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Dynamic economic dispatch based on improved particle swarm optimization and penalty function

机译:基于改进粒子群算法和惩罚函数的动态经济调度

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In order to minimize the system production cost, optimize the power output of thermal unit, this paper proposes a dynamic economic dispatching (DED) model based on wind power and energy — environmental efficiency. An improved particle swarm optimization(IPSO)combined with penalty function is proposed for solving the high dimension, nonlinear, multi-constrained optimization problem. The proposed algorithm can ensure feasibility of the solution by means of feasible regulation scheme; at the same time to prevent precocious convergence of the algorithm and to quicken the search speed. The differential mutation and a kind of random mutation based on variance of the population's fitness are adopted to improve the diversity of the solution. Numerical experiments demonstrate the rationality of optimization model and great practical value of proposed hybrid strategy, besides, efficiency and high optimization accuracy can be guaranteed.
机译:为了最小化系统生产成本,优化热力单元的功率输出,本文提出了一种基于风能和能源—环境效率的动态经济调度(DED)模型。提出了一种结合罚函数的改进粒子群算法(IPSO),解决了高维,非线性,多约束优化问题。所提出的算法可以通过可行的调节方案来保证解决方案的可行性。同时防止算法过早收敛,加快搜索速度。采用差异变异和一种基于种群适应度方差的随机变异来提高解的多样性。数值实验证明了该优化模型的合理性和所提混合策略的实用价值,并且可以保证效率和较高的优化精度。

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