IEEE Access · 2021 · 96 citations · 42 references
ComputingCybersecurityElectrical EngineeringCyber-physical SystemsInternet Of ThingsMachine LearningCyber-physical SecurityCybersecurity For Critical InfrastructureFault DetectionIndustrial Internet Of ThingsIot SecurityThreat DetectionAttack Detection In Cyber-physical SystemsCyber-physical Attack DetectionCyber-physical System SecurityExtreme Gradient BoostingGradient BoostingOnline MonitoringPartial DischargeReal-time Monitoring
IoT enables real‑time monitoring and cybersecurity in electric power stations, yet cyber‑attacks and the classification of gas‑insulated switchgear (GIS) insulation defects remain major challenges. This work proposes a novel IoT architecture for online monitoring of GIS status, replacing traditional observation methods. The architecture, built on a cyber‑physical system framework, extracts partial discharge pulse‑sequence features and applies extreme gradient boosting to detect cyber‑attacks and classify GIS defects across multiple test scenarios. Experimental results show the system accurately visualizes all GIS defects with distinct alarms, detects cyber‑attacks, and presents defect and fake‑data recognition on a dashboard with high accuracy, improving decision‑making.
Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision-making about the GIS status.
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Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Zhiqiang Ge, Zhihuan Song, Steven X. Ding et al. · IEEE Access · 2017 · 895 citations · Full text