Output Tracking Control for Renewable Wind Generators Based on Adaptive Takagi-Sugeno Fuzzy Cerebellar Model Articulation Controller

Professor: Peter Liu Presenter: Chia-Lien Hsu August 31, 2010

Contents

1 Problem

2

2 Literature Review

4

3 Method

6

3.1

Input space:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

3.2

Association memory space:

3.3

Receptive-field space:

3.4

Weight memory space: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.5

Output: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

. . . . . . . . . . . . . . . . . . . . . . . . . . 11

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Anticipated Deliverables and Results

14

4.1

Theory part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.2

Simulation part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.3

Real-time Simulation part . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.4

Product performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Timetable and Milestones

16

1

Chapter 1 Problem Wind power generating system is one of topologies of electric generation hybrid system.The main object of this proposal is that I want to design a controller which can easily track the output of convertor, and fill the tank full as fast as possible by tracking maximum voltage. To investigate the wind power generator, as the diagram is shown in 1.1.The Wind Energy Conversion System(WEGS) is composed by a fixed pitch windmill, a multipolar permanent-magnet synchronous generator(PMSG), a rectifier, and a dc-dc convertor to interface the energy tank. The mathematical nonlinear dynamic model is supposed to use from [7], which is describe that:

Rs ωe ϕm πVb iq uw i˙ q = − iq − ωe id + − √ L L 3 3LIs Rs πVb id uw i˙ d = − id + ωe iq − √ L 3 3LIs 2 3pϕm ωe πpϕm Vb uw p √ (Tt − + ) ω˙ e = 2J 4Rs 4 3Rs 1 v˙ c = (iw − iL ) Cb

(1.1)

with the condition

Vb = Eb + vc + (iw − iL ) √ Is = i2q + i2d π √2 √ iw = iq + i2d uw 2 3 2

(1.2)

Figure 1.1: Wind energy conversion system where iL is the load current; iq and id are the q − d components of the stator current; Is is the line current; ωe is the electrical angular speed; vc is the voltage of the equivalent capacitor of the energy storage tank; and uw =

ktr δ

is a simple function of the dc-dc

converter; δ is the duty cycle of dc-dc converter; and ktr is the winding ratio of the transformer. While the another condition is let the ratio ktr = 1. On the other hand, the other variables such like Rs and L are parameters of stator resistance and inductance; ϕm is the flux linked by the stator windings; Tt is the torque developed from the wind turbine; and p is the poles and J is inertia coefficient; Rb , Cb , Eb are resistance, capacitance and ideal voltage source from the model of energy storage tank. According to the function 1.1 and 1.2 which is describe form [7], we can know that uw is the output what I need to track from the premise known variable.

3

Chapter 2 Literature Review

According to [1] the web site shows that renewable energy conversion systems have became a main electronic producing generators in the world and in [2], we can see that wind power generator is one of those renewable conversion systems. Moreover, the wind power have low cost, easy to build and well-efficiency energy produce. Wind generator has many method and research to discuss, such as [7] which discuss sliding mode control, and like [3] which use fuzzy model and virtual desired variable to design the controller. Both of above are trying to achieve maximum power. The maximum power is provided in [1], which can have a good efficiency to fill up the energy capacity. The main object I want to achieve is making the turbine output maximum, so using the method above is a good way to design a controller, but I want my controller finish tracking itself. That means the controller that I would like to design has on-line selflearning function. In [4], we can see that Neural Network system is a system which can learn automatically. So, I choose a controller called ”Cerebellar Model Articulation Controller(CMAC)” to deal with the renewable energy conversion system. CMAC not only has benefit in neural network, but also use less operation to implement on-line selflearning.we can easily see in [5] that Neural Network can tracking maximum power and the precise value.

In order to make the nonlinear dynamic model easy to synthesis, I will use fuzzy model which described by Takagi and Sugeno, which is called T-S Fuzzy model, in [6], using CMAC with T-S Fuzzy model to ensure the tracking value is correct, and it would 4

more quick and more precise in tracking by using less operational process. So, it’s possible to put a controller with T-S fuzzy mode CMAC in tracking WECS maximum power.

