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  • 1. Ahmad, H.
    et al.
    Coppens, S.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Connection of an Offshore Wind Park to HVDC Converter Platform without Using Offshore AC Collector Platforms2013In: Green Technologies Conference, 2013 IEEE, 2013, 400-406 p.Conference paper (Refereed)
  • 2.
    Aihara, Aya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Goude, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Wind Flow Resource Analysis Of Urban Structures: A Validation Study2016Conference paper (Refereed)
    Abstract [en]

    In order to have better insight into the physics of the urban wind turbines, a Computational Fluid Dynamics (CFD) flow solver has been developed for industrial applications by Uppsala University and SOLUTE Ingenieros. Urban wind resource assessment for small scale wind applications present several challenges and complexities for that are different from large-scale wind power generation. Urban boundary layer relevant in this regime of flows have different horizontal profiles impacted by the buildings, low speed wind regimes, separation and different turbulence characteristics. Preliminary measurement results will be presented for a particular site in Huesca Spain where a measurement campaign is undertaken to validate the CFD results.

  • 3.
    Bayazit, Dervis
    et al.
    Federal Home Loan Bank of Atlanta, Financial Risk Modeling Department, Atlanta GA.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Simplex optimization for particle filter joint parameter estimation of electricity prices with jump diffusion2013In: Journal of financial and economic practice, Vol. 13, no 2, 1-14 p.Article in journal (Refereed)
  • 4. Erduman, A.
    et al.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Storage commitment and placement for an interconnected island system with high wind penetration, Gotland2014In: Renewable Energy Research and Application (ICRERA), 2014 International Conference IEEE, 2014, 605-609 p.Conference paper (Refereed)
  • 5.
    Goude, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Giovannini, Gabriele
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Magdalena, J.
    Fernandez, A.
    A GUI for urban wind flow CFD analysis of small scale wind applications2015In: Cyberworlds, 2015 IEEE, 2015, 193-199 p.Conference paper (Refereed)
    Abstract [en]

    In order to have better insight into the physics of the urban wind turbines, a graphical user interface (GUI) that employs OpenFOAM flow solver has been developed for industrial applications by Uppsala University with Spanish engineering company SOLUTE via EU framework as part of the WINDUR framework 7 project. Urban wind resource assessment for small scale wind applications present several challenges and complexities for that are different from large-scale wind power generation. Urban boundary layer relevant in this regime of flows have different horizontal profiles impacted by the buildings, low speed wind regimes, separation and different turbulence characteristics. This software addresses the project setup and scientific visualization of the results for right investment decision needs. Preliminary numerical results will be presented for a test site in Huesca, Spain where a measurement campaign is undertaken to validate the Computational Fluid Dynamics (CFD) results.

  • 6.
    Kaidis, Christos
    et al.
    MECAL Independent Experts, NL-7521 PL Enschede, Netherlands..
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Amoiralis, Filippos
    MECAL Independent Experts, NL-7521 PL Enschede, Netherlands..
    Wind turbine reliability estimation for different assemblies and failure severity categories2015In: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424, Vol. 9, no 8, 892-899 p.Article in journal (Refereed)
    Abstract [en]

    This study discusses the life-cycle analysis of wind turbines through the processing of operational data from three modern European wind farms. A methodology for supervisory control and data acquisition data processing has been developed combining previous research findings and experience from operational wind farms followed by statistical analysis of the results. The analysis was performed by dividing the wind turbine into assemblies and the failures events in severity categories. Depending on the failure severity category a different statistical methodology was applied, examining the reliability growth and the applicability of the bathtub curve' concept for wind turbine reliability analysis.

  • 7.
    Menin, M.
    et al.
    CDEEE, Ave Independencia Ctr Heroes,Apartado Postal 1428, Santo Domingo, Dominican Rep.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Parametric Sensitivity Study for Wind Power Trading through Stochastic Reserve and Energy Market Optimization2015In: Green Technologies Conference (GreenTech), 2015 Seventh Annual IEEE, 2015, 82-87 p.Conference paper (Refereed)
    Abstract [en]

    Trading optimal wind power in energy and regulation markets offer possibilities for increasing revenues as well as impacting security of the system via additional regulation reserve [1]. The bidding in both energy and regulation markets can be computed through stochastic optimization process of both markets as demonstrated in a previous study by Liang [1]. This paper is furthering the previous study by Liang [1] by analyzing the impact of price ratios between energy and reserve market on the revenues for Swedish market. The parametric study reveals that as long as up-regulation prices are below day-ahead energy, the algorithm will bid in both markets to optimize revenue. When regulation prices surpass or equal to day-ahead energy market price then it only bids energy in the regulation market with the current objective function.

