Wide area monitoring using Common Information Model and Sensor Web

Dahal, Nischal; Mohan, Vivek; Durbha, Surya S.; Srivastava, Anurag K.; King, Roger L.; Younan, Nicolas H.; Schulz, Noel N.

doi:10.1109/psce.2009.4840207

Cited by 16 publications

(11 citation statements)

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“…The noticeable increase in frequency of blackouts during the last decade has influenced the research conveyed by the Mississippi State University that was related to wide area monitoring of an electrical system and its integration within the existing SensorNet management system [25]. SensorNet is a Sensor Web solution intended for real-time detection, identification and assessment of chemical, biological, explosive and other threats.…”

Section: Related Workmentioning

confidence: 99%

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

Stoimenov

Bogdanović

Bogdanović-Dinić

2013

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

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

Stoimenov

Bogdanović

Bogdanović-Dinić

2013

Sensors

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“…In order to support the deployment of these information services it will be incumbent upon the Smart Grids research community to develop new tools aimed at facilitating operational data acquisition and handling in interoperable formats [8,10]. To address this complex issue the smart grid community has identified the deployment of cooperative smart sensor networks as the most promising enabling technology [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Consensus Protocols: The Enabler for Smarter Grids Monitoring

Iacoviello

Loia

Pietrosanto

et al. 2013

2013 27th International Conference on Advanced Information Networking and Applications Workshops

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The large scale deployment of the Smart Grids paradigm is expected to support the evolution of traditional electrical power systems toward active, flexible and self-healing web energy networks composed by distributed and cooperative energy resources. In this field, the application of hierarchical monitoring paradigms has many disadvantages that could hinder their application in modern Smart Grids where the constant growth of grid complexity and the need for supporting rapid decisions in a data rich, but information limited environment, require more scalable, more flexible monitoring paradigms. In trying and addressing these challenges, in this paper the role of decentralized consensus protocols for distributed and cooperative smart grids monitoring is analyzed.

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“…A nonlinear robust controller that uses PMU data and accounts for the delay introduced by remote signals as well as the incomplete information that results when not all generators are equipped with PMUs is described in Reference [6]. A framework based on a sensor web and the Common Information Model (CIM) is discussed in Reference [7]; it incorporates PMU data and considers interoperability and wide-area monitoring. A smart Discrete Fourier Transform-based algorithm to eliminate system noise and errors for fault detection and location with PMU data is presented in Reference [8].…”

Section: Introductionmentioning

confidence: 99%

Wide‐area monitoring and control using the real time digital simulator and a synchrophasor vector processor

Srivastava

Ravikumar²,

Zweigle³

2010

Int Trans Elec Energy Syst

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SUMMARYSeveral major blackouts in the past have shown the need for advanced wide-area monitoring and control (WAMC) techniques. Advanced synchrophasor visualization and monitoring can be realized with timereferenced power system data that are collected by phasor measurement units (PMUs) using synchronized clocks. Presently, PMUs are being deployed around the world at a rapid rate for various power system applications. When a necessary number of PMUs are installed at optimal locations throughout the network, the complete state of the system can be observed. PMUs take real-time measurements to determine the state of the power system and can be used to enhance state estimation efficiency. With PMUs in place, there are many control and protection schemes that can be implemented successfully to take preventive and corrective actions. These remedial action schemes (RASs) are classified by the IEEE Power System Relaying Committee (PSRC) as system integrity protection schemes (SIPSs). This work involved the development of a real-time hardware test bed in order to analyze the transient stability of a simulated power system by using synchrophasors to visualize system stress across a transmission line with and without load-shedding schemes. The real time digital simulator (RTDS 1 ) was used to model the power system in real time. PMUs, a satellite-synchronized clock, and a synchrophasor vector processor (SVP) were used to test the synchrophasor application.

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Wide area monitoring using Common Information Model and Sensor Web

Cited by 16 publications

References 1 publication

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

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

Decentralized Consensus Protocols: The Enabler for Smarter Grids Monitoring

Wide‐area monitoring and control using the real time digital simulator and a synchrophasor vector processor

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