Research On Chaotic Power System Control Based On RBF Neural Network Observer

As the interconnection of large power grids becomes a trend in the development of power systems,the structure of power systems becomes more and more complex,which inevitably leads to nonlinear oscillation phenomena.Among them,the chaotic oscillation phenomenon of power system can be a great threat to the stable operation of power system,in order to solve this problem,it is crucial to study the control method of chaos suppression in power system.From the perspective of uncertainty in the power system,this article considers different situations where the output state of the system can be directly obtained,and designs different controllers and adaptive laws to suppress chaotic oscillations in the power system.The main research contents are as follows:(1)RBF neural network and sliding mode control are widely used in uncertain nonlinear systems.In this paper,some basic theories of RBF neural network control and sliding mode control are briefly described.Then,we take the two-machine interconnected power system as the research object,explore the influence of electromagnetic disturbance power amplitude on the system in the two-machine interconnected power system,draw the corresponding timing and phase plane diagrams,and analyze the chaotic oscillation phenomenon of the system.(2)Based on the chaotic phenomenon of the two-machine interconnected power system,an adaptive sliding mode controller and an adaptive law are designed by combining RBF neural network,disturbance observer and sliding mode control method under the consideration of the system uncertainty and the output state can be directly obtained,and the stability of the system is demonstrated.The numerical simulation part shows that the designed controller has good anti-interference capability,good robustness,and can suppress the chaotic oscillation of the system well.(3)In the above case,this paper further considers the case that the output state cannot be directly obtained,and combines RBF neural network,state observer,disturbance observer and sliding mode control to design an adaptive sliding mode controller based on RBF neural network state and disturbance observer.This control method can not only achieve the observation of the system output state,but also estimate part of the disturbance and compensate the system with the estimated disturbance.The system is found to be stable by stability analysis,and the superiority of this control method is also demonstrated in the simulation part,which can achieve the suppression of the chaotic oscillation phenomenon of the system.(4)In order to reduce the control cost and the complexity of algorithm design to achieve the suppression of system chaotic oscillation,the adaptive sliding mode controller and adaptive law are designed by combining RBF neural network,expansive state observer and sliding mode control.This method expands the internal and external disturbances of the system into a third state of the system,which enables the output state of the system and the large external disturbances to be observed.In addition to this the corresponding controller as well as the adaptive law are designed and the stability of the system is verified.The simulation part also shows the effectiveness of the proposed method,which can suppress the chaotic oscillations of the system very well.