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  • 1.
    Elkington, Katherine
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Nonlinear Power Oscillation Damping Controllers for Doubly Fed Induction Generators in Wind Farms2013In: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424, Vol. 7, no 2, p. 172-179Article in journal (Refereed)
    Abstract [en]

    This study presents two methods for designing power oscillation damping (POD) controllers for wind farms comprising doubly fed induction generators (DFIGs). The first is the residue method, which uses linear feedback. The second method uses a non-linear signal as feedback. Here linear matrix inequalities (LMIs) and regional pole placement are used to determine the feedback gains for multiple wind farms simultaneously so that the power system satisfies a minimum damping ratio. The impact of the designed POD controllers in wind farms is demonstrated in a test power system. Modal analysis is used to design controllers using both the residue and LMI methods, and dynamic simulations are used to demonstrate the contribution of the wind farms to power system damping. Numerical simulations show that DFIGs, such as those found in wind farms, are capable of damping oscillations, and also illustrate the effectiveness of using non-linear feedback controllers.

  • 2.
    Herre, Lars
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Matusevičius, Tadas
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Olauson, Jon
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Exploring Wind Power Prognosis Data on Nord Pool: The Case of Sweden and Denmark2019In: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424, ISSN 1752-1416Article in journal (Refereed)
    Abstract [en]

    A good understanding of forecast errors is imperative for greater penetration of wind power, as it can facilitate planning and operation tasks. Oftentimes, public data is used for system studies without questioning or verifying its origin. In this paper, we propose a methodology to verify public data with the example of wind power prognosis published by Nord Pool. We focus on Swedish data and identify a significant bias that increases over the forecast horizon. In order to explore the origin of this bias, we first compare against Danish forecast and then describe the underlying structure behind the submission processes of this data. Based on the balance settlement structure, we reveal that Swedish "wind power prognoses" on Nord Pool are in fact rather wind production plans than technical forecasts. We conclude with the recommendation for improved communication and transparency with respect to terminology of public data on Nord Pool. We stress the importance for the research community to check publicly available input data before further use. Furthermore, the root-mean-square error and the spatio-temporal correlation between the errors in the bidding areas at different horizons is presented. Even with this compromised data, a stronger correlation is identified in neighbouring areas.

  • 3.
    Kerres, Bertrand
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Internal Combustion Engines.
    Fischer, Katharina
    Fraunhofer Institute for Wind Energy and Energy System Technology IWES.
    Madlener, Reinhard
    Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University.
    Economic evaluation of maintenance strategies for wind turbines: a stochastic analysis2015In: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424, Vol. 9, no 7, p. 766-774Article in journal (Refereed)
    Abstract [en]

    The authors develop a stochastic model for assessing the life-cycle cost and availability of wind turbinesresulting from different maintenance scenarios, with the objective to identify the most cost-effective maintenancestrategy. Using field-data-based reliability models, the wind turbine – in terms of reliability – is modelled as a serialconnection of the most critical components. Both direct cost for spare parts, labour and access to the turbine, as wellas indirect cost from production losses are explicitly taken into account. The model is applied to the case of a VestasV44–600 kW wind turbine. Results of a reliability-centred maintenance analysis of this wind turbine are used to selectthe most critical wind turbine components and to identify possible maintenance scenarios. This study reveals thatcorrective maintenance is the most cost-effective maintenance strategy for the gearbox and the generator of the V44turbine, while the cost benefit of condition-based maintenance using online condition-monitoring systems increaseswith higher electricity price, turbine capacity and remoteness of sites.

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