Research On Power Optimization Control For Speed-sensorless Wave Energy Generation System

To cope with the escalating problems of energy shortage and environmental pollution,countries attach more importance to the development and utilization of clean energy.Wave energy,as a widely distributed clean energy,has great exploration space and broad application prospects,but the harsh and variable environment at sea also increases the difficulty and cost of research.Taking the direct drive wave energy generation system as the subject investigated,the feasibility of advanced control theory and speed sensorless technology are explored in this paper,so as to optimize the system output power and reduce operating costs.First of all,the mathematical model of the system is established based on the construction and operating principle of the direct drive wave energy generation system.By analyzing the output power of the system and utilizing the Fourier transform and superposition principle to solve the dynamic equation of the system,the dynamic conditions for maximizing the system output power is obtained.Secondly,considering that the application of speed sensors at sea may bring higher operating costs,the unscented kalman filter algorithm is utilized to realize the speed sensorless technology.By employing sigma sampling points and unscented transformation,UKF solves the problem that the traditional kalman filter algorithm is not applicable in nonlinear systems,so as to utilize the measurable current and voltage information to estimate the speed and displacement of the system.The simulation results show that the estimation provided by UKF is not only accurate,but also has certain robustness.For the system with unknown parameters,the controller is designed by utilizing the adaptive backstepping method.Taking the dynamic condition of maximizing the system output power as the expected value,the control law of the actual control terms and the update law of the estimates of unknown parameters are obtained through backward derivation based on Lyapunov stability principle,so that the system can track its expected value.The simulation results show that by updating the estimated values of unknown parameters in real time,the controller can obtain more accurate model parameters,thereby improving its control accuracy and robustness,and realizing power optimization of the system.Finally,in order to tackle the problem that the proposed power optimization strategy may not be applicable when the system is constrained,the optimal control theory is applied to analyze the power optimization problem with constraints.Additionally,based on adaptive dynamic programming,the optimal output of the motor is obtained by applying neural network and strategy iteration.And the backstepping method is utilized to design the controller to make the motor output track its optimal output,thereby achieving power optimization.The simulation results verify that utilizing adaptive dynamic programming can make the system satisfy the constraints and still have high output power.