Active Disturbance Rejection Robust Control Of Generator Exciting System

In this paper， we carry out two aspects of research and analysis as following aboutgenerator excitation system using the adaptive particle swarm optimization （APSO）algorithm and active disturbance rejection control technology （ADRC）：Aiming at the shortcoming to the method of exact feedback linearization， weintroduce the active disturbance rejection control technology （ADRC） into the controldesign of the generator excitation control system. By constructing extended stateobserver （ESO）， a new dynamic compensation linearation method is introduced in thenonlinear model of the generator excitation system. Using this method， we can obtain astronger robustness linear model which is only related to the system damping. By theoptimal control theory， we design the linear system considering by the dampingdisturbance， which is in accordance with realistic running condition.When taking into account the parameters of disturbance， the processing of thedisturbance is also a problem. In this paper， we apply the idea of matrix perturbationand linear matrix inequalities （LMI） into the control law design of generator excitationsystem， which makes the operation more simplified. After that we obtain the nonlinearrobust control law of generator excitation system. This control method takes into accountthe impact of parameter perturbations. Simulation results show that， compared with theexact feedback linearization （EFL）， the structure of the control law in this paperproposed is simplified， and the internal and external disturbances on the model is morerobust.Aiming at the shortcoming in that traditional identification methods can not identifythe nonlinear part of excitation system， a new method based on APSO algorithm foridentifying parameters of generator excitation system is introduced in this paper. Thismethod identify parameters according to the sample data of input and output directly inthe time domain and without FFT transform， it is simple and can effectively solve the problem that nonlinear part of excitation system is difficult to identify. The simulationresult shows that，compared with genetic algorithm（GA），APSO algorithm has fasterconvergence speed and higher recognition accuracy.