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  • 1.
    Baudette, Maxime
    et al.
    KTH.
    Castro, Marcelo
    Univ Fed Juiz de Fora, Juiz de Fora, Brazil..
    Rabuzin, Tin
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Lavenius, Jan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Bogodorova, Tetiana
    Ukrainian Catholic Univ, Fac Appl Sci, Lvov, Ukraine..
    Vanfretti, Luigi
    Rensselaer Polytech Inst, Troy, NY 12180 USA..
    OpenIPSL: Open-Instance Power System Library - Update 1.5 to "iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations"2018In: Software Quality Professional, ISSN 1522-0540, Vol. 7, p. 34-36Article in journal (Refereed)
    Abstract [en]

    This paper presents the latest improvements implemented in the Open-Instance Power System Library (OpenIPSL). The OpenIPSL is a fork from the original iTesla Power Systems Library (iPSL) by some of the original developers of the iPSL. This fork's motivation comes from the will of the authors to further develop the library with additional features tailored to research and teaching purposes. The enhancements include improvements to existing models, the addition of a new package of three phase models, and the implementation of automated tests through continuous integration.

  • 2.
    Lavenius, Jan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Vanfretti, Luigi
    Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA..
    PMU-Based Estimation of Synchronous Machines' Unknown Inputs Using a Nonlinear Extended Recursive Three-Step Smoother2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 57123-57136Article in journal (Refereed)
    Abstract [en]

    Knowledge of the synchronous machines' control input signals and internal states can provide valuable insight to system operators for assessing security margins and the stability of the power system. For example, during disturbances in a stressed power system, it can be of great value to monitor the performance of the machine's control system, e.g., the response of the field voltage, mechanical power, and the field current. As there are often no real-time power plant measurements available to power system operators, internal states, and unknown inputs of generator units would need to be estimated from synchrophasor measurements. This paper proposes a new estimation algorithm, the nonlinear extended recursive three-step smoother (NERTSS), to simultaneously estimate the states and the unknown inputs of the synchronous machine using data from phasor measurement units. These quantities can then be used to monitor the performance of the machine's controls. The case studies presented in the paper compare the estimation performance of the NERTSS with the extended Kalman filter with unknown inputs (EKF-UI) when noisy synchrophasor measurements are used. The simulation results show that the proposed estimation method compares favorably with respect to the EKF-UI in terms of the achieved estimation accuracy.

  • 3.
    Lavenius, Jan
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric power and energy systems. Statnett SF, Norway.
    Taranto, Glauco N.
    Performance Assessment of PMU-Based Estimation Methods of Thevenin Equivalents for Real-Time Voltage Stability Monitoring2015In: 2015 IEEE 15TH INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING (IEEE EEEIC 2015), IEEE , 2015, p. 1977-1982Conference paper (Refereed)
    Abstract [en]

    This paper investigates the performance of different methods for calculating the Thevenin equivalent parameters based using only data obtained from real-time synchrophasor measurements. Estimating the parameters from data affected by noisy measurements, transients, and continuously varying power system parameters may yield Thevenin equivalents that do not reflect the actual voltage stability of the system. The paper proposes using a total least squares method to take into account errors in the model from time-varying Thevenin parameters and noise. The paper reviews two different methods for Thevenin equivalent estimation proposed in the literature. The suitability of the three methods described in the paper is tested in case studies, where the performance of the methods are tested with both noise-free and noisy data. The papers also investigates effect of changes in the power consumptions at other buses on the Thevenin parameters seen from the monitored load bus.

  • 4.
    Mahmood, Farhan
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH The Royal Institute of Technology.
    Hooshyar, Hossein
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH The Royal Institute of Technology.
    Lavenius, Jan
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Lund, Per
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH The Royal Institute of Technology.
    Real-time reduced steady state model synthesis of active distribution networks using PMU measurements2016In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 32, no 1, p. 546-555Article in journal (Refereed)
    Abstract [en]

    Due to the increase of generation sources in distribution networks, it is becoming more and more complex to develop and maintain models of these networks. Network operators need to determine reduced models of distribution networks to be used in grid management functions. This paper presents a novel method that synthesizes steady state models of unbalanced active distribution networks by the use of dynamic measurements (time series) from PMUs. As PMU measurements may contain errors and bad data, the paper presents the application of a Kalman Filter technique for real-time data processing. In addition, PMU data captures the power system’s response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter has been improved to extract the steady state component of the PMU measurements to be fed to the steady state model synthesis application. Performance of the proposed methods has been assessed by real-time hardware-in-the-loop simulations on a sample distribution network.

  • 5. Perninge, M.
    et al.
    Lavenius, Jan
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Approximating a post-contingency stable operation region in parameter space through time-domain simulation2013In: Proceedings of IREP Symposium: Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid, IREP 2013, IEEE , 2013Conference paper (Refereed)
    Abstract [en]

    Operating criteria for power systems, such as the (N - 1)-criterion, are often based on evaluating whether the system is vulnerable to a specific set of contingencies. Therefore, a major part of power system security is concerned with establishing regions in parameter space where the system is vulnerable to specific contingencies. In this article we exploit the possibility of using Monte Carlo simulations to build an approximation of the region, in parameter space, where the power system will remain stable following a given contingency.

