| 引用本文: | 龙 文,梁昔明,龙祖强,李朝辉.基于蚁群算法和LSSVM的锅炉燃烧优化预测控制[J].电力自动化设备,2011,31(11): |
| LONG Wen,LIANG Ximing,LONG Zuqiang,LI Zhaohui.Predictive control based on LSSVM and ACO for boiler combustion optimization[J].Electric Power Automation Equipment,2011,31(11): |
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| 摘要: |
| 火电厂锅炉燃烧过程是一个复杂的多输入／多输出系统，具有高度非线性、强耦合的特点。借助燃烧特性试验数据，利用最小二乘支持向量机（LSSVM）建立锅炉燃烧模型，使用非线性模型预测控制（MPC）算法对锅炉燃烧过程进行优化和控制。提出一种改进蚁群算法用于求解预测控制算法中的非线性优化问题，采用动态随机抽取方法来确定目标个体引导蚁群进行全局搜索，同时在最优蚂蚁邻域内进行小步长局部搜索。实例表明，该方法对锅炉燃烧过程具有较好的控制效果。 |
| 关键词: 最小二乘支持向量机 蚁群算法 燃烧 优化 预测控制 电厂 支持向量机 |
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| 基金项目:国家自然科学基金资助项目（61074069）；贵州财经学院博士科研启动基金资助项目 |
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| Predictive control based on LSSVM and ACO for boiler combustion optimization |
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LONG Wen1,2, LIANG Ximing2, LONG Zuqiang3, LI Zhaohui2
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1.Guizhou Key Laboratory of Economics System Simulation，Guizhou College of Finance and Economics，Guiyang 550004，China;2.School of Information Science and Engineering，Central South University，Changsha 410083，China;3.Department of Physics and Electronics Information Science， Hengyang Normal College，Hengyang 421008，China
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| Abstract: |
| The boiler combustion process of coal-fired power plant is a very complicated MIMO system with high nonlinearity and strong coupling. The LSSVM（Least Square Support Vector Machine） is applied to build the boiler combustion model based on the property test data and the nonlinear MPC（Model Predictive Control） is applied to optimize the control of boiler combustion process. The improved ACO（Ant Colony Optimization） is proposed to solve the nonlinear optimization problem of MPC algorithm，which extracts the target individuals dynamically and stochastically to lead the global search of ant colony while carries out the small step search nearby the optimal ant. Case study indicates its effectiveness. |
| Key words: least square support vector machine ant colony optimization combustion optimization predictive control power plants support vector machines |
