Wind Turbine Power Generation: Response Prediction

IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 7, Issue 1 (May. - Jun. 2013), PP 31-39
www.iosrjournals.org
www.iosrjournals.org 31 | Page
Wind Turbine Power Generation: Response Prediction
Akash Deep Tyagi1
, Ankush Yadav1
, Nitin Joshi1
1
(Suresh Gyan Vihar University, Mahal Jagatpura Jaipur Rajasthan)
Abtract: The worldwide interest has been increasing about wind energy for power generation purpose due to
continue increase in fuel cost and the need to have clean source of energy. Wind energy may enhance the power
generation capabilities and maximize its capacity factor, inurn participate in generating power at lower cost. It
has also been notice that renewable power generation through wind energy is also the fast growing energy
technology. The optimization of the efficiency of wind turbine is prudent to complete the conventional power
sources. Wind Turbine Power Generation (WTPG) is a complex phenomenon to understand since the real
process has depends upon the Wind Velocity and the relative turbine dimensions and the outside climatological
parameter like Wind Velocity (WV) nature of wind, etc. In this paper an effort has been made to develop a fuzzy
logic approach to predict an appropriate WTPG considering the WV, AD and Chord Length of Turbine Blades
(CLTB) as input parameters. The complexities of the parameters and the imprecision of linguistic expressions
are taken into consideration; the application combining linguistic variable to optimize WTPG under multiple
conditions is presented in this study.
Keywords: Wind Energy, Wind Power, Wind Velocity, Fuzzy Logic
I. Introduction
Wind energy generation has brought many challenges to the electrical power system engineers [1]. The
problems encountered in the electrical network comprising wind energy systems are due to the continuous
variations in the wind regime [2]. On-line estimation of wind turbine power coefficients using unknown input
observer was reported by Odgaard P et al. [3]. In addition to conventional energy resources renewable energy
can also play a vital role for energy demand of which wind energy is one of them. This is a kinetic energy
associated with the atmospheric wind velocity. Wind energy for irrigation and milling has been used since the
beginning of 20th
century. It is being used to generate electrical power also. The power carried by a flowing
mass of air that is called wind is the product of the Cross-sectional area of the mass and the wind, the density of
the wind, ρ, and the wind speed, v.
𝑊𝑖𝑛𝑑 𝑃𝑜𝑤𝑒𝑟, 𝑃 =
1
2
× 𝜌 × 𝐴 × 𝑣3
Where;
ρ = Air density (kg/m3
)
v = wind velocity (m/s)
The air density is proportional to the air temperature and the air pressure; both vary with height above
sea level. The power in the wind cannot be completely converted into mechanical energy of a wind turbine.
Otherwise the air mass would be stopped completely in the intercepting rotor area and would cause a
“congestion” of the cross-sectional area for the air masses. Theoretically maximum energy extraction from wind
was discovered by Betz in 1926, and is written as
𝑃𝐵𝑒𝑡𝑧 =
1
2
× 𝜌 × 𝐴 × 𝑣3
× 𝐶𝑝𝐵𝑒𝑡𝑧 =
1
2
× 𝜌 × 𝐴 × 𝑣3
× 0.59
Though this is not a hard and fast limit such as the Carnot efficiency it happens to be a useful rule-of-
thumb estimate. According to Betz, even if no losses occurred, a wind turbine could utilize only 59% of the
wind power. In addition when unavoidable swirl losses are included, this figure reduces to about 0.42. This
happens to be observed as the current limit of well-designed turbines today [4].
The competitive efficiency of the wind turbine is necessary to be an alternate the conventional power
sources. Prediction of WTPG is a complex phenomenon to understand and is very difficult to present
mathematically since the real process has the involvement of wind velocity and the relative turbine dimensions.
Turbine Blades Hub above ground has the following phenomena, the higher the wind velocity (to the 1/7th
power) and since power is proportional to the cube of the velocity, an increase in hub elevation from 30m to
50m leads to an Average wind speed 7.6% higher. This becomes a significant cost-benefit trade-off since taller
hubs become more expensive. Spacing of wind turbine on wind frames will prevent the maximum amount of
wind to be intercepted. However, too close a spacing will lead to interference, and downwind units will be less
productive [4]. Recent Progress about wind energy conversion has been reported by Musgrove, P.J. [5].

