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Publications (10 of 10) Show all publications
Johannesson, N., Bogodorova, T. & Vanfretti, L. (2017). Identifying Low-Order Frequency-Dependent Transmission Line Model Parameters. In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings: . Paper presented at 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017, Torino, Italy, 26 September 2017 through 29 September 2017. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Identifying Low-Order Frequency-Dependent Transmission Line Model Parameters
2017 (English)In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes the modeling and parameter identification of a frequency dependent transmission line model from time-domain data. To achieve this, a single-phase transmission line model was implemented in OpenModelica where the frequency dependent behavior of the line was realized by a series of rational functions using the Modelica language. Next, the developed line model was exported as a Functional Mock-up Unit (FMU). The RaPId toolbox was then used for automated parameter optimization of the model within the FMU that was interfaced to RaPId via the FMI Toolbox for MATLAB. Given a reasonable starting guess of the set of parameters, the toolbox improved the model's response significantly, resulting in a good approximation even though low-order representations were used for the identification process. It was found that even though the process was straightforward, it can be enhanced by exploiting the physical/numerical properties of this specific problem.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE PES Innovative Smart Grid Technologies Conference Europe, ISSN 2165-4816
Keywords
EMTP, Parameter estimation, Power system modeling, System identification, Transmission lines
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-226266 (URN)10.1109/ISGTEurope.2017.8260138 (DOI)000428016500047 ()2-s2.0-85046262006 (Scopus ID)978-1-5386-1953-7 (ISBN)
Conference
2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017, Torino, Italy, 26 September 2017 through 29 September 2017
Note

QC 20180424

Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2018-06-04Bibliographically approved
Bogodorova, T., Vanfretti, L., Peric, V. S. & Turitsyn, K. (2017). Identifying Uncertainty Distributions and Confidence Regions of Power Plant Parameters. IEEE Access, 5
Open this publication in new window or tab >>Identifying Uncertainty Distributions and Confidence Regions of Power Plant Parameters
2017 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 5Article in journal (Refereed) Published
Abstract [en]

Power system operators, when obtaining a model's parameter estimates; require additional information to guide their decision on a model's acceptance. This information has to establish a relationship between the estimates and the chosen model in the parameter space. For this purpose, this paper proposes to extend the usage of the particle filter (PF) as a method for the identification of power plant parameters; and the parameters' confidence intervals, using measurements. Taking into consideration that the PF is based on the Bayesian filtering concept, the results returned by the filter contain more information about the model and its parameters than usually considered by power system operators. In this paper the samples from the multi-modal posterior distribution of the estimate are used to identify the distribution shape and associated confidence intervals of estimated parameters. Three methods [rule of thumb, least-squares cross validation, plug-in method (HSJM)] for standard deviation (bandwidth) selection of the Gaussian mixture distribution are compared with the uni-modal Gaussian distribution of the parameter estimate. The applicability of the proposed method is demonstrated using field measurements and synthetic data from simulations of a Greek power plant model. The distributions are observed for different system operation conditions that consider different types of noise. The method's applicability for model validation is also discussed.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Confidence intervals, measurements, parameter identification, particle filters, power plant models, power system identification, power system model validation, power systems
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-217476 (URN)10.1109/ACCESS.2017.2754346 (DOI)000412768000018 ()2-s2.0-85030645570 (Scopus ID)
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2017-11-29Bibliographically approved
Bogodorova, T. & Vanfretti, L. (2017). Model Structure Choice for a Static VAR Compensator Under Modeling Uncertainty and Incomplete Information. IEEE Access, 5, Article ID UNSP 22657.
Open this publication in new window or tab >>Model Structure Choice for a Static VAR Compensator Under Modeling Uncertainty and Incomplete Information
2017 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 5, article id UNSP 22657Article in journal (Refereed) Published
Abstract [en]

To simulate the complex behavior of power systems, operators frequently rely on models. The task of model identification and validation becomes important in this context. The validity of the models has a direct influence on operator's decisions and actions. In other words, erroneous or imprecise models lead to erroneous predictions of the systems' behavior which may result in unwanted operator's actions. This paper addresses the challenge of model structure choice for modeling and parameter identification in power systems. Three types of model structures are analyzed: 1) physical principle-based modeling; 2) black-box modeling (NARX, transfer function, Hammerstein Wiener model); and 3) combination of physical and black-box modeling. This analysis has been performed using real grid measurements and available knowledge about a static VAR compensator (SVC) connected to the U.K.'s transmission network and operated by National Grid. The SVC's modeling is presented in the context of a generalized modeling and identification algorithm, that is offered as a guideline for engineers. The model validity issues of the identified SVC models that include modeling uncertainty are discussed.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-218244 (URN)10.1109/ACCESS.2017.2758845 (DOI)000414737900002 ()2-s2.0-85030757303 (Scopus ID)
Note