  • 8. Tan, M
    et al.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Price, W G
    Rogers, E
    Reduced models for statistically stationary and non-stationary flows with control applications2002In: Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, Vol. 216, no 1, 95-102 p.Article in journal (Refereed)
    Abstract [en]

    Reduced modelling techniques, based on a proper orthogonal decomposition (POD) method, are applied to an investigation of the incompressible Navier-Stokes equations with inputs. A circular cylinder in uniform flow with and without inputs is studied. Reduced dynamic models are created by POD and by extended POD (EPOD) approaches for the forced flow which is statistically non-stationary. A direct control action is applied to the flow at particular points and this investigation provides insights into the applications of the proposed approaches coupled with a full solver.

  • 9.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Adaptive observations in ensemble data assimilation2007In: Computer Methods in Applied Mechanics and Engineering, ISSN 0045-7825, E-ISSN 1879-2138, Vol. 196, no 41, 4207-4221 p.Article in journal (Refereed)
  • 10.
    Uzunoglu, Bahri
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Bayazit, D.
    A generic resampling particle filter joint parameter estimation for electricity prices with jump diffusion2013In: European Energy Market (EEM), 2013 10th International Conference, 2013, 1-7 p.Conference paper (Refereed)
  • 11.
    Uzunoglu, Bahri
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Fletcher, S. J.
    Zupanski, M.
    Navon, I. M.
    Adaptive ensemble reduction and inflation2007In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 133, no 626, 1281-1294 p.Article in journal (Refereed)
  • 12. Uzunoglu, Bahri
    et al.
    Tan, M.
    Price, W. G.
    Low-Reynolds-number flow around an oscillating circular cylinder using a cell viscousboundary element method2001In: International Journal for Numerical Methods in Engineering, ISSN 0029-5981, E-ISSN 1097-0207, Vol. 50, no 10, 2317-2338 p.Article in journal (Refereed)
  • 13.
    Uzunoglu, Bahri
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Florida State Univ, Dept Math, Tallahassee, FL 32310 USA.
    Ulker, Muhammed Akif
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Bayazit, Dervis
    State St Financial Ctr, One Lincoln St, Boston, MA 02111 USA.
    Particle filter joint state and parameter estimation of dynamic power systems2016In: 2016 57th International Scientific Conference On Power And Electrical Engineering Of Riga Technical University (RTUCON): IEEE Conference Publications / [ed] IEEE, 2016Conference 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.

  • 14. Vogstad, Klaus
    et al.
    Bhutoria, Vaibhav
    Amund Lund, John
    Ivanell, Stefan
    Gotland University, School of Culture, Energy and Environment.
    Uzunoglu, Bahri
    Gotland University, School of Culture, Energy and Environment.
    Instant Wind: Model reduction for fast CFD computations2012Report (Other academic)
  • 15. Xiong, X.
    et al.
    Navon, I.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    A Note on the Particle Filter with Posterior Gaussian Resampling2011In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 58, no 4Article in journal (Refereed)
    Abstract [en]

    Particle filter (PF) is a fully non-linear filter with Bayesian conditional probability estimation, compared here with the well-known ensemble Kalman filter (EnKF). A Gaussian resampling (GR) method is proposed to generate the posterior analysis ensemble in an effective and efficient way. The Lorenz model is used to test the proposed method. The PF with Gaussian resampling (PFGR) can approximate more accurately the Bayesian analysis. The present work demonstrates that the proposed PFGR possesses good stability and accuracy and is potentially applicable to large-scale data assimilation problems.

  • 16.
    Yildirim, Nurseda
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Association Rules for Clustering Algorithms for Data Mining of Temporal Power Ramp Balance2015In: Cyberworlds, 2015 IEEE, 2015, 224-228 p.Conference paper (Refereed)
    Abstract [en]

    Power ramp estimation is utmost importance for wind power plants which will be the focus of this paper. Power ramps are caused by intermittent supply of wind power generation. This is an important problem in the power system that needs to keep the load and generation at balance at all times while any unbalance leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In this study, K-means clustering and association rules of apriori algorithm are implemented to analyze and predict wind power ramp occurrences based on 10 minutes temporal SCADA data of power from records of Ayyildiz wind farm. Power ramps are computed from this data. Five wind turbines with no dissimilarity measure in space were clustered based on temporal data. The power ramp data are analyzed by the K-means algorithm for calculation of their cluster means and cluster labels. Association rules of data mining algorithm were employed to analyze temporal ramp occurrences between wind turbines. Each turbine impact on the other turbines were analyzed as different transactions at each time step. Operational rules based on these transactions are discovered by an apriori association rule algorithm for operation room decision making. Discovery of association rules from an apriori algorithm can help with decision making for power system operator.

