Advances in Artificial Neural Systems

Volume 2015 (2015), Article ID 318589, 9 pages

http://dx.doi.org/10.1155/2015/318589

## Sensorless Direct Power Control of Induction Motor Drive Using Artificial Neural Network

Faculty of Electrical & Computer Engineering, University of Kashan, Kashan 87317-51167, Iran

Received 27 September 2014; Revised 31 January 2015; Accepted 2 February 2015

Academic Editor: Matt Aitkenhead

Copyright © 2015 Abolfazl Halvaei Niasar and Hossein Rahimi Khoei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper proposes the design of sensorless induction motor drive based on direct power control (DPC) technique. It is shown that DPC technique enjoys all advantages of pervious methods such as fast dynamic and ease of implementation, without having their problems. To reduce the cost of drive and enhance the reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor’s position and speed of induction motor. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Simulink. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify the proposed methods. Simulation results confirm the performance of ANN based sensorless DPC induction motor drive in various conditions.

#### 1. Introduction

The electrical drive system is used to control the position, speed, and torque of the electric motors. Many works have been done on power converter topologies, control scheme of the electric drive systems, and the motor types in order to enhance and improve the performance of the electric motors so as to exactly perform and do what is required [1]. Induction motors (IMs) are widely used in industrial, commercial, and domestic applications as they are simple, rugged, and easy to maintain and of low cost. Since IMs demand well control performances, precise and quick torque and flux response, large torque at low speed, and wide speed range, the drive control system is necessary for IMs [2].

Control of induction motors can be done using various techniques. Most common techniques are (a) constant voltage/frequency control (*V*/*F*), (b) field orientation control (FOC), and (c) direct torque control (DTC). The first one is considered as scalar control since it adjusts only magnitude and frequency of the voltage or current with no concern about the instantaneous values of motor quantities. It does not require knowledge of parameters of the motor, and it is an open-loop control. Thus, it is a low cost simple solution for low performance applications such as fans and pumps. The other two methods are in the space vector control category because they utilize both magnitude and angular position of space vectors of motor variables, such as the voltage and flux. They are employed in high performance applications, such as positioning drives or electric vehicles [3, 4].

Direct power control is a control method that directly selects output voltage vector states based on the power and flux errors using hysteresis controllers and without using current loops. In this respect, it is similar to the well-known direct torque control (DTC) method described in the literatures for various AC motors [5]. What is in common among these applications is that they all are power output devices needed to provide real power to the load. DPC technique has been basically applied to the generators, but it is tried to use it for control of electrical motors instead of DTC technique, due to the problems of torque estimation and dependency on the motor’s parameters. Therefore, DPC technique enjoys all advantages of DTC such as fast dynamic and ease of implementation, without having the DTC’s problems. However, publications about direct power control are mainly aimed at either rectifiers [6], converters [7, 8], dual-fed induction generators (DFIG) [9, 10], or permanent magnet synchronous generators (PMSG) [11, 12], and there is no research on using the DPC technique for induction motor.

Recently, many researches have been carried on the design of speed-sensorless control schemes of induction motor drives. The main reasons for the development of sensorless drives are reduction of complex hardware and hence cost; increase in mechanical robustness and hence overall ruggedness; working under hostile environment; higher reliability; reduced maintenance; and so forth. Techniques range from open-loop, low performance strategy to closed-loop, high performance over the past decades [13–17].

Speed estimator employing artificial neural network (ANN) is an improvement over the classical mathematical model based approaches [18–23]. It is a major advantage of ANN based techniques that they do not require any mathematical model of the motor under consideration and the drive development time can be substantially reduced. In this study, a speed estimator, based on ANN based model reference adaptive system (MRAS), has been studied and analyzed. For ANN, the back propagation network (BPN) algorithm is used for online training to estimate the motor speed. The paper is organized as follows. Section 2 gives the dynamic model of induction motor. Section 3 proposes the original DPC strategy and employs it for induction motor. Speed estimation technique using neural network is presented in Section 4. Simulation results are presented in Section 5, and the conclusion is given in Section 6.

#### 2. Model of Induction Motor

Neglecting the motor core loss, the saturation, the slot effect, and so forth, the equivalent circuit of the IM in stationary reference frame is shown in Figure 1.