Application of Intelligent Sensors in the Collection of Electrical Engineering Automation Equipment

Aim: The idea behind this research work is to present a fuzzy PID controller based on an RBF neural network and use MATLAB to simulate and test the algorithm in order to understand how intelligent sensors can be used in a collection of electrical engineering automation equipment. Methodology: The initial focus is on the characteristics and requirements of electrical engineering and automation control accuracy and takes a permanent magnet brushless DC motor as the entry point for intelligent technology application. The fuzzy neural network PID algorithm is permanently used to study the control effect of the closing speed of the motor circuit breaker. Results: The simulation results show that, compared with traditional PID algorithms, the fuzzy neural network PID algorithm reduces the error rate of motor closing tracking speed by 0.33 m/s, confirming that the fuzzy neural network PID control algorithm has a good control effect on simulating the motor closing process. Then, a permanent magnet brushless DC motor experimental platform was built, and the fuzzy neural network PID control algorithm was fitted for experiments. Conclusion: The fitting experimental results show that the closing tracking speed error rate of the permanent magnet brushless DC motor is maintained at a relatively low level of 0.22 m/s through fuzzy neural network PID algorithm control. Experimental research has shown that intelligent fuzzy neural network control algorithms can improve the accuracy of electrical engineering automation control.