Presentation is loading. Please wait.

Presentation is loading. Please wait.

Simulation in Wind Turbine Vibrations: A Data Driven Analysis Graduate Students: Zijun Zhang PI: Andrew Kusiak Intelligent Systems Laboratory The University.

Published byCecilia Hodges Modified over 8 years ago

Similar presentations

Presentation on theme: "Simulation in Wind Turbine Vibrations: A Data Driven Analysis Graduate Students: Zijun Zhang PI: Andrew Kusiak Intelligent Systems Laboratory The University."— Presentation transcript:

1 Simulation in Wind Turbine Vibrations: A Data Driven Analysis Graduate Students: Zijun Zhang 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 ParameterDescription y1(t)y1(t)Average Drive Train Acceleration at time t y2(t)y2(t)Tower Acceleration at time t y3(t)y3(t)Generated Power at time t y 1 (t-1)Average Drive Train Acceleration at time t-1 y 2 (t-1)Tower Acceleration at time t-1 x1(t)x1(t)Generator Torque at time t x 1 (t-1)Generator Torque at time t-1 x2(t)x2(t)Average Blade Pitch Angle at time t x 2 (t-1)Average Blade Pitch Angle at time t-1 v1(t)v1(t)Wind Speed at time t v 1 (t-1)Wind Speed at time t-1

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 TimeTorque value Torque Value(T-1) Wind Speed Wind Speed(T- 1) …….Drive Train Acc Drive Train Acc(T-1) 19/10/08 3:01:10 PM 42.158636.66309.02598.2568…….63.265161.5034 19/10/08 3:01:20 PM 45.509342.15868.99739.0259…….59.915163.2651 ……. TimeTorque value Torque Value(T-1) Wind Speed Wind Speed(T- 1) …….Drive Train Acc Drive Train Acc(T-1) 10/19/08 3:01 PM 40.799438.99338.38538.0945…….59.764659.5475 10/19/08 3:02 PM 36.994138.02038.15248.1375…….54.840656.1781 …….

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 models in 10-s dataset Test results of the NN models for 10-s data Predicted ParameterMAEStd of MAEMAPEStd of MAPE Drive train acceleration1.27 0.020.03 Tower acceleration4.738.920.060.10 Generated power9.86 0.030.08

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 models in 10-s dataset The first 50 test points of the tower accelerations for 10-s data

13 Validation of data-driven models in 10-s dataset The first 50 test points of the power output for 10-s data

14 Validation of data-driven models in 1-min dataset Test results of the NN models for 1-min data Predicted ParameterMAEStd of MAEMAPEStd of MAPE Drive train acceleration0.771.580.01 Tower acceleration2.767.970.030.04 Generated power8.9913.830.030.15

15 Validation of data-driven models in 1-min dataset The first 50 test points of the drive train accelerations for 1-min data

16 Validation of data-driven models in 1-min dataset The first 50 test points of the tower acceleration for 1-min data

17 Validation of data driven models in 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 Experiment No. Description 1 Select an instance from the 10-s data set to tune the selection pressure and population size of the ES algorithm that will be implemented in the model extracted from 10-s data set 2 Select an instance from the 1-min data set to tune the selection pressure and population size of the ES algorithm that will be implemented in the model extracted from 1-min data set One instance selected from the 10-s data set for experiment 1 TimeTVTV(t-1)WSWS(t-1)PowerTATA(t-1)BPABPA(t-1)DTADTA(t-1) 10/18/08 10:55:10 PM 100.93100.0612.3214.111484.47164.64167.206.778.21147.43139.09 One instance selected from the 1-min data set for experiment 2 TimeTVTV(t-1)WSWS(t-1)PowerTATA(t-1)BPABPA(t-1)DTADTA(t-1) 10/18/08 10:55 PM 100.43100.5814.4214.961481.49169.72170.2210.6811.41142.68144.27 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 Combinations of selection pressure Converge speed of average drive train acceleration (generations) Converge speed of inverse of power output (generations) Converge speed of tower acceleration (generations) Average converge speed (generations) Ratio1 (2parents/2offsprings) 9681631420850.3333 Ratio2 (2parents/4offsprings) 6374781170761.6667 Ratio3 (2parents/6offsprings) 9796974389.0000 Ratio4 (2parents/8offsprings) 9796974389.0000 Ratio5 (2parents/10offsprings) 13459419204.0000 Ratio6 (2parents/12offsprings) 10860736301.3333 Ratio7 (2parents/14offsprings) 11035277140.6667 Ratio8 (2parents/16offsprings) 8747214116.0000 Ratio9 (2parents/18offsprings) 10641180109.0000 Ratio10 (2parents/20offsprings) 17115306164.0000

