One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. 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 Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator.
This paper is the design of an induction motor drive system that can be controlled using direct power control. First the possibilities of direct power control (DPC) of induction motors (IMs) fed by a voltage source inverter have been studied. Principles of this method have been separately evaluated. Also the drive system is more versatile due to its small size and low cost. Therefore it is advantageous to use the system where the speed is estimated by means of a control algorithm instead of measuring. This paper proposed one novel induction motor speed control system with fuzzy logic. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator.
One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. 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 Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator.
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.
This paper is the design of an induction motor drive system that can be controlled using direct power control. First the possibilities of direct power control (DPC)
IntroductionWith the rapid development of mobile communication technology and the growing popularity of intelligent terminals, there is an urgent need to get information and services from the Internet at anytime and anywhere even during movement. The urgent needs of information and services promoted the convergence of Internet technology and mobile communication technology, and finally formed the Mobile Internet. Among the wide variety of Mobile Internet services, location-based services (LBS) are the most widely used one. It can provide many personalized services for mobile users according to their location information. Users can not only access to basic geography-related services, but also achieve communication, purchasing and sharing based on social network, as well as get news, arrange dating, release and search information during movement [1].In the era of Mobile Internet, cell-phone is much more than a tool for communication; it has already become an "organ" of our body, and has even become an indispensable part of people's social relationships. However, the high adhesion degree of mobile terminals to users not only brings facility but also bring new security risks. Physical location and trajectory data of mobile user contains a large number of personal privacy information. If the specific location information of user has been leaked while using the LBS services, it may disclosure some privacy information of user, such as interests, habits, health status, political affiliation, even cause huge loss of personal property and security.The paper started from the application perspective of mobile cloud computing, analyzed the advantages and necessity to provide LBS services based on mobile cloud computing, introduced the traditional protection methods of location privacy and pointed out the new security risks to LBS system brought by mobile cloud computing. In order to solve the above problems, the paper proposed a systematic framework of LBS system and described the realization process of LBS business based on mobile cloud computing.
Controlled induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. For these speed sensorless AC drive system, it is key to realize speed estimation accurately. This paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. 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 Matlab. Simulation result shows a good performance of speed estimator. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to resistances of stator variations.
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