电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2013年
24期
44-49
,共6页
组件温度%影响因素%多元非线性拟合%BP神经网络%局部遮阴
組件溫度%影響因素%多元非線性擬閤%BP神經網絡%跼部遮陰
조건온도%영향인소%다원비선성의합%BP신경망락%국부차음
module temperature%influencing factors%multivariate nonlinear function fitting%BP neural network%partial shading
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。
為瞭穫得光伏陣列的組件溫度，通過分析組件溫度與影響因素之間的關繫，確定瞭各種外界環境因素與組件溫度的函數規律。將多元非線性函數擬閤與BP神經網絡相結閤，提齣一種計算光伏組件的溫度的方法。針對光伏組件跼部遮陰的情況，通過研究組件溫度與最大功率點之間的關繫，對該方法做齣瞭脩正。經過光伏電站實際數據驗證得知，方法計算精度高，能夠反映外界環境變化，具有較彊的自適應性和實用性。
위료획득광복진렬적조건온도，통과분석조건온도여영향인소지간적관계，학정료각충외계배경인소여조건온도적함수규률。장다원비선성함수의합여BP신경망락상결합，제출일충계산광복조건적온도적방법。침대광복조건국부차음적정황，통과연구조건온도여최대공솔점지간적관계，대해방법주출료수정。경과광복전참실제수거험증득지，방법계산정도고，능구반영외계배경변화，구유교강적자괄응성화실용성。
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.
