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电力大数据:2023,26(9):-
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基于改进的鲸鱼优化算法在ELD上的应用
李庆豪
(中国南方电网)
An Application of Improved Whale Optimization Algorithm on ELD
Liqinghao
(.Huizhou Power Supply Corporation of Guangdong Power Grid)
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投稿时间:2023-06-12    修订日期:2023-06-14
中文摘要: 针对电力系统经济负荷分配这一典型的非凸、非线性、组合优化问题,提出一种将基于自适应权重更新策略和差分进化的随机变异策略的鲸鱼优化优化算法(ADWOA)相结合。该算法首先在鲸鱼优化算法中引入了自适应权重来提高WOA的搜索能力,使算法能够在早期执行精细的全局搜索,在后期执行精确的局部搜索,加速寻优算法的迭代,同时由于随机变异策略,会再次更新位置。然后从更新的结果中选择最优位置,以加速种群的收敛,并有效防止种群陷入局部最优将适应度较好的个体信息更快地保留用于下一次鲸鱼优化算法的迭代,提高了求最优解的速度和精度。最后,对多个算法在电力系统经济负荷分配问题进行了测试,验证了基于自适应权重的的鲸鱼优化算法可以更合理地配置电力系统的经济负荷,能够有效找到可行解,避免陷入局部最优,能实现经济负荷的合理分配。
Abstract:Aiming at the typical non-convex, nonlinear and combinatorial optimization problem of power system Economic Load Distribution(ELD), a Whale Optimization Optimization Algorithm based on adaptive weight update strategy and stochastic mutation strategy of differential evolution is proposed(ADWOA). The algorithm first introduces adaptive weights into the whale optimization algorithm to improve the search ability of WOA, so that the algorithm can perform a fine global search in the early stage, and perform an accurate local search in the later stage, which accelerates the iteration of the optimization algorithm. policy, the location will be updated again. Then the optimal position is selected from the updated results to accelerate the convergence of the population and effectively prevent the population from falling into a local optimum. The individual information with better fitness is retained for the next iteration of the whale optimization algorithm faster, which improves the demand for The speed and accuracy of the optimal solution. Finally, several algorithms are tested in the problem of economic load distribution in the power system, and it is verified that the whale optimization algorithm based on adaptive weights can more reasonably allocate the economic load of the power system, and can effectively find feasible solutions and avoid falling into local optimum. , can realize the reasonable distribution of economic load.
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