Power Intelligent Optimization Control Of Oscillating Buoy Wave Power Generation System

The large-scale exploitation and utilization of ocean wave energy has vitally important strategic and practical significance for the economic and social development of China.However,the commercial application was seriously restricted by the low energy efficiency and high electric energy cost of the existing wave energy converter.Therefore,the power point tracking optimization strategies based on the swarm intelligence algorithm for oscillating buoy wave power generation system is proposed,which can enhance the energy conversion efficiency and increase the system output power effectively.A mathematical model is established for the incident wave and its interaction with the buoy,and then the nonlinear characteristics of the buoy hydrodynamic are analyzed.The maximum power point tracking optimization requirements are derived by analyzing the motion process of the wave power generation system,and then swarm intelligence algorithm optimization schemes are formed.The simulation model of the wave power generation system for maximum power point tracking optimization based on swarm intelligence algorithm is established on the MATLAB/Simulink environment.The analysis shows that significant nonlinearity is existed in the hydrodynamic model of the buoy,which leads to the local optimum problem of the traditional swarm intelligence algorithm.For this reason,the maximum power point tracking optimization scheme based on crisscross optimization algorithm(CSO)is proposed.Under the judgment of vertical crossover probability,the arithmetic crossover algorithms between the individual dimension variables are implemented with the vertical crossover operator of CSO to ensure that the population will avoid the local optimum state.The random pairing and arithmetic crossover algorithms between individuals are completed,and the entire solution space is divided into several subspaces with the horizontal crossover operator of CSO.The pairing individuals are taken as the diagonal vertices of each subspace to attain perfect local search capability by searching the subspaces interior and its neighborhoods.Any information contributed to achieving global optimality is rapidly distributed among the variables of the population to change the search path by the alternation of the crisscross operators.The simulation shows that when the incident wave frequency changes,the performance of the CSO maximum power point tracking optimization scheme is better than the traditional swarm intelligence algorithm.The original artificial bee colony algorithm(ABC)is combined to optimize the performance of CSO,an maximum power point tracking optimization scheme based on modified artificial bee colony algorithm(CABC)is proposed.The horizontal crossover operator of CSO,which implements arithmetic crossover between all variables of two individuals,is introduced to train the searching skill of director bees and forager bees,and the local searching capability of CABC algorithm is improved.Vertical crossover operator of CSO is introduced to enhance the searching ability of detecting bees,so that the known information of nectar sources can be used by detecting bees to explore unknown feasible solution region.Both nectar source selection criterion and artificial bee colony configuration are modified,and performance of CABC algorithm is further improved.Simulation shows that the CABC power point tracking scheme is better than CSO and is suitable for maximum power point tracking.A modified grey wolf optimizer(MGWO)optimized by Fourier analysis method is proposed to optimize the output power control of ocean wave power system.On the basis of reserving the most essential characteristics of grey wolf optimizer,completely new strategies for instructing elite class wolves searching and overall wolves expansion are added,as well as encirclement formation strategy,hunting patterns and wolves organization are improved,in order to ensure that the MGWO algorithm can avoid trapped in local optimum due to the nonlinearity of buoy hydrodynamics.The Fourier analysis method is introduced to decompose the ocean incident wave and the response of motor moving components.The MGWO algorithm is used to solve the optimal motor control parameters corresponding to each wave component of incident wave in the buoy capture frequency range in order to capture the power carried in each wave component as mentioned to the most extent.The simulation shows that the maximum power point tracking of oscillating buoy wave power generation system is achieved effectively by the application of Fourier analysis method and modified grey wolf optimizer.