一种用于计算光伏组件温度的多元非线性函数拟合与修正方法
为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出一种计算光伏组件的温度的方法。针对光伏组件局部遮阴的情况，通过研究组件温度与最大功率点之间的关系，对该方法做出了修正。经过光伏电站实际数据验证得知，方法计算精度高，能够反映外界环境变化，具有较强的自适应性和实用性。...
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| Published in | 电力系统保护与控制 Vol. 41; no. 24; pp. 44 - 49 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
华北电力大学新能源电力系统国家重点实验室,北京,102206
2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
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| Abstract | 为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出一种计算光伏组件的温度的方法。针对光伏组件局部遮阴的情况，通过研究组件温度与最大功率点之间的关系，对该方法做出了修正。经过光伏电站实际数据验证得知，方法计算精度高，能够反映外界环境变化，具有较强的自适应性和实用性。 |
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| AbstractList | 为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出一种计算光伏组件的温度的方法。针对光伏组件局部遮阴的情况，通过研究组件温度与最大功率点之间的关系，对该方法做出了修正。经过光伏电站实际数据验证得知，方法计算精度高，能够反映外界环境变化，具有较强的自适应性和实用性。 TM71; 为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出一种计算光伏组件的温度的方法。针对光伏组件局部遮阴的情况，通过研究组件温度与最大功率点之间的关系，对该方法做出了修正。经过光伏电站实际数据验证得知，方法计算精度高，能够反映外界环境变化，具有较强的自适应性和实用性。 |
| Abstract_FL | In order to obtain the module temperature of PV array, by analyzing the relationship between module temperatures and influencing factors, the function law between various external environmental factors and module temperatures is determined. Combining multivariate nonlinear function fitting with BP neural network, this paper proposes a more comprehensive method to calculate the temperature of PV modules. Aiming at the partial shading of PV arrays, the method is modified by analyzing the relationship between temperature of modules and the maximum power point. The actual data from a photovoltaic power plant verify that the method has high accuracy and strong practicability. It can reflect the changes in the external environment and has a strong self-adaptivity. |
| Author | 刘宝柱 颜洪正 王立国 |
| AuthorAffiliation | 华北电力大学新能源电力系统国家重点实验室,北京102206 |
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| Author_FL | YAN Hong-zheng LIU Bao-zhu WANG Li-guo |
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| DocumentTitleAlternate | A multivariate nonlinear function fitting and modified method for calculating the temperature of PV modules |
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| Keywords | influencing factors 多元非线性拟合 BP neural network BP神经网络 partial shading 影响因素 组件温度 module temperature multivariate nonlinear function fitting 局部遮阴 |
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| Notes | In order to obtain the module temperature of PV array, by analyzing the relationship between module temperatures and influencing factors, the function law between various external environmental factors and module temperatures is determined. Combining multivariate nonlinear function fitting with BP neural network, this paper proposes a more comprehensive method to calculate the temperature of PV modules. Aiming at the partial shading of PV arrays, the method is modified by analyzing the relationship between temperature of modules and the maximum power point. The actual data from a photovoltaic power plant verify that the method has high accuracy and strong practicability. It can reflect the changes in the external environment and has a strong self-adaptivity. This work is supported by National High-tech R ＆ D Program of China （863 Program） （No. 2011AA05A301）. LIU Bao-zhu, YAN Hong-zheng, WANG Li-guo （State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Po |
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| Snippet | 为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出一种计... TM71; 为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出... |
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| SubjectTerms | BP神经网络 多元非线性拟合 局部遮阴 影响因素 组件温度 |
| Title | 一种用于计算光伏组件温度的多元非线性函数拟合与修正方法 |
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