Research On The Intelligent Optimization Algorithms For The Optimal Load Of Oscillating-Body Wave Power Generation Systems

Wave power generation is a new energy power generation method.The wave energy has large power density and good predictability,therefore it has a certain potential for development and utilization.How to search the optimal load to maximize the output power of the wave power generation system is the key technology in the research of wave power generation.In order to increase the average output power of the wave power generation system,various control strategies have emerged.Combining the maximum wave energy capture with the intelligent optimization algorithm can improve the adaptability of the optimization strategy of the load.By analyzing the basic structure of the oscillating-body wave power generation system and its working principle,the equations of motion of the floater and the average output power of the system,the mathematical expression of the capture rate of wave energy and the ratio of the output power to the excitation force were derived.The curves of the above two functions were plotted by the software Matlab,and the dynamic characteristics were analyzed.The particle swarm optimization(PSO)algorithm has low probability in searching global optimization and premature convergence in searching the optimal load of the wave power generation system.A novel simulated annealing particle swarm optimization(SA-PSO)algorithm was proposed to solve the problem of the traditional PSO.When the speed and position of each particle were updated with SA-PSO,the replacement value of the global maximum from all particles was confirmed by comparing the fitness of each particle of the current temperature and the random number value.As a result,the new algorithm can escape local maximum at the premature convergence and quickly discover global optimum solution.The simulation results show that this novel algorithm can improve the capture rate of wave energy.All individual of the population tends to the same state quickly and stops evolution in genetic algorithm(GA),therefore GA has difficulty in discovering the optimum load of the wave power generation system.A novel multiple population genetic algorithm(MPGA)was proposed to solve the problem of the traditional GA.MPGA introduced multiple populations to search simultaneously at the beginning.Different populations were given different crossover probabilities and mutation probabilities so that the novel algorithm can balance global search and local search.At the same time,the immigration operator was added to maintain the connection between populations and the artificial selection operator was used to establish quintessence population.The criteria for the convergence of the algorithm was based on the quintessence population.The simulation results show that the proposed algorithm has much improvement in the speed of optimization and the stability of the algorithm.The seeker optimization algorithm was applied to the solution of the optimal load of the wave generation system.The search direction was selected by the weighted random geometric mean value of the egotistic behavior,altruistic behavior and pro-activeness behavior to enhance the global search ability of the seeker.The simulation results show that the proposed algorithm can converge quickly and increase the average output power of the wave power generation system.The artificial fish swarm algorithm was proposed to seek the optimal load of the wave generation system.By comparing the object function value of the next position getting from the “chasing the trail behavior or preying behavior or random behavior” and “swarming behavior or preying behavior or random behavior”,the better behavior mode was selected to confirm the search direction.The simulation results show that the seek of optimal load under different frequencies was achieved with the proposed algorithm.The novel algorithm can make the system effectively avoid the local optimization.