Abstract
Switched Reluctance Motor (SRM) is becoming popular as a variable speed industrial drive. But the requirement of position sensor to synchronize the rotor position with phase currents makes the SRM drive circuit complex and unreliable. With the advent of high speed digital signal processors, it is possible to implement algorithms to estimate the rotor position based on the electrical signals in motor windings. In addition to this, the latest graphical user interface software aids to reduce the time for the development of control algorithms. This paper presents the simulation study of an artificial neural network(ANN) based algorithm for rotor position estimation from phase voltage and current of a four phase SRM using VISSIM version 6.0B software. Based on the simulation results, a particular artificial neural network (ANN) is selected and checked for real time implementation.
Highlights
Switched Reluctance Motor (SRM) is becoming popular as a variable speed industrial drive
Application of artificial neural network for position estimation in SRM drives is studied by many researchers
Different architectures of artificial neural network are tuned for phase flux linkage and phase current waveforms as input and rotor position as output
Summary
Switched Reluctance Motor (SRM) is becoming popular as a variable speed industrial drive. This paper presents the simulation study of an artificial neural network(ANN) based algorithm for rotor position estimation from phase voltage and current of a four phase SRM using VISSIM version 6.0B software. The following are the various Non-intrusive methods: (i) The model based estimator technique (ii) The flux linkage/current method and (iii) The mutual voltage method [7] For these methods the terminal measurements of phase voltage or mutual voltage and current are used as inputs for an estimator to obtain the rotor position. Based on the works in [1] and [2] the simulation study of an artificial neural network(ANN) based algorithm for rotor position estimation from phase voltage and current of a four phase SRM using VISSIM version 6.0B software is done and the results are shown. The trained ANN structure with minimum error is verified experimentally
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