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 Authors : T. Nageswara Prasad, V. Chandra Jagan Mohan, Dr. V.C. Veera Reddy
 Paper ID : IJERTV2IS2094
 Volume & Issue : Volume 02, Issue 02 (February 2013)
 Published (First Online): 28022013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Mitigation Of Voltage Fluctuations Due To 3P Oscillations Of Wind Farm Using Series Controller
1T. Nageswara Prasad, 2V. Chandra Jagan Mohan, 3DR. V.C. Veera Reddy 1Research Scholar, Department of EEE, S.V.U. College of Engineering, Tirupati, India 2Asst. Professor, Department of EEE, Vaishnavi Institute of Technology, Tirupati, India 3Professor & Head, Department of EEE, S.V.U. College of Engineering, Tirupati, India
Abstract:
Rapid increase in demand for power, depletion of conventional energy sources and their adverse effects on environment is making power engineers to look for alternatives for power generation. Power generation through wind gained more popularity across the world due to technology advancements and low installation costs compared to solar and other sources of renewable energy. Grid integration of wind turbines has negative impact on grid operation due to random nature of wind, wind shear and tower shadow effects. Thus, the wind turbines are fluctuating power sources that influence power quality in distribution system. This paper presents a series controller for blocking these disturbances and maintaining the power quality within the acceptable limits defined by industry standards. The simulation results, carried out by using MATLAB/SIMULINK show the impact of these disturbances on the grid. The proposed series controller is found to be an effective tool in blocking disturbances at the point of common coupling and thereby maintaining the power quality.
Index Terms: Distributed Generation (DG), wind shear, tower shadow effect, 3p oscillations, wind generation, power quality, series controller.

Introduction
Distributed Generation (DG) by the use of renewable energy is gaining wide acceptance in these days due to socioeconomic problems i.e., due to increased public concerns for adverse environmental factors and increased energy costs associated with the use of conventional energy sources. Among the renewable energy sources viz., solar, wind, biomass, geothermal, major portion of power generation through renewable energy sources is by wind turbines [1], [2] as they are environmental friendly, low installation cost compared to other sources of energy. Nowadays, these sources are being interconnected to distribution system in small and large scale. The utilization of these sources indirectly reduces the line losses in the transmission as these sources are installed close to utilization point.
With the increase in wind power penetration into the grid, the power quality becomes a paramount issue for power engineers. Flicker is an important power quality issue for the operation of electrical equipment and may become a serious limitation to wind power especially in case of weak grids i.e., grids with low short circuit ratio. Many factors affect flicker emission of grid connected wind turbines during continuous operation such as wind characteristics and grid conditions [3][5]. The wind power fluctuations produced by grid connected variable speed wind turbines during continuous operation is mainly caused by fluctuations in aerodynamic torque due to wind speed variations, the wind shear and the tower shadow effects [4], [6] and [7]. The wind shear and tower shadow effects are referred to as 3p oscillations. These 3p oscillations result in output power to drop three times per revolution for a three bladed wind turbine [8]. This frequency is normally referred to as 3p. The 3p oscillation frequency component is transmitted to the output active power of the wind turbine, which will induce voltage fluctuation and flicker in the grid [8]. There are other factors that affect flicker emission of gridconnected wind turbines during continuous operation that include
wind characteristics (e.g., mean wind speed, turbulence intensity), grid conditions (e.g., short circuit capacity, grid impedance angle) and types of wind turbines (e.g., fixed speed and variable speed).
The flicker level is more [9], [10], [11]

at higher wind speeds due to higher turbulence in the wind

with lower short circuit level at the Point of Common Coupling (PCC) of the wind turbine

if the difference between grid impedance angle and the wind turbine power factor angle approaches zero from 900

in fixed speed wind turbines compared to variable speed wind turbines. For fixed speed wind turbines, flicker level increases around three times from lower to higher wind speeds [4]

Several methods have been proposed for mitigation of flicker origination from wind farms using Flexible AC Transmission Systems (FACTS) devices by controlling active power, reactive power and energy storage.
In one of the active power control methods, the flicker is eliminated by curtailing the active power being extracted from the wind turbine [12]. Another scheme of control known as Flicker Mitigation Controller (FMC) has been proposed [8]. This method dampens the flicker by varying dclink voltage. FMC uses a vector control scheme for the Pulse Width Modulation (PWM) converter. The most commonly used technique for flicker mitigation is the reactive power compensation. For this Static Var Compensator (SVC) [13], Static Compensator (STATCOM) [14], [15], Unified Power Flow Controller [16] have been proposed. STATCOM proves to be superior to the SVC with respect to flicker mitigation [17], [18]. Another method known as Energy storage scheme of control

is also proposed and has not become popular because of its own drawbacks. STATCOMs serve two fold, they not only provide reactive power support but also with appropriately designed control strategy can eliminate flicker during continuous operation of fixed speed wind farms.
An effort is made here to overcome the integration barriers that help sustainable and clean DG technologies which contribute to the Power System in a way that enhances the overall grid performance. It is proved in this paper that the series controller can effectively be utilized to perform the following functions in the event of integration of wind power generators to power distribution system.

