| 基于BP神经网络与改进粒子群的光伏MPPT算法 |
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| 引用本文： | 李季,阎鑫,孙文涛,徐晓宁,邵磊.基于BP神经网络与改进粒子群的光伏MPPT算法[J].电源技术,2022,46(2):186-189. |
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| 作者姓名： | 李季 阎鑫 孙文涛 徐晓宁 邵磊 |
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| 作者单位： | 天津理工大学电气电子工程学院天津市复杂系统控制理论及应用重点实验室,天津300384 |
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| 基金项目： | 天津市自然科学基金项目(17JCTPJC53100)。 |
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| 摘 要： | 针对光伏阵列在环境突变情况下尤其是局部阴影下的多峰值现象，提出一种基于反向传播(BP)神经网络与改进粒子群的最大功率点跟踪(MPPT)算法。该算法利用BP神经网络近似定位最大功率点，并利用对粒子群算法中的惯性权重值进行非线性动态优化后的改进粒子群精确定位最大功率点。仿真结果表明，复合算法可以更好地跟踪最大功率点，有效避免前期易陷入局部极值的问题，提高了精度，减小了功率振荡。
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| 关 键 词： | 光伏系统 最大功率跟踪 改进粒子群 BP神经网络 |
Photovoltaic MPPT algorithm based on BP neural network and improved particle swarm |
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| LI Ji,YAN Xin,SUN Wentao,XU Xiaoning,SHAO Lei.Photovoltaic MPPT algorithm based on BP neural network and improved particle swarm[J].Chinese Journal of Power Sources,2022,46(2):186-189. |
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| Authors: | LI Ji YAN Xin SUN Wentao XU Xiaoning SHAO Lei |
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| Affiliation: | (Tianjin Key Laboratory for Control Theory&Application in Complicated Systems,School of Electrical and Electronic Engineering,Tianjin University of Technology,Tianjin 300384,China) |
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| Abstract: | Aiming at the multi-peak phenomenon of photovoltaic arrays under sudden environmental changes,especially under partial shadow,a MPPT algorithm based on BP neural network and improved particle swarm was proposed.The BP neural network was used to approximate the maximum power point,and the improved particle swarm after nonlinear dynamic optimization of the inertia weight value in the particle swarm algorithm was used to accurately locate maximum power point.The simulation results show that the hybrid algorithm can better track the maximum power,and effectively avoid the problem of falling into local extremes in the early stage,and it improves the accuracy and reduces the power oscillation. |
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| Keywords: | photovoltaic system maximum power tracking improved particle swarm BP neural networks |
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