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Particle filter joint state and parameter estimation of dynamic power systems
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Florida State Univ, Dept Math, Tallahassee, FL 32310 USA. (Computational Renewables)
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
State St Financial Ctr, One Lincoln St, Boston, MA 02111 USA.
2016 (English)In: 2016 57th International Scientific Conference On Power And Electrical Engineering Of Riga Technical University (RTUCON): IEEE Conference Publications / [ed] IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Intermittent renewable energy sources in distributed generation will increase the chance of sudden unpredictable changes in the system states and parameters of dynamic power systems. To track the changes of the power systems, system state and parameter estimation methods that can track the near real-time dynamics of the power systems are needed. Power system operators still employ simulation studies using off-line models that are built based on prior knowledge gained through information via simulated typical scenarios which does not make use of posterior knowledge of neither parameter space nor state space of the dynamics of the power systems. Dynamic models of a power system has increasingly more important role in power system operations since they impact the operational conditions of dynamical power system. In this study, we propose a particle filter based state and parameter estimation method to improve modelling accuracy, which determines the best set of model parameters using realtime measurement data. This can be achieved via measurements by Phasor Measurement Units (PMU) or Remote Terminal Units (RTU) that can capture the system dynamic responses in real time. In addition, parameters of the system can also be estimated. Herein the load will he the parameter of the system that needs to be estimated jointly with the states. Joint state and parameter estimation for power systems via employing Bayesian particle filter is being introduced in this study.

Place, publisher, year, edition, pages
2016.
Keyword [en]
State estimation, Power system dynamics, Power system stability
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-310708DOI: 10.1109/RTUCON.2016.7763152ISI: 000391423000073ISBN: 9781509037315 (print)OAI: oai:DiVA.org:uu-310708DiVA: diva2:1057694
Conference
IEEE Conference 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) OCT 13-14, 2016
Projects
MIDAS
Funder
Swedish Energy Agency
Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2017-02-27Bibliographically approved

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CiteExportLink to record
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