Research And Optimization Of Passive Control Strategy For Superconducting Energy Storage System Based On CLLG Converte

When the superconducting energy storage system was used as a power compensation device,it can realize four-quadrant operation,and can quickly and independently compensate the active power and reactive power of the power system according to the needs of the power system,greatly improving the transient stability of the power system.However,because the superconducting energy storage system had the characteristics of nonlinearity,strong coupling,and large capacity,an effective transfer function cannot be established,and the traditional system strategy was to linearize the system,which cannot accurately describe the complex nonlinear system.There were problems such as complex parameter setting,high harmonic content,and reduced transient stability.In order to solve the above problems,this paper adopted the method of theoretical analysis and simulation verification to study the topology and control strategy of the superconducting energy storage system converter.Specifically reflected in the following four aspects:The first is to adopt a superconducting energy storage converter based on CLLC resonant converter,determine that the AC side of the converter was composed of VSC,and the DC side was composed of CLLC resonant converter,analyzed the working principle of VSC and CLLC resonant converter,according to the basic Deriving and establishing the mathematical model of the superconducting energy storage system based on the CLLC resonant converter based on Erhoff’s voltage and current law;and established its interface equation with the power system.The second was to introduce a passive control strategy.Based on the passive control theory,the system was given based on the port-controlled dissipation Hamilton model,combined with the passivity of SMES.Established the energy equation,analyzed the expected balance point of active power and reactive power respectively,and then solved the energy equation to obtain the inner loop control rate;then analyzed the stability of the passive controller through the second Lyapunov method,and analyzed the damping matrix The influence of the value of on the stability of the controller.The third was to use the variable damping injection method based on BP neural network to optimize the passive control strategy.By injecting the damping that changes with the system parameters into the system,the response of the control system to the passive control using constant damping injection is reduced.The sensitivity of parameter changes and the influence of controller stability solved the problem of controller stability reduction caused by constant damping injection in passive control.The fourth was to use Matlab/Simulink to construct a simulation model of a superconducting energy storage system based on CLLC resonant converter and adopt PI control strategy and passive control strategy to control the harmonic content of the system,phase voltage waveform,phase current waveform,and system output respectively.The active power,reactive power,generator angle of attack oscillation,power system transient stability analysis,the results show that compared with the traditional PI control strategy,the power compensation device based on the passive control strategy does not need to establish a transfer function and the parameter setting is simple,can quickly track the active power and reactive power commands,and can effectively reduce the angle of attack oscillation of the generator,and make the system have lower harmonic content,thereby improving the transient stability of the power system.A simulation model of variable damping injection passive control strategy based on BP neural network was established,and the system’s active power response waveform,reactive power response waveform,DC side voltage response waveform,and the corresponding waveform of the system when a short-circuit fault was encountered were analyzed respectively.It shows that compared with the constant damping injection passive control strategy,the variable damping injection method based on BP neural network has a faster response time and the system can reach the steady state faster,thereby improving the transient stability of the power system.