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This article deals with the parameter identification of the generalized time-varying systems. The time-varying parameter vector can be expressed as a coefficient matrix multiplied by a measurable disturbance vector, the common identification methods cannot be used to estimate the parameters of the generalized time-varying systems directly. This motivates us to develop new iterative identification algorithms. The gradient-based iterative (GI) algorithm is proposed by means of the iterative technique. Moreover, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. The MDW GI algorithm is proposed to improve the parameter estimation accuracy. The numerical simulation is provided and the simulation results show that the proposed identification methods are effective for the generalized time-varying systems.

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Polynomial loss models are introduced for the economic dispatch problem. The models are based on interpolations of load flow solutions. An appoximate error estimation method for the loss models is also presented. The effect of approximate loss models on the economic dispatch is evaluated according to the deterioration of total generation cost in addition to the relative values of the coefficients of the loss formula Case study shows that loss expressions have characteristics which have not been considered previously. Comparisons between the proposed models and the generalii generation distribution factor (GGDF) based models show the advantages of the proposed models.