5

Chapter 3 Method Before design the output tracking control, we can see that in [8], it says the tip-speed ratio is given from the turbine which can be mapped as:

λ=

rωm v

(3.1)

where r is the blade length; ωm is the angular shaft speed; and v is the wind speed. The power extracted from the wind can be maximized once the power coefficient Cp is maximized. Figure3.1 is a typical diagram of the λ V.S. Cp , and we can see that Cp has a unique maximum value at a constant value of λ, so the output power of wind turbine can be inferred as:

1 Pt = Cp (λ)ρAs v 3 2

(3.2)

Which ρ is the air density; and As is the wind turbine swept area. We can also represent the function 3.2 as another form:

3 Pt = Kωm 2we 3 ) = K( p

Where K =

1 C (λ)ρAs r3 2λ2 t

and Ct (λ) =

Cp (λ) λ

(3.3)

is the torque coefficient, so the book

confirmed that the output of WECS model, may be expressed as: 6



Figure 3.1: Polynomial approximation of a typical power coefficient

Tt =

Pt 1 = Ct (λ)ρAs rv 2 ωm 2

Let x1 = iq , x2 = id , x3 = ωe , x4 = vc , x5 = uw . Then the WECS nonlinear model can be described as:

x(t) ˙ = A(x, t)x(t) + Bu(t) + η

(3.4)

Where 

−Rs L

   0   A(x, t) =  0    0  0

0

ϕm L

−Rs L

0

pTt 2Jx3

− x2

0

x1

0



3p2 ϕ2m 8JRs

0

0

0

0

0

0

0

−πV √ b x1 3 3LIs −πV √ b x2 3 3LIs πp√2 ϕm Vb 8 3JRs πIs √ 2 3Cb

          

0

B = [ 0 0 0 0 1 ]T , η = [ 0 0 0 − CiL 0 ]T b with Is =



x21 + x22 and the system output y(t) = Pt (x) =

8K 3 x p3 3

.

The nonlinear system 3.4 can be expressed through T-S fuzzy model which rule is:

7

P lant Rule i

: IF z1 (t) is F1i and · · · and zg (t) is Fgi .T hen

x(t) ˙ = Ai x(t) + Bu(t) + η, i = 1, 2, . . . , r

where z1 (t)˜zg (t) are the premise variables which consist of the states of the systems; Fji (j = 1, 2, · · · , g) are the fuzzy sets; r is the number of fuzzy rules; x ∈ Rn is the state vector; Ai , B , and η are the system matrices with appropriate dimensions. Using the singleton fuzzifier, product inferred, and weighted average defuzzifier, the nonlinear system can be expressed as follows:

x(t) ˙ =

r ∑

µi (z(t))Ai (x, t)x(t) + Bu(t) + η

(3.5)

i=1

where µi (z(t)) =

∑rωi (z(t)) i=1 ωi (z(t))

with ωi (z(t)) =

g ∏

Fji (zj (t)). Note that

j=1

∑r i=1

µi (z(t)) =

1 for all t, where µi (z(t)) ≥ 0, for i = 1, 2, . . . , r, are regarded as the grade function. Deal with a nonlinear dynamic plant, I’m considering a controller design such is Cerebellar Model Articulation Controller(CMAC), figure (3.2) is the basic structure, it quantize the input function to several values, then through the figure ??, we know that CMAC will put the separated parameters in x-y axis, then us ”point to point” way to find out the representative value, and follow this memory address choosing method, we infer the function which is shown as: 

ys = aTs w(xs ) =

[

as,1 as,2 · · ·

as,Nh

w  1  ]  w2 ·  ..  .  wNh

   Nh  ∑ = aa,j wj   j=1 

(3.6)

Where s is the number of learning sample; Nh is the size of memory; aTs = [ as,1 as,2 · · · as,Nh ] is the cross value of (??), if they crossover, the value will be 1 and vice versa; w = [ w1 w2 · · ·

wN h ]T is the weighting value of each representation region, and through

(3.2), we also known that memory size Nh = NbNv × Ne , which Nb is the number of each 8

layer; Nv is the number of input vector; and Ne is the number of block each element contain. The controller will keep learning until the minus of expect value and output value will converge into an available range. the output between minus is called update value, and the controller learning formula is:

