Modification Of UPFC Circuit To Enhance Dynamics...

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A Publisher for Research Motivation........ Email: [email protected] Volume 2, Issue 9, September 2014 ISSN 2321-600X

Volume 2, Issue 9, September 2014 Page 43

ABSTRACT Unified Power Flow Controller (UPFC) is the most widely applicable FACTS device to control the power flow and to optimize the system stability in the transmission lines. This paper presents a novel modification of components of UPFC to improve its performance, and then the parameters of the modified section are optimized using soft computing selection based on Artificial Neural Network (ANN). The proposed modification realizes enhancing on the dynamic response of the system in presence of UPFC achieving global benefits in both directions; steady state and dynamics. Modeling and results are verified to model UPFC and to verify the performance of UPFC with this modification. Keywords: Artificial Neural Network (ANN), Dynamic Response, FACTS Devices, UPFC.

1. INTRODUCTION FACTS technology consists of devices depended on using the reliable and high speed power electronic devices instead of mechanical controllers. Thus, the utilization of the existing power system comes into optimal conditions and the controllability of the system is increased [1]. UPFC allows simultaneous control of active power flow, reactive power flow, and voltage magnitude at UPFC terminals. These characteristics give UPFC the capability to enhance the performance of the power system during various operating conditions [2]. Researches on UPFC have focused on its effect on the power flow control, improvement of stability, and damping of power swing [3, 4]. However, for practical design and application of UPFC devices, there are two aspects that need to be investigated in more details. First one, components-based simulation including feasible GTO switches and converter topologies and semiconductor switch control. Second one, evaluation of the influences of DC capacitance on the system dynamic performance or selection of the inductance of boosting transformer for technical performance requirements of the system [5]. Transient power flow inside the UPFC has been theoretically discussed in [6], and design of capacitance of the DC-link capacitor has been presented based on the theoretical analysis of power flow inside UPFC. Furthermore, the UPFC is highly nonlinear because it consists of converters, power transformers, filters, and surge arrestors. Uncertainties in the power system make it difficult to model the transmission system accurately [7]. Control schemes based on soft computing principles such as fuzzy logic, neural networks, and adaptive techniques are useful for modeling and/or controlling systems characterized by uncertainties and nonlinearities. In [8], Artificial Neural Networks applied UPFC for damping low frequency oscillations. In [9], Fuzzy Logic Controller is used instead of PI controller in UPFC to better voltage sag compensation. In this paper, studying the effect of UPFC on system voltage overshoot at the instant of device insertion to the system, and modifying UPFC to reduce the voltage overshoot is considered and presented. Artificial Neural Network has been applied to adjust the parameters of the modeling to improve the UPFC performance, the proposed work has been tested on IEEE 6 and IEEE 30 bus systems using the MATLAB SIMULINK Program.

2. UNIFIED POWER FLOW CONTROLLER (UPFC) The UPFC may be considered to be constructed of two VSCs sharing a common capacitor on their DC side and a unified control system. A simplified schematic representation of the UPFC is given in Figure 1.Converter 2 provides

Modification Of UPFC Circuit To Enhance Dynamics Performance Using Soft Computing

Selection

Ahmed. M. Othman1, Mahdi M. M. El-Arini2 and Raef S. S. Ahmed3

1 Electrical Power Dept., Faculty of Engineering, Zagazig University

2 Head of Electrical Power Dept., Faculty of Engineering, Zagazig University

3 Electrical Power Dept., Faculty of Engineering, Zagazig University




the main function of the UPFC by injecting an ac voltage Vpq with controllable magnitude and phase angle in series with the transmission line via a series insertion transformer. This injected voltage acts essentially as a synchronous ac voltage source. The basic function of converter 1 is to supply or absorb the real power demand by converter 2 at the common dc link. It can also generate or absorb controllable reactive power and provide independent shunt reactive compensation for the line. Converter 2 supplies or absorbs locally the required reactive power and exchanges the active power as a result of the series injection voltage [10], so it follows that, with proper controls, it can also achieve the task of an independent static capacitor developing compensation for reactive power at the transmission line. The inverter output voltage inserted in series with the line is considered mainly as an AC voltage source. The current flowing through the injected voltage source is the transmission line current; it depends on the transferred electric power and the transmission line impedance. The total of the maximum injected voltage defines the VA rating of the injected voltage source for Inverter 2 and the maximum line current during the power flow control is still developed.

