Torsional system parameter identification of turbine-generator sets

Accurate low order linear models that represent the torsional motion of turbine-generator sets are needed for determining shaft torsional responses resulting from subsynchronous resonance conditions, electric system faults and planned/unplanned switching actions in the electric network. This paper outlines the theoretical background and the methodology used for identification of linear state-space models of turbine-generator systems. These analytic mass-spring-damper models are lumped-parameter approximations, which in reality represent a continuous nonlinear system. For transient torque studies these models are adequate representations of the torsional dynamics of interest. Reduced analytic models of any particular turbine-generator unit, however, usually do not match precisely the behavior of the real machine. The paper describes an optimization method that can give a more precise representation of a particular turbine-generator based on actual plant tests and an assumed model of that unit. The parameter identification process is illustrated using plant test data from a 618 MVA turbine-generator unit     

Coherency-based low order models for shaft systems ofturbine-generator sets

Accurate low order lumped models that represent the low frequency torsional motion of turbine-generator sets are needed for determining shaft torsional responses resulting from subsynchronous resonance conditions, electric power system faults and planned/unplanned switching actions in the electric network. This paper presents a coherency-based method that resolves a high order inertia-spring lumped model into a low order inertia-spring lumped model, while preserving the selected group of natural torsional frequencies and their associated mode shapes. Forced eigen-frequency matching and conservation of angular momentum form the basis of the iterative procedure developed in the paper. Numerical examples are included to illustrate the capabilities of the proposed method in determining accurate low order dynamic equivalents