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access icon openaccess Research on the RBF-PID control method for the motor actuator used in a UHV GIS disconnector

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 28/08/2018, Accepted 19/09/2018, Published 24/10/2018

To improve the reliability and intelligent level of the AC transmission system, the radial basis function-based proportional-integral-derivative (RBF-PID) control method for the motor actuator used in the 550 kV gas-insulated switchgear (GIS) disconnector is proposed. According to the dynamic mathematical model of the motor actuator, the main structure of the RBF neural network based on the gradient descent method for learning algorithm is constructed. An identification function is formed by taking values of the output error square, and then by gathering information on the real-time tuning parameters of PID. Based on that, the simulation of the control system is constructed. The comparative analyses of the tracking control characteristics and the servo following error of the disconnector's contact speed between double-loop PID and RBF-PID are done through the computational and experiment methods, respectively. The results show that 550 kV GIS disconnector with the motor actuator by the RFB-PID control method has better controllability, and the servo following error is controlled within 0.1 m/s.

Inspec keywords: learning (artificial intelligence); radial basis function networks; gas insulated switchgear; three-term control; neurocontrollers

Other keywords: motor actuator; gradient descent method; 550 kV GIS disconnector; UHV GIS disconnector; velocity 0.1 m/s; 550 kV gas-insulated switchgear; double-loop PID; dynamic mathematical model; AC transmission system; servo following error; intelligent level; RBF-PID control method; RBF neural network; RFB-PID control method; reliability; tracking control characteristics; control system; radial basis function-based proportional-integral-derivative control method; controllability; computational experiment methods; voltage 550.0 kV

Subjects: Neural computing techniques; Optimisation techniques; Switchgear; Control of electric power systems

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