Figure 1 Schematic diagram of UPFC

3. MODELING OF NORMAL UPFC The IEEE 6 bus test system is used to investigate the proposed work which will be presented in this paper; this system consists of three generators, six buses, eleven transmission lines and three loads as shown in single line diagram of this system in Fig. 2 [11]. The dynamic SIMULINK model of the IEEE 6 bus system with UPFC is presented in the Fig.3, in which the UPFC is placed between bus 1 and bus 5 in line 3 according to optimal location and installation [12] that achieved solution of steady state problems, the UPFC is used to control the real power flow in the system and regulate the system voltage.

Figure 2 Single line diagram of IEEE 6 bus system




Figure 3 MATLAB SIMULINK Model of the system with UPFC Assume that the Bypass breaker of the UPFC is initially closed, then it is opened at time (t = 5 sec), so the UPFC will be inserted in the network at t = 5 sec, the voltages at the load buses are shown in Fig. 4 which shows that there is a voltage overshoot at the instant of insertion the UPFC. From Fig. 4, a sudden insertion of UPFC in the network may result in considerable voltage overshoot at the load level. Since loads are voltage sensitive, the overshoot will temporarily increase the loading of the feeding transmission system. This type of disturbance is likely to arise during voltage instability incidents as a result of tripped lines or generation units in the transmission system. During such incidents, the transmission system is already operating close to its transfer limits and increased loading is highly undesirable.

Figure 3 Load bus voltage with inserting original UPFC

4. MODELING OF NORMAL UPFC To overcome the problem, it is required to study the dynamic performance of the UPFC in the system voltage. At the instant of insertion of UPFC, the fast power flow control may causes fluctuation of the DC link voltage because active power is produced between the output voltage of the series device and the current of transmission line and flows into capacitor (C) in transient states. If a large amount of active power flows into the series device, the dc link voltage rapidly rises up, and over voltage maybe applied to C which affect on the performance of the UPFC and the dc link voltage stability between the series and shunt inverters. This problem is solved by selecting a proper size of the DC capacitor according to [6].




Figure 5 voltage of bus4 with varying the UPFC inductance Figure 6 voltage of bus 5 with varying the UPFC

inductance

Figure 7 voltage of bus 6 with varying the UPFC inductance

For the shunt converter of UPFC, it is only used to provide the active power to the series converter of UPFC. So it is has no a great effect on improving the system voltage overshoot. The series circuit of UPFC controls the line power flow by means of adjusting the magnitude and phase angle of its output inserted voltage, so it can be used to study the series circuit parameter effect on the system voltage overshoot at the UPFC insertion time. Fig. 5, 6, and 7 show the load bus voltages with varying the inductance of series circuit of UPFC (in per unit value), it is observed that with increasing the UPFC series inductance the voltage overshoot is reduced and on the other hand the steady state voltage value is also reduced.

5. THE MODIFICATION OF UPFC CIRCUIT From the above graphs and characteristics, it can be proposed a UPFC circuit so that the UPFC can present a high series inductance at the insertion in the system, and then reduce the inductance linearly to its base value within the voltage overshoot period.

Figure 8 The modified UPFC




The modified UPFC is shown in Fig. 8. Where the thyristor controlled reactor (TCR) is inserted in the series circuit of UPFC, the firing angle is controlled of TCR to express high inductance at the UPFC insertion in the system and the reduced gradually to zero within the voltage overshoot period.

