Model identification using tie line power and frequency measurements

The identification of dynamic models which relate power and frequency deviations on a tie line of a power system is investigated. The identification problem is posed and three identification algorithms are presented which produce least squares models with different structural properties. Model order is determined by applying residual and system structure tests to a sequence of models of increasing order. These tests indicate the model order for both equivalent realizations and predictive models. Equivalent realizations are identified on one data set and then their performance as a dynamic equivalent is evaluated on a second data set. These equivalent realizations are also used to predict frequency in an iterative frequency prediction algorithm. Predictive models are also identified and their performance as frequency predictors is evaluated using a direct prediction algorithm. The identification of dynamic equivalents provides information about the structural properties of power systems. The use of dynamic equivalents and predictive models for frequency prediction indicates the tradeoff in accuracy vs the prediction interval which can be obtained using these least squares algorithms and the measurement device presently available.