Nonlinear Modeling And Parameter Identification On Hydraulic Turbine Regulating System

Hydraulic Turbine Regulating system is a complex system that contains water, and other mechanical and electrical subsystems with strong non-linear and non-minimum phase characteristics. Its components are very complex and dynamic process. Hydraulic Turbine Regulating control system’s safe, stable and efficient operation, has been a hot aspect of engineering applications and scientific research workers. Hydraulic Turbine Regulating control system’s parameter identification purpose is to promptly and accurately identify the real-time status of the system parameters. System’s parameter identification not only provides the basic conditions for system diagnostics but also for Turbine System’s Control Strategy research. Parameter identification has a big significance on Hydraulic turbine system’s security, stable and efficient operation.In this paper, hydraulic turbine regulation system is the mainly research object, the system summarizes the overall structure of the system consisting of turbine generators and speed control system and various parts of the structure, the turbine governor regulating system is divided into subsystems, sub-pressure water diversion system systems, subsystems turbine, generator and load subsystem four parts. According to research focus, establish the model of hydraulic turbine regulation system.On the basis of the establishment of the mathematical model of hydraulic turbine regulation system, using recursive least squares method on system parameters identification. In the modern theory of identification, least squares algorithm is relatively mature, more complete theory development, realized by a computer is very convenient, in a wide range of practical applications, it is one of the primary means of system identificationFinally, establish the turbine transient simulation platform based on the model of the turbine system, and verified by simulation system parameter identification accuracy. And on an experimental basis in the recursive least squares based on adding forgetting factor to optimize the calculation of the effect of parameter identification.