Figure 9 The schematic diagram of TCR

Fig. 9 shows the schematic diagram of thyristor controlled reactor, it consists of two thyristor back to back in series with small reactance XLm in shunt with a large reactance XL2. With firing angle (α) equal to 90 degree, the thyristor back to back acts as open circuit and the total inductance is increased, and at zero firing angle, the thyristors back to back acts as short circuits and there is a very small addition of inductance, according to the following equation [13]:

Xeq(α) = XL2 // XL1(α) =

2 1

2 1

. ( )( )

L L

L L

X XX X

(1)

Where:

XL1(α) = XLm = 2 sin(2 )

(2)

Xeq(α): is the equivalent reactance of the added element. XL2 : the very large added inductance. XLm: the very small added inductance. The equivalent circuit of series inductance in the simulink model is shown in fig. 9.

Figure 10 The modification of the series inductance model

Fig. 10 shows the proposed modification in the UPFC Simulink model Where: base L: is the value of the inductance of the series circuit of UPFC. L modified: is the resulted value of the series inductance after modification. Fig. 11 shows the inductance variation with time, where at time (t = 5 sec) the UPFC is inserted in the network with high series inductance, and through 1.5 second the series inductance is reduced gradually to the base inductance of the UPFC circuits.




Figure 11 Value of the proposed series inductance

So this modification can be achieved by adding a variable inductance to the series inductance of the UPFC at the instant of the UPFC insertion in the system and then reduce the added inductance gradually to zero as described above. Fig. 12, 13, and 14 show the load bus voltage of IEEE 6 bus system before and after modification of UPFC. From Fig. 12, 13, and 14, it is observed that the system voltage overshoot is improved using the modified UPFC.

Figure 12 Voltage at bus 4 with and without modification

Figure 13 Voltage at bus 5 with and without modification




6. THE MODIFICATION OF UPFC CIRCUIT The proposed modification applied to the UPFC improves its step response to the system bus voltage at the instant of the UPFC insertion of the system. It is required to apply the Soft Computing Selection [14] using the Artificial Neural Network for optimal adjustment of the UPFC parameters and selecting the series inductance of the UPFC. It is required from the applied Neural Network to select the series inductance values (the added inductance and the base value) of UPFC to get the required step response of the bus voltages.

Figure 15 Artificial Neural Network architecture Fig. 15 shows the proposed Artificial Neural Network (ANN) architecture, it shows that the ANN has five inputs (the Bus No. , rise time, settling time, settling value and max. overshoot of the step response of the voltage signal of that bus) and has two outputs (the values of the added inductance and original inductance of the UPFC). ANN is trained using the data obtained from the simulation results of the test system with variation in the added modification of UPFC. The resilient backpropagation (trainrp) training algorithm will be used to train the presented neural network, in which the problems of the variable learning rate algorithm (traingdx) are eliminated such as sigmoid transfer functions in the hidden layers which are often called “squashing” functions, because they compress an infinite input range into a finite output range. Sigmoid functions are characterized by the fact that their slopes must approach zero as the input gets arge. This cause small changes in the weights and biases, even though the weights and biases are far from their optimal values. In The resilient backpropagation the size of the weight change is determined by a separate update value [15].

Figure 16 Comparison between (MSE) of the training algorithms

Fig. 16 shows the comparison between Mean Square Errors (MSE) of the two training algorithm presented above. It shows that the number of epochs (iterations) and the MSE is lower in Resilient backpropagation with lower MSE The neural network will be designed to get the series inductance values of the UPFC to improve the step response of the bus voltage at the UPFC insertion in the network, the required voltage signal specifications of a certain system bus are applied to the network to give the values of the series inductance. The ANN is trained through the resilient backpropagation (trainrp) as a training algorithm with the following design parameters in table 1:

Artificial Neural

Network

Bus No.

