly handling renewable energy instability in supporting a cloud datacenter. In 2020 IEEE international parallel and distributed processing symposium (IPDPS) (pp. 769–778). IEEE.

Amudha, G. (2021). ACDS—Assisted Cooperative Decision-Support for Reliable Interaction based Navigation Assistance for Autonomous Vehicles. Microprocessors and Microsystems, 104241.

Yılmaz, İ. H., Mwesigye, A., and Göksu, T. T. (2020). Enhancing the overall thermal performance of a large aperture parabolic trough solar collector using wire coil inserts. Sustainable Energy Technologies and Assessments, 39, 100696.

Amudha, G., and Narayanasamy, P. (2018). Distributed location and trust-based replica detection in wireless sensor networks. Wireless Personal Communications, 102(4), 3303–3321.

Sharma, P., Shankar, A., and Cheng, X. (2021). Reduced PAPR Model Predictive Control based FBMC/OQAM signal for NB-IoT paradigm. International Journal of Machine Learning and Cybernetics, 1–15.

Shanmugam, L., Mani, P., and Joo, Y. H. (2020). Stabilisation of event-triggered-based neural network control system and its application to wind power generation systems. IET Control Theory & Applications, 14(10), 1321–1333.

Gunasekaran, N., Thoiyab, N. M., Muruganantham, P., Rajchakit, G., and Unyong, B. (2020). Novel global robust stability analysis results for dynamical delayed neural networks under parameter uncertainties. IEEE Access, 8, 178108–178116.

Collazos, J. D., Gaona-García, E. E., Gaona-García, P. A., Montenegro, M. C. E., and Gómez-Acosta, A. (2020, June). Prediction model for energy consumption and generation based on artificial neural networks. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1–6). IEEE.

Khamparia, A., Singh, S. K., Luhach, A. K., and Gao, X. Z. (2020). Classification and analysis of users review using different classification techniques in an intelligent e-learning system. International Journal of Intelligent Information and Database Systems, 13(2–4), 139–149.

Indukuri, C. L., and Kottursamy, K. (2021). Advanced Accident Avoiding, Tracking, and SOS Alert System Using GPS Module and Raspberry Pi. In Artificial Intelligence Techniques for Advanced Computing Applications (pp. 167–178). Springer, Singapore.

Billah, M. F. R. M., Saoda, N., Gao, J., and Campbell, B. (2021, May). BLE Can See A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) (pp. 132–147).

Do, D. T., Van Nguyen, M. S., Nguyen, T. N., Li, X., and Choi, K. (2020). Enabling multiple power beacons for the uplink of noma-enabled mobile edge computing in wirelessly powered IoT. IEEE Access, 8, 148892–148905.

Abouloula, K., Ou-Yassine, A., Krit, S. D., and Elhoseny, M. (2021). Artificial Intelligence–Based Methods of Financial Time Series for Trading Experts in a Relational Database to Generate Decisions. In the Internet of Everything and Big Data (pp. 101–114). CRC Press.

Choi, C., Esposito, C., Wang, H., Liu, Z., and Choi, J. (2018). Intelligent power equipment management based on distributed context-aware inference in smart cities. IEEE Communications Magazine, 56(7), 212–217.

Liu, Y. (2019, October). The Influence of Smart Grid on Electric Power Automation. In International Conference on Advanced Intelligent Systems and Informatics (pp. 1036–1043). Springer, Cham.

Hashim, M. S., Yong, J. Y., Ramachandaramurthy, V. K., Tan, K. M., Mansor, M., and Tariq, M. (2021). Priority-based vehicle-to-grid scheduling for minimization of power grid load variance. Journal of Energy Storage, 39, 102607.

Fan, M., and Zhang, X. (2019). Consortium blockchain-based data aggregation and regulation mechanism for smart grid. IEEE Access, 7, 35929–35940.

