Artificial Neural Network PI Controlled Superconducting Magnetic Energy Storage, SMES for Augmentation

of Power Systems Stability Ashraf Mohamed Hemeida, Ph.D. Associate Professor

E.E. Dept, Higher Institute of Energy, South Valley University, Aswan, EGYPT Currently with Computer Science Dept. Arrass Teachers College, Qassim University, P.O. Box:53,

Arrass, Kingdom of Saudi Arabia

Abstract: This paper aimed to apply artificial neural network proportional, plus integral, PI controlled superconducting magnetic energy storage SMES to improve the transient stability of power systems. The PI controller parameters is firstly determined based on eigenvalue assignment approach. The artificial neural network, ANN is used to determine the optimum gains of the PI controller at different load values. The ANN is trained off line using matlab software to obtain the optimum parameters of the PI controller. The speed deviation, ∆ω and load angle deviation ∆δ are used as input signal to the PI controller. The studied power system consists of single machine connected to an infinite bus via double transmission lines. The studied system is modeled by a set of nonlinear differential and algebraic equations and simulated by the Matlab software. The simulation results indicates the effect of the proposed ANN PI controlled SMES. Keywords: Artificial neural network – Proportional plus integral, PI controller - Superconducting magnetic energy storage, SMES – Power systems stability. 1. Introduction: Superconducting magnetic energy storage, SMES systems have received a great attention in power systems applications[1-3]. The SMES unit can deliver or absorb the real power to and from the AC system according to the system response. The amount of the delivered or absorbed power by SMES system can be controlled via the firing angle of the converters of the SMES systems. The effective of the SMES on power system stabilization depends on the used control technique[4]. So that the control system strategies for the SMES devices have been proposed in literature[5-6]. However, uptill now the SMES systems still controlled by the conventional controller, such as P, PI and PID controller. Those control

techniques cannot cope with the system variations during large scale disturbance. Artificial intelligent neural network, Fuzzy logic, Nuerofuzzy and wavelet neural network control strategies are recently proposed to be used in several applications in power systems[7-9]. Power system stability is considered one of the main important consideration in synchronous machine. There are many techniques has been proposed by literature for improving the stability of power systems[10]. Flexible AC transmission systems, FACTS technology, which utilizes fast acting thyristors, are used widely for this purpose. Static VAR compensators, static phase shifter, STATCOM, variable series capacitor and static var generator are used for improvement of power system stability and increase the amount of power transmitted[11-12]. The performance of these devices dependent on the strategies of the control system. In this study the artificial neural network, ANN PI controller is designed to control the superconducting magnetic energy storage, SMES unit for damping power systems oscillations and improve the dynamic performance. The conventional proportional plus integral PI controller is designed based on eigenvalue assignment approach. The artificial neural network, ANN is trained off line to obtain the optimum gains of the PI controller parameters. Two input signals are used for the PI controller which is the speed deviation, ∆ω and load angle deviation, ∆δ. The proposed ANN PI Controlled SMES is validated for the studied single machine connected to infinite bus via double transmission lines. The superconducting magnetic energy storage, SMES system can draw or release the power from and to the AC system during the dynamic period. The simulation results show the ability of the proposed control system in damping the oscillations very fast with less overshoot and undershoot. 2 Studied System Modeling The studied power system consists of synchronous generator connected to an infinite

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bus via double transmission line with SMES unit as shown in Fig.1

There are different models have been used for stability analysis. In the present paper the 7th order model is used for time simulating of the synchronous generator[10].