Rise Time

Settling Time Settling Value

Max. Overshoot

Added Inductance

Original

Induct




Table 1. The designed values for the resilient backpropagation parameters for IEEE 6 bus system No. of hidden Layer 2

No of neurons 40 and 20

The activation functions Tansigmoid

The learning rate 0.1

Figure 17 voltage response of bus 4 with and without ANN


Figure 19 Voltage response of bus 6 with and without ANN

Table 2 shows the step response specifications, and Fig. 17, 18, and 19 show the voltage response of bus 4, 5, and 6 respectively before the modification applied to the UPFC and after modification with and without using ANN in controlling the series inductance values of UPFC, it shows that before modification, the voltage response has a high overshoot, the modification without ANN gives high oscillation and higher settling time, but the modification with ANN gives lower overshoot and settling time and smoothly changing in voltage response.




Table 2. The specifications of voltage signals before and after modification

7. THE MODIFICATION OF UPFC CIRCUIT For more validation of the proposed modification, it has been tested on the following test system, an IEEE-30 bus system. The SIMULINK model of the IEEE 30 bus system is presented in the fig.20. The ANN is trained through the resilient backpropagation (trainrp) as a training algorithm with the following design parameters:

Table 3. The designed values for the resilient backpropagation parameters for IEEE 30 bus system No. of hidden Layer 2 No of neurons 30 and 20 The activation functions Tansigmoid The learning rate 0.1

Figure 20 MATLAB SIMULINK Model of the IEEE 30 bus system with UPFC

Table 4 shows the step response specifications, and fig. 21 and 22 show the voltage response of bus10 and 16respectively before the modification applied to the UPFC and after modification with and without using ANN in

settling value Settling time Rise time Overshoot % 0.9831 1.9443 0.0534 32.41 % Before modification

Bus voltage (4) 0.9818 2.6811 0.006546 3.11 % After traditional

modification 0.9867 2.0908 0.1091 9.94 % After ANN

modification 0.9928 1.0798 0.1474 33.51 % Before modification



modification 0.9878 1.4311 0.1187 36.06 % Before modification



modification




choosing the series inductance values of UPFC, it is shown that, before modification the voltage signal has a high overshoot, the modification without ANN gives high oscillation and higher settling time, but the modification with ANN gives lower overshoot and settling time and smoothly changing in voltage signal.


Figure 22 voltage response of bus 16 with and without

Table 4. The specifications of voltage signals before and after modification

settling value Settling time Rise time Overshoot

%

1.0104 1.5583 0.1537 55.62 % With original UPFC

Bus voltage (6)

1.0106 2.2926 1.3552 7.70 % With proposed traditional

modification 1.0136 1.6647 0.2529 2.07 % With proposed ANN

modification 1.0043 2.7331 0.1384 67.32 % With original UPFC

Bus voltage (10)




Bus voltage (16)




Bus voltage (24)



modification




8. CONCLUSIONS The main objective of this paper is to propose a modification for UPFC to improve its performance in the power systems, the modification is proposed to improve the system voltage overshoot and its step response at the UPFC insertion time. the principle of this modification is on controlling the series inductance of UPFC by adding a TCR, as a variable inductance, to the UPFC circuit and adjusting its firing angle. So this modification can be achieved by adding a variable inductance to the series inductance of the UPFC at the instant of the UPFC insertion in the system and then reduce the added inductance gradually to zero. For more strength results , the Soft Computing Selection is applied using the Artificial Neural Network for optimal adjustment of the UPFC modification and choosing the series inductances of the UPFC. The resilient back propagation Neural Network has been applied to control the added series reactance of this modification; the proposed works have been tested on IEEE 6 and 30 bus system and give good results on the step response the system bus voltages.

REFERENCES [1] N.G. Hingorani and L.Gyugi, Understanding FACTS –Concepts and Technology of Flexible Ac Transmission

Systems, Standard Publishers Distributors, IEEE Press, New York, 2001. [2] A. M. Othman, A. Gaun, M. Lehtonen, and M. M. Alarini, Real World Optimal UPFC Placement and its Impact

on Reliability, 5th IASME / WSEAS International Conference on (EE'10), Cambridge University, UK, pp. 90-95, February 2010.