(i)

es(i) = yˆs − aTs−1 ws−1 α △ws(i) = αs · e(i) s Ne (i) ws(i) = ws−1 + △ws(i) where s is still the number of learning sample; α is the rate of learning rule; yˆs is the (i)

(i)

desired output in this sample input; aTs−1 ws−1 is the output in last sample input, so es−1 (i)

is the output error of different input; and △ws−1 is the update value. Chaos system is a non-predictable system, so i use a preciseness predictor such like CMAC, the system might be track. Because of the Chaos system is described through T-S fuzzy model, i would also use CMAC through T-S fuzzy. It would make the system match. T-S fuzzy mode CMAC is the main object I need to achieve, It not only includes the advantages of CMAC, but also solve the nonlinear problem by using T-S fuzzy rule. [10][11][12][13][14][15] As shown is the figure (3.4), the control system includes input space, association memory space, receptive-field space, weight memory space, and output space. Each space represent the system working stage, I would like to introduce each as follows:

3.1

Input space:

According to the figure(3.4), we assumed the input vector is S which S = si = [ s1 s2 s3 s4 ]T ∈ Rni , and the variable ni is dimension of input vector.

9

Figure 3.2: CMAC basic structure

Figure 3.3: CMAC separate each input to each memory region 10

Figure 3.4: CMAC structure with T-S fuzzy model

3.2

Association memory space:

The association memory space also called the quantize area, before describe this space, I need to choose the quantize way first. There are many ways to quantize the vector, and I choose Gaussian to quantize the input because it is a ripe math which has been used by many scholar. And the definition of Gaussian function is:

ϕij (sj ) = exp(−

(sj − mij )2 ), f or i = 1, 2, . . . , ni and j = 1, 2, . . . , nR σij2

Where ϕij represents jth layers of ith input si with the mean term mij and the variance term σij , each terms can also be represented as:

mij = [ mTi1 mTi2 · · ·

T n ·n mTinR ] ∈ R i R

T T T ]T ∈ Rni ·nR σij = [ σi1 · · · σin σi2 R

where mj = [ m1j m2j · · · mni j ]T ∈ RnR , and σj = [ σ1j σ2j · · · RnR , respectively.

11

σni j ]T ∈

3.3

Receptive-field space:

Each location of memory space corresponds to a receptive-field.The multi-dimensional receptive-field function is defined as:

bj (s, mj , σj ) =

ni ∏

ni ∑ (si − mij )2 ϕij (sj ) = exp( ) for j = 1, 2, . . . , nR 2 σ ij i=1 i=1

where bi is associate with the ith receptive-field. The multi-dimensional receptive-field function can be expressed in a vector as Γ(s, m, σ) = [ b1 b2 · · · bnR ]T ∈ RnR

3.4

Weight memory space:

Each location of receptive-field space to a set of adjustable value in the weight memory space with nR . The weight vj is linear combination with state variable z , then the corresponding weight can be expressed as:

wj = vjT z where the weight vj is initialized from zero and is automatically adjusted during on-line operation.

3.5

Output:

The output of TS-SCMAC is the algebraic sum of the activated weight memory, and is expressed as

yc =

nR ∑

z T bj (s, mj , σj )vj = V T ZΓ = W T Γ

(3.7)

j=1

where W T = V T Z, V = [ v1T v2T · · · vnTR ]T ∈ Rnv ·nR , and Z = { z z · · ·

z }∈

R(nv ·nR )×nR . In this study, the TS-SCMAC is utilized to estimated the idea compensator, so that a TS-SCMAC output ucn can be written as follows: 12

ucn = V T ZΓ = W T Γ And my main object is make ucn = uw , so that I can proof that output tracking control wind generator based on TS-SCMAC is work.

13

Chapter 4 Anticipated Deliverables and Results According to the problem, I would take apart my work to four parts as follows:

4.1

Theory part

In this part, I would show the synthesis of WECS and the theory of CMAC method, then I will infer the mathematical model and design the controller.

4.2

Simulation part

In this part, I would program the simulation results through MATLAB, and the simulation would might proof that WECS system with CMAC is stable and work.