II. Soft Computing Approaches
Soft computing technology is an interdisciplinary research field in computational science. Presently
various techniques of soft computing are being used for data analysis such as neural network, and fuzzy logic.
For analytical purposes, it is considered that the fuzzy logic controller is a “black box” which receives inputs
parameters and imprecision of linguistic expressions, whereas output is the prediction of desired resultant.
Figure 1 shows the Black Box process of fuzzy controller.
Figure 1: Black Box Process of Controller
In recent year the artificial intelligence have largely used in the different area including the
transportation, agriculture, industrial application etc. In the power generation many expert system have been
designed. Hossain et al have been reported an intelligent system using Fuzzy logic for the prediction of power
generation [6]. A classical set theory can be extended to handle the partial membership enabling to express
indistinct human concept using fuzzy set also describe by Kalogirou in 2003 [7]. A computational Model of a
Novel, Two-Cup Horizontal Wind-Turbine System has been developed by Greg, M et al [8]. Recently a fuzzy
logic controller for predicting wind turbine power generation has been reported by the Badrana et al [9] in which
authors utilizes the fuzzy logic methodology to assess wind sites to decide which sites should be given the
highest priority with respect to their benefits and costs, and to predict the annual generation for different
turbines in the best sites. The criterion of evaluation using fuzzy logic is based on different parameters, i.e.,
wind resources, prevailing wind direction, above ground level (AGL), site capacity, soil conditions, site
elevation, land cost, land roughness, temperature, cultural and environmental concerns,
Aviation/telecommunications conflicts, nearby resident's concerns, site environmental issues (corrosion,
humidity), and distance to transmission etc. Simulink model is developed to study the parameters that affect the
power generation by wind turbines using MATLAB.
There are a number of different techniques that would work here and therefore a design choice must be
made. Some of the techniques require a relatively accurate model of the system in order to develop a satisfactory
system. Fuzzy Logic system, on the other hand, does not require a model of the system, instead, of that they
depend the knowledge of an expert for the particular system. Therefore, with all of these kept in mind, a Fuzzy
Logic expert system is introduced for the prediction of wind power generation from the wind turbine. The main
advantage of Fuzzy Logic is that it can be tuned and adapted and if necessary, the degree of freedom of the
system may be enhanced [10].
III. Fuzzy Logic Modeling
There are number of different methods are available some of the method require a comparatively truthful
model in order to develop a satisfactory system. The concept of fuzzy set theory is a membership function,
which represents numerically to what degree an element belongs to a set. In fuzzy set theory, an element can be
a member of a particular set to a certain degree and at the same time be a member of a different set to a certain
degree. To what degree an element belongs to a certain set is called the membership degree. In fuzzy rule-based
systems, knowledge is represented by if–then rules. Fuzzy rules consist of two parts: an antecedent part stating
conditions on the input variable(s) and a consequent part describing the corresponding values of the output
variable(s) [11]. The aim of this study was to construct fuzzy knowledge-based models for the prediction of the
power generation by controlling wind velocity of wind turbine (based on the Mamdani) approach. Fuzzy
modeling involves the following three steps
i. Fuzzification
ii. Fuzzy Rule Inference
iii. Defuzzification
3.1 Fuzzification
The fuzzification comprises the process of transforming crisp values into grades of membership for
linguistic terms of fuzzy sets. The Membership Function (MF) is used to associate a grade to each linguistic
term. The terminology of linguistic variable was introduced by Zadeh [12] as an approach to capture natural
experience commonly used by controller black box.
Fuzzy Logic Controller
BLACK BOX
OutputInput
Parameters

