coordinated control for optimal wind farm operation -...

Coordinated Control for Optimal Wind Farm Operation

Coordinated WF Operation, WILL4WIND, July 2013

Mato Baotić

Vedrana Spudić, Mate Jelavić, Nedjeljko Perić

AeolusDistributed Control of Large-Scale Offshore Wind Farms

ACROSSCentre of Research Excellence for Advanced Cooperative Systems

Offshore wind farm in operation


Thanet offshore wind farm

Vestas V90 3MW turbines100 turbines, in 7 rows (12, 14, 15, 17, 17, 14, 11)


Control problem


Centralized controller

Control system requirements:– track wind farm power reference– reduce fatigue loads of wind turbines – tower and shaft loads considered– obtain scalability of control algorithm to different wind farm sizes

Control problem features:– optimal control approach required– variable constraints – nonlinear system– the system model size and complexity increases drastically with wind farm size

Conventional optimal control approaches not suitable for the problem!


Wind farm control – simplistic approach


Wind farm coupling - static optimization


Wind farm coupling – dynamic optimization (AEOLUS)


Wind farm optimization (AEOLUS)


Hierarchical centralized wind farm controller


Reconfigurable controller overview


Reconfigurable controller factsheet

Objectives:

Track wind farm power reference

Minimize fatigue loads on tower and shaft

Respect optimal operating point distribution from the supervisory controller

Requirements:

Control sampling time 1sRelevant for wind turbine mechanical systemFeasible for wind farm control systemRequires dedicated optimal control approach

Respects system constraints

Can be applied in the entire operation region

Provides fast response to:Wind gusts and turbulenceChanges in system constraintsChanges in wind turbine availabilityChanges in wind farm power reference


Reconfigurable controller factsheet

Interface:

Approach:Assumption:

no coupling between wind turbines at fast time scalesModeling:

wind farm is a cooperative control systemconsists of dynamically independant subsystems – wind turbineseach subsystem has an independant control objective – minimize fatigue loads, stay close to optimal operating pointssubsystems cooperate to achieve a common objective – deliver required wind farm power

Control design based on precomputation of control laws via parametric programming


Local optimal control problem


Local control objective: Minimize fatigue loads on tower and shaftStay close to optimal operating points – loads and powers

Solution: Introduce optimal load and power tracking into the controller cost functionLoad tracking can reduce fatigue load

Local control objective

Time [s]

Sha

ft to

rque

[Nm

]

0 5 10 15 20 25 303.1

3.15

3.2

3.25

3.3

3.35

3.4

3.45

3.5

3.55

3.6x 10

6


Local control objectiveLocal control objective:

Minimize fatigue loads on tower and shaftStay close to optimal operating points – loads and powers


0 5 10 15 20 25 303.1

3.15

3.2

3.25

3.3

3.35

3.4

3.45

3.5

3.55

3.6x 10

6

1

2 3

4

56

7

8

9

10

11

12

13

14

1516

17 1819 20

21

22

2324

2526

2728 29

3031

32

33

3435

36

37

38

39

404142

43

44 4546

474849

5051

52

53

54

55

56

57

58 5960

6162

63

64

65

66 67

68

69

70

71

72

73

Rainflow cycles extracted from signal

Time [s]

Sha

ft to

rque

[Nm

]


Local control objectiveLocal control objective:

Minimize fatigue loads on tower and shaftStay close to optimal operating points – loads and powers


0 5 10 15 20 25 303.1

3.15

3.2

3.25

3.3

3.35

3.4

3.45

3.5

3.55

3.6x 10

6

1

2 3

4

56

7

8

9

10

11

12

13

14

1516

17 1819 20

21

22

2324

2526

2728 29

3031

32

33

3435

36

37

38

39

404142

43

44 4546

474849

5051

52

53

54

55

56

57

58 5960

6162

63

64

65

66 67

68

69

70

71

72

73

Rainflow cycles extracted from signal

Time [s]

Sha

ft to

rque

[Nm

]


Local optimal control problem

Wind model – persistance assumptionPrediction horizon N=4References assumed constant in prediction

horizon

Track power and loads

WT model

Available power constraint

Other constraints


Cooperation of turbines


Cooperation of turbines

A new variable for cooperation of wind turbines introduced in the local control problem

Proposed wind farm controller


Results


Results – Scalability

20 40 60 800

1

2

3

4

5

6

7

8

Number of wind turbines

Mean t

ime r

equired f

or

one c

ontr

ol co

mputa

tion [

s]

Classical approach

Reconfigurable controller


Results – Hierarchical controller

Supervisory and reconfigurable controller merged into hierarchical controllerNo stability issuesThe supervisory control actions practicaly the same after adding the reconfigurable controller


Results – Hierarchical controller

Wind farm layout:

The hierarchical controller was extensively tested

Test results can be found in deliverable 3.3a and deliverable 5.6

Here we present the basic tests from D5.6 – tracking of the constant power reference for high and low wind speed

Measures for comparison:Wind farm power tracking standard deviation Damage equivalent load on shaftDamage equivalent load on tower


Results – Performance evaluation

Wind farm power reference (36 MW)Mean wind speed 15 m/s


Results – Performance evaluation

400 420 440 460 480 500 520 540 560 580

3.58

3.585

3.59

3.595

3.6

3.605

x 107

Win

d f

arm

pow

er

[W]

Time[s]

SUPSUP+REC

300 320 340 360 380

-8

-6

-4

-2

0

x 107 WT1

Tow

er

bendin

g m

pm

ent

[Nm

]

Time[s]

120 140 160 180 200

3.5

3.6

3.7

3.8

3.9

4

4.1x 10

6 WT1

Shaft

tors

ional m

om

ent

[Nm

]

Time[s]


coordinated control for optimal wind farm operation -...

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