摘要
针对变电站定容选址的优化问题,建立了以变电站年综合费用最小为目标的数学模型,不仅考虑变电站投资、馈线投资和网损维护等可量化费用,还将状态变量约束以罚函数形式计入目标函数中,使变电站定容选址模型更具实用价值。在此基础上,提出了一种基于K-均值聚类粒子群的智能算法对其进行求解,该算法有效地克服了传统粒子群算法容易陷入局部最优以及早熟的缺点。最后通过实例验证了模型和方法的实用性和有效性。
Aiming at the optimal substation locating and sizing planning, this paper establishes an optimization model to minimize the total substation investment cost including substation investment, teeder investment and network loss cost. The model improves the practical value by adding the state variables constraint to the objective function in the form of penalty function. Meanwhile, the paper uses an intelligence algorithm based on K-mean clustering particle swarm algorithm to get the solution of the models, which can avoid the premature phenomenon or easy to fall into local optimum. Finally, the effectiveness and feasibility of the model and improved algorithm is verified with an example.
出处
《陕西电力》
2015年第11期60-64,共5页
Shanxi Electric Power
基金
国家自然科学基金项目资助(51407035)
中国南方电网公司科技项目资助(K-GD2014-0891)
关键词
粒子群算法
K-均值聚类分析
变电站
定容选址
particle swarm optimization
K-mean clustering analysis
substation
locating and sizing
