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1

DTC strategy for a DSP-based Five-Level Flying-Capacitor Drive with improved flux estimation

Arasteh M., Rahmati A., Farhangi S., Abrishamifar A.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 6

132-139

EN

This paper gives a detailed analysis of direct torque control (DTC) strategy in a five-level drive and proposes a 24-sector switching table. Also, flux estimation has been improved by a discrete-time low-pass filter (LPF) with variable cut-off frequency. Algorithm to compensate the amplitude and phase errors is introduced and algorithm to determine digital filter coefficients at different speeds is presented. Simulation and practical results on a prototype using an induction motor 400V, 3kVA are given. To implement the DTC strategy, processor TMS320F2812 is used.

PL

Przedstawiono analizę strategii DTC w napędzie pięciopoziomowym i zaproponowano 24-sektorową tabelę przełączeń. Strumień był ulepszony dzięki zastosowaniu cyfrowego filtru dolnoprzepustowego o różnej częstotliwości odcięcia. Wprowadzono algorytm kompensacji błędu amplitudy i fazy dla różnych prędkości. Przeprowadzono symulację i badania prototypu dla silnika indukcyjnego 400 V, 3 kVA. Wykorzystano procesor TMS320F2812.

2

Sensorless DTC of induction motor using improved neural network switching state selector controller

Messaif I., Berkouk E. M., Saadia N.

Archives of Control Sciences

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2010

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Vol. 20, no. 4

435-456

EN

The paper deals with development of sensorless Direct Torque Control (DTC) system based on neural network. This network is built to solve the task of proper switching states selection based on information about electromagnetic torque and stator flux (position and magnitude) of induction motor. In fact, this technique which uses conventional switching table is not convenient for one-line and real time control for its high computation time. In order to avoid this problem a solution based on neural network is proposed. Well trained Artificial Neural Network structure can replace successfully the switching table. However, in the Neutral-Point-Clamped topology, it has an inherent problem of Neutral Point Potential (NPP) variation. In this way, a Neural Network-Direct Torque Control technique has been applied and the estimated value of the Neutral Point Potential is used, which is calculated by motor currents. This control strategy offers the possibility of selecting appropriate switching state to achieve the control of Neutral Point Potential. Simulation results verify the validity of the proposed method.

3

Direct torque control for induction machines using neural networks

Messaif I., Berkouk E-M., Saadia N.

Archives of Control Sciences

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2007

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Vol. 17, no. 1

93-104

EN

In this work, a novel switching vector selector in Direct Torque Control of an induction machine using Artificial Neural Network is studied. In the first part, we describe design of a speed sensor-less Direct Torque Control (DTC) strategy of an induction motor supplied by a two-level voltage source. For this, a conventional look up table is applied which improves the performances. Due to the high computation load, this technique is not convenient for an one-line and real-time control. Thus, a simplified method of choosing the output vector for two-level voltage source inverter-fed induction machine is proposed in the second part, and a novel switching vector selector using Artificial Neural Network (ANN) is trained under the tutor of the method mentioned above. The ANN receives attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of plant or process. In fact, when the stator flux and electromagnetic torque are different from theirs respective references, the output vector can be expediently acquired. Simulation results showed that the ANN structure can replace successfully the conventional look up table of the DTC.