25 Tuning parameters of SPEA Convergence for 10 values of the selection pressure in experiment 2 Combinations of selection pressure Converge speed of average drive train acceleration (generations) Converge speed of inverse of power output (generations) Converge speed of tower acceleration (generations) Average converge speed (generations) Ratio1 (2parents/2offsprings) 190253190211.0000 Ratio2 (2parents/4offsprings) 46631466321.0000 Ratio3 (2parents/6offsprings) 258178258231.3333 Ratio4 (2parents/8offsprings) 35803550.0000 Ratio5 (2parents/10offsprings) 99529983.3333 Ratio6 (2parents/12offsprings) 29248292210.6667 Ratio7 (2parents/14offsprings) 14928149108.6667 Ratio8 (2parents/16offsprings) 83418369.0000 Ratio9 (2parents/18offsprings) 151181549.3333 Ratio10 (2parents/20offsprings) 36553642.3333

26 Tuning parameters of SPEA Convergence of the ES algorithm for two populations of experiment 1 Population Sizes Converge speed of average drive train acceleration (generations) Converge speed of inverse of power output (generations) Converge speed of tower acceleration (generations) Average converge speed (generations) PS1(2parents/18offsprings)10641180109.0000 PS2(10parents/90offsprings ) 1815123.3333 Convergence of the ES algorithm for two populations of experiment 2 Population Size Converge speed of average drive train acceleration (generations) Converge speed of inverse of power output (generations) Converge speed of tower acceleration (generations) Average converge speed (generations) PS1(2parents/20offsp rings) 36553642.3333 PS2(10parents/100of fsprings) 491374978.3333

27 Simulation results and discussion

28 Simulation Results of Single Point Optimization Partial solution set generated by the evolutionary strategy algorithm Solution No. Solution (TV, BPA) Drive Train Acceleration Gain in Drive Train Acceleration Tower Acceleration Gain in Tower Acceleration Power Gain in Power 1(90.0, 8.81)136.867.17%160.612.45%1460.96-1.58% 2(90.0, 7.34)136.857.18%164.470.10%1460.80-1.59% 3(63.9, 15.00)136.967.10%119.4227.47%1007.14-32.16% 4(67.6, 15.00)136.717.27%120.3426.90%1031.21-30.53% 5(50.9, -3.23)122.5716.86%356.37-116.45%785.72-47.07% 6(90.0, 8.09)136.887.15%162.461.33%1462.77-1.46% 7(63.4, 15.00)136.987.09%119.4127.48%1005.11-32.29%

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) Case 1 (Minimize average drive train acceleration) Minimum value (mean)Original value (mean)Gain (mean) Average drive train acceleration 119.61131.679.16%

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 Mean Value Minimize Drive Train Acceleration Optimized Drive Train Acceleration Original Drive Train Acceleration Gain 10-s data set119.53131.499.10% 1-min data set124.06131.795.87% Minimize Tower Acceleration Optimized Tower Acceleration Acceleration Gain 10-s data set87.22127.8231.76% 1-min data set106.26130.3218.46% Maximize Power Output Optimized Power Output Original Power OutputGain 10-s data set1497.991481.721.10% 1-min data set1497.791482.571.03%

35 Thank You !

Download ppt "Simulation in Wind Turbine Vibrations: A Data Driven Analysis Graduate Students: Zijun Zhang PI: Andrew Kusiak Intelligent Systems Laboratory The University."

Similar presentations

Empirical Algorithmics Reading Group Oct 11, 2007 Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach by Thomas Bartz-Beielstein.

Empirical Algorithmics Reading Group Oct 11, 2007 Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach by Thomas Bartz-Beielstein.
MOEAs University of Missouri - Rolla Dr. T’s Course in Evolutionary Computation Matt D. Johnson November 6, 2006.

MOEAs University of Missouri - Rolla Dr. T’s Course in Evolutionary Computation Matt D. Johnson November 6, 2006.
Angers, 10 June 2010 Multi-Objective Optimisation (II) Matthieu Basseur.

