Dynamic Performance Investigation of DQ Model

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 9, Number 21 (2014) pp. 10651-10670 © Research India Publications http://www.ripublication.com

Dynamic Performance Investigation of D-Q Model Based UPFC with Various Controlling Techniques K. Pounraj1 and Dr. S. Selvaperumal2 1

Assistant Professor, Department of EIE, Bharath Niketan Engineering College, Theni, India 2 Professor and Head (PG Studies), Department of EEE, Syed Ammal Engineering College, Ramanathapuram, India E-mail: [email protected], [email protected]

Abstract Reactive power compensation is a main predicament in the control of electric power system. Reactive power from the source increases the transmission losses and decreases the power transmission aptitude of the transmission lines. In adding together, reactive power must not be transmitted throughout the transmission line to a longer distance. Accordingly Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are worn to effortlessness these problems. The unified power flow controller is the primarily pliable and composite power electronic apparatus that has emerged as the very important paraphernalia for the control and optimization of power flow in electrical power transmission system. In this manuscript a DQ model based UPFC is developed with PI, PID, Fuzzy and ANN controllers. The above controllers are simulated using MATLAB and their performance is analyzed. Outcome of the Analysis shows the dominance of ANN control over the conventional control method. Also this script presents relative estimation (Conventional and ANN Controllers) of dynamic performance when initial and final load strife. Keywords: DQ model; PID control; sssc; upfc.

1. Introduction The unified power-flow controller (UPFC) is the second generation of the flexible ac transmission systems (FACTS) devices that is able to provide series and shunt

Paper Code: 27086–IJAER

10652

K. Pounraj & Dr. S. Selvaperumal

compensations in transmission systems. Since the proposal of the UPFC in 1992 [1], there has been increasing interest in finding a suitable control method to suit a range of system operating conditions [2]. In previous studies, various controllers have been proposed to regulate the operation of the UPFC. Cross-coupling proportional- integral (PI) controllers are proposed to minimize the interaction between the real and reactive power flow [3]. In addition, the use of decoupling PI controllers with a predictive internal control loop has also been investigated in order to reduce the effect of harmonics in the current measurement [4]. A hybrid (direct coupling and cross coupling) PI controller was suggested to dampen the transient power fluctuation caused by the controller [5]. A common limitation of PI controllers is that they do not always perform satisfactorily over a wide range of operating points. This is caused by the control parameters being determined based on certain system conditions. The robust control H theory based on has been used in the UPFC controller design [6]. This requires a defined mathematical model of the power system, including the UPFC, and, consequently, the required online computation to solve the optimization equations is intense. Methods based on fuzzy control theory have also been proposed [7]–[9]. Despite the qualitative approach that enables fast response and stable operation, a drawback of these methods (using fuzzy logic) is that the chosen membership functions are not adapted according to the system operating condition. An adaptive neurofuzzy inference system (ANFIS) combines the fuzzy qualitative approach with the adaptive capabilities of neural networks to achieve improved performance [10]. UPFC is the most comprehensive multivariable FACTS controller. Multiple power system parameters like bus voltage, real and reactive power exchange, modification of line impedance as per requirement, can all be carried out using the UPFC, however with certain difficulties. The UPFC consists of a series and a shunt element each responsible for independently controlling the power system parameters [11]-[13]. The two constituent elements of the UPFC are STATCOM the shunt element and SSSC the series element. The STATCOM can independently exchange reactive power with the bus bar. The SSSC can also independently exchange reactive power with the bus bar [14]-[16]. However these two in combination exchange real power with the bus bar. While exchanging real power with bus bar, which happens both during steady state condition and transient condition there must be a co ordination between the operations of these two entities [17]-[18]. FACTS devices are the most multifarious devices used to control real and reactive power in transmission line for economic, flexible operation in the power system. To control real and reactive power we must be investigated about the voltage magnitude of the transmission, power angle and line impedance. But UPFC allows simultaneous or independent control of all these three parameters, with possible switching from one control scheme to another in real time [9]. Damping of oscillations and transient stability are investigated profoundly throughout the world [20]-[21]. This research work aims at developing a control strategy that guarantees automatic coordination between the STATCOM and the SSSC while real power is being transferred through the UPFC.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10653

2. UPFC Model

Fig. 1: Schematic diagram of UPFC. The Unified Power Flow Controller (UPFC) has the capabilities of real time control and dynamic compensation of ac transmission systems. As seen in figure1 UPFC is a combination of STATCOM and SSSC which was operated from a common dc capacitor. The configuration which is depicted in Fig. 1 act as an ideal ac to ac power converter. The real power can freely flow in either direction between the ac terminals of these two converters. Also each converter can exchange reactive power at its own ac terminal with the system.