QC 20171124

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-29Bibliographically approved
Vanfretti, L., Baudette, M., Bogodorova, T., Lavenius, J. & Gómez, F. J. (2016). RaPId: A modular and extensible toolbox for parameter estimation of Modelica and FMI compliant models. SoftwareX, 5, 144-149
Open this publication in new window or tab >>RaPId: A modular and extensible toolbox for parameter estimation of Modelica and FMI compliant models
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2016 (English)In: SoftwareX, ISSN 2352-7110, Vol. 5, p. 144-149Article in journal (Refereed) Published
Abstract [en]

This paper describes the Rapid Parameter Identification toolbox (RaPId), developed within the EU FP7 iTesla project. The toolbox was designed to carry out parameter identification on models developed using the Modelica language, focusing in particular on power system model identification needs. The toolbox has been developed with modularity and extensibility in mind, using Matlab/Simulink as a plug-in environment, where different tasks of the identification process are carried out. The identification process uses different optimization algorithms to improve the fitting of the model’s response to selected criteria. The modular architecture of RaPId gives users complete freedom to extend and adapt the software to their needs, e.g. to implement or link external solvers for simulation or optimization. The compatibility with Modelica models is brought by the use of technologies compliant with the Functional Mock-up Interface (FMI) standard.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
FMI; Modelica; Model validation; System identification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-204927 (URN)10.1016/j.softx.2016.07.004 (DOI)
Projects
iTESLA
Note

QC 20170519

Available from: 2017-04-04 Created: 2017-04-04 Last updated: 2017-05-19Bibliographically approved
Bogodorova, T., Vanfretti, L. & Turitsyn, K. (2015). Bayesian Parameter Estimation of Power System Primary Frequency Controls under Modeling Uncertainties. In: IFAC-PapersOnLine: . Paper presented at 17th IFAC Symposium on System Identification (pp. 461-465). Elsevier, 48
Open this publication in new window or tab >>Bayesian Parameter Estimation of Power System Primary Frequency Controls under Modeling Uncertainties
2015 (English)In: IFAC-PapersOnLine, Elsevier, 2015, Vol. 48, p. 461-465Conference paper, Published paper (Refereed)
Abstract [en]

Nonlinear Bayesian filtering has been utilized in numerous fields and applications. One of the most popular class of Bayesian algorithms is Particle Filters. Their main benefit is the ability to estimate complex posterior density of the state space in nonlinear models. This paper presents the application of particle filtering to the problem of parameter estimation and calibration of a nonlinear power system model. The parameters of interest for this estimation problem are those of a turbine governor model. The results are compared to the performance of a heuristic method. Estimation results have been validated against real-world measurement data collected from staged tests at a Greek power plant.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Nonliner Systems, Parameter Estimation, Electric Power Systems, Recursive filters, Monte Carlo Method
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-184110 (URN)10.1016/j.ifacol.2015.12.171 (DOI)2-s2.0-84988674854 (Scopus ID)
Conference
17th IFAC Symposium on System Identification
Funder
EU, FP7, Seventh Framework Programme, iTesla
Note

QC 20160408

Available from: 2016-03-24 Created: 2016-03-24 Last updated: 2016-11-03Bibliographically approved
Perić, V. S., Bogodorova, T., Mete, A. N. & Vanfretti, L. (2015). Model order selection for probing-based power system mode estimation. In: 2015 IEEE Power and Energy Conference at Illinois, PECI 2015: . Paper presented at 2015 IEEE Power and Energy Conference at Illinois, PECI 2015, 20 February 2015 through 21 February 2015.
Open this publication in new window or tab >>Model order selection for probing-based power system mode estimation
2015 (English)In: 2015 IEEE Power and Energy Conference at Illinois, PECI 2015, 2015Conference paper, Published paper (Refereed)
Abstract [en]

The paper discusses model order selection for probing mode estimation algorithms. Four methods are analyzed and compared: 1) Residual analysis based model order selection, 2) Model order selection based on singular values, 3) Akaike Information Criterion, and 4) Variance-Accounted-For (VAF) as a measure of optimal fitting between measured data and model response. The methods are assessed using synthetic PMU measurements from the simulation of the KTH Nordic 32 Test system and the IEEE test system with 50 generators and 145 buses.

