Load modeling is widely used in power system studies. Two types of modeling, namely, static and dynamic, are employed. The current industrial practice is the static modeling. Static modelss are algebraic equations of active and reactive power changes in terms of voltage and frequency deviations. In this paper, a component based on static modeling is employed in which the aggregate model is derived based on the sensitivity coefficients and participation factors of load components. As an induction motor comprises a significant portion of industrial loads, Artificial Neural Network (ANN) is employed to derive its static model readily from nameplate data as accurately as possible.
G. R. Yousefi and H. Seifi, (2001). Induction Motor Electric Parameters Estimation Using Artificial Neural Networkds and its Application in industrial Load Modeling. Journal of Advanced Materials in Engineering (Esteghlal), 19(2), 19-30.
MLA
G. R. Yousefi and H. Seifi. "Induction Motor Electric Parameters Estimation Using Artificial Neural Networkds and its Application in industrial Load Modeling", Journal of Advanced Materials in Engineering (Esteghlal), 19, 2, 2001, 19-30.
HARVARD
G. R. Yousefi and H. Seifi, (2001). 'Induction Motor Electric Parameters Estimation Using Artificial Neural Networkds and its Application in industrial Load Modeling', Journal of Advanced Materials in Engineering (Esteghlal), 19(2), pp. 19-30.
VANCOUVER
G. R. Yousefi and H. Seifi, Induction Motor Electric Parameters Estimation Using Artificial Neural Networkds and its Application in industrial Load Modeling. Journal of Advanced Materials in Engineering (Esteghlal), 2001; 19(2): 19-30.
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