Angers, 10 June 2010 Multi-Objective Optimisation (II) Matthieu Basseur.
Biased Random Key Genetic Algorithm with Hybrid Decoding for Multi-objective Optimization Panwadee Tangpattanakul, Nicolas Jozefowiez, Pierre Lopez LAAS-CNRS.

Biased Random Key Genetic Algorithm with Hybrid Decoding for Multi-objective Optimization Panwadee Tangpattanakul, Nicolas Jozefowiez, Pierre Lopez LAAS-CNRS.
Exact and heuristics algorithms

Exact and heuristics algorithms
Multi-Objective Optimization NP-Hard Conflicting objectives – Flow shop with both minimum makespan and tardiness objective – TSP problem with minimum distance,

Multi-Objective Optimization NP-Hard Conflicting objectives – Flow shop with both minimum makespan and tardiness objective – TSP problem with minimum distance,
Genetic Algorithms Contents 1. Basic Concepts 2. Algorithm

Genetic Algorithms Contents 1. Basic Concepts 2. Algorithm
Surrogate Model Based Differential Evolution for Multiobjective Optimization (GP - DEMO) Šmarna gora, 20. 9. 2012Miha Mlakar Algoritmi po vzorih iz narave.

Surrogate Model Based Differential Evolution for Multiobjective Optimization (GP - DEMO) Šmarna gora, Miha Mlakar Algoritmi po vzorih iz narave.
Tutorial 2, Part 1: Optimization of a damped oscillator.

Tutorial 2, Part 1: Optimization of a damped oscillator.
Multiobjective Optimization Chapter 7 Luke, Essentials of Metaheuristics, 2011 Byung-Hyun Ha R1.

Multiobjective Optimization Chapter 7 Luke, Essentials of Metaheuristics, 2011 Byung-Hyun Ha R1.
Spie98-1 Evolutionary Algorithms, Simulated Annealing, and Tabu Search: A Comparative Study H. Youssef, S. M. Sait, H. Adiche

Spie98-1 Evolutionary Algorithms, Simulated Annealing, and Tabu Search: A Comparative Study H. Youssef, S. M. Sait, H. Adiche
Content Based Image Clustering and Image Retrieval Using Multiple Instance Learning Using Multiple Instance Learning Xin Chen Advisor: Chengcui Zhang Department.

Content Based Image Clustering and Image Retrieval Using Multiple Instance Learning Using Multiple Instance Learning Xin Chen Advisor: Chengcui Zhang Department.
1 Lecture 8: Genetic Algorithms Contents : Miming nature The steps of the algorithm –Coosing parents –Reproduction –Mutation Deeper in GA –Stochastic Universal.

1 Lecture 8: Genetic Algorithms Contents : Miming nature The steps of the algorithm –Coosing parents –Reproduction –Mutation Deeper in GA –Stochastic Universal.
1 Simulation Modeling and Analysis Session 13 Simulation Optimization.

1 Simulation Modeling and Analysis Session 13 Simulation Optimization.
A New Evolutionary Algorithm for Multi-objective Optimization Problems Multi-objective Optimization Problems (MOP) –Definition –NP hard By Zhi Wei.

A New Evolutionary Algorithm for Multi-objective Optimization Problems Multi-objective Optimization Problems (MOP) –Definition –NP hard By Zhi Wei.
Multi-Objective Evolutionary Algorithms Matt D. Johnson April 19, 2007.

Multi-Objective Evolutionary Algorithms Matt D. Johnson April 19, 2007.
A new crossover technique in Genetic Programming Janet Clegg Intelligent Systems Group Electronics Department.

A new crossover technique in Genetic Programming Janet Clegg Intelligent Systems Group Electronics Department.
Introduction to Genetic Algorithms Yonatan Shichel.

Introduction to Genetic Algorithms Yonatan Shichel.
Multimodal Problems and Spatial Distribution Chapter 9.

Multimodal Problems and Spatial Distribution Chapter 9.
Automating Keyphrase Extraction with Multi-Objective Genetic Algorithms (MOGA) Jia-Long Wu Alice M. Agogino Berkeley Expert System Laboratory U.C. Berkeley.

Automating Keyphrase Extraction with Multi-Objective Genetic Algorithms (MOGA) Jia-Long Wu Alice M. Agogino Berkeley Expert System Laboratory U.C. Berkeley.

Similar presentations

Ads by Google