- sωωδi

i

dtd

= (1)

) -(D -I )IX E ( - I ) IX -E ( - T M sdq\q

\dqd

\d

\qmi ωωω

ii

dtd

+= (2)

fdd\dd

\q

\\ E I )X-(X - E- +=

dtdE

T qdo (3)

q\qq

\d

\\ I )X-(X E- +=

dtdE

T dqo (4)

RfdfdEE V E )](ES [K- ++=dt

dET fd

E (5)

)V -V (K E K - R V- trefAfdA

fAR ++=F

FRA T

KKdt

dVT (6)

fdFFF E )T/ (K R - +=dt

dRT F

F (7)

The algebraic equations are:

0 = ( Rs +Re )Id – ( Xq\ + Xe ) Iq –

Ed\ + Vb sin δ (8)

0 = ( Rs +Re )Iq – ( Xd\ + Xe ) Id –

Eq\ + Vb cos δ (9)

Vt =(Vd2 +Vq

2)1/2 (10) Vd = Re Id – Xe Iq + Vb sin δ (11) Vq = Re Iq – Xe Id + Vb cos (12) 3 SMES Model: The configuration of the superconducting magnetic energy storage, SMES system is as shown in Fig. 2. The system consists of superconducting inductor, which is the heart of the system and 12-pulse AC/DC converter connected to three-phase AC power system via Υ-∆ / Y-Y step down transformer. 3.1 SMES Operation: The current ISM passing through the superconducting inductor is unidirectional, so that the voltage VSM across the inductor terminals can be varied between negative and positive values through the control of the firing angles ά1 and ά2. By this way the active and reactive power of the power system can be modulated. According to the converter theory, the voltage VSM in the DC side of the bridge can be expressed by the following equation: VSM = VSM0 (cos ά1 +cos ά2 ) (13) Where the VSM0 is the no load ideal maximum DC voltage of the 6-pulse bridge. Where the current of the superconducting inductor can be expressed as:

I d L

1I SM0

t

t0SMSM += ∫ τSMV (14)

Where ISM0 is the initial current of the inductor. At any time the active and reactive power delivered or abosrbed by the SMES device can be given by: PSM = VSM0 ISM (cos ά1 + cos ά2 ) (15) QSM = VSM0 ISM (sin ά1 + sin ά2 ) (16) 4 Artificial Neural Network PI controller The conventional Proportional plus integral, PI controller is firstly designed using the eigenvalue assignment approach. The synchronous machine speed deviation ∆ω and load angle deviation, ∆δ are considered to be the input signals to the controller. To improve the PI controller performance the parameters is

Connected Tr.

To 3-ph. AC Bus

LSM

ISM

Fig. 3 Schematic diagram of SMES Device

12 –pulse converter

ά1

ά2

G

SMES

Re Xe Vt V∞

Re Xe

Fig. 1 Studied Single machine infinitebut power system with SMES device

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recalculated using the artificial neural network ANN technique.

The best values for controller parameters is obtained by training the ANN off line at different load parameters. The ANN consists of two input nodes, three hidden layers and two output nodes. The data used in training stage is obtained from pole assignment approach. The trained ANN predicts the best values of the PI parameters for the input data which are not considered in the training stage. Fig. 3 shows the schematic diagram of the proposed ANN PI SMES controller. Firstly the PI gains is determined and those values is provided to the controller to obtain the output value of the thyristor firing angle ά. There are two values of ά are required and can be obtained depends on the system status. 5 Results and Discussions In order to indicate the effectiveness of the proposed artificial neural network PI SMES controller two faults were considered. The first is a three-phase short circuit fault for 100 m.sec. at the generator terminal recovered without any variations on the system configuration. The second one is input mechanical power disturbance for 2 sec. and system return to it's normal conditions. The two considered faults with the studied power system performance can be described as follows: 5.1 Three-Phase Short circuit Fault: Figs. 4,5 and 6 depicts the studied power system dynamic performance when a recovered three-phase short circuit to ground

fault occurs at the generator terminal for 100 m.sec with the proposed ANN PI SMES and with PI SMES for two different loading conditions. The effectiveness of the proposed ANN PI SMES over the PI SMES in improving the system disturbance and damping the oscillations is clear. The comparative study between the proposed ANN PI SMES controller and PI SMES controller is evident in damping the system oscillations

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.995

1

1.005

Time in sec.ω

in p

u -.-. ANN PI SMES__ PI SMES

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

50

55

60

65

70

75

Time in sec.

δ in

deg

ree

-.-. ANN PI SMES__ PI SMES

Fig.4 Studied System response due to 3-ph.

