The PMSM based full speed control method is an effective way to realize sensorless control of permanent magnet synchronous motor (PMSM), but because of the complex structure of PMSM, high performance requirements, and the existence of nonlinear, strong coupling and other problems, this method can not achieve high-speed operation in the full speed range. To solve these problems, this paper proposes a sensorless control method for PMSM in the full speed range based on adaptive sliding mode control. Firstly, an adaptive sliding mode filter is loaded on the stator resistance to reduce the influence of current component in the sliding mode controller; Then, the stator resistance is estimated according to the stator resistance, torque and speed variation; Finally, the nonlinear mathematical model of the motor is established to estimate the variation of torque and speed. The PMSM sensorless full speed control model is built in Matlab/Simulink. The simulation results show that this method can effectively realize the PMSM sensorless control in the full speed range.
In order to achieve high efficiency and energy saving electromechanical integration zero transmission structure, a permanent magnet synchronous motor using an external rotor is adopted (PMSM) direct drive mode of coal mine belt conveyor. Through the research and analysis of the special application environment and control strategy of this kind of motor, vector control strategy method based on active disturbance rejection technology and sensorless control is adopted. To improve the speed regulation performance of belt conveyor when the load frequency changes conduct modeling and simulation analysis The improved sliding mode observer can effectively improve the accuracy of rotor position estimation and tracking performance, the effectiveness of the algorithm is verified.
KEYWORDS: Sensor networks, Real-time computing, Control systems, Signal intensity, Sensors, Signal attenuation, Ranging, Radio propagation, Environmental sensing, Wave propagation
Today's wireless sensor networks are limited in the computing power, data storage and communication bandwidth of nodes, so the research on ultra-bandwidth wireless sensor network technology, media access control and routing protocol has important application value. An important application of wireless sensor networks is real-time monitoring of mine safety. In this paper, the wireless sensor network method based on real-time system is studied, and the effectiveness of the wireless sensor network technology based on real-time system is studied and verified.
Aiming at the problems of low accuracy and poor timeliness of complex background images processed by deep learning network models, this paper proposes to optimize the YOLOv5s model. The threshold function is used to denoise the image, and the original loss function GIOU_Loss is optimized into CIOU_Loss function and fine-tuned. The optimized model has good generalization ability and robustness, and the bird's nest detection as a case verifies the effectiveness of the method, which can be used for the detection and identification of foreign objects in high-voltage transmission lines.
In the study of UAV detection of transmission line defects, in order to improve the accuracy of detecting insulator defects, a self-explosion insulator detection method based on optimized YOLOv5 is proposed. In the training, the activation function is optimized, and the original ReLU activation function is optimized to SiLU activation function. Through the training and verification of a large number of transmission line image data collected by UAV inspection, the experimental results show that this method can effectively detect the self-explosion and falling defects of glass insulators under various complex background conditions. The mean average accuracy is used to evaluate the method. The detection rate of insulator self-explosion and flake defect is 94.7 %.
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