[3] Vibhor Gupta, Study and Effects of UPFC and its Control System for Power Flow Control and Voltage Injection in a Power System, International Journal of Engineering Science and Technology Vol. 2(7) 2558-2566, 2010.

[4] N.K.Sharma, P.P.Jagtap, Modelling and application of Unified Power Flow Controller (UPFC), Third International Conference on Emerging Trends in Engineering and Technology, 2010

[5] Kang Y.L, Consideration Of Dc Capacitor And Line Inductance In The Design Of UPFC To Improve Transient Stability, Power Engineering Society Winter Meeting, 2001. IEEE ,vol.3, pp. 1277 – 1282, 2001.

[6] Hideaki Fujita, Yasuhiro Watanabe, Transient Analysis of a Unified Power Flow Controller, and its Application to Design of the DC-Link Capacitor, IEEE Transactions on power electronics, vol. 16 , pp. 735 – 740, Sep 2001.

[7] Sebaa MORSLI, A robust adaptive fuzzy control of a unified power flow Controller, Turk J Elec Eng & Comp Sci, Vol.20, No.1, 2012.

[8] Sangu Ravindra, Artificial Neural Networks based UPFC for Damping Low Frequency Oscillations, International Journal of Computer Applications (0975 – 8887) Volume 53– No.7, September 2012.

[9] Krishna Chaitanya Diggavi, M.Ramu, Fuzzy Logic Based Unified Power Flow Controller For Improvement of Voltage Sag in a Transmission System, International Electrical Engineering Journal (IEEJ) Vol. 3 (2012) No. 1, pp. 602-606.

[10] M. Noroozian, L. Angquist, M. Ghandhari and G. Anderson, Use of UPFC for Optimal Power Flow Control, IEEE Trans. Power Delivery, vol. 12, no. 4, , pp 1629-1633, October 1997

[11] http://www.pserc.cornell.edu/matpower/ [12] Mahdi M. M. El-arini, Raef S. S. Ahmed, Optimal Location of FACTS Devices to Improve Power Systems

Performance,Journal of Electrical Engineering, Vol. 3 , pp. 73-80, Romania 2012 [13] Mohamed Zellagui and Abdelaziz Chaghi . Impact of Series FACTS Devices (GCSC, TCSC and TCSR) on

Distance Protection Setting Zones in 400 kV Transmission Line, ISBN: 978-953-51-1079-8, InTech, DOI: 10.5772/54257.

[14] Urszula Stańczyk ,Man-Machine Interactions advances in Intelligent and Soft Computing, Vol 59, 2009, pp 335-344

[15] http://www.mathworks.com/help/nne

AUTHORS Ahmed M. Othman (1980) He received the B.Sc. (with first class honors) and M.Sc degrees from the Electrical Power Engineering Department, Faculty of Engineering, Zagazig University, Egypt in 2002 and 2007, respectively., he worked towards the Ph.D. degree at Power Systems Lab., Electrical Engineering Dept., Helsinki University of Technology (TKK), Finland under joint supervision with Zagazig University. His main interest research is FACTS devices and their control. Also the AI systems and its application to power systems.




Mahdi M. El-Arini (1955) has been a professor atZagazig University, Egypt since 1999. Where he is now Head of Electronics Department in Zagazig University. He received his Master’s in 1984 from Cairo University, Egypt and Ph. D. in Electrical Engineering from Dusiburg University (West Germany), in 1989. The main activities of Dr. Mahdi include Voltage and Reactive Power Control, power system optimal operation and control of power systems, contingency evaluation of power systems and system security and system stability.

Raef S. S. Ahmed received the B.S. and M.S. degrees in Electrical Engineering from faculty of engineering Zagazig university in 2009 and 2013, respectively. Currently, he is working towards the Ph.D. degree at Power Systems Lab., Electrical Engineering Dept. Zagazig university His main interest research is FACTS devices and their control. Also the AI systems and its application to power system.

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Modification Of UPFC Circuit To Enhance Dynamics...

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