Fig. 2: Power system including UPFC model

10654

K. Pounraj & Dr. S. Selvaperumal

Circuit scheme of the UPFC system has been depicted in Fig. 2. To simplify the dynamic analysis instantaneous three phase variables has converted to d-axis and qaxis components in a synchronously rotating d-q frame .UPFC system is a two machine system. Vs and Vr are sinusoidal voltage sources which generates balanced three phase voltages. UPFC converters are represented by voltage sources Vsh and Vse respectively. Converters here are assumed that ideal controllable voltage sources. Rsh and Xsh are shunt converter’s coupling transformer resistance and leakage reactance respectively. UPFC dc link equivalent is not take place in this circuit but presented in UPFC dynamic model. In Fig. 2 energy transmission systems named as transmission line 1 and transmission line 2. Rs and Xs are transmission line resistance and reactance respectively. Transmission line resistance and reactance also includes the series transformer leakage reactance and resistance.

3. Control Strategy 3.1 DC Voltage Controller As real power is sourced to the remote end through the SSSC the DC link capacitor voltage shows a fall in voltage across its terminal. The purpose of coordinated control is to maintain this DC voltage at the constant level irrespective of the flow of real power to the remote end through the SSSC. The function of the DC voltage control is to monitor the actual Vdc across the capacitor and compute the error as the difference between Vdc actual and the set point Vdc. This error is fed as input to the PID module where a representative Fig. of the error in the DC voltage is arrived. The output of the PID controller is treated as the reference for the real component of current drawn by the STATCOM. Since the direct component of current is responsible for real power transfer the direct component reference is derived from the DC voltage controller. The toping up operation of the capacitor DC voltage has nothing to do with the reactive current drawn from the AC side. Therefore the q component reference is set to zero. Now we have a direct and a quadrature component of current viz. Id and Iq. These two quantities are just the references. These two reference quantities are compared with the actual Id and Iq quantities in a cross coupled controller. The ultimate output of the cross coupled controller are ed and eq references. Using the eq and ed reference quantities the Vabc reference is generated. The Vabc is the three phase reference to be supplied to the STATCOM inverter’s pulse generating unit. Since the Vabc is derived from eq and ed references which have been derived from the DC voltage controllers and the cross coupled controller the STATCOM switching will be arranged in such a manner so as to track the Vdc across the capacitor and to maintain it at the set point Vdc. The Fig. 3 shows the schematic arrangement of the cross coupled control system. Here, Iq and Id are used along with the line reactance (Xl) of the series filter, in association with a set of PID controllers, the required eq and ed are derived.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10655

Fig. 3: Decoupled current controllers. 3.2 Design of PI and PID controller Ziegler and Nichols conducted numerous experiments and proposed rules for determining values of KP , KI and KD based on the transient step response of system [22] . It applies to UPFC with neither integrator nor dominant complex-conjugate poles, whose unit-step response resemble an S-shaped curve with no overshoot. This S-shaped curve is called the reaction curve.

Fig. 4: S-shaped reaction curve. The S-shaped reaction curve (shown in Fig. 4) can be characterized by two constants, delay time (L) and time constant (T), which are determined by drawing a tangent line at the inflection point of the curve and finding the intersections of the tangent line with the time axis and the steady-state level line. Using the parameters L and T, we can set the values of KP, KI and KD according to the formula [23] shown in the table I. These parameters will typically give a response with an overshoot about

10656

K. Pounraj & Dr. S. Selvaperumal

25% and good settling time. Based on the Ziegler-Nichols Tuning Rule, Proportional gain Constant (KP)=6, Integral Time Constant (KI) =0.0675 and Differential Time Constant (KD)=6 are obtained for the UPFC under study. Table I: Ziegler-Nichols Tuning Rule Table Controller P PI PID