100% evacuation of active power harvested from the wind

Mitigation of voltage fluctuations at PCC due to 3p oscillations resulting from wind farm

Unity power factor operation

In addition to the above objectives, the power quality is strictly maintained within the standards prescribed by IEEE519 [20] and IEC61000 standards.

System description and modeling:

System description:
The single line diagram of the power system under consideration with series controller is shown in Fig. 1. The network consists of 33KV, 50 Hz, grid supply point, feeding a 33KV distribution system. There are four load centers in the system L1, L2, L3 and L4. The four load centers comprise of Linear and NonLinear loads. The Wind farm comprises of 3 wind turbines using squirrel cage induction generators each rated 3MW, 690V, 50Hz. Each generator is provided 170 KVAr fixed reactive power compensation through a bank of capacitors to give necessary reactive power support at the time of starting. The total wind farm of capacity 9MW is connected to the 33KV distribution system at the node Wind through a 690V/33KV transformer. In this study a mean wind speed of 9
m/s is considered. The Squirrel Cage Induction Generator model available in Matlab / Simulink SimPowerSystem libraries is used.
Fig. 1 OneLine diagram of distribution system with wind farm and Series controller The power in the ar flow is given by
Pair
1 R2 v3
2
(1)
The power transmitted to the wind turbine rotor, is always less [21], and gets reduced by the power coefficient, CP .
CP Pwind turbine
Pair
(2)
Therefore, the power that can be extracted from wind turbine is determined by the following expression:
Pwind turbine 1 R2 3 CP
2
(3)
where is air density (approximately 1.25kgm3), R is the radius of the swept area, is the
wind speed and CP is the power coefficient. For the turbines used in the study, the values of R
and CP
are 31.2 m and 0.45 respectively. The total power PT
from the wind farm is the arithmetic
sum of the power generated by each turbine and is given by
3
PT Pi
i 1
(4)
The wind speeds at different points of the swept area may substantially differ both in its mean and turbulence components. The speed variation at a rotating point can be separated into deterministic and stochastic components. The former is due to the spatial distribution of the mean speed whereas the latter is due to the turbulence. The factors that contribute to the deterministic component are primarily wind shear, tower shadow and the presence of other wind turbines and obstacles in the surroundings. On the other hand, the stochastic component is caused by the temporal and spatial distribution of turbulence.
In this paper, the disturbances due to wind shear and tower shadow which result in 3p oscillations is considered. The model developed for wind shear and tower shadow [6] is used here to study their impact on the power quality of distribution system to which the FSWTSCIG is integrated. The wind speed ( v ) can be expressed as (5) the combination of these three factors shown in (6) to (8).
v v0 vws vts
v0 mVH
(5)
(6)
( 1) R 2 ( 1)( 2) R 3
vws VH 1
8 H
60 H
cos3
(7)
mVH 3 a2 R2sin 2b
2a2 R2
vts 2 2
ln
2
1
R
R
2 2 2
(8)
x
x
3R b 1sin b
sin
b x
where m [1 (( 1)R2 / 8H 2 )] is a coefficient of the wind turbine, VH is the wind speed at
hub height (in meters per second), is the empirical wind shear exponent, H is the elevation of rotor hub (in meters), is the azimuthal angle of each blade (in degrees), a is the tower radius (in
meters) and x is the distance from the blade origin to the tower midline (in meters). The values chosen for these parameters for the study are given in appendix.

Compensation scheme:
Many methods have been proposed for mitigation of flicker. But the solutions proposed are for each turbine. The 3p oscillations resulting from one turbine appear as flicker in voltage at the PCC. Since flicker calculation requires long simulation time, the fluctuation in voltage is considered for the design of control scheme of series controller. A series controller is proposed in this paper for mitigation of fluctuations in voltage at PCC. The series controller employs a voltage source inverter (VSI) connected in series between the nodes PCC and wind as illustrated in Fig. 1. The VSI based series controller through suitable transformer eliminates voltage fluctuations whenever arises from wind farm. The series controller mainly consists of a converter and a control unit. The inverter is a three arm diode clamped IGBT based inverter with capacitor as dc storage element. The gate signals are generated with Pulse Width Modulation (PWM) scheme.
The SineTriangle Pulse Width Modulator (STPWM) control is used here. The switches of the inverter are controlled based on a comparison of a sinusoidal control and a triangular switching signal. A Phase Locked Loop (PLL) is used for extracting the frequency f of the voltage at PCC (VPCC). Reference voltage (Vref) for each phase is obtained using the frequency f in reference voltage manipulator.
Fig. 2 Control scheme of Series controller