4.3

Real-time Simulation part

In this part, I will show the results by using MATLAB-Dspace interface, then try different input to see the result whether if stable or not.

14

4.4

Product performance

The final part is a ITA (If Time Available) part, I will program the theory in a board or design a small chip to implement the result.

15

Chapter 5 Timetable and Milestones I would set some target to achieve which can be seen in Table5.1, and Figure5.1 is the working days of each goal, this can make sure that I would have done it or not.

Figure 5.1: Timetable 16

Date

Target to goal

9/30

Finish T-S fuzzy mode CMAC simulation

10/15 Paper submitted(Asian Control Conference Paper Submission) 10/25

graduate examination(Application)

1/1

Find an extendable or program a PCB code

1/10

Graduate examination(Test)

2/28

Alternative military from technology(Start searching)

5/20

Finish thesis writing(Graduation)

6/11

Graduation oral Table 5.1: Milestones

17

Reference [1] ”Global wind 2009 report, ”Global Wind Energy Council.2009. http://www.gwec. net/ [2] ”Wind Energy Conversion System, ”The Encyclopedia of Alternative Energy and Sustainable Living. http://www.daviddarling.info/encyclopedia/AEmain.html [3] K.-Y. Lian, Y.-L. Ouyang and T.-K. Chiu, ”Fuzzy Model-Based Control For Renewable Wind Generators, ”IEEE Tran. unpublished, 2007. [4] Prof. Leslie Smith,”An Introduction to Neural Networks. ” http://www.cs.stir. ac.uk/~lss/NNIntro/InvSlides.html [5] H.Li, K.L. Shi, and P.G. Mclaren, ”Neural-network-based sensorless maximum wind energy capture with compensated power coefficient, ”IEEE Trans. Ind. Appl, Vol. 41, No.6, 1548-1556, 2005. [6] K.-H Liao, ”Output tracking control based on adaptive Takagi-Sugeno Fuzzy Cerebellar Model Articulation Controller, ”Thesis, Ching Yun University, 2010. [7] F.Valenciaga, P.F. Puleston, P.E. Battaiotto, and R.J. Mantz, ”Passivity/sliding mode control of a stand-alone hybrid generation system, ”Proc. Inst. Elect. Eng. Contr. Theory Appl., Vol.147, No. 6, 680-686, 2000. [8] L.L. Freris, ”Wind Energy conversion System, ”Book, publish from Prentice Hall, p.83, 1990 [9] T.-S. Chiang, C.-S. Chiu, p. Liu, ”Output tracking control via TS-SCMAC and VDVs Approch For Time-Delay Systems, ”Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, 270-275, 2010.

18

[10] C.M. Lin, Y.F. Peng and C.F. Hsu, ”Robust cerebellar model articulation controller design for unknown nonlinear systems, ”IEEE Trans. on circuit and systems: II: Express Briefs, Vol. 51, no. 7, 354-358, 2004. [11] C.M. Lin, Y.F. Peng, ”Adaptive CMAC-based supervisory control for uncertain nonlinear systems, ”IEEE Trans. on systems, and Cybernetics-Part B: Cybernetics, Vol. 34, no. 2, 1248-1260, 2004. [12] C.S. Lin, C.T. Chiang, ”Learning converence of CMAC technique, ”IEEE Transactions on Neural Networks,Vol. 8, no.6, 1281-1292, 1997. [13] C.T. Chiang, and C.S. Lin, ”Integration of CMAC and radial basis function techniques, ”Proceeding of the 1995 IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, 3263-3268, 1995. [14] C.T. Chiang, T.S. Chiang and C.K. li, ”A simple and converged structure of addressing technique for CMAC GBF, ”IEEE In. Conf. SMC, 6097-6101, 2004. [15] Y.F. Peng, R.J. Wai, and C.M.Lin, ”Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor, ”IEEE Trans. on Industrial Electronics, Vol. 51, no. 1, 35-48, 2004.

19

Output Tracking Control for Renewable Wind Generators Based on ...

The Wind Energy Conversion System(WEGS) is composed by a fixed pitch windmill, ..... mode control of a stand-alone hybrid generation system, ”Proc.

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