  • 6.
    Rabuzin, Tin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Lavenius, Jan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Taylor, Nathaniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Bayesian Detection of Islanding Events Using Voltage Angle Measurements2018In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018, IEEE, 2018, article id 8587561Conference paper (Refereed)
    Abstract [en]

    The growing presence of distributed generation in power systems increases the risk for the unintentional creation of electrical islands. It is important to apply reliable and quick is landing protection methods. At the same time, the deployment of phasor measurement units facilitates the usage of data-oriented techniques for the development of new wide-area protection applications, one of which is islanding protection. This paper presents a Bayesian approach to detecting an islanding event, which utilizes measurements of voltage angles at the system's buses. A model of mixtures of probabilistic principal component analysers has been fitted to the data using a variational inference algorithm and subsequently used for islanding detection. The proposed approach removes the need for setting parameters of the probabilistic model. The performance of the method is demonstrated on synthetic power system measurements.

  • 7.
    Vanfretti, Luigi
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Baudette, Maxime
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Bogodorova, Tetiana
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Lavenius, Jan
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Gómez, Francisco José
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    RaPId: A modular and extensible toolbox for parameter estimation of Modelica and FMI compliant models2016In: SoftwareX, ISSN 2352-7110, Vol. 5, p. 144-149Article in journal (Refereed)
    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.

  • 8.
    Vanfretti, Luigi
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Olsen, Svein
    Statnett SF, Oslo.
    Arava, Venkata Satya Narasinham
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Laera, Giuseppe
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Bidadfar, Ali
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Rabuzin, Tin
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Lavenius, Jan
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Baudette, Maxime
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Gómez, Francisco José
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Jakobsen, Sigurd H.
    Norwegian University of Science and Technology, Trondheim, Norway.
    An open data repository and a data processing software toolset of an equivalent Nordic grid model matched to historical electricity market data2017In: Data in Brief, E-ISSN 2352-3409, Vol. 11, p. 349-357Article in journal (Refereed)
    Abstract [en]

    This article presents an open data repository, the methodology to generate it and the associated data processing software developed to consolidate an hourly snapshot historical data set for the year 2015 to an equivalent Nordic power grid model (aka Nordic 44), the consolidation was achieved by matching the model׳s physical response w.r.t historical power flow records in the bidding regions of the Nordic grid that are available from the Nordic electricity market agent, Nord Pool.

    The model is made available in the form of CIM v14, Modelica and PSS/E (Siemens PTI) files. The Nordic 44 model in Modelica and PSS/E were first presented in the paper titled “iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations” (Vanfretti et al., 2016) [1] for a single snapshot. In the digital repository being made available with the submission of this paper (SmarTSLab_Nordic44 Repository at Github, 2016) [2], a total of 8760 snapshots (for the year 2015) that can be used to initialize and execute dynamic simulations using tools compatible with CIM v14, the Modelica language and the proprietary PSS/E tool are provided. The Python scripts to generate the snapshots (processed data) are also available with all the data in the GitHub repository (SmarTSLab_Nordic44 Repository at Github, 2016) [2].

    This Nordic 44 equivalent model was also used in iTesla project (iTesla) [3] to carry out simulations within a dynamic security assessment toolset (iTesla, 2016) [4], and has been further enhanced during the ITEA3 OpenCPS project (iTEA3) [5]. The raw, processed data and output models utilized within the iTesla platform (iTesla, 2016) [4] are also available in the repository. The CIM and Modelica snapshots of the “Nordic 44” model for the year 2015 are available in a Zenodo repository.

  • 9.
    Zhang, Mengjia
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Baudette, Maxime
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Lavenius, Jan
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Løvlund, Stig
    Statnett SF, Oslo, Norway.
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Modelica Classes of the Norwegian Grid for iTesla and SW-to-SW Validation2015Conference paper (Refereed)
    Abstract [en]

    Following the European network of transmission systemoperators for electricity (ENTSO-E) R&D Road Map,efforts of collaboration between the European transmissionsystem operators (TSO) have led to common researchprojects concerning power system operation suchas iTesla. iTesla aims to develop a common toolbox tosupport the future operation of pan-European power grid.This toolbox was developed to use Modelica models.

    The work presented here encompasses the developmentof Modelica classes for power gird componentsused by Nordic TSOs to model the Nordic synchronousgrid. The performance of these Modelica models havebeen validated through different test cases implementedin both Power System Simulator for Engineering (PSS/E) as domain-specific tool and a Modelica simulation environment.The results from dynamic simulations with thepresence of different perturbations have been compared tothe PSS/E reference to validate the Modelica implementation,reaching almost identical records between simulationresults from both tools.

1 - 9 of 9
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