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Würth, I., Valldecabres, L., Simon, E., Mohrlen, C., Uzunoglu, B., Gilbert, C., . . . Kaifel, A. (2019). Minute-Scale Forecasting of Wind Power - Results from the Collaborative Workshop of IEA Wind Task 32 and 36. Energies, 12(4), Article ID 712.
Open this publication in new window or tab >>Minute-Scale Forecasting of Wind Power - Results from the Collaborative Workshop of IEA Wind Task 32 and 36
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2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 4, article id 712Article in journal (Refereed) Published
Abstract [en]

The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on Very Short-Term Forecasting of Wind Power in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop's main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
wind energy, minute-scale forecasting, forecasting horizon, Doppler lidar, Doppler radar, numerical weather prediction models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-381196 (URN)10.3390/en12040712 (DOI)000460667700138 ()
Funder
EU, Horizon 2020, 642108
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-04-11Bibliographically approved
Uzunoglu, B. (2018). Bayesian approach with subjective opinion fusions for wind turbine maintenance. Journal of Physics, Conference Series, 1037
Open this publication in new window or tab >>Bayesian approach with subjective opinion fusions for wind turbine maintenance
2018 (English)In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 1037Article in journal (Refereed) Published
Abstract [en]

An optimal Bayesian update strategy that implements the subjective opinions of several experts are introduced for preventive maintenance of wind turbines while single expert opinion has been introduced by the author in the previous studies. This work is introducing the opinion of the wind farm manager or technician via subjective opinions based on a Bayesian adaptive update strategies for optimal preventive maintenance. Subjective opinion will be implemented to Bayesian cycles while experts can impact the distribution parameters with no knowledge of statistics but just by presenting their opinion as belief, disbelief or uncertainty. Statistical parameters such as minimal time of maintenance and cost of new strategy will be impacted by the interaction of wind farm manager and technician that interact with quantitative data with their opinions. The approach employs and complements the quantitative data from turbine Supervisory control and data acquisition (SCADA).

National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:uu:diva-368746 (URN)10.1088/1742-6596/1037/6/062021 (DOI)000454822700176 ()
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-01-23Bibliographically approved
Uzunoglu, B. (2018). Bayesian approach with subjective opinion fusions for wind turbine maintenance: Journal of Physics: Conference Series. In: : . Paper presented at TORQUE 2018 The Science of Making Torque from Wind,20-22 June, Milan, Italy.
Open this publication in new window or tab >>Bayesian approach with subjective opinion fusions for wind turbine maintenance: Journal of Physics: Conference Series
2018 (English)Conference paper, Published paper (Refereed)
National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:uu:diva-368804 (URN)
Conference
TORQUE 2018 The Science of Making Torque from Wind,20-22 June, Milan, Italy
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-03-06Bibliographically approved
Uzunoglu, B. & Ulker, M. A. (2018). Maximum Likelihood Ensemble Filter State Estimation for Power Systems. IEEE Transactions on Instrumentation and Measurement, 67(9), 2097-2106
Open this publication in new window or tab >>Maximum Likelihood Ensemble Filter State Estimation for Power Systems
2018 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 67, no 9, p. 2097-2106Article in journal (Refereed) Published
Abstract [en]

Maximum likelihood ensemble filter (MLEF) is an ensemble-based deterministic filtering method. It optimizes a nonlinear cost function through maximum likelihood and utilizes low-dimensional ensemble space on the calculation of Hessian preconditioning of the cost function. This paper implements the MLEF as a state estimation tool for the estimation of the states of a power system, and presents the first MLEF application study on a power system state estimation. The MLEF methodology is introduced into power systems and the simulations are implemented for a three-node benchmark power system and 68-bus test system which have been employed in several previous studies to address a discontinuous problem where derivative is not defined. This is in contrast to gradient-based methods in the literature that needs gradient and Hessian information which is not defined in jumps. The performance of the filter on the presented problem is analyzed and the results are presented. Results indicate that the estimation convergence is achieved with the MLEF method.

Keywords
Control systems, dynamic state estimation, optimization, power system measurements, power systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering with specialization in Automatic Control; Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-362635 (URN)10.1109/TIM.2018.2814066 (DOI)000441423100008 ()
Funder
Swedish Energy Agency
Available from: 2018-10-09 Created: 2018-10-09 Last updated: 2018-11-05
Uzunoglu, B. (2017). Locating distribution power system fault employing Bayes theorem with subjective logic. In: : . Paper presented at 2017 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy. (pp. 130-134). NEW YORK: IEEE
Open this publication in new window or tab >>Locating distribution power system fault employing Bayes theorem with subjective logic
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Prompt and effective power system restoration is essential for the minimization of downtime and cost which can get substantial rapidly after a system blackout. Most grids do not have the sensors to diagnose faults for algorithms that employ these measurements. Instead, assessment depends on customer calls that have lost power while the input of the field technician is not reflected into assessment in a formal way. This paper investigates fault location detection for service restoration based on a Distribution Automation System (DAS) with a centralized intelligence Bayesian probabilistic approach that takes into account field technician subjective opinion. The approach employs probabilistic logic and subjective logic based on evidence theory. A simplified model of distribution power system is employed to introduce new concepts that employ evidence theory with subjective and probabilistic logic to address the insufficient information.

