In engineering practice, multi-objective optimization problems (Multi-objective Optimization Problems, MOPs) are common, but multi-objective optimization problems usually cannot be solved directly. In order to solve the multi- objective optimization problem, the goals to be optimized are usually analyzed, and the problems to be studied are transformed into computable mathematical models by using mathematical theories and methods. At the same time, the mathematical model and calculation method are studied, and a more appropriate algorithm is selected to further solve the mathematical model established. After obtaining all feasible schemes that meet the research objectives, the best scheme is selected according to the needs of the research objectives. In the process of multi-objective function optimization, the problem of constraint complexity and high dimension is encountered, so the choice of the algorithm has a great impact on the accuracy of the solution. Therefore, in this paper, the hybrid algorithm of NSGA-II and WOA is adopted to solve the optimization scheduling problem, which is a mixture of NSGA-II and WOA. The hybrid algorithm of NSGA-II and WOA can obtain the optimal solution set in each calculation and select the optimal solution.
Due to the randomness of wind speed and wind turbine unit status in the wind field, the output of wind turbines is subject to randomness. If large-scale wind power is integrated into the power system, it is necessary to evaluate the output of wind turbines. In order to more accurately evaluate the output of wind turbines under multiple random states, this paper considers four states of operation, decoupling, shutdown, and random startup for each wind turbine unit, and considers the sum of the outputs of independent wind turbines under these four states. Sequential Monte Carlo method is used for sampling evaluation, and two-parameter Weibull distribution is used to simulate wind speed in the wind field. The impact of parameter changes on the output of wind turbines is observed by changing the parameters. Considering the stochastic impact of wind speed on the overall wind turbines in the wind field and considering the random states of individual wind turbines, a model for the output of wind turbines under multiple states is established. Finally, the probability density of the output of wind turbines under various states is compared and analyzed, and the simulation results verify the accuracy and effectiveness of the proposed method in evaluating the output of wind turbines.
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