Probabilistic eigenvalue sensitivity analysis and PSS design in multimachine systems

This paper presents an application of probabilistic theory to the selection of robust PSS locations and parameters. The aim is to enhance the damping of multiple electromechanical modes in a multimachine system over a large and prespecified set of operating conditions. Conventional eigenvalue analysis is extended to the probabilistic environment in which the statistical nature of eigenvalues corresponding to different operating conditions is described by their expectations and variances. Probabilistic sensitivity indices to facilitate "robust PSS" site selection and a probabilistic eigenvalue-based objective function for coordinated synthesis of PSS parameters are then proposed. The quasi-Newton technique of nonlinear programming is used to solve the objective function and its convergence properties are discussed and compared with the conventional steepest descent approach. The effectiveness of the proposed stabilizers, with a classical lead/lag structure, is demonstrated on an eight-machine system.

[1]  Lei Wang,et al.  Increasing power transfer limits at interfaces constrained by small-signal stability , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[2]  C. Y. Chung,et al.  Selection of the location and damping signal for static var compensator based on a versatile modeling , 2000 .

[3]  G. T. Heydt,et al.  Probabilistic Methods For Power System Dynamic Stability Studies , 1978, IEEE Transactions on Power Apparatus and Systems.

[4]  B. C. Papadias,et al.  Power System Stabilization via Parameter Optimization - Application to the Hellenic Interconnected System , 1987, IEEE Transactions on Power Systems.

[5]  S. K. Tso,et al.  Design optimisation of power system stabilisers based on modal and eigenvalue-sensitivity analyses , 1988 .

[6]  K. M. Tsang,et al.  Improved probabilistic method for power system dynamic stability studies , 2000 .

[7]  P. Kundur,et al.  Application of Power System Stabilizers for Enhancement of Overall System Stability , 1989, IEEE Power Engineering Review.

[8]  M. L. Kothari,et al.  Radial basis function (RBF) network adaptive power system stabilizer , 2000 .

[9]  Lamberto Cesari,et al.  Optimization-Theory And Applications , 1983 .

[10]  K. M. Tsang,et al.  Algorithm for power system dynamic stability studies taking account of the variation of load power , 1998 .

[11]  A. K. David,et al.  Partial pole placement of H∞ based PSS design using numerator-denominator perturbation representation , 2001 .

[12]  Roy Billinton,et al.  Probabilistic transient stability studies using the method of bisection [power systems] , 1996 .

[13]  P. Kundur,et al.  Power system stability and control , 1994 .

[14]  Roger Fletcher,et al.  A Rapidly Convergent Descent Method for Minimization , 1963, Comput. J..

[15]  S. K. Tso,et al.  Refinement of conventional PSS design in multimachine system by modal analysis , 1993 .

[16]  M. Brucoli,et al.  Probabilistic approach for power system dynamic stability studies , 1981 .

[17]  C. Y. Chung,et al.  Multimachine eigenvalue sensitivities of power system parameters , 2000 .

[18]  E. Larsen,et al.  Applying Power System Stabilizers Part I: General Concepts , 1981, IEEE Transactions on Power Apparatus and Systems.

[19]  Lei Wang,et al.  A tool for small-signal security assessment of power systems , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[20]  Kewen Wang,et al.  Robust PSS design based on probabilistic approach , 2000 .