Electric power systems are nonlinear and complicate in nature. Along with increasing size of power systems, their complexity becomes more. Besides, by using additional and auxiliary components such as Flexible AC Transmission Systems (FACTS) devises, Power System Stabilizers (PSSs), excitation systems, turbine-governor systems and etc, the dynamic model of network is increased and power system becomes more complicate. The nonlinear dynamic model of these large power systems is easily obtained. But, the complexity makes it difficult to obtain a linear dynamic model of power system. Obtaining an appropriate linear model is necessary in order to analysis and study of power system. In tradition method, a linear dynamic model of power system is obtained by linearization of nonlinear dynamic model around an operating condition. But in large electric power systems, the linearization technique is very sophisticate and maybe impossible. Concerning this matter, in this paper a Recursive Least Square (RLS) technique is used to parameter identification in a multi machine electric power system. In this method a linear model is assumed for power system and its parameters are accurately computed by using RLS method. In order to verifying the results, the obtained linear model is compared with the nonlinear model. The simulation results show the validity of identified model, as the response of identified linear model is very near to nonlinear model. 

1. Astrom KJ and Wittenmark B (1994) Adaptive control, 

Prentice Hall, second edition. New Jersey. USA. pp:185-260.

2. Krneta R, Anti´c S and Stojanovi´c D (2005) 

Recursive least squares method in parameters 

identication of DC motors models.  FACTA Univ. 

(NI_S). Electrical Engg. 18(3), 467-478.

3. Li W, Meng Ch, Sheng D, Chen J and Ren H (2007) 

Application of recursive least squares method for 

parameter identification of DEH governing system. 

Challenges of Power Engg. & Environ. 5, 286-289.

4. Mehdi Nikzad, Shoorangiz Shams Shamsabad 

Farahani, Mohammad Bigdeli Tabar, Mehdi Ghasemi 

Naraghi and Ali Javadian (2011) Adjustment of PSS 

in a multi machine power system based on particle swarm optimization technique. Indian J.Sci.Technol.

4 ( 8), 876-880. Domain site: http://www.indjst.org.

5. Paleologu C, Benesty J and Ciochin S (2008) A 

robust variable forgetting factor recursive leastsquares algorithm for system identification.  IEEE 

Signal Processing Lett. 15, 597-600.

6. Reza Hemmati, Sayed Mojtaba Shirvani Boroujeni, 

Hamideh Delafkar and Amin Safarnezhad Boroujeni 

(2011) Fuzzy load frequency control in multi area 

electric power system. Indian J.Sci.Technol. 4 (7), 

796-800. Domain site: http://www.indjst.org.

7. Rodriguez S, Pizarro P, Rodriguez D and Bueno FJ 

(2007) Complex-space recursive least squares power 

system identification. In: Power electronics specialists 

conference. PESC 2007. IEEE  pp: 2478 – 2484.

8. Sayed Mojtaba Shirvani Boroujeni, Babak Keyvani 

Boroujeni, Hamideh Delafkar, Elahe Behzadipour and 

Reza Hemmati (2011) Harmony search algorithm for 

STATCOM controllers tuning in a multi machine 

environment. Indian J.Sci.Technol. 4 ( 9), 1031-1035. 

Domain site: http://www.indjst.org.

9. Shoorangiz Shams  Shamsabad Farahani, Mehdi 

Nikzad, Mohammad Bigdeli Tabar, Mehdi Ghasemi 

Naraghi and Ali Javadian (2011a) Multi-machine 

power system stabilizer adjustment using genetic 

algorithms. Indian J.Sci.Technol. 4 (8), 881-885. 

Domain site: http://www.indjst.org.

10. Shoorangiz Shams Shamsabad Farahani, Mehdi 

Nikzad, Mohammad Bigdeli Tabar, Mehdi Ghasemi 

Naraghi and Ali Javadian (2011b) Multi-machine 

power system stabilizer adjustment using Simulated 

Annealing. Indian J.Sci.Technol. 4 (8), 886-889. 

Domain site: http://www.indjst.org.

11. University of Washington, electrical engineering 

department, power system test case archive, power 

flow test cases, IEEE 14 bus test system. 

http://www.ee.washington.edu/research/pstca/.