Prof. Reza Arghandeh
Western Norway University of Applied Sciences
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Academic Career Length
19Years (2008-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 May 2016 in Renewable and Sustainable Energy Reviews
Journal Article
Reza ArghandehAMAlexandra von Meier
Alexandra von Meier
LMLaura Mehrmanesh
Laura Mehrmanesh
LMLamine Mili
Lamine Mili
1 February 2014
Conference Paper
AMAlexandra von Meier
Alexandra von Meier
DCDavid Culler
David Culler
AMAlex McEachern
Alex McEachern
Reza ArghandehThis paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.
4 December 2017 in IEEE Transactions on Power Systems
Journal Article
GCGuido Cavraro
Guido CavraroReza ArghandehThis paper proposes a novel method for topology detection in distribution networks called the Time-Series Signature Verification Method for Topology Detection (TSV-Top). The TSV-Top analyzes data from phasor measurement units (PMU or μPMU) installed on power distribution feeders. The TSVTop relies on the fact that measurement data time series from power systems contain similar trends when network topology changes occur, and basically performs the projection of actual voltage phasorial patterns onto a library of signals associated with possible topology transictions of a given distribution network. The proposed algorithm is validated using a 33-bus testbed and the IEEE 123-bus feeder, and then compared with another state-ofthe- art algorithm using the IEEE 37-bus feeder.
1 December 2014 in Applied Energy
Journal Article
Reza ArghandehJWJeremy Woyak
Jeremy Woyak
Ahmet ONENJJJaesung Jung
Jaesung Jung
RBRobert P. Broadwater
Robert P. Broadwater
13 March 2017 in IEEE Transactions on Smart Grid
Journal Article
The power system has been incorporating increasing amount of unconventional generations and loads such as distributed renewable resources, electric vehicles, and controllable loads. The induced dynamic and stochastic power flow require high-resolution monitoring technology and agile decision support techniques for system diagnosis and control. This paper discusses the application of micro-phasor measurement unit (PMU) data for power distribution network event detection. A novel datadriven event detection method, namely Hidden Structure Semi- Supervised Machine (HS $^{3}$ M), is established. HS $^{3}$ M only requires partial expert knowledge: it combines unlabeled data and partly labeled data in a large margin learning objective to bridge the gap between supervised learning, semi-supervised learning, and learning with hidden structures. To optimize the non-convex learning objective, a novel global optimization algorithm, namely Parametric Dual Optimization Procedure (PDOP), is established through its equivalence to a concave programming. Finally, the proposed method is validated on an actual distribution feeder with installed PMUs, and the result justifies the effectiveness of the learning-based event detection framework, as well as its potential to serve as one
1 July 2015
Conference Paper
GCGuido Cavraro
Guido CavraroReza ArghandehKPK. Poolla
K. Poolla
AMA. von Meier
A. von Meier
This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on the real time analysis of time-series voltages measurements. The analysis approach draws on data from high-precision phasor measurement units (μPMUs or synchrophasors) for distribution networks. The key fact is that time-series measurement data taken from the distribution network has specific patterns representing state transitions such as topology changes. The proposed algorithm is based on comparison of actual voltage measurements with a library of signatures derived from the possible topologies simulation. The IEEE 33-bus model is used for the algorithm validation.
About Me
Reza Arghandeh is a Professor at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences. He currently serves as the Leader of the Data Science and Artificial Intelligence Group (HVL DS-AI) and as the Director of the Connectivity, Information & Intelligence Lab (Ci2Lab) at the Western Norway University of Applied Sciences. He also holds the title of Research Professor in the Electrical and Computer Department at Florida State University, USA. He previously served as an Assistant Professor at the same institution from 2015 to 2018. Prior to his tenure at Florida State University, he conducted groundbreaking research as a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences at UC Berkeley from 2013 to 2015, USA. He earned his Ph.D. in Electrical Engineering with a specialization in Power Systems at Virginia Tech, USA, and holds his master’s degrees in Industrial and System Engineering from Virginia Tech and Energy Systems from the University of Manchester and KNTU. His research interests include applied artificial intelligence for spatiotemporal and geospatial data analysis related to complex networks. Core applications include climate adaptation solutions for electricity networks and transportation systems.