A SCADA platform architecture for cloud-based micro-service system with real time data process

Liang, Shen; He, Chunhua; Fang, Wenchong; Zhou, Zhifeng; Li, Yingchen; Wang, Yichang

doi:10.1088/1757-899x/677/4/042056

Cited by 2 publications

(2 citation statements)

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“…With the rapid development of smart grids and microservices, some research studies are exploring the application of microservice technology in the field of smart grids [15]- [17]. Liang et al [15] proposed a cloud-based microservice architecture for a real-time data process in the SCADA power control system. As an advantage, in this paper the functions of the data collection, processing, storage, interaction, and display in the SCADA system are analyzed.…”

Section: Related Workmentioning

confidence: 99%

“…With the rapid development of microservices, some studies have explorered the application of microservice technology in the field of smart grids [15]- [17]. Although a microservicebased architecture offers many benefits for smart grids, with the deregulation of the power systems [18], a power grid system is run by many different companies competing with each other.…”

Section: Introductionmentioning

confidence: 99%

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A Subgrid-Oriented Privacy-Preserving Microservice Framework Based on Deep Neural Network for False Data Injection Attack Detection in Smart Grids

Yin

Zhu

2022

IEEE Trans. Ind. Inf.

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False data injection attacks (FDIAs) have recently become a major threat to smart grids. Most of the existing FDIAS detection methods have focused on modeling the temporal relationship of time-series measurement data, but have paid less attention to the spatial relationship between bus/line measurement data, and have failed to consider the relationship between sub-grids. To address these issues, we propose a sub-grid-oriented microservice framework by integrating a well-designed spatialtemporal neural network for FDIA detection in AC-model power systems. First, a well-designed neural network was developed to model the spatial-temporal relationship of bus/line measurements for sub-grids. A microservice-based supervising network is then proposed for integrating the representation features obtained from sub-grids for the collaborative detection of FDIAs. To evaluate the proposed framework, three types of FDIA datasets are generated based on a public benchmark power grid. Case studies on the FDIA datasets show that our method outperforms state-of-the-art methods for FDIA detection in these datasets.

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Section: Related Workmentioning

confidence: 99%