KP T L

T L T 1.2 L

0 .9

KI 0 0.27

T L2

T 0.6 2 L

KD 0 0 0.6T

3.3 Vabc to dqo Converter The entire control system in this work deals with quantities such as id, iq, ed, eq ,vd and vq. The three phase quantities such as Vabc and Iabc from the system are first measured and then converted into equivalent Vd, Vq, Id and Iq. These components are the direct and quadrature components of voltage and current. Using the direct and quadrature components of the system parameters along with the direct and quadrature components of references the control system is framed. The MATLAB Vabc to dqo converter blocks is used for this purpose. The conversion of the Vabc into vq and vd involves the consideration of the speed of the rotating reference vector so we use the PLL blocks also to find out ώt of the reference three phase system. Once the required eq, and ed are decided the required three phase reference for the three phase inverter’s pulse generating unit are found using the dqo to Vabc conversion block. 3.4 Block description of the VSC controller

Fig. 5: Block Diagram of the VSC controller

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10657 The Fig. 5 shows the typical control strategy for the VSC used as the STATCOM or the SSSC used in the UPFC. The generalized control procedure is discussed below. The VSC used in an UPFC injects a controllable voltage at the AC terminals given by

E  mE0  e  



(1)

Where e is modulation index and  is the phase angle. E 0  K Vdc

(2) where K is a constant depending on the converter configuration. For a two level converter 6 (3) k  The normalized modulation index and alpha are control variables used to control real power and reactive power.  is the angle of AC bus of the converter. Note that e is less than 1 and alpha can be positive or negative depending on whether the converter is operating as an inverter or rectifier. The reactive power injected by the converter is a function of Vs, the bus voltage magnitude, in contrast it is to be noted that line commutated thyristors based converter can only draw reactive power depending on the power flow through the converter. The block diagram of the controllers for VSC is shown in the Fig. 5. Here Ip and Ir the active and reactive components of the AC current drawn by the converter. That is P (4) Vs Q (5) Ir  Vs The reference values for the active current Ip* is obtained either from the power controller or DC voltage controller. The reference value for the reactive current Ir* is obtained from the specified reactive power or AC voltage controller. The outputs of the current controllers (active and reactive current) are the desired converter voltage components ep and er which are defined as Ip 

ep  m  k V dc  cos  and er   m  K V dc  sin 

(6)

From the above equation m and α can be calculated as

m

ep2  er2 K Vdc

 er  and    tan     ep 

(7)

10658

K. Pounraj & Dr. S. Selvaperumal

4. Principle of decoupled current controllers and simulation The real and reactive current references are used to generate the reactive and real component references. The generation of ep and er are calculated as treated below. With reference to the Fig. 6.  X  e p  Vs  u p   Ir   B 

(8)

Fig. 6: MATLAB Simulink Model for the decoupled Controller.. The following Matlab subsystem is used for generating the eq , ed signal from Id , Iq and Id, Iq references. 4.1 Algorithm for STATCOM  Convert measured Vabc and Iabc into respective d q components.  Measure the Vdc across the capacitor.  Use the PID controller to decide the id reference for the STATCOM.  Set iq for the STATCOM as zero.  Use Id (ref), Iq (ref) and Id Iq actual in a cross coupled PI control unit to generate ed and eq references.  Use eq and ed in a dqo to Vabc converter and get the three phase reference for the inverter. 4.2 Algorithm for SSSC  Convert measured Vabc and Iabc into respective d-q components.  Measure the actual real power at the receiving end.  Measure the actual voltage at the receiving end.  Find the error between set point power and actual power.  Find the error between set point voltage and actual voltage.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10659     

Use the error in either case in a PID controller. Combine the output of each PID controller. The output is the reference ed and eq.Set iq for the STATCOM as zero. Use Id Iq (ref) and Id Iq actual in a cross coupled PID control unit to generate ed and eq references. Use eq and ed in a dqo to Vabc converter and get the three phase reference for the inverter.

4.2 Control Algorithm for sine triangular PWM generation

Fig. 7: Flow chart for control algorithm.

