**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**31105

##### Multimachine Power System Stabilizers Design Using PSO Algorithm

**Authors:**
H. Shayeghi,
A. Safari,
H. A. Shayanfar

**Abstract:**

In this paper, multiobjective design of multi-machine Power System Stabilizers (PSSs) using Particle Swarm Optimization (PSO) is presented. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electro-mechanical modes of all machines to a prescribed zone in the s-plane. A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The PSSs parameters tuning problem is converted to an optimization problem which is solved by PSO with the eigenvalue-based multiobjective function. The proposed PSO based PSSs is tested on a multimachine power system under different operating conditions and disturbances through eigenvalue analysis and some performance indices to illustrate its robust performance.

**Keywords:**
Multiobjective optimization,
Particle Swarm Optimization,
PSS Design,
Dynamic
Stability

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1080201

**References:**

[1] X. Hugang, Ch. Haozhong, L. Haiyu, Optimal reactive power flow incorporating static voltage stability based on multi-objective adaptive immune algorithm, Energy Conversion and Management, Vol. 49, 2008, pp. 1175-1181.

[2] H. Ping, Y. W. Kewen, T. Chitong, B. Xiaoyan, Studies of the improvement of probabilistic PSSs by using the single neuron model, Electrical Power and Energy Systems, Vol. 29, 2007, pp. 217-221.

[3] C. Y. Chung, K. W. Wang, C. T. Tse, X. Y. Bian, and A. K. David, Probabilistic eigenvalue sensitivity analysis and PSS design in multimachine systems, IEEE Trans. on Power Systems, Vol. 18, No. 4, 2003, pp.1439 - 1445.

[4] S. Lee, Optimal decentralised design for output-feedback power system stabilizers, IEE Proc. Generation. Transmission and Distribution, Vol. 152, No. 4, 2005, pp. 494-502.

[5] J. Fraile-Ardanuy, P.J. Zufiria, Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms, Neurocomputing, Vol. 70, 2007, pp. 2902-2912.

[6] Z. Barto, Robust control in a multimachine power system using adaptive neuro-fuzzy stabilizers, IEE Proc. Generation. Transmission and Distribution, Vol. 151, No. 2, 2004, pp.261-267.

[7] R. Segal, A. Sharma, M.L. Kothari, A self-tuning power system stabilizer based on artificial neural network, Electrical Power Energy Systems, Vol. 26, 2004, pp. 423-430.

[8] E. Larsen and D. Swann, Applying power system stabilizers, IEEE Trans. Power App. Systems, Vol. PAS-100, 1981, pp. 3017-3046.

[9] G. T. Tse and S. K. Tso, Refinement of conventional PSS design in multimachine system by modal analysis, IEEE Trans. on Power Systems, Vol. 8, 1993, pp. 598-605.

[10] P. Kundur, M. Klein, G. J. Rogers, and M. S. Zywno, Application of power system stabilizers for enhancement of overall system stability, IEEE Trans. on Power Systems, Vol. PAS-108, 1989, pp. 614-626.

[11] M. J. Gibbard, Robust design of fixed-parameter power system stabilizers over a wide range of operating conditions, IEEE Trans. on Power Systems, Vol. 6, pp. 794-800, 1991.

[12] V. A. Maslennikov, S. M. Ustinov, The optimization method for coordinated tuning of power system regulators, Proceedings of the 12th Power Systems Computation Conference, Dresden, Germany, 1996, pp. 70-75.

[13] Y. L. Abdel-Magid, M. A. Abido, S. AI-Baiyat, A. H. Mantawy, Simult-aneous stabilization of multimachine power systems via genetic algorithms, IEEE Trans. on Power Systems, Vol. 14, No. 4, 1999, pp. 1428-1439.

[14] M. A. Abido, Y. L. Abdel-Magid, Hybridizing rule-based power system stabilizers with genetic algorithms, IEEE Trans. on Power Systems, Vol. 14, No. 2, 1999, pp.600 -607.

[15] P. Zhang, A. H. Coonick, Coordinated synthesis of PSS parameters in multi-machine power systems using the method of inequalities applied to genetic algorithms, IEEE Trans. on Power Systems, Vol. 15, No. 2, 2000, pp. 811-816.

[16] Y. L. Abdel-Magid, M. A. Abido, Optimal multiobjective design of robust power system stabilizers using genetic algorithms, IEEE Trans. on Power Systems, Vol. 18, No. 3, , 2003, pp. 1125 -1132.

[17] Y. L. Abdel-Magid, M. A. Abido, A. H. Mantawy, Robust tuning of power system stabilizers in multimachine power systems. IEEE Trans. on Power Systems, Vol. 15, No. 2, 2000, pp. 735 -740.

[18] M. A. Abido, Robust design of multimachine power system stabilizers using simulated annealing , IEEE Trans. on Energy Conversion, Vol. 15, No. 3, 2003, pp. 297-304.

[19] M. A. Abido, Y. L. Abdel-Magid, Optimal design of power system stabilizers using evolutionary programming, IEEE Trans. on Energy Conversion, Vol. 17, No. 4, 2002, pp. 429-436.

[20] S. Mishra, M. Tripathy, J. Nanda, Multi-machine power system stabilizer design by rule based bacteria foraging, Electric Power Systems Research, Vol. 77, 2007, pp. 1595-1607.

[21] S. Yang, M. Wang, L. Jiao, A quantum particle swarm optimization, Proceedings of the Congress on Evolutionary Computation, Vol. 1, 2004, pp. 320-324.

[22] J. Kennedy, R. Eberhart, Y. Shi, Swarm intelligence, Morgan Kaufmann Publishers, San Francisco, 2001.

[23] M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. on Evolutionary Computation, Vol. 6, No. 1, , 2002, pp. 58-73.

[24] V. Mukherjee, S.P. Ghoshal, Comparison of intelligent fuzzy based AGC coordinated PID controlled and PSS controlled AVR system, Electrical Power Energy Systems, Vol. 29, 2007, pp. 679-689.

[25] S. H. Zahiria, S. A. Seyedin, Swarm intelligence based classifiers, Journal of Franklin Institute, Vol. 344, 2007, 362-376.

[26] X. M. Yu, X. Y. Xiong, Y. W. Wu, A PSO-based approach to optimal 28 capacitor placement with harmonic distortion consideration, Electric Power Systems Research, Vol. 71, 2004, pp. 27-33.