基于改进莱维飞行粒子群算法的光伏系统MPPT方法
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摘要
在光伏发电系统的MPPT控制中,标准粒子群算法存在跟踪精度不高,易收敛于局部极值点的问题。为了提升MPPT的准确度,引入了改进的莱维飞行粒子群算法(LFPSO),优化了算法流程及收敛条件,提升了算法的准确度,并减少了莱维飞行过程所需的额外控制时间。通过仿真表明:新算法能够显著提高跟踪的精确度,可以很好地收敛到全局最优点。
In the MPPT control of PV system,the standard PSO control method has the problem of low tracking accuracy and easy convergence to local extremum points.In order to improve the accuracy,the improved Levy Flight Particle Swarm Optimization( LFPSO) is introduced to optimize the algorithm flow and convergence conditions,which improves the accuracy of the algorithm and reduces the extra control time required for Levy Flight.The simulation shows that the new algorithm can significantly improve the accuracy of tracking,which can well converge to the global optimum.
In the MPPT control of PV system,the standard PSO control method has the problem of low tracking accuracy and easy convergence to local extremum points.In order to improve the accuracy,the improved Levy Flight Particle Swarm Optimization( LFPSO) is introduced to optimize the algorithm flow and convergence conditions,which improves the accuracy of the algorithm and reduces the extra control time required for Levy Flight.The simulation shows that the new algorithm can significantly improve the accuracy of tracking,which can well converge to the global optimum.
引文
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[14]杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,6(5):87-94.
[15] YANG X S. Cuckoo search via levy flights[C]//World Congress on Nature&Biologically Inspired Computing.Coimbatore:IEEE,2009:210-214.
[16]杨娇,叶春明.应用新型萤火虫算法求解Job-Shop调度问题[J].计算机工程与应用,2013,49(11):213-215.
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[18] MANTEGNA R N,STANLEY H E.Stochastic process with ultraslow convergence to a gaussian the truncated lévy flight[J].Physical Review Letters,1995,74(24):4959
[19] HAKLI H,UGUZ H.A novel particle swarm optimization algorithm with levy flight[J]. Applied Soft Computing,2014,23(5):333-345.
[2]丁明,王伟胜,王秀丽,等.大规模光伏发电对电力系统影响综述[J].中国电机工程学报,2014(1):1-14.
[3] XIAO W,DUNFORD W G. A modified adaptive hill climbing MPPT method for photovoltaic power system[C]//2004 IEEE 35th Annual Power Electronics Specialists Conference(PESC 04),2004:3105-3109.
[4] FERMIA N,GRANOZIO D,PETRONE G,et al.Predictive adaptive MPPT perturb and observe method[J]. IEEE Transactions on Aerospace and Electronic Systems,2007,43(3):934-950.
[5]蔡明想.改进的电导增量法在光伏系统MPPT中的应用[J].电气传动,2011,41(7):21-24.
[6]崔东君,彭宇,杨进.固定电压法与改进电导增量法结合的光伏发电系统最大功率点跟踪[J].四川电力技术,2015,18(3):78-81.
[7] ISHAQU E K,SALAM Z.A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition[C]//Proceedings of IEEE Conference on Neural Networks. Perth:IEEE,2012:3195-3206.
[8]刘晓彦,姚玉霞,隋庆茹.改进粒子群算法的光伏最大功率点跟踪控制[J].激光杂志,2016,37(10):129-132.
[9] MIYATAKE M,TORIUMI F,ENDO T,et al.A novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique[C]//European Conference on Power Electronics and Applications,2007:1-10.
[10]林虹江.基于遗传优化BP神经网络算法的光伏系统最大功率点跟踪研究[J].电测与仪表,2015,52(5):35-40.
[11]杨旭,基于广义动态模糊神经网络的光伏电池MPPT控制[J].电力系统保护与控制,2010,38(13):22-25.
[12]惠晶,方光辉.新能源转换与控制技术[M].北京:机械工业出版社,2008:135-149.
[13] KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of IEEE Conference on Neural Networks.Perth:IEEE,1995:1942-1948.
[14]杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,6(5):87-94.
[15] YANG X S. Cuckoo search via levy flights[C]//World Congress on Nature&Biologically Inspired Computing.Coimbatore:IEEE,2009:210-214.
[16]杨娇,叶春明.应用新型萤火虫算法求解Job-Shop调度问题[J].计算机工程与应用,2013,49(11):213-215.
[17] YANG X S. Engineering optimisation by cuckoo search[J].International Journal of Mathematical Modelling and Numerical Optimization,2010,1(4):330-343.
[18] MANTEGNA R N,STANLEY H E.Stochastic process with ultraslow convergence to a gaussian the truncated lévy flight[J].Physical Review Letters,1995,74(24):4959
[19] HAKLI H,UGUZ H.A novel particle swarm optimization algorithm with levy flight[J]. Applied Soft Computing,2014,23(5):333-345.