10660

K. Pounraj & Dr. S. Selvaperumal

Fig. 7 shows the flowchart for the sine triangular PWM generation. Hear at the start of the program event manager module is initialized. The phase angle is set to zero and at the same time the modulation index value is made constant to 0.5. The key press is done through switches S1 and S2.If switch S1 is closed then phase angle is increased by 4 degree for every 1msec key press. If S2 is closed then phase angle is decreased by 4 degree for every 1msec of key press. According to the phase angle the sine triangular PWM is generated. 4.3 Neural Network Implementation of Conventional Controller Data processing in PI controller is a complex task that requires heavy computation time. The neural network is a nonlinear algorithm that can be worked out because of its mathematical nature [24]. In this section, the solution of implementing conventional PI controller in a neural network is discussed. The ANN controllers designed in most of the work use a complex network structure for the controller. The aim of this work is to design a simple ANN controller with as low neurons as possible while improving the performance of the controller. A two layer feed forward neural network is constructed with two neurons in the input layer and one neuron in the output layer. The structure of the neural network used to implement PI controller is shown in Fig. 8. As the inputs to the neuron controller are the error and the change in error, two neurons are used for input layer. The neurons are biased. The activation functions used for the input and output neurons are pure linear and tangent sigmoid respectively. The network is trained for the set of inputs and desired outputs [25]. The training patterns are extracted from the conventional controller designed. Supervised back propagation training algorithm is used [26]. A back propagation neural network-training algorithm is used with a fixed error goal. The network is trained for an error goal of 0.0005. The error (e) and change in error (ce) are the inputs to the controller. The output corresponds to the change in the duty cycle for the motor control. The details of the trained network are shown in Fig. 9.

Fig. 8: Neural network implementation of PI controller.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10661

Fig. 9: Details of the trained network. 4.4 Fuzzy Logic for the management of UPFC The purpose of the system to be built is to maintain a constant DC voltage across the DC link capacitor that lies in between the STATCOM and the SSSC. In the management of the UPFC for real and reactive power control we have used Fuzzy Logic system instead of the usual PI control system. The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership. This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement. After the input and output parameters are adopted in the membership functions the next step is to find out “if the input is this, then the output is what?” This is the formation of rules. For the rule formation knowledge and experience are used. For the system to be built the rule formation uses the rule matrix, which is explained below. Rule firing is the process of pointing out the existence of a condition found in the rule matrix. The rule firing contains the following steps.  From the membership functions of the input 1, the lingual under whose range, the actual values is present is found. Also its decimal height DOB –Degree of Belief is got.  From the membership functions of input 2, the corresponding lingual is also found along with the DOB.  For these two inputs, what is the output lingual? It is fetched from the rule matrix. For the system under control the rule firing process can be explained as follow:

10662

K. Pounraj & Dr. S. Selvaperumal

The required DC voltage or AC voltage is given as input (set point). From the plant or the system the actual DC voltage or actual AC voltage is read. The error is got in terms of percentage. From the membership functions of error, the lingual corresponding to that percentage value is taken. As the membership a function is in the form of interesting triangles, the percentage may lie within the range of two triangles. But their DOB will vary. So the two adjacent linguals, which include the error percentage value, are taken along with their corresponding heights (degree of beliefs). Similarly the error rate is calculated as the percentage of previous error. It is also parallel to the above case ie. The value may be contained by two adjacent linguals with different degree of beliefs, which are l3, and l4 with heights h3 and h4. Now there are four linguals with different degree of beliefs. So by probability there is a possibility of making four rules as, If (l1–x1 AND l2=y1) then U=Z1 If (l1=x1) AND l4=y2) then U=Z2 If (l2=x2 AND l3=y1) then U=Z3 If (l2=x2 AND l3=y1) then U=Z3 If (l2=x2 AND l4=y2) then U=Z4 Where X1 and X2 are the obtained linguals of ep. Y1 and Y2 are obtained linguals of erp. Z1, Z2, Z3 & Z4 are the linguals obtained from the table of rule matrix. There are three methods of compositions.  Minimum composition.  Product of maximum composition  Maximum of minimum composition. Out of these, we can prefer maximum of minimum composition. Under the knowledge about the influence of ep and erp, the possible outputs are tabulated in a matrix form called rule matrix [22]. 4.5 Simulation Results and Discussion The co ordination controller for real and reactive power control for the UPFC has been modeled in the Simulink of Matlab. Various relevant blocks were arranged and their parametric values were appropriately set and the simulation was run. The simulation worked well and it was observed that the power transmission system exhibits better performance with the UPFC, in terms of maintenance of the DC link voltage, the power flow and the AC voltage profile. The function of the DC voltage controller block is to maintain the DC voltage across the DC link capacitor [27]. The capacitor in the DC link loses its charge and as a result the voltage across the capacitor falls if power flows from the capacitor to the AC main lines through the SSSC. When this happens, the STATCOM charges up the capacitor automatically. The objective of the PID controller is to change the manipulated variable accordingly so that the charging up of the capacitor through the STATCOM is carried out. The manipulated variable is the control pulses applied to the bridge converter, in this case the STATCOM.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10663