Keywords
Akaike Information Criterion (AIC), Model order selection, Probing mode estimation, Variance-Accounted-For, Energy management, Energy resources, Akaike information criterion, IEEE test systems, Mode estimation, Model-order selection, PMU measurements, Residual analysis, Singular values, Algorithms
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-167394 (URN)10.1109/PECI.2015.7064883 (DOI)000380464400006 ()2-s2.0-84928024680 (Scopus ID)9781479979493 (ISBN)
Conference
2015 IEEE Power and Energy Conference at Illinois, PECI 2015, 20 February 2015 through 21 February 2015
Note

QC 20150528

Available from: 2015-05-28 Created: 2015-05-22 Last updated: 2016-08-23Bibliographically approved
Vanfretti, L., Bogodorova, T. & Baudette, M. (2014). A Modelica Power System Component Library for Model Validation and Parameter Identification. In: Proceedings of the 10th International Modelica Conference, March 10-12, 2014, Lund, Sweden: . Paper presented at The 10th International Modelica Conference 2014, March 10-12, 2014, Lund, Sweden (pp. 1195-1203).
Open this publication in new window or tab >>A Modelica Power System Component Library for Model Validation and Parameter Identification
2014 (English)In: Proceedings of the 10th International Modelica Conference, March 10-12, 2014, Lund, Sweden, 2014, p. 1195-1203Conference paper, Published paper (Refereed)
Abstract [en]

This paper summarizes the work performed in one of the work-package of the FP7 iTesla project. This work consisted in the development of a power system component library for phasor time domain simulation in Modelica. The models were used to build power system network models, used in experiments for parameter identification. The experiments were carried out with the RaPId toolbox, which has been developed at SmarTS Lab within the same project. The toolbox was written in MatLab, making use of FMI Technologies for interacting with Modelica models.

Keywords
Power Systems, Phasor Simulation, Modelica, FMI, Parameter Identification, Model Validation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-143773 (URN)10.3384/ECP140961195 (DOI)
Conference
The 10th International Modelica Conference 2014, March 10-12, 2014, Lund, Sweden
Projects
iTesla
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20150210

Available from: 2014-03-28 Created: 2014-03-28 Last updated: 2015-02-10Bibliographically approved
Vanfretti, L., Bogodorova, T. & Baudette, M. (2014). Power System Model Identification Exploiting the Modelica Language and FMI Technologies. In: 2014 IEEE International Conference on Intelligent Energy and Power Systems, IEPS 2014 - Conference Proceedings: . Paper presented at 2014 IEEE International Conference on Intelligent Energy and Power Systems, IEPS 2014, Kyiv, Ukraine, 2 June 2014 through 6 June 2014 (pp. 127-132). IEEE Computer Society
Open this publication in new window or tab >>Power System Model Identification Exploiting the Modelica Language and FMI Technologies
2014 (English)In: 2014 IEEE International Conference on Intelligent Energy and Power Systems, IEPS 2014 - Conference Proceedings, IEEE Computer Society, 2014, p. 127-132Conference paper, Published paper (Refereed)
Abstract [en]

This article provides an overview of the work performed at SmarTS Lab on power system modeling and system identification within the FP7 iTesla project. The work was performed using Modelica as the modeling language for phasor time domain simulation and FMI (Flexible Mock-up Interface) Technologies for coupling Modelica models with simulation and optimization tools. The article focuses on use case examples of these Modelica models in an FMI driven environment to perform parameter identification.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Keywords
Modelica, FMI, model identification, parameter estimation, system identification
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-158303 (URN)10.1109/IEPS.2014.6874164 (DOI)000345748200025 ()2-s2.0-84906557500 (Scopus ID)978-1-4799-2266-6 (ISBN)
Conference
2014 IEEE International Conference on Intelligent Energy and Power Systems, IEPS 2014, Kyiv, Ukraine, 2 June 2014 through 6 June 2014
Note

