A UPFC Damping Control Scheme using Lead-Lag and ANN based Adaptive Controllers

A UPFC Damping Control Scheme using Lead-Lag and ANN based Adaptive Controllers

Sangu Ravindra, Associate Professor, Department of EEE, QISCET, Ongole.

Dr.V.C.Veera Reddy, Professor of EEE, S.V.University, Tirupathi.

Dr.S.Sivanagaraju, Associate Professor, Department of EEE, JNTUK, Kakinada.

Abstract

Low Frequency Oscillations (LFO) occur in power systems because of lack of the damping torque in order to dominance to power system disturbances as change in mechanical input power. In the recent past Power System Stabilizer (PSS) was used to damp LFO. FACTs devices, such as Unified Power Flow Controller (UPFC), can control power flow and increase transient stability. So UPFC may be used to damp LFO instead of PSS. UPFC damps LFO through direct control of voltage and power. In this research the linearized model of synchronous machine (Heffron-Philips) connected to infinite bus (Single Machine-Infinite Bus: SMIB) with UPFC is used and also in order to damp LFO, adaptive ANN damping controller for UPFC is designed and simulated. Simulation is performed for various types of loads and for different disturbances. Simulation results demonstrate that the developed ANN damping controller would be more effective in damping electromechanical oscillations in comparison with the conventional lead- lag controller.

Keywords- Low Frequency Oscillations (LFO), Unified Power Flow Controller (UPFC), Single Machine-Infinite Bus (SMIB) power system, Artificial Neural Network (ANN) damping controller.

1. Introduction

   The Benefits of Flexible AC Transmission Systems (FACTs) usages to improve power systems stability are well known [1], [2]. The growth of the demand for electrical energy leads to loading the transmission system near their limits. Thus, the occurrence of the LFO has increased. FACTs controllers has capability to control network conditions quickly and this feature of FACTs can be used to improve power system stability. The UPFC is a FACTS device that can be used to the LFO. The primarily use of UPFC is to control the power flow in power systems. The UPFC consists of two voltage source converters (VSC) each of them has two control parameters namely me, e, mb and b [3]. The UPFC used for power flow control, enhancement of transient stability, mitigation of system oscillations and voltage regulation [3]. A comprehensive and systematic approach for mathematical modeling of UPFC for steady-state and small signal (linearized)

   dynamic studies has been proposed in [4-7]. The other modified linearized Heffron-Philips model of a power system installed with UPFC is presented in [8] and [9]. For systems which are without power system stabilizer (PSS), excellent damping can be achieved via proper controller design for UPFC parameters. By designing a suitable UPFC controller, an effective damping can be achieved. It is usual that Heffron-Philips model is used in power system to study small signal stability. This model has been used for many years providing reliable results [10].

   In this study, Applying artificial neural networks has many advantages such as the ability of adapting to changes, fault tolerance capability, recovery capability, High-speed processing because of parallel processing and ability to build a DSP chip with VLSI Technology. Matlab toolbox is used as a computing tool to implement the ANN. To show performance of the designed adaptive artificial neural network controller, a conventional lead-lag controller that is designed in [11] is used and the simulation results for the power system including these two controllers are compared with each other.

2. Power System Modeling with Upfc

   UPFC is one of the famous FACTs devices that is used to improve power system stability. Fig.1 shows a single machine-infinite-bus (SMIB) system (Heffron- Philips model of a power system installed with UPFC) with UPFC. It is assumed that the UPFC performance is based on pulse width modulation (PWM) converters. In figure 1 me, mb and e, b are the amplitude modulation ratio and phase angle of the reference voltage of each voltage source converter respectively. These values are the input control signals of the UPFC.

   Fig 1: Heffron-Philips model of a power system installed with UPFC

   As it mentioned previously, a linearized model of the power system is used in dynamic studies of power system. In order to consider the effect of UPFC in

   Where mE, E, mB and, B are the variations of UPFC control parameters considered as the inputs of state space model

   damping of LFO, the dynamic model of the UPFC is employed; In this model the resistance and transient of

   0 o 0 0

   k D k

   0

   k pd

   the transformers of the UPFC can be ignored.

