| 引用本文: | 蒋燕君,姜建国,张宇华.采用多目标网格进化算法并面向对象的舰船电网重构[J].电力自动化设备,2013,33(3): |
| JIANG Yanjun,JIANG Jianguo,ZHANG Yuhua.Object-oriented reconfiguration of shipboard power network using multi-objective grid evolutionary algorithm[J].Electric Power Automation Equipment,2013,33(3): |
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| 摘要: |
| 考虑系统失电负荷量、电网有功损耗、线路负荷分配失衡度和开关操作次数，构造出舰船电网重构模型。以网格为载体，在邻域范围内进行选择、交叉和变异，采用精英策略，提出基于多目标进化并面向对象的舰船电网智能重构方法。该方法在不影响解的全局最优性的基础上，极大缩短了算法执行时间，并将各种Pareto重构算法的共同属性和操作抽象出来形成公共基础平台，改进超体积指标计算方法，实现不同算法性能间的公平比较。算例分析结果表明，在算法运行时间及所获解集的趋近度和分布度方面，所提方法均优于NSGA-Ⅱ和SPEA2。 |
| 关键词: 网络重构 多目标网格进化算法 Pareto最优 超体积 舰船电网 进化算法 |
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| 基金项目:国家高技术研究发展计划（863计划）资助项目（2011AA050403） |
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| Object-oriented reconfiguration of shipboard power network using multi-objective grid evolutionary algorithm |
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JIANG Yanjun1,2, JIANG Jianguo1, ZHANG Yuhua1
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1.Key Laboratory of Control of Power Transmission and Conversion，Ministry of Education，Shanghai Jiao Tong University，Shanghai 200240，China;2.School of Information Science and Technology，Zhejiang Shuren University，Hangzhou 310015，China
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| Abstract: |
| With the consideration of out-of-service loads，network active power loss，line load distribution imbalance and switch operation times，a reconfiguration model of shipboard power network is built. An object-oriented intelligent reconfiguration approach based on the multi-objective grid evolutionary algorithm is proposed for shipboard power network，which takes the grid as a carrier，carries out the selection，crossover and mutation in the neighborhood range and adopts the elitist strategy. Without affecting the global optimality of solution，its execution time is greatly reduced. A public base platform is formed by extracting the common attributes and operations from different Pareto-based reconfiguration algorithms. The calculation method of hypervolume metric is improved to realize the fair comparison among different algorithms. Case analysis shows that，the proposed approach is better than NSGA-Ⅱ and SPEA2 in the tendency and distribution degree of obtained solution set，as well as the calculation time. |
| Key words: network reconfiguration multi-objective grid evolutionary algorithm Pareto optimality hypervolume shipboard power network evolutionary algorithms |
