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ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability
Leonid Stoimenov1, Milos Bogdanovic, Sanja Bogdanovic-Dinic
1Faculty of Electronic Engineering, University of Niš, Niš 18000, Serbia. leonid.stoimenov@elfak.ni.ac.rs
Sensors (Basel, Switzerland)
|August 20, 2013
Summary
This study introduces an Enterprise Service Bus (ESB)-based Sensor Web solution to predict electric power supply network vulnerability. The system integrates sensor and GIS data to forecast defect probability in network elements, enhancing grid reliability.
Area of Science:
- Computer Science
- Electrical Engineering
- Geographic Information Systems (GIS)
Background:
- Electric power companies depend on enterprise IT systems for distribution network monitoring.
- Existing systems collect data from metering devices, crucial for predicting power supply network vulnerability.
- There is a need for integrated solutions combining diverse data sources for accurate vulnerability assessment.
Purpose of the Study:
- To present an Enterprise Service Bus (ESB)-based Sensor Web integration solution for predicting power supply network vulnerability.
- To enable the prediction of defect probability for specific electric power network elements.
- To demonstrate a vulnerability prediction model using real-world data from power supply companies.
Main Methods:
- Extension of the GinisSense Sensor Web architecture to operate within an ESB environment.
- Integration of Sensor Web and Geographic Information System (GIS) technologies.
- Collection and aggregation of electrical and ambient sensor data using an adapted Omnibus data fusion model.
- Application of decision-making logic and visualization through a specialized Web GIS application.
Main Results:
- The developed ESB-based Sensor Web solution successfully integrates heterogeneous data sources.
- The vulnerability prediction model demonstrated its capability on data from two power supply companies.
- The system effectively visualizes detected vulnerabilities to end-users via a Web GIS interface.
Conclusions:
- The proposed GinisSense upgrade, operating in an ESB environment, enhances electric power supply system vulnerability prediction.
- Combining Sensor Web and GIS technologies provides a comprehensive approach to grid monitoring and risk assessment.
- The solution offers a practical tool for utilities to proactively identify and address potential network vulnerabilities.
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