Detection of voltage fluctuation:

The algorithm proposed in [22], [23] is adopted here for quick detection of voltage fluctuation. It is schematically represented in Fig. 2. The voltage at PCC and the reference voltage is transformed to dqo reference frame. The difference between the reference voltage and the measured voltage at PCC in dqo reference frame is calculated as follows:
Vd
Vref ,d VPCC,d
(9)
Vq Vref ,q VPCC,q
Further (5) & (6) are combined as
(10)
V V 2 V 2
(11)
d q
From (7), the fluctuation (F) in the voltage at PCC is expressed as
V
F (12)
V
V
V
V
2 2
ref ,d ref ,q
The deviation in voltages is processed by controller for generation of gate pulses. The controller can be PI, Fuzzy or ANFIS. Many reports claim that the fuzzy logic controller is superior compared to PI controller [24][27] as it can handle imprecise data, complex systems and can adopt to any changes in the systems. The PI controller is to be tuned properly for any change in system parameters. One main drawback of fuzzy controller is the lack of learning ability. The learning ability [28][30] and [22] of neural networks is combined with fuzzy and a new scheme of controller known as Adaptive NeuroFuzzy Inference System (ANFIS) [24] and [31] is considered in this work. An initial fuzzy inference system is taken from PI controller and is tuned with back propagation algorithm based on the collection of inputoutput data. For training the ANFIS controller, triangular membership function with seven input data pairs are used. The rule base used for the ANFIS controllers is illustrated in Table 1.
Table 1 Rule base for ANFIS controllers
Input 1 (error (e)) 
Input 2 (error (e)) 

NB 
NM 
NS 
ZE 
PS 
PM 
PB 

NB 
NB 
NB 
NB 
NB 
NM 
NS 
ZE 

NM 
NB 
NB 
NB 
NM 
NS 
ZE 
PS 

NS 
NB 
NB 
NM 
NS 
ZE 
PS 
PM 

ZE 
NB 
NM 
NS 
ZE 
PS 
PM 
PB 

PS 
NM 
NS 
ZE 
PS 
PM 
PB 
PB 

PM 
NS 
ZE 
PS 
PM 
PB 
PB 
PB 

PB 
ZE 
PS 
PM 
PB 
PB 
PB 
PB 
The gate pulses are used for generation of three phase voltages by PWM converter. The output of the PWM converter is injected in series with the line as indicated in Fig. 2. The overall control logic of series controller works in such a way that the value of F is zero.

Results & Discussion:
Simulations are carried out to study the operation of wind farm integrated to the power distribution system. The total simulation time considered is 3 sec. Simulations are carried out to show that the series controller mitigates the fluctuations in voltage at PCC. It is also shown that the disturbance originating from wind farm penetrates into the dstribution system to with it is connected. A node MV2 to which a load, L2, of 1.25MW is connected is chosen to show the influence of voltage fluctuation from wind farm and the effectiveness of series controller. The connected 3phase load details at MV2 are Voltage: 33KV; Frequency: 50Hz; Active Power: 1.25MW; Inductive Reactive Power: 0.25MVar; Capacitive Reactive Power: 1KVar. The effects of wind shear and tower shadow on the power quality are dealt separately as detailed below:
Case 1 (with only tower shadow):
In this case, the simulation was conducted with the following chronology to analyze the effect of tower shadow:

at t = 0.0 sec, the simulation starts with series controller not connected to the system

at t = 1.0 sec, the voltage fluctuation originates from wind farm due to tower shadow only

at t = 2.0 sec, the series controller is turned ON

at t = 3.0 sec, the simulation stops

In the entire analysis the wind speed is considered to be
v v0
in the range 0 < t < 1. To analyze
the effect of tower shadow on the power quality, vws is set to zero in Eq. 5 in the time interval 2 < t
< 5. The wind speed will be influenced only by tower shadow which is given by v v0 vts . Fig. 3 shows the flicker (1 < t < 2) in voltage in phasea at PCC due to tower shadow. The deviation in voltage is found to be 20%. This is objectionable as per the industry standards. The disturbance in voltage propagates into the distribution system and affects the operation of electrical equipment. This is evident from the Fig. 4. The real and reactive powers at the node MV2 is illustrated in Fig. 5 and Fig. 6 respectively.
Fig. 3 Voltage in phasea at PCC Fig. 4 Voltage in phasea at node MV2
Fig. 5 Real power in phasea at MV2 Fig. 6 Reactive power in phasea at MV2
With the series controller brought into operation at t = 2 sec., the voltage fluctuation due to tower shadow effect is mitigated and the power quality is restored. This is evident from the illustrations Fig. 3 to Fig. 6.
Case 2 (with only wind shear):
In this case, the simulation was conducted with the following chronology to analyze the effect of wind shear:

