ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

Abstract

Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

UEFI-based Research on the Inner Operation Mechanism and Characteristics of Firmware Vulnerabilities in Key Devices of Electric Power Monitoring Systems

“…Leonid et al proposed an enterprise service bus-based sensor integration scheme for power network vulnerability prediction that achieves the prediction of the probability of defects in specific network elements. The effectiveness of combining sensor networks and GIS technology for vulnerability prediction is demonstrated by comparing vulnerability data collected by different power monitoring systems [8]. Sperstad, I et al focus on establishing a methodological basis for vulnerability analysis of power monitoring systems that complements traditional risk and reliability analysis of power systems.…”

Geospatial Assessment Methodology to Estimate Power Line Restoration Access Vulnerabilities After a Hurricane in Puerto Rico

“…The access to lines perspective differs from other work that focused on defining power system damage risks. For instances, past work proposed ESB-based sensors to understand electric power system vulnerabilities [32]. Or, other work used meteorological data to perform early identification of potentially harmful weather events [33]- [35].…”

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin

“…One SWE specification—Sensor Model Language (SensorML) [ 9 ]—is a flexible carrier for describing sensor information used for sensor discovery that has been applied in many SWE projects, including the European Sensors Anywhere (SANY) project [ 10 ], the Namibia Sensor Web project [ 11 ] and the Sensor Web 2.0 project [ 12 ]. The Sensor Instance Registry (SIR) [ 13 ] catalogue service adopts the SensorML 1.0-based discovery profile [ 14 ] to restrict the basic information model, which can address the sensor web resource discovery issue [ 15 ]. Although SensorML 2.0 [ 9 ] notes that the restriction of generic information models by defining the application-specific metadata profiles of specialized sensors should be included in future work, to date, the SensorML 2.0-based metadata profile for the accurate and fine-grained selection of Earth observation (EO) sensors has not been further formulated.…”