Article Details

Abstract

To address the challenges of dispersed power quality monitoring points, heterogeneous monitoring terminals, complex network environments, and the difficulty of centralized management of massive monitoring data in industrial sites, this paper proposes and implements a device-edge-cloud collaborative online power quality monitoring system for industrial site deployment. The system adopts a three-layer architecture consisting of field acquisition terminals, edge gateways, and a cloud platform. The edge layer is responsible for the unified access of multi-source heterogeneous terminals, protocol adaptation, standardized data aggregation, and connection maintenance, while the cloud platform performs unified registration of multiple monitoring points, real-time monitoring, trend analysis, limit violation detection, and event management. The engineering performance of the system is validated through practical deployment and continuous operation tests at the power distribution site of a typical industrial user. The results show that the system can reliably acquire and centrally manage key power quality data, including three-phase voltage, current, and harmonics, and can identify and record abnormal events such as voltage sags. Under complex operating conditions such as weak network connectivity, the system demonstrates automatic reconnection capability, achieving a reconnection success rate of 100%. The proposed system provides an engineering reference for the long-term online monitoring of power quality in industrial sites.

Keywords

power quality; online monitoring; device-edge-cloud collaboration; edge gateway; industrial site

References

1. [1] Zhang Shaoru Research on Power Quality Monitoring System and Evaluation Method Based on Internet of Things [D]. Shanghai: East China University of Science and Technology, 2018
2. [2] Qu Zhijian, Yuan Shengao, Fan Mingming Design of dual row family storage for massive data in online monitoring system for power quality [J]. Power System Protection and Control, 2019, 47 (2): 154-160
3. [3] Teng Zhaowei Research on Online Monitoring System for Power Quality Based on Embedded Platform [D]. Beijing: North China Electric Power University, 2020
4. [4] Xiang Song Design of Portable Network Side Overvoltage and Power Quality Comprehensive Monitoring System for High speed EMU [D]. Chengdu: Southwest Jiaotong University, 2024
5. [5] Shi Weisong, Zhang Xingzhou, Wang Yifan, etc Edge computing: Status Quo and Prospects [J]. Computer Research and Development, 2019, 56 (1): 69-89
6. [6] Zhou Z，Chen X，Li E，et al. Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing[J]. Proceedings of the IEEE，2019，107（8）：1738-1762.
7. [7] Ren J K，Yu G D，He Y K，et al. Collaborative Cloud and Edge Computing for Latency Minimization[J].IEEE Transactions on Vehicular Technology，2019，68（5）：5031-5044.
8. [8] Liu Y，Yang C，Jiang L，et al. Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities[J]. IEEE Network，2019，33（2）：111-117.
9. [9] Zhu Bingeng, Chen Xiaoman Design of edge computing power quality gateway based on Modbus protocol [J]. Modern Information Technology, 2023, 7 (22): 174-178
10. [10] Guan Guofei, Song Qingwu, Zhang Wansheng, etc Research and Application of Distribution IoT Architecture Based on edge computing and Reinforcement Learning [J]. Mechanical and Electronics, 2020, 38 (7): 14-22
11. [11] Wang Haitao Research on distributed collaborative optimal dispatching strategy of integrated energy system based on edge computing [D]. Shenyang: Northeast University, 2022
12. [12] Zhu Bingeng, Chen Xiaoman Design and Development of an Online Monitoring Platform for Power Quality Based on Cloud Computing SaaS [J]. Automation and Instrumentation, 2023 (8): 49-51, 55
13. [13] Zhang Mingze Research on intelligent perception application technology of distribution IoT based on edge computing of distribution transformer terminal [D]. Beijing: North China Electric Power University, 2024
14. [14] Li Wu, Gao Qi, Yang Hui, etc Active distribution network real-time optimal dispatching strategy based on edge computing and deep reinforcement learning [J]. Electric Measurement and Instrumentation, 2026,63 (1): 105-114
15. [15] Tong Jie, Qi Zihao, Pu Tianjiao, etc Edge Intelligence of Power Internet of Things: Concept, Architecture, Technology, and Application [J]. China Electrical Engineering Journal, 2024, 44 (14): 5473-5496
16. [16] Dai Yuhang Research on Transmission Scheduling and Resource Allocation Optimization of Power Distribution IoT Business under Cloud Edge Collaboration [D]. Nanchang: East China Jiaotong University, 2025
17. [17] Zhong Jiayong Research on Cloud Edge Collaboration Model and Application of Intelligent Fusion Terminal for Power Distribution Internet of Things [D]. Chongqing: Chongqing University, 2021
18. [18] Liu Yuanyuan Research on the Scheduling Method of Protection and Control Services in Distribution Networks under Cloud Edge Collaborative Architecture [D]. Guangzhou: South China University of Technology, 2024

About the authors

Chen Xiaoman (1987-), female, Han ethnicity, from Jieyang, Guangdong, senior engineer, master's student, research interests include intelligent sensing and detection technologies.