Research On Key Control Techniques Of Active Resonance C-Type Buoyancy Pendulum Wave Power System

Compared with the green energy of wind energy,ocean wave energy has large reserves and high energy flow density.In order to solve the urgent demand for new green energy,the research on ocean wave power generation theory and technology has higher application prospects.Due to its wide frequency response,high energy conversion efficiency,and strong energy gathering ability,pendulum wave energy power generation devices are widely used in the field of wave power generation and have high practical and commercial value.In the research on the wave energy power generation control system,the key issue is how to improve the overall energy conversion efficiency of the wave energy power generation device.Based on the active resonance C-type buoyancy pendulum wave energy power generation platform,this paper conducts research on the active resonance control of the buoyancy pendulum and the maximum power tracking control of the power generation system.In the maximum power tracking control,according to the buoyant pendulum motion equation and the maximum power point under resonance conditions,the efficiency of mechanical energy conversion to electrical energy can be maximized.In the realization of the above research,it is necessary to detect the wave amplitude and frequency parameters and predict the wave amplitude to achieve.This paper proposes the wave feature parameter identification algorithm,the wave amplitude prediction algorithm and the maximum power tracking algorithm according to the required functions of the system and carries out the experiment simulation.The wave amplitude prediction algorithm uses the predicted amplitude in the maximum power tracking system to achieve the maximum power macro-control and prevent the mechanical damage caused by excessive swing Angle.In this paper,t he thought evolutionary algorithm is introduced into the support vector machine regression algorithm to generate the MEA-SVR algorithm to predict the wave amplitude.Compared with the previous proposed algorithm,the results show that the algorithm is supe rior and practical in the application of wave amplitude prediction.The wave amplitude prediction algorithm uses the predicted amplitude in the maximum power tracking system to achieve the maximum power macro-control and prevent the mechanical damage caused by excessive swing Angle.In this paper,the mind evolutionary algorithm is introduced into the support vector machine regression algorithm to generate the MEA-SVR algorithm to predict the wave amplitude.Compared with the previous proposed algorithm,the results show that the algorithm is superior and practical in the application of wave amplitude prediction.The wave characteristic parameter recognition algorithm uses the measured frequency parameter as a signal in the active resonance system of the buo yancy pendulum,and the frequency and amplitude are used as the signal in the maximum power tracking system to realize the micro-control of the maximum power.This algorithm uses quaternion to calculate the attitude of the sensor,and applies the neighborhood centroid opposition-based particle swarm optimization algorithm to the fitting identification parameters to realize the identification of wave characteristic parameters.In the research of the maximum power tracking control algorithm,the predicted amplitude and the detected amplitude frequency are used as the input of the maximum power tracking control strategy.The motor-side converter adopts the optimal power point tracking control,and the grid-side converter adopts the converter.Decoupling control realizes grid connection,and the two converters are relatively independent and cooperate together to realize the maximum power feeding into the grid.Finally,a simulink control model is constructed for simulation,and the simulation results show the eff ectiveness of the maximum power control strategy.