//ESR 10
ESR 10 2018-01-12T13:28:30+00:00

ESR 10

Wind Farm management cost optimization

Work Package 5 of AWESOME project focuses on cost effective strategies for wind energy O&M tasks. As a part of this package, our main goals can be stated as follows;

  • To create a decision making tool for operation and maintenance strategy selection in terms of scheduling, investment type, risk consideration.

  • To define main input parameters and their metrics for O&M optimization task.

  • To search for correlation and dependency relationships between input clusters.

  • To model a dynamical simulation for the wind farms in operation.

  • To design dynamic simulation which is based on synchronously updating input-output structure.

  • To make an economical evaluation for wind farm O&M and provision of financial summary parameters.

  • To establish a new model which includes the effects of crucial factors such as tax, logistics and three layered health conditions for component, turbine and wind farm.

  • To use non-commercial software environments such as R and nonlinear optimization techniques.

Whole wind farm evaluation will be main target rather than an individual wind turbine. Also cost of electricity which is taken from the grid will be modeled. Instead of weak assumptions such as all failures are repairable (repair condition) or un-repairable (replace condition), we will assess the deterioration level of components.

Dynamical model of insurance costs will be established which is based on life time and failure occurrences of each turbine. Wind direction, wake effects and the seasonality components will be taken into consideration in our power model.

Finally dynamical electricity market, subsidy, investment structure, interest rate changes, varying effects of loan amounts will be analyzed. The model flexibility will be achieved to adapt on the changes of the future policies in economical evaluation such as Green House Gas (GHG) emission tax and GHG trading effects on cost of electricity.

Integration of wind farm management decision tools will be performed with a multi-objective and multidisciplinary optimization perspective.

Planned Secondments


Compañia Eólica de Tierras Altas (CETASA)
Tentative schedule: Spring – Summer 2016


Loughborough University (LBORO)
Tentative schedule: Spring – Summer 2017


  • Yurusen, N. Y., & Melero, J. J. (2016, September). Probability density function selection based on the characteristics of wind speed data. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032067). IOP Publishing. DOI: 10.1088/1742-6596/753/3/032067 Full text: http://iopscience.iop.org/1742-6596/753/3/03206

  • Reder M, Yurusen NY, Melero JJ (2018) Data-driven learning framework for associating weather conditions and wind turbine failures, Reliability Engineering and System Safety 169  pp. 554–569, ISSN: 09518320, DOI: 10.1016/j.ress.2017.10.004, Full text: https://www.sciencedirect.com/science/article/pii/S0951832017300832?via%3Dihub

  • Yurusen, NY, Tautz-Weinert J, Watson SJ, Melero JJ (2017) The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain), Journal of Physics: Conference Series, 926, 012014, DOI: 1088/1742-6596/926/1/012014. Full text: http://iopscience.iop.org/article/10.1088/1742-6596/926/1/012014

  • Yurusen NY, Reder M, Melero JJ (2017) Failure Event Definitions & their Effects on Survival and Risk Analysis of Wind Turbines, Safety and Reliability – Theory and Applications, CRC Press Taylor & Francis, DOI: 10.1201/9781315210469-93.



Nurseda Yildirim Yurusen
Bachelor: Mechanical Engineer
MSc: Wind Power Project Management
MSc: Energy Engineer


Julio J. Melero

Country: Spain
Host Institution: Circe