基于T细胞调节的免疫遗传优化算法对逆变器PWM控制序列的优化
针对遗传算法（GAs）收敛速度慢、易于陷入局部最优等不足，以逆变器PWM最优控制序列为目标，提出了一种改进的免疫遗传优化算法（IGOAs）。该算法设计基于单相全桥逆变器输出电流与参考电流的误差模型，采用0／1编码、自适应突变概率及T细胞调节算子，从而增强了算法收敛的快速性和种群的多样性。在逆变器负载端电阻为定值和受随机扰动值两种情形下进行了数值实验，并将IGOAs和GAs在两种情形下的实验结果分别进行了比较。结果表明，IGOAs具有较好的收敛性和稳定性，负载电阻受随机扰动时，IGOAs能更快速地跟踪参考电流，获得总谐波失真（THD）更小的输出波形。...
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| Published in | 计算机应用研究 Vol. 33; no. 1; pp. 50 - 55 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
南京航空航天大学 自动化学院,南京,210016%南京航空航天大学 自动化学院，南京 210016
2016
安顺学院 数理学院，贵州 安顺 561000 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2016.01.011 |
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| Abstract | 针对遗传算法（GAs）收敛速度慢、易于陷入局部最优等不足，以逆变器PWM最优控制序列为目标，提出了一种改进的免疫遗传优化算法（IGOAs）。该算法设计基于单相全桥逆变器输出电流与参考电流的误差模型，采用0／1编码、自适应突变概率及T细胞调节算子，从而增强了算法收敛的快速性和种群的多样性。在逆变器负载端电阻为定值和受随机扰动值两种情形下进行了数值实验，并将IGOAs和GAs在两种情形下的实验结果分别进行了比较。结果表明，IGOAs具有较好的收敛性和稳定性，负载电阻受随机扰动时，IGOAs能更快速地跟踪参考电流，获得总谐波失真（THD）更小的输出波形。 |
|---|---|
| AbstractList | TP306.3%TP301.6; 针对遗传算法（GAs）收敛速度慢、易于陷入局部最优等不足，以逆变器 PWM最优控制序列为目标，提出了一种改进的免疫遗传优化算法（IGOAs）。该算法设计基于单相全桥逆变器输出电流与参考电流的误差模型，采用0／1编码、自适应突变概率及 T 细胞调节算子，从而增强了算法收敛的快速性和种群的多样性。在逆变器负载端电阻为定值和受随机扰动值两种情形下进行了数值实验，并将 IGOAs 和 GAs 在两种情形下的实验结果分别进行了比较。结果表明，IGOAs 具有较好的收敛性和稳定性，负载电阻受随机扰动时，IGOAs 能更快速地跟踪参考电流，获得总谐波失真（THD）更小的输出波形。 针对遗传算法（GAs）收敛速度慢、易于陷入局部最优等不足，以逆变器PWM最优控制序列为目标，提出了一种改进的免疫遗传优化算法（IGOAs）。该算法设计基于单相全桥逆变器输出电流与参考电流的误差模型，采用0／1编码、自适应突变概率及T细胞调节算子，从而增强了算法收敛的快速性和种群的多样性。在逆变器负载端电阻为定值和受随机扰动值两种情形下进行了数值实验，并将IGOAs和GAs在两种情形下的实验结果分别进行了比较。结果表明，IGOAs具有较好的收敛性和稳定性，负载电阻受随机扰动时，IGOAs能更快速地跟踪参考电流，获得总谐波失真（THD）更小的输出波形。 |
| Abstract_FL | Aiming at the genetic algorithms (GAs)’s disadvantages of slow convergence and easy of falling into local opti-mum,and finding optimal PWMcontrol sequence of inverter,this paper proposed an improved immune genetic optimization al-gorithm (IGOAs),based on the error model of the output current and reference current of single-phase full-bridge inverter.In the algorithm,antibodies were coded with 0 and 1.It took advantage of an adaptive mutation probability and T cell-mediated operators to increase the diversity of population and improve convergence of IGOAs.Numerical experiments considered both fixed resistance and resistance with random perturbation of the load side of inverter.Simulation results show that IGOAs have better convergence when resistance is static.In addition,IGOAs can track standard current when load resistance is exposed to random value quickly,and obtain current with smaller THD. |
| Author | 王永兴 钱淑渠 叶永强 |
| AuthorAffiliation | 南京航空航天大学自动化学院,南京210016 安顺学院数理学院,贵州安顺561000 |
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| Author_FL | Wang Yongxing Ye Yongqiang Qian Shuqu |
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| Author_xml | – sequence: 1 fullname: 王永兴 钱淑渠 叶永强 |
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| DocumentTitleAlternate | Immune genetic optimization algorithm based on T cell-mediated for sequence optimization of PWM inverter |
| DocumentTitle_FL | Immune genetic optimization algorithm based on T cell-mediated for sequence optimization of PWMinverter |
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| Keywords | 单相全桥逆变器 免疫算法 convergence T cell-mediated single-phase full-bridge inverter 收敛性 PWMcontrol sequence immune algorithm T细胞调节 PWM控制序列 |
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| Notes | 51-1196/TP single-phase full-bridge inverter; PWM control sequence; immune algorithm; T cell-mediated; convergence Aiming at the genetic algorithms （GAs） ＇ s disadvantages of slow convergence and easy of falling into local opti- mum, and finding optimal PWM control sequence of inverter, this paper proposed an improved immune genetic optimization al- gorithm （ IGOAs）, based on the error model of the output current and reference current of single-phase full-bridge inverter. In the algorithm, antibodies were coded with 0 and l. It took advantage of an adaptive mutation probability and T cell-mediated operators to increase the diversity of population and improve convergence of IGOAs. Numerical experiments considered both fixed resistance and resistance with random perturbation of the load side of inverter. Simulation results show that IGOAs have better convergence when resistance is static. In addition, IGOAs can track standard current when load resistance is exposed to random value quickly, and obtain current with |
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| PublicationTitle | 计算机应用研究 |
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| PublicationYear | 2016 |
| Publisher | 南京航空航天大学 自动化学院,南京,210016%南京航空航天大学 自动化学院，南京 210016 安顺学院 数理学院，贵州 安顺 561000 |
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| Snippet | ... TP306.3%TP301.6; 针对遗传算法（GAs）收敛速度慢、易于陷入局部最优等不足，以逆变器 PWM最优控制序列为目标，提出了一种改进的免疫遗传优化算法（IGOAs）。该算法设计基于单相全桥逆变器输出电流与参考电流的误差模型，采用0／1编码、自适应突变概率及 T... |
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| StartPage | 50 |
| SubjectTerms | PWM控制序列 T细胞调节 免疫算法 单相全桥逆变器 收敛性 |
| Title | 基于T细胞调节的免疫遗传优化算法对逆变器PWM控制序列的优化 |
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