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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 iead/lag structure, is demonstrated on an eight-machine system. Index Terms--Eigenvalue, optimization, power system stabilizer (PSS), probabilistic theory, sensitivity.

A probabilistic power system stabilizer (PSS) design considering multioperating conditions is proposed in this paper. Under the assumption of normal distribution, the conventional eigenvalue sensitivity analysis is extended to probabilistic environment by describing the statistical nature of eigenvalues as expectations and variances. Two probabilistic indexes derived from the sensitivities of eigenvalue expectations and variances are introduced to the PSS site and parameter selection. The robustness of the system can be guaranteed because wide range of the load variation has been taken into account in probabilistic representation. The effectiveness of the proposed PSS is demonstrated on a 3-machine system by probabilistic eigenvalue analysis and transient response simulation. Index Terms--Eigenvalue, power system stabilizer (PSS), residue index, sensitivity and probability.