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2025 | OriginalPaper | Buchkapitel

Research and Application of Customer Side Security Energy Use Monitoring Technology Based on Artificial Intelligence and Digital Power Room

verfasst von : Jincan Li, Ying Dai, Wanting Zhu, Pei Li, Xiaqin Yang, Ying Liu

Erschienen in: Advances in Communication, Devices and Networking

Verlag: Springer Nature Singapore

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Abstract

With the development of the power industry, smart grid has become the development direction of the future global power grid. With the help of a new generation of information technology, the collection of power consumption data at the power terminal, and the analysis of power consumption data, and then timely prediction or identification of electrical faults, timely warning to the user or automatic cut off the power supply, can effectively reduce the risk of electrical faults caused by accidents, reduce the loss caused by accidents. Therefore, this paper mainly introduces the development and implementation of intelligent electricity safety monitoring system and fault arc identification algorithm. Firstly, the intelligent electricity safety monitoring system is designed. The main functions of the system are as follows: terminal data collection function, LoRa network wireless transmission function, intelligent gateway data exchange function, server human–computer interaction function and intelligent processing algorithm function. Finally, through the system test, it is found that the intelligent electricity monitoring system can identify the fault arc with 96% accuracy, which is suitable for customer-side safety energy monitoring. Through the research and application of intelligent electricity safety monitoring technology, users can grasp the status information and operation of electrical equipment in real time, and establish a perfect operation and maintenance management system based on intelligent electricity monitoring technology.

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Literatur
1.
Zurück zum Zitat Wang H (2021) Design of power line safety operation and maintenance monitoring system based on cloud computing. Int J Inf Commun Technol 19(3):242–257 Wang H (2021) Design of power line safety operation and maintenance monitoring system based on cloud computing. Int J Inf Commun Technol 19(3):242–257
2.
Zurück zum Zitat Luo F, Xu J, Zhang T (2021) Quantitative evaluation of power supply reliability improvement in distribution network by customer-side integrated energy system. Energy Rep 7(6):233–241 Luo F, Xu J, Zhang T (2021) Quantitative evaluation of power supply reliability improvement in distribution network by customer-side integrated energy system. Energy Rep 7(6):233–241
3.
Zurück zum Zitat Sarker E, Halder P, Seyedmahmoudian M et al (2021) Progress on the demand side management in smart grid and optimization approaches. Int J Energy Res 45(1):36–64CrossRef Sarker E, Halder P, Seyedmahmoudian M et al (2021) Progress on the demand side management in smart grid and optimization approaches. Int J Energy Res 45(1):36–64CrossRef
4.
Zurück zum Zitat Kumar P, Lin Y, Bai G et al (2019) Smart grid metering networks: a survey on security, privacy and open research issues. IEEE Commun Surv Tutor 21(3):2886–2927CrossRef Kumar P, Lin Y, Bai G et al (2019) Smart grid metering networks: a survey on security, privacy and open research issues. IEEE Commun Surv Tutor 21(3):2886–2927CrossRef
5.
Zurück zum Zitat Hasan MK, Alkhalifah A, Islam S et al (2022) Blockchain technology on smart grid, energy trading, and big data: security issues, challenges, and recommendations. Wirel Commun Mob Comput 2022(9):1–26 Hasan MK, Alkhalifah A, Islam S et al (2022) Blockchain technology on smart grid, energy trading, and big data: security issues, challenges, and recommendations. Wirel Commun Mob Comput 2022(9):1–26
6.
Zurück zum Zitat Hashmi SA, Ali CF, Zafar S (2021) Internet of things and cloud computing-based energy management system for demand side management in smart grid. Int J Energy Res 45(1):1007–1022CrossRef Hashmi SA, Ali CF, Zafar S (2021) Internet of things and cloud computing-based energy management system for demand side management in smart grid. Int J Energy Res 45(1):1007–1022CrossRef
7.
Zurück zum Zitat Ibrahem MI, Nabil M, Fouda MM et al (2020) Efficient privacy-preserving electricity theft detection with dynamic billing and load monitoring for AMI networks. IEEE Internet Things J 8(2):1243–1258CrossRef Ibrahem MI, Nabil M, Fouda MM et al (2020) Efficient privacy-preserving electricity theft detection with dynamic billing and load monitoring for AMI networks. IEEE Internet Things J 8(2):1243–1258CrossRef
8.
Zurück zum Zitat Rathor SK, Saxena D (2020) Energy management system for smart grid: an overview and key issues. Int J Energy Res 44(6):4067–4109CrossRef Rathor SK, Saxena D (2020) Energy management system for smart grid: an overview and key issues. Int J Energy Res 44(6):4067–4109CrossRef
9.
Zurück zum Zitat Jenssen R, Roverso D (2019) Intelligent monitoring and inspection of power line components powered by UAVs and deep learning. IEEE Power Energy Technol Syst J 6(1):11–21CrossRef Jenssen R, Roverso D (2019) Intelligent monitoring and inspection of power line components powered by UAVs and deep learning. IEEE Power Energy Technol Syst J 6(1):11–21CrossRef
10.
Zurück zum Zitat Zhang Y, Shi X, Zhang H et al (2022) Review on deep learning applications in frequency analysis and control of modern power system. Int J Electr Power Energy Syst 136:107744.1–107744.18 Zhang Y, Shi X, Zhang H et al (2022) Review on deep learning applications in frequency analysis and control of modern power system. Int J Electr Power Energy Syst 136:107744.1–107744.18
11.
Zurück zum Zitat Bohra SS, Anvari-Moghaddam A (2022) A comprehensive review on applications of multicriteria decision-making methods in power and energy systems. Int J Energy Res 46(4):4088–4118CrossRef Bohra SS, Anvari-Moghaddam A (2022) A comprehensive review on applications of multicriteria decision-making methods in power and energy systems. Int J Energy Res 46(4):4088–4118CrossRef
12.
Zurück zum Zitat Jabehdar Maralani P, Kapadia A, Liu G et al (2022) Canadian association of radiologists recommendations for the safe use of MRI during pregnancy. Can Assoc Radiol J 73(1):56–67 Jabehdar Maralani P, Kapadia A, Liu G et al (2022) Canadian association of radiologists recommendations for the safe use of MRI during pregnancy. Can Assoc Radiol J 73(1):56–67
13.
Zurück zum Zitat Cheng L, Yu T (2019) A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int J Energy Res 43(6):1928–1973CrossRef Cheng L, Yu T (2019) A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int J Energy Res 43(6):1928–1973CrossRef
14.
Zurück zum Zitat Kumar P, Hati AS (2021) Review on machine learning algorithm based fault detection in induction motors. Arch Comput Methods Eng 28(4):1929–1940CrossRef Kumar P, Hati AS (2021) Review on machine learning algorithm based fault detection in induction motors. Arch Comput Methods Eng 28(4):1929–1940CrossRef
15.
Zurück zum Zitat Georgijevic NL, Stojic D, Radakovic Z (2020) Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring. Int Trans Electr Energy Syst 30(2):e12234.1–e12234.15 Georgijevic NL, Stojic D, Radakovic Z (2020) Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring. Int Trans Electr Energy Syst 30(2):e12234.1–e12234.15
Metadaten
Titel
Research and Application of Customer Side Security Energy Use Monitoring Technology Based on Artificial Intelligence and Digital Power Room
verfasst von
Jincan Li
Ying Dai
Wanting Zhu
Pei Li
Xiaqin Yang
Ying Liu
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-6465-5_37