QC 20150107

Available from: 2015-01-07 Created: 2015-01-07 Last updated: 2016-04-13Bibliographically approved
Bogodorova, T., Sabate, M., Leon, G., Vanfretti, L., Halat, M., Heyberger, J.-B. & Panciatici, P. (2013). A Modelica Power System Library for Phasor Time-Domain Simulation. In: 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE): . Paper presented at 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe, ISGT Europe 2013; Lyngby, Denmark, 6-9 October, 2013 (pp. 1-5). IEEE conference proceedings
Open this publication in new window or tab >>A Modelica Power System Library for Phasor Time-Domain Simulation
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2013 (English)In: 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), IEEE conference proceedings, 2013, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

Power system phasor time-domain simulation is often carried out through domain specific tools such as Eurostag, PSS/E, and others. While these tools are efficient, their individual sub-component models and solvers cannot be accessed by the users for modification. One of the main goals of the FP7 iTesla project [1] is to perform model validation, for which, a modelling and simulation environment that provides model transparency and extensibility is necessary.1 To this end, a power system library has been built using the Modelica language. This article describes the Power Systems library, and the software-to-software validation carried out for the implemented component as well as the validation of small-scale power system models constructed using different library components. Simulations from the Modelica models are compared with their Eurostag equivalents. Finally, due to its standardization, the Modelica language is supported by different modelling and simulation tools. This article illustrates how Modelica models can be shared across different simulation platforms without loss of information and maintaining consistency in simulation results.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
Series
IEEE PES Innovative Smart Grid Technologies Conference Europe, ISSN 2165-4816
Keywords
power system simulation, time-domain analysis, Eurostag, Eurostag equivalent, FP7 iTesla project, Modelica language, Modelica model, Modelica power system library, PSS-E, domain specific tool, model transparency, model validation, power system phasor time-domain simulation, simulation platform, small-scale power system model, software-to-software validation, subcomponent model, Adaptation models, Computational modeling, Libraries, Load modeling, MATLAB, Mathematical model, Power systems, Modelica, Power system simulation, model validation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-141195 (URN)10.1109/ISGTEurope.2013.6695422 (DOI)000330939800188 ()2-s2.0-84893620772 (Scopus ID)978-147992984-9 (ISBN)
Conference
2013 4th IEEE/PES Innovative Smart Grid Technologies Europe, ISGT Europe 2013; Lyngby, Denmark, 6-9 October, 2013
Projects
FP7 iTesla project
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20140422

Available from: 2014-02-11 Created: 2014-02-11 Last updated: 2014-06-02Bibliographically approved
Vanfretti, L., Li, W., Bogodorova, T. & Panciatici, P. (2013). Unambiguous Power System Dynamic Modeling and Simulation using Modelica Tools. In: 2013 IEEE Power and Energy Society General Meeting (PES): . Paper presented at 2013 IEEE Power and Energy Society General Meeting, PES 2013; Vancouver, BC; Canada; 21 July 2013 through 25 July 2013 (pp. 21-25). IEEE
Open this publication in new window or tab >>Unambiguous Power System Dynamic Modeling and Simulation using Modelica Tools
2013 (English)In: 2013 IEEE Power and Energy Society General Meeting (PES), IEEE , 2013, p. 21-25Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic modeling and time-domain simulation for power systems is inconsistent across different simulation platforms, which makes it difficult for engineers to consistently exchange models and assess model quality. Therefore, there is a clear need for unambiguous dynamic model exchange. In this article, a possible solution is proposed by using open modeling equation-based Modelica tools. The nature of the Modelica modeling language supports model exchange at the 'equation-level', this allows for unambiguous model exchange between different Modelica-based simulation tools without loss of information about the model. An example of power system dynamic model exchange between two Modelica-based software Scilab/Xcos and Dymola is presented. In addition, common issues related to simulation, including the extended modeling of complex controls, the capabilities of the DAE solvers and initialization problems are discussed. In order to integrate power system Modelica models into other simulation tools (Matlab/Simulink), the utilization of the FMI Toolbox is investigated as well.

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE Power and Energy Society General Meeting, ISSN 1944-9925
Keywords
Dymola, FMI, Model exchange, Modelica, Power system modeling and simulation, Scilab/Xcos
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-141198 (URN)10.1109/PESMG.2013.6672476 (DOI)000331874301116 ()2-s2.0-84893169914 (Scopus ID)978-147991303-9 (ISBN)
Conference
2013 IEEE Power and Energy Society General Meeting, PES 2013; Vancouver, BC; Canada; 21 July 2013 through 25 July 2013
Projects
FP7 iTesla Project
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20140319

Available from: 2014-02-11 Created: 2014-02-11 Last updated: 2014-04-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3312-9244

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