针对柔性高压直流输电系统的交互式教-学优化算法
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摘要
提出一种针对柔性高压直流输电系统(VSC-HVDC)的交互式教-学优化算法(ITLO)以获取最优PI控制增益.首先在原始教-学优化算法中引入多个班级来扩大搜索范围;然后在不同班级的教师或学生之间建立小世界网络(SWN),通过深度交互学习实现精确搜索.交互式教-学优化算法能够合理权衡搜索范围和搜索精度,从而有效避免算法陷入局部最优.通过3个算例对所提出算法的有效性进行测试,即有功功率和无功功率追踪、电网短路故障和风电并网,仿真结果验证了其相较于现有启发式优化算法的优越性.
This paper designs an interactive teaching-learning optimization(ITLO) algorithm for voltage source converter based high-voltage direct-current(VSC-HVDC) systems, which is used to optimize the control gains of proportionalintegral(PI) control loops. Firetly, a wider exploration is achieved by introducing multiple classes into the teachinglearning based optimization(TLBO) algorithm. Then, a small world network(SWN) is employed for a deep interactive learning among the teachers or students from different classes, so that a more accurate exploitation can be realized.As a result, ITLO is able to effectively avoid a local optimum thanks to its proper trade-off between explorations and exploitations. Three case studies are undertaken, such as active and reactive power tracking, short-circuit fault at power grid, and wind farm integration. Simulation results show that the proposed approach has great advantage compared with typical meta-heuristic optimization algorithms.
This paper designs an interactive teaching-learning optimization(ITLO) algorithm for voltage source converter based high-voltage direct-current(VSC-HVDC) systems, which is used to optimize the control gains of proportionalintegral(PI) control loops. Firetly, a wider exploration is achieved by introducing multiple classes into the teachinglearning based optimization(TLBO) algorithm. Then, a small world network(SWN) is employed for a deep interactive learning among the teachers or students from different classes, so that a more accurate exploitation can be realized.As a result, ITLO is able to effectively avoid a local optimum thanks to its proper trade-off between explorations and exploitations. Three case studies are undertaken, such as active and reactive power tracking, short-circuit fault at power grid, and wind farm integration. Simulation results show that the proposed approach has great advantage compared with typical meta-heuristic optimization algorithms.
引文
[1]Nikolas F,Vassilios G A,Georgios D D.VSC-based HVDC power transmission systems:An overview[J]IEEE Trans on Power Electronics,2009,24(3):592-602
[2]王鲁浩,李歧强,王桂荣,等.多可再生能源冷热电联供微网系统环境经济优化调度[J].控制与决策,201631(5):913-918.(Wang L H,Li Q Q,Wang G R,et al.Optimal environmental economic scheduling of combined cooling heating and power microgrid system with multi-renewable energy[J].Control and Decision,2016,31(5):913-918.)
[3]徐政,王世佳,李宁璨,等.适用于远距离大容量架空线路的LCC-MMC串联混合型直流输电系统[J].电网技术,2016,40(1):55-63.(Xu Z,Wang S J,Li N C,et al.A LCC and MMC series hybrid HVDC topology suitable for bulk power overhead line transmission[J].Power System Technology,2016,40(1):55-63.)
[4]Ruan S Y,Li G J,Peng L,et al.A nonlinear control for enhancing HVDC light transmission system stability[J].Int J of Electric Power and Energy Systems,2007,29(7):565-570.
[5]杜欣慧,张立伟,张强,等.高压直流输电系统模糊自适应控制器的设计[J].电网技术,2007,31(2):158-161.(Du X H,Zhang L W,Zhang Q,et al.Design of fuzzy self-adaptive controller of HVDC system[J].Power System Technology,2007,31(2):158-161.)
[6]Yang B,Sang Y Y,Shi K,et al.Design and real-time implementation of perturbation observer based sliding-mode control for VSC-HVDC systems[J].Contro Engineering Practice,2016,56:13-26.
[7]李天云,张志华,陈峰,等.基于扩张状态观测器和Terminal滑模的HVDC非线性鲁棒控制器设计[J].电网技术,2012,36(10):190-195.(Li T Y,Zhang Z H,Chen F,et al.Design of nonlinear robust controller of HVDC power transmission system based on extended state observer and termina sliding mode control[J].Power System Technology,201236(10):190-195.)
[8]Li S,Haskew T A,Xu L.Control of HVDC light system using conventional and direct current vector contro approaches[J].IEEE Trans on Power Electronics,201025(12):3106-3118.