   The systems dynamic relations are expressed

   1 2 0

   T

   M M M M

   as follows:

   A k4

   0 k3 1

   T

   T

   T

   • kqd

     '

     d 0

     ' '

     d 0 d 0

     '

     d 0

     o kAk5

     0 kAk6 1

   • kAkvd

   (Pm Pe D) / M

   TA

   TA TA TA

   E q (Eq E fd ) / Tdo

   k7

   0 k8 0 k9

   (8)

   1

   T

   E fd

   A

   E K A (v v )

   T

   fd to t

   A

   (1)

   0

   k pe

   M

   0

   • k pe

     M

     0

   • k pb

     M

     0

   • k pb

     M

     There exit several models for UPFC depending on several study cases.The following equation describe the

     k k

     T

     T

     B qe qe

     k k

     T

     T

     qb qb

     dynamic behavior of UPFC :

     ' ' '

     d 0 d 0 d 0

     '

     d 0

     E

     E

     dc

     m v cos

     kAkve kAkve kAkvb

   • kAkvb

   	TA		TA		TA		TA
   	kce		kce		kcb		kcb

   v 0 x i

   Etd

   E Ed

   2 (2)

   vEtq

   xE

   0 iEq

   mE vdc sin E

   2

   (9)

   The k coefficients are obtained during the Linearization of (1) and (2) around the operating point[6].

   0 i

   mBvdc cos B

   vBtd 0

   xB iBd 2

   (3)

   v x

   Btq B

   Bq

   m v sin

   B dc B

   2

3. Design of Damping Controllers

3.1. Lead-Lag Controller Design

dvdc 3mE cos

sin

iEd 3mB cos

sin

iBd

dt 4C E

E i 4C B

B i

As mentioned before, in this study two different

dc Eq dc

Bq

(4)

controllers have been used to damp LFO. The first one is conventional lead-lag controller. It consists of gain block, washout block, lead-lag compensator block. The

By combining the above linear dynamic equations ,The

state equations expressed as follows:

= AX + BU (5)

Where the matrixces

X E '

Q

washout block is considered as a high-pass filter, with the time constant TW. Without this block steady changes in input wold modify the output. The value of TW is not critical and may be in the range of 1 to 20 seconds. In this study, the parameters obtained from lead-lag controller design that is presented in [11], were used.

3.2 Adaptive Artificial neural networks Controller Design

Before the ANN can be used to adapt the controller gains in real time, it is necessary to determine

E fd

(6)

a proper set of values for the connection weights .The

v

dc

process of reaching the connection weights is normally carried out off-line and is usually referred to as the training process. In the training process, we first compile a set of training patterns and store these training patterns

mE

U E

mB

B

(7)

in the training set. Each training pattern comprises a set of input data and the corresponding output data. A training pattern set of training patterns, which cover a wide range of operating conditions, is finally used to train the desired ANN [16]. It should be noted that we use two hidden layers. Main purpose of ANN is used for the reducing the error in the system, for that we are going to use training data method. In this method, we

have to give both input values and desired output value for estimating the weight values, in that initial value taken as a random value. The input are and , desired output is the function of f { b , e , mb ,

me }.For each input having 20 membership functions and Two rule base is considered.

ANN architecture for a two input sugeno model with two rules is shown in figure2.in which we are using the XOR gate.

Fig 2: ANN Architecture for a two input sugeno model with two rules

For the training data the error reducing method is the following steps are taken those are

1. Set the Learning Rate Parameter () is greater than the one and error value E=0.

2. First Layer k =1.

3. Calculate the output value

   fi (n) = tansig (<Wi (µ( )+µ() )>)+ r ; for i=1,2,20.

4. Calculate error E=E+1/2||f – fi (n)||2.

5. Weights are updated

   Wi+1 = Wi + (f-tansig(<Wi (µ( )+µ())>) + r); for i=1,2..20.

6. Check error E is not zero, then take the layer value as k =k+1,

7. Repeat the above process until when the error E as zero.

   From above process we get the desired output function, the output function

   f =(1-e-(Wi (µ( )+µ())+r) /(1+e-(Wi (µ(

   )+µ())+r) .

   The range of output function is -1 to +1.