Li, L., Ren, X., Yang, Y., Zhang, P., and Chen, X. (2018). Analysis and recommendations for onshore wind power policies in China. Renewable and Sustainable Energy Reviews, 82, 156–167.

Marten, A. K., Akmatov, V., Sørensen, T. B., Stornowski, R., Westermann, D., and Brosinsky, C. (2018). Kriegers flak-combined grid solution: coordinated cross-border control of a meshed HVAC/HVDC offshore wind power grid. IET Renewable Power Generation, 12(13), 1493–1499.

Mao, D., Gao, Z., and Wang, J. (2019). An integrated algorithm for evaluating plug-in electric vehicle’s impact on the state of power grid assets. International Journal of Electrical Power & Energy Systems, 105, 793–802.

Rafique, S. F., Shen, P., Wang, Z., Rafique, R., Iqbal, T., Ijaz, S., and Javaid, U. (2018). Global power grid interconnection for sustainable growth: concept, project and research direction. IET Generation, Transmission & Distribution, 12(13), 3114–3123.

Mao, D., Tan, J., and Wang, J. (2020). Location planning of PEV fast charging station: an integrated approach under traffic and power grid requirements. IEEE Transactions on Intelligent Transportation Systems, 22(1), 483–492.

Hayakawa, N., Maeno, Y., and Kojima, H. (2018). Fault current limitation coordination in electric power grid with superconducting fault current limiters. IEEE Transactions on Applied Superconductivity, 28(4), 1–4.

Zecchino, A., and Marinelli, M. (2018). Analytical assessment of voltage support via reactive power from new electric vehicles supply equipment in radial distribution grids with voltage-dependent loads. International Journal of Electrical Power & Energy Systems, 97, 17–27.

Ilyushin, P. V. (2018). Analysis of the specifics of selecting relay protection and automatic (RPA) equipment in distributed networks with auxiliary low-power generating facilities. Power Technology and Engineering, 51(6), 713–718.

He, Y., Xiong, W., Yang, B., Yang, H. Y., Zhou, J. F., Cui, M. L., and Li, Y. (2021). Combined game model and investment decision-making of power grid-distributed energy system. Environment, Development, and Sustainability, 1–24.

Ding, L., Shi, Y., He, C., Dai, Q., Zhang, Z., Li, J., and Zhou, L. (2021). How does the satisfaction of solar PV users enhance their trust in the power grid?-Evidence from PPAPs in rural China. Energy, Sustainability and Society, 11(1), 1–19.

Adetokun, B. B., and Muriithi, C. M. (2021). Impact of integrating large-scale DFIG-based wind energy conversion system on the voltage stability of weak national grids: a case study of the Nigerian power grid. Energy Reports, 7, 654–666.

Babu, D. V., Saravanan, V., Kumar, P., and Singh, S. (2015). Automated robotic receptionist with embedded touch screen. Journal of Chemical and Pharmaceutical Sciences, 415–417.

Xue, M., Al-Turjman, F., and Saravanan, V. (2021). A Labor Safety Performance and Involvement of Workers in Accident Reduction and Prevention. Aggression and Violent Behavior, 101560.

Yu, L., Nazir, B., and Wang, Y. (2020). Intelligent power monitoring of building equipment based on Internet of Things technology. Computer Communications, 157, 76–84.

Abdulla, A. I., Abdulraheem, A. S., Salih, A. A., Sadeeq, M. A., Ahmed, A. J., Ferzor, B. M., …and Mohammed, S. I. (2020). Internet of things and smart home security. Technol. Rep. Kansai Univ, 62(5), 2465–2476.

Published

2023-05-18

How to Cite

He, P. ., Zhu, Z. ., Wang, X. ., Zhang, C. ., Yuan, W. ., & Hao, J. . (2023). Research on Supervision System of Power Safety Tools and Equipment Based on Internet of Things Technology. Distributed Generation &Amp; Alternative Energy Journal, 38(04), 1223–1254. https://doi.org/10.13052/dgaej2156-3306.3847

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