Short circuit fault for 100 m.s. at the bus terminal P=1.0 p.u. Q=0.2 p.u.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.1

0

0.1

0.2

Time in sec.

Po

of S

ME

S in

p.u

PI SMES

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.1

0

0.1

0.2

Time in sec.

Po

of S

ME

S in

p.u

ANN PI SMES

Fig.5 Dynamic response of SMES with ANN PI and PI controller when 3-ph. Short circuit

fault for 100 m.s. at the bus terminal P=1.0 p.u. Q=0.2 p.u.

P

pf KI

KPinput layer

Output layer

Fig. 3 The Proposed ANN PI controller

SK

K ip +

ά

π/2

π

∆ω ∆δ

Hidden layer

KI

KP

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very fast with less overshoot and undershoot. The effect of both controller has nearly the same effect on the terminal voltage performance.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.995

1

1.005

Time in sec.

ω in

pu

-.-. ANN PI SMES__ PI SMES

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 545

50

55

60

65

70

Time in sec.

δ in

deg

ree -.-. ANN PI SMES

__ PI SMES

Fig.6 Studied System response due to 3-ph.

Short circuit fault for 100 m.s. at the bus terminal P=0.7 p.u. Q=0.1 p.u.

5.2 Input Mechanical Power Disturbance Figs 7,8 and 9 shows the studied power system dynamic response when the system is subjected to 0.1 p.u torque step disturbance for 2 sec at two different operating conditions with the proposed ANN PI SMES controller and PI SMES controller. The effectiveness of the proposed ANN PI SMES controller over the conventional PI SMES controller is very clear in damping the oscillations very fast with less overshoot and undershoot. The response of the SMES output power in ANN PI controller less than that of PI SMES controller. But both ANN PI SMES and PI SMES has the same effect when the system return to it's prefault conditions The System data as in Ref. [13]. 6 Conclusions In this paper the artificial neural network, ANN is used to design proportional plus integral, PI controller for superconducting

magnetic energy storage, SMES device to improve the power system dynamic performance.

0 1 2 3 4 5 6 7 8 9 100.998

0.999

1

1.001

1.002

Time in sec.

ω in

pu

-.-. ANN PI SMES__ PI SMES

0 1 2 3 4 5 6 7 8 9 10

55

60

65

70

75

80

Time in sec.

δ in

deg

ree -.-. ANN PI SMES

__ PI SMES

Fig.7 Studied System response due to 0.1 p.u torque step disturbance when P=1.0 p.u. Q=0.2 p.u with PI SMES and ANN PI SMES

0 1 2 3 4 5 6 7 8 9 10-0.1

0

0.1

0.2

Time in sec.

Po o

f S

MES

in p

.u

ANN PI SMES

0 1 2 3 4 5 6 7 8 9 10-0.1

0

0.1

0.2

Time in sec.

Po o

f SM

ES

in p

.u

PI SMES

Fig. 8 Dynamic response of SMES with ANN PI and PI controller due to 0.1 p.u torque step

disturbance when P=1.0 p.u. Q=0.2 p.u with PI SMES and ANN PI SMES

0 1 2 3 4 5 6 7 8 9 100.998

0.999

1

1.001

1.002

Time in sec.

ω in

pu

-.-. ANN PI SMES__ PI SMES

0 1 2 3 4 5 6 7 8 9 10

55

60

65

70

75

80

Time in sec.

δ in

deg

ree

-.-. ANN PI SMES__ PI SMES

Fig.9 Studied System response due to 0.1 p.u

torque step disturbance when P=0.7 p.u. Q=0.1 p.u with PI SMES and ANN PI SMES

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The eigenvalue assignment approach is used to determine the parameters of the PI controller. However the system is affected by the load parameters, so that the controller parameters must be recalculated to improve the system performance. So that the ANN is used to predict the best values of the PI parameters at each value of load parameters. The ANN is trained off line using matlab software package for the prediction of the suitable parameters for the PI controllers. The time simulation show the effectiveness of the ANN PI controlled SMES in damping the system oscillations very fast. Also the SMES device should not be used for stability improvement only but can be used for load leveling or load management. 7 Nomenclature ω: angular speed δ : torque angle