2 Vdc _ref

PI

1 Id _Ref

Discrete PI Controller 1

1 Vdc

Display

Fig. 10: MATLAB Simulink Model for the DC Voltage Controller. With reference to the above segment of the MATLAB Simulink model (Fig. 10) , it is clear that the above model block has two inputs and one output. The two inputs are the actual DC link voltage and the set point DC link voltage. The difference between these two, the error, is first found. Fall in DC voltage can be made up by increasing the direct component of the current drawn by the STATCOM from the main power line. The decrease or increase of the DC link voltage is an indication of positive or negative real power transaction. Since Id is the current responsible for real power transaction the PID controller output is used as the Id reference. Iq is set to be zero. Now using this Id and Iq as the references and using the actual Id and Iq the cross coupled controller is formed. The actual values of Id and Iq are obtained by using the Matlab Simulink blocks as shown in Fig. 11.The Matlab Simulink subsystem is used for the control of the real power transmission through the transmission line.

Freq

Vabc (pu)

1 In 1

wt

Sin_Cos

Discrete 3-phase PLL 2

1 Out 1

abc

3 Out 3

dq0

2 Out 2

sin_cos

Vmes 1

Fig. 11: MATLAB Simulink Model SubsystemGeneration of d q and 0 quantities from Vabc. Fig. 12 shows Matlab Simulink diagram of reference controller for SSSC. The coordination controller presented in this work is capable of regulating the DC voltage, the AC bus bar voltage and the real power transmission. The regulation of the real power at the receiving end is controlled by the SSSC.

10664

K. Pounraj & Dr. S. Selvaperumal

1 Vabc (pu )

abc dq0 s in_cos

PI dq0

Selector Vd Vq

abc _to_dq 0 Transformation

abc

0

2 Vref

V0

Uref

Puls es

s in_cos

dq 0_to_abc Transformation

1 pulses

Discrete PWM Generator

5 q

Vr

Freq Vabc (pu)

PI

3 Pref

wt

Sin_Cos

Discrete 3-phase PLL 1

K-

4 Pmeas

Fig. 12. Reference Controller for the SSSC

Fig. 13: Fall and rise of real and reactive powers as viewed from the sending end. It is the SSSC that governs the subsidy real power demand and caters to any sudden real power demand at the receiving end. The actual real power measured at the receiving end and the set point real power requirement is used in a combiner to get the error. This error is used in a PID controller to get the direct component of voltage reference for the SSSC. The relevant waveforms at different locations are shown in the following Fig. 13 to Fig. 15.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10665

Fig. 14: The fall and rise of P and Q powers as viewed from the receiving end.

Fig. 15: Power Oscillations in the event of a fault without UPFC.

10666

K. Pounraj & Dr. S. Selvaperumal

Fig. 16: X-axis Device, Y-axis: Maximum overshoot. Fig. 16 shows, maximum overshoot are very less for UPFC compared to STATCOM and SSSC. Fig. 17 shows, settling time are very less for UPFC compared to STATCOM and SSSC.

