A Gray-Box Single-Phase PWM Rectifier Modeling and Health Parameter Monitoring Method Based on Digital Twins
Since single-phase,two-level PWM rectifiers are widely used and undertake important functions,their working conditions are complex,and the operating environment is variable.Therefore,the demand for their reliability in engineering application sites has gradually increased,and the problem of accurate monitoring and evaluation of reliability urgently needs to be solved.Non-invasive monitoring is a better choice to reduce the impact on the original system during system status monitoring.Considering the situation of unknown system control parameters in practical applications,this paper proposes a digital twin gray-box model for single-phase,two-level PWM rectifiers.This model can simulate the external voltage and current characteristics of actual rectification systems,achieving non-invasive monitoring of the control parameters of the system and key parameters of the main circuit.Firstly,a digital twin gray-box model of single-phase,two-level PWM rectifiers has been established.A mathematical model of the main circuit is established.Combined with the mathematical model of the corresponding closed-loop control system and the sampling part in the actual system,a discretized mathematical model of the closed-loop rectification system can be obtained using the fourth-order Runge-Kutta method for discretization.Based on the actual rectification circuit's external voltage and current data,an improved particle swarm optimization algorithm is used to iterate the discrete mathematical model.The parameters in iterations include system control parameters and health status characteristic parameters.When the external characteristics of the discrete mathematical model approximate the actual circuit,the final digital twin gray-box model has been obtained.Secondly,system control parameters and health status characteristic parameters are monitored.Due to the unknown control parameters,the control parameters are introduced as the measured values during the algorithm iteration when establishing the digital twin gray-box model.Based on the external characteristic data of the actual system,the control parameters can be well monitored,making the digital twin model similar to the actual system.Meanwhile,key parameters of the main circuit are the focus of monitoring,i.e.,the equivalent inductance L and resistance R of the AC-side inductor,the capacitance C of the DC-side supporting capacitor,and the saturation voltage drop of IGBT.Finally,the digital twin gray-box model and parameter monitoring method have been validated under different parameters and operating conditions.The results indicate that the established model can simulate the actual system's dynamic and static operating states.At the same time,the control parameters and key parameters are effectively monitored,the errors are less than 5%,and the changing trend of parameters can be identified.In addition,the influence of different intelligent optimization algorithms on monitoring performance is explored,considering algorithm complexity,average iterations,and local optimal escaping ability.The superiority of the particle swarm optimization algorithm under the application conditions has been demonstrated.
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