3.2 Fuzzy Rule Inference
Fuzzy rule inference method is used to define some sets of fuzzy logic operators along with production
rules. The most common rule is called IF-THEN Rule, which can be used to formulate the conditional
statements that comprise fuzzy logic.
3.3 Defuzzification
In Defuzzification, Fuzzy sets act as the input in the process and output is the single number. The
aggregate of fuzzy set encompasses a range of output values and so must be defuzzified in order to get the single
output value from the set. A Conceptual Fuzzy Logic Modeling Approach has been depicted in Figure 2.
Figure 2: Conceptual Fuzzy Logic Modeling
IV. FUZZY INFERENCE ENGINE (FIE)
Fuzzy inference is the process of formulating the mapping from a given input set to an output using
fuzzy logic. Fuzzy logic [11]-[13] is a superset of conventional Boolean logic and extends it to deal with new
aspects such as partial truth and uncertainty. The basic elements of fuzzy logic are linguistic variables, fuzzy
sets, and fuzzy rules [14]. The linguistic variables’ values are words, specifically adjectives like “very low”,
“low”, “medium”, “high”, and ”very high” A fuzzy set is a collection of couples of elements. It generalizes the
concept of a classical set, allowing its elements to have a partial membership. The degree to which the generic
element “x” belongs to the fuzzy set A (expressed by the linguistic statement x is A) is characterized by a
membership function (MF), fA(x). The membership function of a fuzzy set corresponds to the indicator function
of the classical sets. It can be expressed in the form of a curve that defines how each point in the input space is
mapped to a membership value or a degree of truth between 0 and 1. The most common shape of a membership
function is triangular, although trapezoidal and bell curves are also used. This operation normalizes all inputs to
the same range and has a direct effect on system performance and accuracy.
A fuzzy set “A” defined within a finite interval called universe of discourse U as follows:
A = {(x, fA (x)), fA (x): U → [0,1]}
U is the whole input range allowed for a given fuzzy linguistic variable. All fuzzy sets related to a
given variable make up the term set, the set of labels within the linguistic variable described or, more properly,
granulated. Fuzzy rules form the basis of fuzzy reasoning. They describe relationships among imprecise,
qualitative, linguistic expressions of the system’s input and output. Generally, these rules are natural language
representations of human or expert knowledge and provide an easily understood knowledge representation
scheme.
A typical conditional fuzzy rule assumes a form such as
IF Load is “High” AND Backup is “Poor” THEN Performance is “Not satisfactory”.
Load is “High” and Backup is “Poor” is the rule’s premise; while Performance is “Not Satisfactory” is the
consequent. The premise predicate might not be completely true or false, and its degree of truth ranges from 0 to
1. We compute this value by applying the membership functions of the fuzzy sets labeled “High” and “Poor” to
the actual value of the input variables Load and Backup. After that, fuzzification is applied to the conclusion;
the way in which this happens depends on the inference model.
There are two types of fuzzy inference models:
1. Mamdani [15],

2. TSK or Sugeno [16].
Interpreting an if-then rule involves two distinct parts: first evaluating the antecedent and then applying
results to the consequent (known as implication) [17]-[18]. In the case of two-valued or binary logic, if-then
rules do not present much difficulty. If the premise is true, then the conclusion is true, whereas with fuzzy
approach, if the antecedent is true to some degree of membership, then the consequent is also true to that same
degree.
Mamdani-type [16] inference expects the output membership functions to be fuzzy sets. After the
aggregation process, there is a fuzzy set for each output variable that needs defuzzification. It is possible, and in
many cases much more efficient, to use a single spike as the output’s membership function rather than a
distributed fuzzy set. This is sometimes known as a singleton output membership function, and it can be thought
of as a pre-defuzzified fuzzy set. It enhances the efficiency of the defuzzification process because it greatly
simplifies the computation required by the more general Mamdani method, which finds the centroid of a two-
dimensional function. Rather than integrating across the two-dimensional function to find the centroid,
Sugenotype systems use weighted sum of a few data points. In general, Sugeno-type systems can be used to
model any inference system in which the output membership functions are either linear or constant.
V. Proposed Model
As shown in Fig. 3, the three input parameter WV, AD and CLTB are considered for the prediction of
output WTPG; the fuzzy logic toolbox of MATLAB was used. For fuzzification of these factors the linguistic
variables Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH) were used for the input and
output. For the study, the bisector method for defuzzification was used because these operators assure that the
vertical line divides the region into two sub-regions of equal area of the output between the rules. The units of
the used factors were WV (m/s), AD (kg/cubic meter) and CLTB (m), and WTPG (W). With the fuzzy sets
defined, it is possible to associate the fuzzy sets in the form of fuzzy rules. For the three inputs and one output, a
fuzzy rule is developed. The main advantage of Fuzzy Logic is that it can be tuned and adapted if necessary,
thus enhancing the degree of freedom of the system Rajagopalan et al., 2003 [10].
Figure 3: Inference system Block Diagram
5.1 Membership Functions
For the fA(x) as the Membership Function (MF), a large class of functions can be taken viz. triangular,
trapezoidal, Gaussian and bell functions. However we have selected Gaussian function. MFs considered for AD,
WV, CLTB and WTPG are represented by 5 Gaussian curves for this model illustrated in Fig. 4, 5, 6 and 7.
Figure 4. Membership Functions for the corresponding to WV