[9]喻锋,王西田,杨煜,等.一种混合遗传算法在HVDC定电流控制器参数优化中的应用[J].电力系统保护与控制,2014,42(9):126-131.(Yu F,Wang X T,Yang Y,et al.An application of hybrid genetic algorithm in the parameters optimization of HVDC constant current controller[J].Power System Protection and Control,2014,42(9):126-131.)
[10]胡江,魏星.基于自适应粒子群算法的直流输电PI控制器参数优化[J].电网技术,2008,32(2):71-74.(Hu J,Wei X.Parameter optimization of HVDCtechnology PI controller based on adaptive particle swarm optimization[J].Power System Technology,2008,32(2)71-74.)
[11]刘杰勋.基于差分进化算法的VSC-HVDC系统控制参数优化[J].贵州电力技术,2015,18(2):55-58.(Liu J X.Parameter optimization on the VSC-HVDCsystem based on differential evolution algorithm[J]Guizhou Electric Power Technology,2015,18(2):55-58.)
[12]郭春义,赵成勇,李广凯,等.基于Simplex算法的VSC-HVDC控制参数优化[J].电力自动化设备,201030(9):13-17.(Guo C Y,Zhao C Y,Li G K,et al.Contro parameter optimization based on Simplex algorithm for VSC-HVDC[J].Electric Power Automation Equipment2010,30(9):13-17.)
[13]Rao R V,Savsani V J,Vakharia D P.Teaching-learningbased optimization:A novel method for constrained mechanical design optimization problems[J].ComputerAided Design,2011,43(3):303-315.
[14]Rao R V,Kalyankar V D.Parameter optimization of modern machining processes using teaching-learningbased optimization algorithm[J].Engineering Applications of Artificial Intelligence,2013,26(1):524-531.
[15]Haghighi A S,Seifi A R,Niknam T.A modified teaching-learning based optimization for multi-objective optimal power flow problem[J].Energy Conversion and Management,2014,77:597-607.
[16]Kanwar N,Gupta N,Niazi K R,et al.Simultaneous allocation of distributed resources using improved teaching learning based optimization[J].Energy Conversion and Management,2015,103:387-400.
[17]Watts D J,Strogetz S H.Collective dynamics of‘small-world’networks[J].Nature,1998,393(6684):440-442.
[18]Yang B,Jiang L,Yao W,et al.Perturbation observer based adaptive passive control for damping improvement of multi-terminal voltage source converter-based high voltage direct current systems[J].Trans on the Institute of Measurement and Control,2017,39(9):1409-1420.
[19]崔建峰,张科,吕梅柏.基于均匀设计的张量积分布补偿控制系统设计[J].控制与决策,2015,30(4):745-750.(Cui J F,Zhang K,Lv M B.Tensor product distributed compensation control method based on uniform design[J].Control and Decision,2015,30(4):745-750.)
[20]He S,Wu Q H,Saunders J R.Group search optimizer:An optimization algorithm inspired by animal searching behaviour[J].IEEE Trans on Evolutionary Computation,2009,13(5):973-990.
[21]陈国初,杨维,张延迟,等.简化的分类微粒群算法及其在风电场建模中的应用[J].控制与决策,2011,26(3):381-386.(Chen G C,Yang W,Zhang Y C,et al.Simpilified classification particle swarm optimization algorithm and its application in wind farm modeling[J].Control and Decision,2011,26(3):381-386.)
[2]王鲁浩,李歧强,王桂荣,等.多可再生能源冷热电联供微网系统环境经济优化调度[J].控制与决策,201631(5):913-918.(Wang L H,Li Q Q,Wang G R,et al.Optimal environmental economic scheduling of combined cooling heating and power microgrid system with multi-renewable energy[J].Control and Decision,2016,31(5):913-918.)
[3]徐政,王世佳,李宁璨,等.适用于远距离大容量架空线路的LCC-MMC串联混合型直流输电系统[J].电网技术,2016,40(1):55-63.(Xu Z,Wang S J,Li N C,et al.A LCC and MMC series hybrid HVDC topology suitable for bulk power overhead line transmission[J].Power System Technology,2016,40(1):55-63.)
[4]Ruan S Y,Li G J,Peng L,et al.A nonlinear control for enhancing HVDC light transmission system stability[J].Int J of Electric Power and Energy Systems,2007,29(7):565-570.
