Abstract
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.
Highlights
Gas-insulated switchgears (GISs) have a superior interruption and insulation performance compared to traditional air-insulated switchgears [1]–[3]
This paper presents new online monitoring and tracking for gas-insulated switchgear (GIS) defects based on a novel Internet of Things (IoT) architecture and machine learning technique
The defects of the GIS are classified based on effective new machine learning techniques
Summary
Gas-insulated switchgears (GISs) have a superior interruption and insulation performance compared to traditional air-insulated switchgears [1]–[3]. To cover the abovementioned gap in the literature, this study is aiming to propose a novel IoT topology for the online monitoring and defect diagnoses of GIS in an effective manner. Various test scenarios are simulated on diverse GIS defects that prove the efficiency and security of the proposed IoT topology. The merit of the proposed IoT is to visualize all GIS defects with diverse alarms and the cyber-attacks on the networks efficiently. Introducing intelligent online monitoring for the status of the GIS to diagnose various defects based on partial discharge pulse sequence features. The experimental results emphasize the superiority of the proposed IoT architecture integrating machine learning to monitor and diagnose partial discharges in GIS towards an effective, reliable, and securing power system
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