Study On SoC Estimation And Health Evaluation Method Of Backup Power Supply Of Supercapacitor For Wind Turbine

At present,the wind turbine only monitors the voltage of the supercapacitor cell module in the SCADA systems,which are equipped with the supercapacitor cell module as the backup power.But the State of Charge(SoC)of the supercapacitor cell module is not estimated,with the result of the SoC and the real-time performance of the backup power would not be known in time.To a step further,it leads to a potential risk to the emergency feathering of wind turbines.Aiming at the above problems,the SoC estimation and health evaluation methods of the backup power supply of wind turbine supercapacitor is studied in this paper through field investigation and reading a large number of literatures.(1)Taking the supercapacitor cell module as an object,the experimental testing platform of the supercapacitor cell module is built and the experimental characteristics of the supercapacitor cell module are analyzed.(2)In order to accurately identify the parameters of the equivalent model of supercapacitor cell module in the backup power supply of the pitch system of megawatt wind turbine and to solve the problem that the gain decreases too fast due to the data saturation phenomenon,the three-branch equivalent circuit model for the supercapacitor cell module is established,and a parameter identification method of the equivalent circuit model of supercapacitor cell module based on variable forgetting factor recursive least squares(RLS)is proposed in this paper.Then,the Simulink simulation model is also established for the multi-method parameter identification of supercapacitor cell module,and the simulation and analysis are performed.The comprehensive error in the static self-discharge phase of this new method is 0.19%,which is 6.92% and 0.09% lower than circuit analysis method and segmentation optimization method,respectively.Its comprehensive error in the whole process is 1.22%,which is reduced by 9.5% and 1.6% compared with circuit analysis method and segmentation optimization method,respectively.The results show that the new method has higher identification accuracy than circuit analysis method and segmentation optimization method.(3)In order to accurately estimate the SoC(State of Charge)of supercapacitor cell module,and to solve the problem that the SoC rises suddenly due to the sudden change of its terminal voltage,a new SoC estimation method based on Extended Kalman Filtering and Median Filtering(EKF-MF)hybrid filtering algorithm for supercapacitor cell module is proposed.The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model,and the parameter matrix of the system discrete state space model is solved,then SoC of supercapacitor cell module is estimated.The results show that the comprehensive error of this method in the whole process is 0.259%,which is 1.259% lower than that of the ampere-hour integration method,Although the comprehensive error of EKF-MF method is only 0.004% lower than Extended Kalman Filtering(EKF)method,it effectively solves the problem that the SoC estimated by EKF rises suddenly with the sudden change of the terminal voltage.The EKF-MF hybrid filtering algorithm is of higher estimation accuracy than the ampere-hour integration method and the EKF method,and it is not sensitive to the sudden change of the module terminal voltage.It will lay the foundation for effectively evaluating the health status of supercapacitor cell module.(4)In order to evaluate the health of supercapacitor cell module and solve the problem of premature elimination of supercapacitor cell module caused by single evaluation index,the comprehensive evaluation index of supercapacitor cell module is put forward.