Figure 5. Membership Functions for the corresponding to CLTB
Figure 6. Membership Functions for the corresponding to AD
Figure 7. Membership Functions for the corresponding to WTPG
When multiple input combinations are to get executed, the basic concept of switching and time slices
comes into picture. In our proposed algorithm as shown under, a newly WV (), AD () and CLTB() value, will be
added to the input queue. This queue consists of the remaining input values from last cycle that has not yet been
executed
Loop
For each state of the WTPG, do the following:
1. For each value of power generation, feed its AD, WV and CLTB into the inference engine. Consider the
output of inference module as WTPG.
2. Store the values of WTPG in an array WTPG ().
3. Execute the corresponding WTPG () value of highest magnitude until a scheduling event occurs.
4. Update the system states.
End Loop
VI. Results and Discussion
Fuzzification of the used parameter is made by aid follows functions. These formulas were determined by
using assumed values.
𝐴𝐷 𝑖1 =
𝑖1; 1.14 ≤ 𝑖1 ≤ 1.45
0; 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑊𝑉 𝑖2 =
𝑖2; 4 ≤ 𝑖2 ≤ 8
𝐶𝐿𝑇𝐵 𝑖3 =
𝑖3; 0.01 ≤ 𝑖3 ≤ 0.16
𝑊𝑇𝑃𝐺 𝑜1 =
𝑜1;20 ≤ 𝑜1 ≤ 350
AD, WV, CLTB and WTPG are set up as shown in 4 to 7. The membership values used for the FIE
were obtained from the above formulas. These membership functions helped in converting numeric variables
into linguistic terms. For example, the linguistic expressions and membership functions for wind velocity (WV)
obtained from the developed rules and above the formula are given as following.
The Gaussian fuzzy set is defined as:
2
2
1
),;(





 

 

cx
ecxgaussian

Where c represents the MFs centre and σ determines the MFs width.
2
)(
2
1
)( 
cx
VL ex


 , where c = 4,σ = 0.427











5
064.0
9.4
108.0
8.4
173.0
7.4
261.0
6.4
373.0
5.4
504.0
4.4
645.0
3.4
781.0
2.4
896.0
1.4
973.0
4
1
)(xVL
2
)(
2
1
)( 
cx
L ex


 , where c = 5,σ = 0.427











6
064.0
8.5
173.0
6.5
373.0
4.5
645.0
2.5
896.0
0.5
1
8.4
896.0
6.4
645.0
4.4
373.0
2.4
173.0
4
064.0
)(xL
2
)(
2
1
)( 
cx
M ex


 , where c = 6, σ = 0.427











7
064.0
8.6
173.0
6.6
373.0
4.6
645.0
2.6
896.0
6
1
8.5
896.0
6.5
645.0.0
4.5
373.0
2.5
173.0
5
064.0
)(xM
2
)(
2
1
)( 
cx
H ex


 , where c = 7, σ = 0.427











8
064.0
8.7
173.0
6.7
373.0
4.7
645.0
2.7
896.0
7
1
8.6
896.0
6.6
645.0
4.6
373.0
2.6
173.0
6
064.0
)(xH
2
)(
2
1
)( 
cx
VH ex


 , where c = 8, σ = 0.427
 8
1
8.7
896.0
6.7
645.0
4.7
373.0
2.7
173.0
7
064.0)( xVH
The determination of conclusion is taken when the rules that are applied to deciding what the wind
power generation to the plant (wind turbine) should be. For this the recommendations from all the rules are
combined to determine the wind power generation input to the wind turbine.
The composition of two fuzzy relations is defined as follows:
Let P be a fuzzy relation from X to Y and Q be a fuzzy relation from Y to Z such that the membership
degree is defined by P(x, y) and Q(y, z). Then, a third fuzzy relation R from X to Z can be produced by the
composition of P and Q, which is denoted as PοQ. Fuzzy relation R is computed by the formula
R(x, z) = (P ο Q)
(x, z) = max {min [P(x, y), Q( y, z)]}
For example, for AD= 1.295 kg/cubic meter, WV = 6.5 m/s and CLTB = 0.1 m, the rules 28, 29, 33 and
34 will be applied which notices that μM(AD) = 1 but that the other membership functions for the AD inputs are
all “off” (i.e., their values are zero). For the WV input it is seen that μM(WV) =0.5 and μH(WV) = 0.5 and that the
other membership functions are off. Again for the CLTB input it is seen that μM(CLTB) =0.5 and μH(CLTB) =
0.5 and that the other membership functions are off.
5.1 For rule 28
The premise (M), we have μM(28) = min{ μM(AD), μM (WV), μM(CLTB)} = min{1,0.5,0.5} = 0.5, by
using the minimum of the three membership values. The notation μ28 represents for rule (28) so that we are 0.5
certain that this rule applies to the current situation. This rule indicates that if its premise is true then the action
indicated by its consequent should be taken. For rule (28) the main consequent is “wind power is medium”. The
membership function for the conclusion reached by rule (28), which is denoted as μ28, is given by μ28(WTPG) =
min {0.5, μM(WTPG)}. This membership function defines the implied fuzzy set for rule (28).
5.2 For Rule 29
The premise (M), we have μM(29) = min{ μM(AD), μM (WV), μH(CLTB)} = min{1,0.5,0.5} = 0.5, by