at t = 0.0 sec, the simulation starts with series controller not connected to the system

at t = 1.0 sec, the voltage fluctuation originates from wind farm due to wind shear only

at t = 2.0 sec, the series controller is turned ON

at t = 3.0 sec, the simulation stops
Throughout the analysis the wind speed is considered to be v v0 in the range 0 < t < 1. To analyze
the effect of wind shear on the power quality,
vts
is set to zero in Eq. 5 in the time interval 2 < t <
5. The wind speed will be influenced only wind shear which is given by
v v0 vws . Fig. 7 shows
the flicker (1 < t < 2) in voltage in phasea at PCC due to wind shear. The deviation in voltage is found to be 1.3%. The voltage disturbance propagates into the distribution system and effects the operation of electrical equipment. This can be noticed from the Fig. 8. The real and reactive powers at the node MV2 is illustrated in Fig. 9 and Fig. 10 respectively. The effect of disturbance in voltage due to wind shear is negligible compared to the flicker due to tower shadow.
Fig. 7 Voltage in phasea at PCC Fig. 8 Voltage in phasea at node MV2
Fig. 9 Real power in phasea at MV2 Fig. 10 Reactive power in phasea at MV2
From the above illustrations, it is clear that the fluctuation in voltage is mainly due to tower shadow effect. The results are in good agreement with the results of [6]. Fluctuation in voltage due to tower shadow is an alarming condition that affects the quality of power. Power quality is a serious issue as the electronic equipment, induction motors etc., are very sensitive to flicker. Computers, adjustable speed drives and process control equipment frequently trip as a consequence of a flicker in voltage.
The voltage pulsations arising from wind farm due to either wind shear or tower shadow at t > 1 sec. is mitigated with the series controller by turning it ON at t = 2 sec. From Fig. 3 to Fig. 10, it is evident that the series controller is able to maintain the power quality within the allowable limits of power quality standards.
Conclusion:
The effects of wind shear and tower shadow on power quality is analyzed separately. It is observed that the power quality is much affected due to tower shadow than wind shear. Series controller is suggested to overcome the power quality issues due to 3p oscillations. The importance of series controller is presented in this paper. The proposed controller is found extremely satisfactory for mitigation of voltage fluctuations originating from wind farm due to either wind shear or tower shadow. The disturbances in voltage are mitigated and the voltage is restored to its normal value. Thus, the proposed series controller is found more effective and efficient in mitigating voltage fluctuations while meeting the industry standards IEEE519 and IEC61000.
Appendix
Table A
Parameters of SCIG coupled to Fixedspeed wind turbine
Parameter 
Value 
Rated power 
3MW 
Rated voltage 
690V 
Wind speed at hub height (VH ) 
9 m/sec 
Empirical wind shear component ( ) 
0.3 
Wind turbine Rotor radius ( R ) 
40 m 
Elevation of rotor hub ( H ) 
80 m 
Distance from the blade origin to the tower midline ( x ) 
5 m 
Tower radius ( a ) 
2 m 
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AUTHORS PROFILE
T. Nageswara Prasad is a Research Scholar at Sri Venkateswara University College of Engineering, Department of Electrical & Electronics Engineering, Tirupati, Andhra Pradesh, India. He obtained B.Tech. from Vellore Institute of Technology, Vellore and M.Tech. from JNTUCE, Anantapur. His area of interest is Power Quality, Power System Operation & Control, Microprocessors, Machines etc.,. He has more than a decade of teaching experience in engineering college. He is a Life member of ISTE.
V. Chandra Jagan Mohan obtained B.Tech. degree from RMK Engineering College, Chennai and M.Tech. degree from Sree Vidyanikethan Engineering College, Tirupati. He is presently working as Assistant Professor, Department of EEE, Vaishnavi Institute of Technology, Tirupati, Andhra Pradesh, India. His areas of interest include Power Quality, Power System Operation & Control.
Dr. V. C. Veera Reddy obtained his ME & Ph.D. degrees from Sri Venkateswara University, Tirupati, Andhra Pradesh, India in 1981 & 1999. He is presently working as Professor and Head, Department of EEE, S.V.U College of Engineering. He pursued research in the area of Power Systems. He has 31 years of teaching experience. He is a life member of CSE, IETE, and former member of IEEE. He has
published 47 papers in the area of Power Systems in various journals.