基于交叉随机粒子群优化算法的机组组合优化模型  

A Unit Commitment Optimization Model Based On Crossing Stochastic PSO Algorithm

作　　者：陈碧云[1] 程鹏飞[1] 

机构地区：[1]广西大学电气工程学院,南宁530004

出　　处：《广西电力》2012年第1期5-8,29,共5页Guangxi Electric Power

摘　　要：针对机组运行时故障的不确定性,利用威布尔失效概率函数来详细描述机组的故障概率,并以此为基础提出了兼顾机组故障率的机组组合优化模型。根据所建模型的特点,提出了带有随机权重和带有异步变化学习因子的粒子群算法,将机组组合问题划分为离散量和连续量两部分,通过在机组编码矩阵中进行交叉计算来解决机组组合问题。以5台机组24 h的机组组合优化问题为例进行计算,验证了所建模型的正确性及所提算法在求解机组组合优化模型时的有效性。According to the uncertainty of fault occurrence in unit operation, the Weibull distribution function is applied to describe the fault probability, based on which a unit commitment optimization model considering fault rate is proposed. Based on features of the model, a PSO （particle swarm optimal） algorithm with random weights and asynchronous learning factors is proposed which divides the problem into continuous and discrete variables and solves the problem by the crossing calculation in the encoded matrix of unit. A 5 units and 24 hours unit commitment optimization problem is calculated as an example by the proposed method which prove the correctness of the model and the efficient of the algorithm.

关 键 词：机组组合 机组故障率 离散粒子群算法 连续粒子群算法 

分 类 号：TM732[电气工程—电力系统及自动化] O158[理学—数学]