[5]杜欣慧,张立伟,张强,等.高压直流输电系统模糊自适应控制器的设计[J].电网技术,2007,31(2):158-161.(Du X H,Zhang L W,Zhang Q,et al.Design of fuzzy self-adaptive controller of HVDC system[J].Power System Technology,2007,31(2):158-161.)
[6]Yang B,Sang Y Y,Shi K,et al.Design and real-time implementation of perturbation observer based sliding-mode control for VSC-HVDC systems[J].Contro Engineering Practice,2016,56:13-26.
[7]李天云,张志华,陈峰,等.基于扩张状态观测器和Terminal滑模的HVDC非线性鲁棒控制器设计[J].电网技术,2012,36(10):190-195.(Li T Y,Zhang Z H,Chen F,et al.Design of nonlinear robust controller of HVDC power transmission system based on extended state observer and termina sliding mode control[J].Power System Technology,201236(10):190-195.)
[8]Li S,Haskew T A,Xu L.Control of HVDC light system using conventional and direct current vector contro approaches[J].IEEE Trans on Power Electronics,201025(12):3106-3118.
[9]喻锋,王西田,杨煜,等.一种混合遗传算法在HVDC定电流控制器参数优化中的应用[J].电力系统保护与控制,2014,42(9):126-131.(Yu F,Wang X T,Yang Y,et al.An application of hybrid genetic algorithm in the parameters optimization of HVDC constant current controller[J].Power System Protection and Control,2014,42(9):126-131.)
[10]胡江,魏星.基于自适应粒子群算法的直流输电PI控制器参数优化[J].电网技术,2008,32(2):71-74.(Hu J,Wei X.Parameter optimization of HVDCtechnology PI controller based on adaptive particle swarm optimization[J].Power System Technology,2008,32(2)71-74.)
[11]刘杰勋.基于差分进化算法的VSC-HVDC系统控制参数优化[J].贵州电力技术,2015,18(2):55-58.(Liu J X.Parameter optimization on the VSC-HVDCsystem based on differential evolution algorithm[J]Guizhou Electric Power Technology,2015,18(2):55-58.)
[12]郭春义,赵成勇,李广凯,等.基于Simplex算法的VSC-HVDC控制参数优化[J].电力自动化设备,201030(9):13-17.(Guo C Y,Zhao C Y,Li G K,et al.Contro parameter optimization based on Simplex algorithm for VSC-HVDC[J].Electric Power Automation Equipment2010,30(9):13-17.)
[13]Rao R V,Savsani V J,Vakharia D P.Teaching-learningbased optimization:A novel method for constrained mechanical design optimization problems[J].ComputerAided Design,2011,43(3):303-315.
[14]Rao R V,Kalyankar V D.Parameter optimization of modern machining processes using teaching-learningbased optimization algorithm[J].Engineering Applications of Artificial Intelligence,2013,26(1):524-531.
[15]Haghighi A S,Seifi A R,Niknam T.A modified teaching-learning based optimization for multi-objective optimal power flow problem[J].Energy Conversion and Management,2014,77:597-607.
[16]Kanwar N,Gupta N,Niazi K R,et al.Simultaneous allocation of distributed resources using improved teaching learning based optimization[J].Energy Conversion and Management,2015,103:387-400.
[17]Watts D J,Strogetz S H.Collective dynamics of‘small-world’networks[J].Nature,1998,393(6684):440-442.
[18]Yang B,Jiang L,Yao W,et al.Perturbation observer based adaptive passive control for damping improvement of multi-terminal voltage source converter-based high voltage direct current systems[J].Trans on the Institute of Measurement and Control,2017,39(9):1409-1420.
[19]崔建峰,张科,吕梅柏.基于均匀设计的张量积分布补偿控制系统设计[J].控制与决策,2015,30(4):745-750.(Cui J F,Zhang K,Lv M B.Tensor product distributed compensation control method based on uniform design[J].Control and Decision,2015,30(4):745-750.)
[20]He S,Wu Q H,Saunders J R.Group search optimizer:An optimization algorithm inspired by animal searching behaviour[J].IEEE Trans on Evolutionary Computation,2009,13(5):973-990.
[21]陈国初,杨维,张延迟,等.简化的分类微粒群算法及其在风电场建模中的应用[J].控制与决策,2011,26(3):381-386.(Chen G C,Yang W,Zhang Y C,et al.Simpilified classification particle swarm optimization algorithm and its application in wind farm modeling[J].Control and Decision,2011,26(3):381-386.)