Search results for "Stationary Reference Frame"

showing 4 items of 4 documents

Current fault signatures of Voltage Source Inverters in different reference frames

2016

This paper considers different current patterns used to identify the correct fault signatures in Voltage Source Inverters (VSI). At the beginning, the Authors consider the currents patterns from which a simple or a double fault can be encompassed both in the case of controllable device only or with its free wheeling companion diode. After the discussion of diagnosis algorithm suitable for electrical drives and principally based on a persistent near zero current condition current in the natural phase reference frame, the stationary reference frame is then considered as a tool to identify both the faulted phase as the device or various combination of faulted devices. On the contrary, the Auth…

0209 industrial biotechnologyEngineeringControl and Optimization02 engineering and technologyFault (power engineering)020901 industrial engineering & automationControl theory0202 electrical engineering electronic engineering information engineeringElectronic engineeringVoltage sourceDouble faultPWM inverterElectrical and Electronic EngineeringStationary Reference Framebusiness.industryMechanical Engineering020208 electrical & electronic engineeringControl reconfigurationFault toleranceRotating reference frameVoltage Source InverterFaultAutomotive EngineeringbusinessReference frameDiagnosi
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Neural based MRAS sensorless techniques for high performance linear induction motor drives.

2010

This paper proposes a neural based MRAS (Model reference Adaptive System) speed observer suited for linear induction motors (LIM). Starting from the dynamical equation of the LIM in the synchronous reference frame in literature, the so-called voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been deduced. Then, while the inductor equations have been used as reference model of the MRAS observer, the induced part equations have been discretized and rearranged so to be represented by a linear neural network (ADALINE). On this basis, the so called TLS EXIN neuron has been used to compute on-line, in recursive form, the machi…

EngineeringArtificial neural networkObserver (quantum physics)business.industrySettore ING-INF/04 - AutomaticaControl theoryLinear induction motorAdaptive systembusinessMRASReference modelStationary Reference FrameLinear Induction Motor (LIM) Sensorless control Model Reference Adaptive Systems (MRAS) Neural Networks (NN) Field Oriented Control (FOC)Reference frame
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MRAS speed observer for high performance linear induction motor drives based on linear neural networks

2011

This paper proposes a Neural Network (NN) MRAS (Model Reference Adaptive System) speed observer suited for linear induction motor (LIM) drives. The voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been obtained. Then, equations of the induced part have been discretized and rearranged so as to be represented by a linear neural network the TLS EXIN neuron, which has been used to compute the machine linear speed on-line and in recursive form. The proposed NN MRAS observer has been tested experimentally on a suitably developed test setup. Its performance has been also compared to the classic MRAS speed observer.

EngineeringObserver (quantum physics)Artificial neural networkDiscretizationControl theorybusiness.industryAdaptive systemLinear induction motorbusinessMRASStationary Reference FrameMachine control2011 IEEE Energy Conversion Congress and Exposition
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Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

2010

This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a cla…

evolutionary algorithms (EAs)induction-motor (IM) drivesvelocity controlspeed sensorlessProportional controlcovariance matricesKalman filteralgorithmsSliding mode controlControl and Systems EngineeringRobustness (computer science)Control theoryAC motor drivesDifferential evolutionoptimization methodsstate estimationElectrical and Electronic EngineeringRobust controlparameter estimationAlgorithmStationary Reference FrameKalman filteringInduction motorMathematics
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