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IEEE PowerTech 2017

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Special Session (SS15): Data Analytics Applications and Methods for Distribution Systems

Thursday, 22 June 2017
09:00 - 10:40

Theatre A

Organiser(s): Reza Arghandeh, Florida State University, USA; Matthias Stifter, AIT, Austria

Abstract

This panel will address current research activities and development in the field of data-driven application for distribution systems. With the increase of measurement and sensor data, big data methods, like machine learning algorithms, parallel processing and real-time data stream processing, enable new insights for network planning and operation. System observability, state estimation, operational optimization, diagnosis, asset management, fault or intrusion detection, power quality monitoring are examples which will benefit from applying data analytical methods.
This session includes:
Cut through the hype – demystifying data and analytics for network companies by clearly defining the different ways data can be used and giving practical examples of how to get started based on those companies ahead in this space.
PMU and sensor based data-driven analytical methods for various applications like fault identification and topology detection are presented. Machine learning algorithms and methods used to identify particular pattern in faults and categorize them into several fault types.
Exploration on how machine learning can be used to extract value from this smart meter data to improve our lives, and also touches on the required technologies to process and store such volumes of data.
Data Analytic applications in power system industry include energy data service hubs, which in line with regulation in some European markets or an outage management tool that mines data across social media and diverse external information sources which is integrated with an Advanced Distribution Management System.
Examples from utility projects using parallel processing database environments for load modeling and characterizing for network applications, e.g. state estimation, event detection in load patterns as input to load forecasting and planning.

Presentations and Speakers

1. “Cutting through data driven hype and confusion” by Iain Stewart, Teradata, UK

2. “Data-Driven Approaches for Event Detection in Power Distribution Networks” by Reza Arghandeh, Florida State University, USA

3. “Non-intrusive Appliance Load Monitoring (NIALM) or energy disaggregation” by Oliver Parson, Centrica Connected Home, UK

4. “Applications of data analytics in the power system industry” by Neil Walls, Omnetric, UK

5. “Data-driven Methods for Distribution System Applications” by Matthias Stifter, AIT, Austria

 

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