Place, publisher, year, edition, pages
NEW YORK: IEEE, 2017
Series
2017 2nd International Conference on System Reliability and Safety (ICSRS)
Keywords
distribution system, fault detection, subjective logic, Bayesian methods, power outage calls, customer calls, technician opinion
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-336514 (URN)10.1109/ICSRS.2017.8272808 (DOI)000426453100021 ()978-1-5386-3322-9 (ISBN)
Conference
2017 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy.
Funder
Swedish Energy Agency
Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2018-08-28Bibliographically approved
Ulker, M. A. & Uzunoglu, B. (2017). Maximum likelihood ensemble filter state estimation for power systems fault diagnosis. In: 2017 2nd International Conference on System Reliability and Safety (ICSRS): . Paper presented at 2nd International Conference on System Reliability and Safety (ICSRS),Milan, Italy, December 20-22, 2017 (pp. 140-145). IEEE
Open this publication in new window or tab >>Maximum likelihood ensemble filter state estimation for power systems fault diagnosis
2017 (English)In: 2017 2nd International Conference on System Reliability and Safety (ICSRS), IEEE, 2017, p. 140-145Conference paper, Published paper (Refereed)
Abstract [en]

Maximum Likelihood Ensemble Filter (MLEF) is a deterministic filtering approach that employs the ensembles. The method applies low dimensional ensemble space for the computation of a nonlinear cost function Hessian preconditioning and implements the optimization of the cost function. The MLEF is utilized as state estimation instrument that estimates states of dynamic systems and contributes to reliable and safe operation and monitoring of dynamic systems. In this article, MLEF is employed as a state estimation tool to track the states of a nonlinear power system to assist the fault diagnosis and bad data analysis of the system. A three-node benchmark power system model is considered in this study and a disconnection event is implemented as a fault scenario on the system with measurement data which contains some bad data. The scenario refers to a discontinuous problem which has non-derivable points and this is contrary to gradient based techniques. The MLEF practice on the introduced problem is examined and the results are illustrated. The obtained results shows that the estimation convergence of the MLEF technique on the considered benchmark model is satisfactory.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
maximum likelihood ensemble filter, dynamic state estimation, power system fault diagnosis, power system measurements, power systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering
Identifiers
urn:nbn:se:uu:diva-336517 (URN)10.1109/ICSRS.2017.8272810 (DOI)000426453100023 ()978-1-5386-3322-9 (ISBN)
Conference
2nd International Conference on System Reliability and Safety (ICSRS),Milan, Italy, December 20-22, 2017
Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2018-11-02
Ulker, M. A. & Uzunoglu, B. (2017). Simplex optimization for particle filter joint state and parameter estimation of dynamic power systems. In: IEEE (Ed.), IEEE EUROCON 2017 CONFERENCE PROCEEDINGS: . Paper presented at IEEE EUROCON 2017 - 17th International Conference on Smart Technologies, 6-8 July, 2017, Ohrid, Macedonia. IEEE
Open this publication in new window or tab >>Simplex optimization for particle filter joint state and parameter estimation of dynamic power systems
2017 (English)In: IEEE EUROCON 2017 CONFERENCE PROCEEDINGS / [ed] IEEE, IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