voltage transientaxis-d : E \d

voltage transientaxis-q : E \q

Efd: Field voltage ebut voltag infinite : V∞

Vd : d-axis terminal voltage Vq : q-axis terminal voltage

\dX : trans. reactance of d-axis stator winding

dX : synch. reactance of d-axis stator winding \qX : trans. reactance of q-axis stator winding

qX : synch. reactance of q-axis stator winding \d0T : d-axis trans. open circuit time constant. \

q0T : q-axis transient open circuit time constant. KA: regular gain KE: exciter gain KF: stabilizing transformer gain: TA: regulator time constant. TF: stabilizing transformer time constant. TE: exciter gain. LSM: SMES inductance. PSM: SMES output real power. QSM: SMES output reactive power. References [1] Hassenzahl, W.;"Will superconducting magnetic energy storage be used on electric utility systems" IEEE Trans. On Magnetics, Vol. 11, Issue 2, Mar 1975, pp. 482-488. [2] Hassenzahl, W. et al "Electric Power Applications of Superconductivity", Proceeding of the IEEE, Vol. 92, No. 10, Oct 2004, pp. 1655-1674. [3] Chi-Jui Wu; Yuang-Shung Lee; "Application of superconducting magnetic

energy storage unit to improve the damping of synchronous generator" IEEE Trans on Energy Conversion, Vol. 6, Issue 4, Dec. 1991 pp. 573 – 578. [4] Mohd. Hassan Ali, Toshiaki Murate and Junji Tamura," A Fuzzy Logic Controlled Superconducting Magnetic Energy Storage For Transient Stability Augmentation", IEEE Trans on Control Systems Technology, Vol. 15, Issue 1, Jan. 2007 pp. 144 – 150. [5] Gaber El-Saady, A. M. Hemeida, ”Damping of Subsynchronous Resonance Oscillations by Fuzzy Superconducting Magnetic Energy Storage Unit” Presented at the IEEE 7th International conference on Intelligent Systems Engineering Systems, INES 2003, March 4-6, Assiut, Luxor, Egypt, pp.282-287. [6] Y. Mitani, K. Tsuji and Y. Murakami,"Application of Superconducting Magnetic Energy Storage to Improve Power System Dynamic Performance" IEEE Trans. On Power Systems, Vol. 3 No. 4, Nov. 1988, pp. 1418-1424. [7]V. Vesely, and D. Mudroncik, “Power System Nonlinear Adaptive Control” Electric Power Systems Research, 22 (1991) pp. 235-242. [8] G. P. Chen, O. P. Malik, G. S. Hope, Y. H. Qin, G. Y. Xu, “ An Adaptive Power System Stabilizer Based On the Self-Optimizing Pole Shifting Control Strategy” IEEE Trans. On Energy Conversion, Vol. 8, No. 4, Dec. 1993, pp.639-645. [9] R. J. Wai, R. Y. Duan, J. D. Lee, and H. H. Chang, “Wavelet Neural Network Control For Induction Motor Drive Using Sliding Mode Design Technique” IEEE Trans. On Industrial Electronics, Vol. 50, No. 4, August 2003, pp.733-748.

[10] P. Kundur, “Power Systems Stability and Control” Book, Mc-Graw Hill Inc. 1994. [11]Lo, K.L.; Lin, Y.J.; "Strategy for the control of multiple series compensators in the enhancement of interconnected power system stability", IEE Proc. Gen., Trans. And Dist. Vol. 146, Issue 2, Mar. 1999, pp. 149-158. [12]Lo, K.L.; Sadegh, M.O.;"Systematic method for the design of a full-scale fuzzy PID controller for SVC to control power system stability" IEE Proceedings-Generation, Transmission and Distribution, Volume 150, Issue 3, May 2003, pp.297 – 304. [13] A. M. Sharaf, M. Z. El-Sadek, F. N. Abdelbar, and A. M. Hemeida,"A Global Dynamic Error Driven Control Scheme For Static VAR Compensators", Electric Power Systems Research Journal, Vol. 51, 1999, pp.131-141.

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