Fig. 17: X-axis: Device, Y-axis: Settling Time.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10667 Table II: Dynamic Performance Comparison of UPFC When Initial Load Disturbance. Parameters

PI Controller

PID Fuzzy Controller Controller Peak Overshoot 0V 25V 24V Settling time 12 milliseconds 4 milliseconds 3.5 milliseconds Steady state error 1.5V 0.4V 0.35 V

ANN Controller 22 V 3 milliseconds 0.3 V

Table III: Dynamic Performance Comparison of UPFC When Final Loads Disturbance 0 Parameters Peak Overshoot Settling time

PI Controller 24V 8 milliseconds

Steady state error 1.5V

PID Fuzzy Controller Controller 19V 18.5V 4 milliseconds 3.5 milliseconds 0.4V 0.35 V

ANN Controller 18 V 3 milliseconds 0.3 V

Transient stability analysis is used to investigate the stability of power system under sudden and large disturbances, and plays an important role in planning and operation of the power system. From the above comparison (Table II and III), it could be concluded that the ANN exhibits better performance in terms of reduced settling time, peak overshoot and steady state error when initial and final load disturbances. Hence the ANN controller based UPFC system is quickly settled compared to other controller based FACTS devices.

Fig. 18: Transient response of local mode of oscillation.

10668

K. Pounraj & Dr. S. Selvaperumal

The performance of the conventional P controller, PI controller and PID controller in damping the local mode of oscillations of the generators are presented in Fig. below. In this case the control of series voltage source is taken. The performance of proposed ANN controller is quite promising in comparison to the P, PI controller and single PID controller. Based on the results obtained in the present investigations, the following line of research seems to be worth pursuing for further research work. 1. The performance of the system is good. However certain modifications and developments can be carried out to the work. The implementation of the control system is very complex and it involves various calculations. The system as a whole is computation intensive. Therefore instead of going for computation intensive techniques if Meta-heuristic based search techniques like GA, PSO, etc, are studied then the co ordination controller can be used for any system of any complexity with reduced mathematical implications. 2. The ultimate goal of the study is to practically develop a control system for the UPFC with improved control strategies. VLSI techniques can be investigated for the development of a common coordination controller for the UPFC.

5. Conclusion This paper made an effort to achieve the dynamic performance of UPFC using PI, PID, Fuzzy and ANN controller in MATLAB Simulink surroundings. The performance of UPFC is analyzed by giving initial and final load disturbance. The settling time, peak overshoot and steady state errors of ANN controller as compared with conventional PI and PID Controller were gritty by simulation studies. It is observed that the UPFC under ANN controller is having less peak overshoot and steady state error. It is also having faster response than PI controller. Hence ANN controller is performing better than PI and PID controller. Also the real and reactive power co ordination control technique for the UPFC using ANN control model can be easily achieved.

References [1]

[2]

[3]

[4]

L. Gyugyi, “A unified power flow control: Concept for flexible ac transmission systems,” Proc. Inst. Elect. Eng. C, vol. 139, no. 4, pp. 323–331, 1992. K. R. Padiyar and K. UmaRao, “Modelling and control of unified power flow controller for transient stability,” Elect. Power Energy Syst., vol. 21, pp. 1–11, 1999. Q. Yu, L. Norum, T. Undeland, and S. Round, “Investigation of dynamic controllers for a unified power flow controller,” in Proc. IEEE 22nd Int. Conf. Ind. Electron., Control Instrum., Taiwan, Aug. 1996, pp. 1764–1769. P. Papic, D. Povh, and M. Weinhold, “Basic control of unified power flow controller,” IEEE Trans. Power Syst., vol. 12, no. 4, pp. 1734–1739, Nov. 1997.

Dynamic Performance Investigation of D-Q Model Based UPFC with Various 10669 [5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15] [16]

[17]

[18]

[19]

