Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks
نویسندگان
چکیده
منابع مشابه
Robust Backstepping Control of Induction Motor Drives Using Artificial Neural Networks and Sliding Mode Flux Observers
In this paper, using the three-phase induction motor fifth order model in a stationary twoaxis reference frame with stator current and rotor flux as state variables, a conventional backsteppingcontroller is first designed for speed and rotor flux control of an induction motor drive. Then in orderto make the control system stable and robust against all electromechanical parameter uncertainties a...
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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 deri...
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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 deri...
متن کاملinduction motor electric parameters estimation using artificial neural networkds and its application in industrial load modeling
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 deri...
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ژورنال
عنوان ژورنال: Advances in Fuzzy Systems
سال: 2009
ISSN: 1687-7101,1687-711X
DOI: 10.1155/2009/241809