indicated by its consequent should be taken. For rule (29) the main consequent is “wind power is medium”. The
min {0.5, μM(WTPG)}. This membership function defines the implied fuzzy set for rule (29).
5.3 For rule 33
The premise (H), we have μH(33) = min{ μM(AD), μH (WV), μM(CLTB)} = min{1,0.5,0.5} = 0.5, by
indicated by its consequent should be taken. For rule (33) the main consequent is “wind power is High”. The
min {0.5, μH(WTPG)}. This membership function defines the implied fuzzy set for rule (33).
5.4 For rule 34
The premise (H), we have μH(34) = min{ μM(AD), μH (WV), μH(CLTB)} = min{1,0.5,0.5} = 0.5, by
indicated by its consequent should be taken. For rule (34) the main consequent is “wind power is High”. The
min {0.5, μH(WTPG)}. This membership function defines the implied fuzzy set for rule (34).
It is noticed that we are certain that all rules (28), (29), (33) and (34) apply to the current situation.
From Mamdani max–min inference, the membership function of system will be found as max (μ28, μ29, μ33, μ34)
= 0.5 and then defuzzification operation is applied to the final component of the fuzzy controller produced by
the inference mechanism and combines their effects to provide the “most certain” controller output (plant input).
Then the output denoted by “WTPGcrisp
” can be calculated that best represents the conclusions of the fuzzy
controller that are represented with the implied fuzzy sets.
The Bisection for defuzzification is being used to know the output membership values whereas, the
value of membership functions are multiplied by their corresponding singleton values and then the result is
divided by the sum of the membership value. The bisection of “WTPGcrisp
” can be computed by the flowing
formula:


)(
)(
ii
iiicrisp
a
WTPG


Where, ia is the position of the singleton in i the universe, and )(i is the corresponding membership function.
For the rules 28, 29, 33 and 34 we have 17528 a , 17529 a ,
262.533 a ,
262.534 a
75.218
0.50.55.05.0
5.0*5.2625.0*5.2625.0*1755.0*175



crisp
WTPG
The diagram of the whole computational fuzzy modeling is presented in Figure 8.
Figure 8: Fuzzy rule viewer

Once all the variables of WV, and CLTB get executed by the FIE, a decision surface can generate showing the
value of the output variable WTPG as shown in figure 9. The decisions surface is the dynamic combination of
WV, and CLTB affecting WTPG. Figure 9 present a three-dimensional curve representing the mapping from
WV and CLTB as inputs (axis X and Y) and WTPG as an output (axis Z). In varying conditions of WV, and
CLTB parameters, the maximum value of WTPG likely to be changes accordingly that depends on the inference
engine mechanism. That is wind power generation increased as increase in wind velocity depicted in figure 10.
Figure 9: Decision Surface
Figure 10: Wind power generation v/s wind velocity
VII. Conclusion
In this paper an effort has been made to develop a fuzzy logic approach for the prediction of appropriate
WTPG considering the WV, AD and Chord Length of Turbine Blades (CLTB) as input parameters which is
necessary for the current renewable energy utilization in many countries particularly in the rural areas. In
comparison to other predictive modeling techniques, fuzzy models have the advantage of being simple due to
the Fuzzy Inference System(FIS) capability to deal with problems that are based on user knowledge and
experience. This system can be developed further by increasing the knowledge rules and by addition of Genetic-
Fuzzy and Neuro-Fuzzy to the system
Acknowledgement
It is our pleasure and privilege to acknowledge and express our deep sense of gratitude to our teacher and guide,
Mr. Amit Mandal and co-guide Dr. Pradeep Yadav. They inspired and guided us every step towards achieving
this goal, despite their busy academic schedule.
.
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