   1. Simulation Results

      In this research, two different cases are studied. In the first case mechanical power and in the second case reference voltage has step change and deviation in () and deviation in rotor angle () is observed. The parameter values of system are gathered in Appendix. In first case, step change in mechanical input power is studied. Simulations are performed when mechanical input power has 10% increase (Pm=0.1 Pu) at t = 1s. Simulation results for different types of loads and controllers (mE, E, mB, B) and step change in mechanical input power are shown in figures 3 to 7.

      Fig 3: Angular velocity deviation during step change in mechanical input power for nominal load (me Controller).

      Fig4: Angular velocity deviation during step change in mechanical input power for light load (me Controller)

      Fig 5: Angular velocity deviation during step change in mechanical input power for nominal load (eController).

      Consequently simulation results show that ANN damping controller successfully increases damping rate and decreases the amplitude of low frequency oscillations. Results comparison between conventional lead-lag compensator and the proposed ANN damping controller for the UPFC indicates that the proposed ANN damping controller has less settling time and less overshoot in comparison with the conventional lead lag compensator.

   2. Conclusions

Fig 6: Angular velocity deviation during step change in mechanical input power for nominal

load (mb Controller)

Fig.7 Angular velocity deviation during step change in mechanical input power for nominal

load (b Controller)

As it can be seen from figures 3 to 7, lead-lag controller response is not as good as ANN damping controller response and also ANN damping controller decreases settling time. In addition maximum overshoot has decreased in comparison with lead-lag controller response.

In second case, simulations were performed when reference voltage has 5% increase (Vref =0.05 Pu) at t=1s. Figure 8 demonstrates simulation result for step change in reference voltage, under nominal load and for b Controller.

Fig. 8 Response of angular velocity for 5% step change in reference voltage in the case of

nominal load (b Controller)

With regard to UPFC capability in transient stability improvement and damping LFO of power systems, an adaptive ANN damping controller for UPFC was presented in this paper. The controller was designed for a single machine infinite bus system. Then simulation results for the system including ANN damping controller were compared with simulation results for the system including conventional lead-lag controller. Simulations were performed for different kinds of loads. Comparison showed that the proposed adaptive ANN damping controller has good ability to reduce settling time and reduce amplitude of LFO.

Appendix

Generator: M = 2H = 8.0MJ / MVA, D=0.0,

Tdo = 5.044s, Xd = 1.0 pu, Xq = 0.6pu , Xd = 0.3pu Exciter (IEEE Type ST1): KA=100, TA=0.01s

Reactances: XlE = 0.1pu, XE = XB = 0.1pu XBv= 0.3pu, X e = 0.5pu

Operation Condition: Pe = 0.8pu, Vt = 1pu, Vb = 1pu UPFC parameters: mE = 0.4013, mB = .0789,

E = – 85.3478 0 B = – 78.2174 0

DC link: Vdc = 2pu, Cdc=1pu

References

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Bibliography

1. Mr. Sangu Ravindra received his B.Tech in Electrical Engineering from JNT University, Hyderabad & M.E in Power Electronics & Indstrial Drives from Satyabhama University, Chennai. He is now a Ph.D candidate at JNT University, Kakinada. His research interest area is FACTS controllers for power quality improvement. Presently he is working as Associate Professor in department of EEE at QISCET ongole.

   Mail ID: sanguravindra@rediffmail.com

2. Dr.V.C.Veera Reddy received his B.Tech in Electrical Engineering from JNT University, Anantapur in 1979 & M.Tech in Power System Operation & Control from S.V University Tirupati, in 1981. He got Ph.D degree in Modeling & Control of Load Frequency using new optimal control strategy from S.V. University Tirutati, in 1999. Presently he is working as Professor of EEE S.V.U.College of Engineering S.V.University Tirupati-517502.

   Mail ID: veerareddyj1@rediffmail.com.

3. Dr. S. Sivanagaraju received his B.Tech in Electrical Engineering from JNT University, Hyderabad in 1998 & M.Tech in Power Systems from IIT Kharagpur in 2000. He got Ph.D degree in Electrical distribution systems & power system Analysis from JNT University, Hyderabad in 2004. Presently he is working as Associate Professor in the Department of EEE, University College of Engineering, and JNT University Kakinada.

Mail ID: sirigiri70@yahoo.co.in.