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Simulation in Wind Turbine Vibrations: A Data Driven Analysis

Simulation in Wind Turbine Vibrations: A Data Driven Analysis. Graduate Students: Zijun Zhang PI: Andrew Kusiak Intelligent Systems Laboratory The University of Iowa. Outline. Modeling wind turbine vibrations Multi-objective optimization model Evolutionary strategy algorithm

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Simulation in Wind Turbine Vibrations: A Data Driven Analysis

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  1. Simulation in Wind Turbine Vibrations:A Data Driven Analysis Graduate Students: ZijunZhang PI: Andrew Kusiak Intelligent Systems Laboratory The University of Iowa

  2. Outline • Modeling wind turbine vibrations • Multi-objective optimization model • Evolutionary strategy algorithm • Simulation results and discussion

  3. Modeling wind turbine vibrations

  4. Parameter description

  5. Models of wind turbine vibrations Wind turbine vibration models: Data-derived model to predict drive train acceleration Data-derived model to predict tower acceleration

  6. Models of wind turbine vibrations Parametric model of power output: • Data-derived model of power output:

  7. Power Curve • Power curve of a 1.5 MW turbine

  8. Sample datasets • 10-s dataset • 1-min dataset

  9. Validation of data-driven models • Four metrics to assess the performance of data driven models: Mean absolute error: Standard deviation of the mean absolute error: Mean absolute percentage error: Standard deviation of Mean absolute percentage error:

  10. Validation of data-driven modelsin 10-s dataset • Test results of the NN models for 10-s data

  11. Validation of data-driven models in 10-s dataset • The first 50 test points of the drive train acceleration for 10-s data

  12. Validation of data-driven modelsin 10-s dataset • The first 50 test points of the tower accelerations for 10-s data

  13. Validation of data-driven modelsin 10-s dataset • The first 50 test points of the power output for 10-s data

  14. Validation of data-driven modelsin 1-min dataset • Test results of the NN models for 1-min data

  15. Validation of data-driven modelsin 1-min dataset • The first 50 test points of the drive train accelerations for 1-min data

  16. Validation of data-driven modelsin 1-min dataset • The first 50 test points of the tower acceleration for 1-min data

  17. Validation of data driven modelsin 1-min dataset • The first 50 test points of the power output 1-min data

  18. Multi-objective optimization model

  19. Multi-objective optimization

  20. Evolutionary strategy algorithm

  21. Strength Pareto Evolutionary Algorithm 1. Initialize three sets, parent set (Sp ), offspring set ( So) and elite set (Se ). Generate u individuals (solutions) randomly to conduct the first generation of population. 2. Repeat until the stopping criteria (number of generation, N) is satisfied 2.1. Search the best non-dominated solutions in So. Copy all non-dominated solutions to Se. 2.2. Search and delete all dominated solutions in Se. 2.3. A clustering technique is applied to reduce size of Se if the size of Se is too large. 2.4. Assign fitness to solutions in Se and So. 2.5. Apply a binary tournament selection to select u parents from the SoUSe to form the population of parents and this population is stored in Sp. 2.6. Recombine two parents from Sp to generate a new population. 2.7. Mutate individuals in So by the mutation operator and assign fitness values to them. 3. Check number of generation, if it is equal to N , then stop.

  22. Strength Pareto Evolutionary Algorithm • Recombination of parents in SPEA • Mutation operator

  23. Tuning parameters of SPEA • Two experiments for tuning selection pressure and population size

  24. Tuning parameters of SPEA • Convergence for 10 values of the selection pressure in experiment 1

  25. Tuning parameters of SPEA • Convergence for 10 values of the selection pressure in experiment 2

  26. Tuning parameters of SPEA • Convergence of the ES algorithm for two populations of experiment 1 • Convergence of the ES algorithm for two populations of experiment 2

  27. Simulation results and discussion

  28. Simulation Results of Single Point Optimization • Partial solution set generated by the evolutionary strategy algorithm

  29. Simulation Results of Single Point Optimization • Solution of the elite set in a 3-dimensional space

  30. Multi-points Optimization Simulation Results • Gains in vibration reductions of the drive train for Case 1 • (10/19/2008 2:43:00 AM - 10/19/2008 2:54:00 AM)

  31. Simulation Results • The optimized and original drive train acceleration of Case 1 for 10-s data • (10/19/2008 2:43:00 AM - 10/19/2008 2:54:00 AM)

  32. Simulation Results • The computed and original torque value of Case 1 for 10-s data • (10/19/2008 2:43:00 AM - 10/19/2008 2:54:00 AM)

  33. Simulation Results • The computed and original average blade pitch angle of Case 1 for 10-s data • (10/19/2008 2:43:00 AM - 10/19/2008 2:54:00 AM)

  34. Simulation Results • Comparison of computational results for 10-s data set and 1-min data set over 10 min horizon

  35. Thank You !

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