Power System Voltage Control using LP and Artificial Neural Network

Optimization and control of reactive power distribution in the power systems leads to the better operation of the reactive power resources. Reactive power control reduces considerably the power losses and effective loads and improves the power factor of the power systems. Another important reason of the reactive power control is improving the voltage profile of the power system. In this paper, voltage and reactive power control using Neural Network techniques have been applied to the 33 shines- Tehran Electric Company. In this suggested ANN, the voltages of PQ shines have been considered as the input of the ANN. Also, the generators voltages, tap transformers and shunt compensators have been considered as the output of ANN. Results of this techniques have been compared with the Linear Programming. Minimization of the transmission line power losses has been considered as the objective function of the linear programming technique. The comparison of the results of the ANN technique with the LP shows that the ANN technique improves the precision and reduces the computation time. ANN technique also has a simple structure and this causes to use the operator experience.

Authors:

• A. Sina
• A. Aeenmehr
• H. Mohamadian

Keywords:

• voltage control
• linear programming
• artificial neural network
• power systems

References:

[1] A. O.Ekwue. JF. Macqueen, " Artificial Intelligence Techniques for
Voltage Control ", IEE Colloquim On-1997
[2] K.R.C.Mamandur, R.D.Cheauth, "Optimal Control of Reactive Power
flow for Improvements in Voltage Profiles and for Real Power Loss
Minimization " IEEE Trans. On Power Apparatus and System, Vol.
PAS100, No.7, July 1981
[3] A.A.EL- Samahy, W.M.Refaey, "An Artificial Neural Network Scheme
for Reactive Power and Voltage Control of Power System." UPEC 1996
[4] Horward Demuch, Mark Beale, "Neural network Toolbox for Use with
MATLAB", Users Guide, Version4.0
[5] A. J. Conejo, Senior Member , F. D.Galiana, I. Kockar. "Z-Bas Loss
Allocation", IEEE Trans on Power System, Vol.16, No,1, February 2001
[6] G. B.Sheble, "Power Basics Problems and Solutions", IEEE Tutorial
Course Reactive, 1987
[7] L. Fausett, " Fundamentals of Neural Networks Architectures Algorithm
and applications", Printice Hall International. Inc, 1994
[8] M. R. Gerald, T. Heydt, "Phasor Measurement Unit Data in Power
System State Estimation",. January 2005
[9] H. Seifi, "The Operation of Power Systems", Tehran university, 1992
[10] S. Kamel, M. Kodsi,"Modeling and Simulation of IEEE 14 Bus system
with FACTS Controllers", IEEE Student Member - 2003
[11] H. Yoshida, K. Kawata,"A Particle SWARM optimization for Reactive
Power and Voltage control Considering Voltage Stability", IEEE,
International Conference on Power System (ISA P99)- 1999
[12] J. Qiu, S. M. Shidehpour, "A new Approach for Minimizing Power
Losses and Improving Voltage Profile", IEEE Trans on Power System.
Vol. PWRS -2, No 2, May 1987
[13] T. Fukuda, T. Shibata,"Theory and Application of Neural Network for
Industrial Control System", IEEE Trans. On Industrial Electronics,
Vol.39, No.6, 1992
[14] M. H. Hagan, M.B. Menhaj,"Training Feed Forward Network with the
Marquardt Algorithm", IEEE Trans on Neural Network, vol.5 ,No.6 -
1994
[15] "IEEE guide for operation and maintenance of hydro generator", IEEE
Std429 -1999