Least squares models for power systems

The identification of dynamic models which relate power and frequency deviations on a tie line is investigated. The identification problem is posed and three identification algorithms are presented which produce least squares models with different structural properties. Equivalent realizations are identified and used as dynamic equivalents and recursively in an iterative frequency prediction algorithm. Predictive models are also identified and used as frequency predictors in a direct prediction algorithm. The identification of dynamic equivalents and frequency predictors is shown to be practical and provide information about the structure of interconnected power systems.