H. Fujita, Y. Watanabe, and H. Akagi, “Control and analysis of a unified power flow controller,” IEEE Trans. Power Electron., vol. 14, no. 6, pp. 1021–1127, 1999. M. Vilathgamuwa et al., “A synchronous reference frame based control of a unified power flow controller,” in Proc. Int. Conf. Power Electron. Drive Syst., 1997, vol. 2, pp. 844–849. K. Sreenvasachar, S. Jayaram, and M. M. A. Salama, “Intelligent autonomous control of a unified power flowcontroller,” in Proc. Int. Conf. Power Electron. Drive Syst., 1997, vol. 2, pp. 862–868. S. Mishra, P. K. Dash, and G. Panda, “TS-fuzzy controller for UPFC in a multimachine power system,” Proc. Inst. Elect. Eng., Gen. Transm. Distrib., vol. 147, no. 1, pp. 15–22, 2000. M. E. A. Farrag, G. A. Putrus, and L. Ran, “Advanced control of the unified power flow controller,” in Proc. UPEC, Leicester, U.K., 1999, vol. 1, pp. 74– 77. J. Shing and R. Jang, “ANFIS: Adaptive network-based fuzzy inference system,” IEEE Trans. Syst., Man Cybern., vol. 23, no. 3, pp. 665–685, May/Jun. 1993. Edris, A. S. Mehraban, M. Rahman, L. Gyugyi, S. Arabi, and T. Reitman, “Controlling the flow of real and reactive power,” IEEE Comput. Appl. Power, vol. 11, no. 1, pp. 20–25, Jan. 1998. S. A. Taher and A. A. Abrishami, “UPFC location and performance analysis in deregulated power system,” Math. Problem Eng., 2009, VCol. 2009, Article ID 109501, 20 pages. Kumar and S. Chanana, “DC model of UPFC and its use in competitive electricity market for loadability enhancement,” presented at the World Congr. Eng. Comput. Sci., San Francisco, CA, Oct. 22–24, 2008. J. Y. Liu and Y. H. Song, “Determining maximum regulating capability of UPFC based on predicting feasibility limit of power systems,” Elect. Mach. Power Syst., vol. 20, pp. 789–800, 1998. J. J. Bukley, “Theory of fuzzy controllers,” Fuzzy Sets Syst., vol. 51, pp. 249– 258, 1992. F. A. Alturki and A. Abdennour, “Design and simplification of adaptive neuro-fuzzy inference controllers for power plants,” Elect. Power Energy Syst., vol. 21, pp. 465–474, 1999. H. Fujita, Y. Watanabe, and H. Akagi, “Transient analysis of a unified power flow controller and its application to design of the dc-link capacitor,” IEEE Trans. Power Electron., vol. 16, no. 5, pp. 735–740, Sep. 2001. Zarghami M, Crow ML, Sarangapani J, Liu Y and Atcitty S “Anovel approach to inter-area oscillations damping by Unified Power Flow Controllers utilizing ultra-capacitors,” IEEE Transactions on Power Systems, 2010, volume 25. no. 1 , pp. 404–412. Jiang. X, Chow J. H, Edris A, and Fardanesh B “Transfer path stability enhancement by voltage-sourced converter-based FACTS controllers,” IEEE Transactions on Power Delivery. 2010, Volume 25, no. 2, pp. 1019–1025.

10670

K. Pounraj & Dr. S. Selvaperumal

[20] Guo. J, Crow M. L and Sarangapani. J, “An improved UPFC control for oscillation damping” IEEE Transactions on Power Systems. 2010, Volume 25, no.1, pp. 288–296. [21] Mohamed E. A. Farrag, and Ghanim A. Putrus “Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller” IEEE Transactions On Power Delivery, 2012,Vol. 27, No. 1,pp.53-61. [22] Selvaperumal.S. Rajan.C.C.A. And Muralidharan.S, “Stability and Performance Investigation of a Fuzzy-Controlled LCL Resonant Converter in an RTOS Environment” IEEE Trans. Power Elect. 2012, Vol.28, No.4, pp. 1817-1832. [23] Selvaperumal.S and Rajan.C.C.A, “Investigation of closed-loop performance for an LCL resonant converter in a real-time operating system environment” IET Power Elect., 2012, Vol.5, No.5, pp. 511-523. [24] N. Barsoum, Artificial neuron controller for DC drive, IEEE Power Engineering Society Winter Meeting, 2000. [25] J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing–A Computational approach to learning & Machine Intelligence, Prentice Hall Inc., 1997. [26] J.M. Zurada, Introduction to Artificial Neuron Systems, Jaico Publishing House, New Delhi, 1992. [27] K.PounRaj, V.Rajasekararn and S.Selvaperumal, “Dynamic Performance Investigation of D-Q Model with PID Controller Based UPFC” IET Power Elect., 2013, Vol.6, No.5, pp. 843-850.