The incidence of sudden unanticipated variations in power system states and parameters will tend to increase due to higher intermittent renewable energy penetration in distributed generation. It is needed to have proper state and parameter estimation tools that can follow-up these variations and can reflect the real-time system dynamics. In this paper, a particle filter with Nelder-Mead simplex optimization algorithm is implemented to estimate the states and a parameter of a three-node benchmark test model. The performance of Bayesian particle filter for joint estimate of the states and parameter for the benchmark non-linear power system model has been analysed and favorable results were obtained by minimizing approximated negative log-likelihood function via Nelder-Mead simplex algorithm.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Particle filter, state and parameter estimation, power system modelling, Bayesian Monte Carlo methods, simplex optimization, Nelder-Mead algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
urn:nbn:se:uu:diva-330290 (URN)10.1109/EUROCON.2017.8011142 (DOI)000426823600070 ()978-1-5090-3843-5 (ISBN)978-1-5090-3844-2 (ISBN)978-1-5090-3842-8 (ISBN)
Conference
IEEE EUROCON 2017 - 17th International Conference on Smart Technologies, 6-8 July, 2017, Ohrid, Macedonia
Projects
MIDAS
Funder
EU, Horizon 2020, 77744
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2018-06-29Bibliographically approved
Aihara, A. & Uzunoğlu, B. (2017). Vortex induced vibration energy extraction modeling via forced versus free vibration. In: Proceedings Of Oceans 2017 - Aberdeen: . Paper presented at Oceans 2017 - Aberdeen, 19-22 June, 2017, Aberdeen, UK.. IEEE
Open this publication in new window or tab >>Vortex induced vibration energy extraction modeling via forced versus free vibration
2017 (English)In: Proceedings Of Oceans 2017 - Aberdeen, IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Vortex induced vibrations (VIV) for energy extraction have been revisited in last years by both marine power and wind power communities. Even though vortex induced vibrations have been focus of research for many years, energy extraction from vortex induced vibrations is relevantly new field which needs more detailed investigation and modeling. To this end, there has been recent experimental and modeling parametric studies where VIV was modeled by solution of one-degree-of-freedom ordinary differential equation spring system where engineering modeling of vortex induced vibration for energy extraction has been investigated based on a spring system with the forces defined from forced oscillation experiments where full coupling of free oscillations were not taken into account. Herein a Computational Fluid Dynamics (CFD) modeling of a circular cylinder will be studied to compare forced and free vibrations in the context of vortex-induced energy extraction. The model is essentially solved by partial differential isothermal incompressible Navier-Stokes equations to model fully mathematical model of the fluid-structure interaction of vortex induced vibration. The comparison between forced and free oscillation response studies of this paper will serve to improve the scientific knowledge where vortex induced vibration modeling are comparatively more limited. The preliminary results are presented herein for forced and free oscillations for the Reynolds number regimes Re = 100 and Re = 3800 in two dimensions for combinations of amplitudes and frequency of oscillations in the context of energy extraction modeling.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Navier-Stokes equations, computational fluid dynamics, partial differential equations, shapes (structures), turbulence, vibrations, vortices, CFD, Reynolds number, circular cylinder, fluid-structure interaction, forced oscillation, free oscillation response studies, free vibration, isothermal incompressible Navier-Stokes equations, ordinary differential equation, partial differential, spring system, vortex-induced energy extraction, Computational modeling, Energy exchange, Force, Mathematical model, Numerical models, Oscillators
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:uu:diva-336513 (URN)10.1109/OCEANSE.2017.8084724 (DOI)000426997000155 ()978-1-5090-5279-0 (ISBN)978-1-5090-5278-3 (ISBN)978-1-5386-2111-0 (ISBN)
Conference
Oceans 2017 - Aberdeen, 19-22 June, 2017, Aberdeen, UK.
Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2018-07-30Bibliographically approved
Yildirim, N. & Uzunoglu, B. (2016). Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization. In: Gavrilova, Marina L.; Tan, C.J. Kenneth; Sourin, Alexei (Ed.), Gavrilova, ML; Tan, CJK; Sourin, A (Ed.), Transactions on Computational Science XXVIII: Special Issue on Cyberworlds and Cybersecurity. Paper presented at 15th International Conference on Cyberworlds, Uppsala Univ, Gotland, SWEDEN, OCT 07-09, 2015 (pp. 163-176). Springer Berlin/Heidelberg
Open this publication in new window or tab >>Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization
2016 (English)In: Transactions on Computational Science XXVIII: Special Issue on Cyberworlds and Cybersecurity / [ed] Gavrilova, ML; Tan, CJK; Sourin, A, Springer Berlin/Heidelberg, 2016, p. 163-176Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9590
Keywords
Data mining, Big data, Power ramp, Clustering, Optimization, Association rules, Apriori algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-307326 (URN)10.1007/978-3-662-53090-0_9 (DOI)000389498600009 ()9783662530900 (ISBN)9783662530894 (ISBN)
Conference
15th International Conference on Cyberworlds, Uppsala Univ, Gotland, SWEDEN, OCT 07-09, 2015
Available from: 2016-11-12 Created: 2016-11-12 Last updated: 2017-01-23Bibliographically approved
Uzunoglu, B., Ulker, M. A. & Bayazit, D. (2016). Particle filter joint state and parameter estimation of dynamic power systems. In: IEEE (Ed.), 2016 57th International Scientific Conference On Power And Electrical Engineering Of Riga Technical University (RTUCON): IEEE Conference Publications. Paper presented at IEEE Conference 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) OCT 13-14, 2016.
Open this publication in new window or tab >>Particle filter joint state and parameter estimation of dynamic power systems
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.

Keywords
State estimation, Power system dynamics, Power system stability
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-310708 (URN)10.1109/RTUCON.2016.7763152 (DOI)000391423000073 ()9781509037315 (ISBN)
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3484-6771

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