  • 17.
    Yildirim, Nurseda
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Florida State Univ, Dept Math, Tallahassee, FL 32310 USA..
    Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization2016In: Transactions on Computational Science XXVIII: Special Issue on Cyberworlds and Cybersecurity / [ed] Gavrilova, ML; Tan, CJK; Sourin, A, Springer Berlin/Heidelberg, 2016, 163-176 p.Conference paper (Refereed)
    Abstract [en]

    Power ramp estimation has wide ranging implications for wind power plants and power systems which will be the focus of this paper. Power ramps are large swings in power generation within a short time window. This is an important problem in the power system that needs to maintain the load and generation at balance at all times. Any unbalance in the power system leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In addition, power ramps decrease the lifetime of turbine and increase the operation and maintenance expenses. In this study, power ramps are detected by data mining and optimization. For detection and prediction of power ramps, data mining K means clustering approach and optimisation scoring function approach are implemented [1]. Finally association rules of data mining algorithm is employed to analyze temporal ramp occurrences between wind turbines for both clustering and optimization approaches. Each turbine impact on the other turbines are analyzed as different transactions at each time step. Operational rules based on these transactions are discovered by an Apriori association rule algorithm for operation room decision making. Discovery of association rules from an Apriori algorithm will serve the power system operator for decision making.

  • 18.
    Yildirim, Nurseda
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Spatial Clustering for Temporal Power Ramp Balance and Wind Power Estimation2015In: Green Technologies Conference (GreenTech), 2015 Seventh Annual IEEE, 2015, 214-220 p.Conference paper (Refereed)
    Abstract [en]

    Power estimation and power ramp estimation is of crucial importance in renewable energy applications especially for wind power plants that is going to be the focus of this study. Intermittent supply of wind power generation can cause power ramps which are sudden change of power production in time. This is an important problem in power system that aims to keep the load and generation balance. Unbalance in the system can lead to price volatility and grid security issues that will create power stability problems and financial losses. Herein a spatial clustering methodology for improving spatio-temporal relations are investigated to improve wind power estimation and power ramp estimation. To validate the data with the model that will be used in clustering analysis, spatial results of Computational Fluid Dynamics (CFD) tool simulations are employed to test suggested methodology in space. CFD results generate the input data for space clustering process. Via the CFD generated spatial information, the relationship between spatial clustering to wind power ramp rate characteristics of wind Turbines are introduced for the spatio-temporal power and power ramp rate relations. Spatial relations for power ramp characteristics of each node in wind resource map is introduced. In space scales, K-means algorithm is used to create spatial Power Ramp Rate (PRR) based clusters to define most related clusters in space so that impact of each spatial cluster can be introduced.

  • 19. Zupanski, M.
    et al.
    Fletcher, S.
    Navon, I.
    Uzunoglu, Bahri
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
    Heikes, R.
    Randall, D.
    Ringler, T.
    Daescu, D.
    Initiation of ensemble data assimilation2006In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 58, no 2Article in journal (Refereed)
    Abstract [en]

    The specification of the initial ensemble for ensemble data assimilation is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but is also applicable to other ensemble data assimilation algorithms. Two methods are considered: the first is based on the use of the Kardar- Parisi-Zhang (KPZ) equation to form sparse random perturbations, followed by spatial smoothing to enforce desired correlation structure, while the second is based on the spatial smoothing of initially uncorrelated random perturbations. Data assimilation experiments are conducted using a global shallow-water model and simulated observations. The two proposed methods are compared to the commonly used method of uncorrelated random perturbations. The results indicate that the impact of the initial correlations in ensemble data assimilation is beneficial. The root-mean-square error rate of convergence of the data assimilation is improved, and the positive impact of initial correlations is notable throughout the data assimilation cycles. The sensitivity to the choice of the correlation length scale exists, although it is not very high. The implied computational savings and improvement of the results may be important in future realistic applications of ensemble data assimilation.

1 - 19 of 19
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