AGA在太阳能电池阵寿命预测中的应用  

Application of Adaptive Genetic Algorithm in Life Prediction of Solar Cell

作　　者：简献忠 武涛 郭强[2] 应怀樵[3] 姜冠祥 JIAN Xian-zhong;WU Tao;GUO Qiang;YING Huai-qiao;JIANG Guan-xiang(Shanghai Key Laboratory of Modern Optical System,School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;National Satellite Meteorological Center,Beijing 100081,China;China Orient Institute of Noise & Vibration,Beijng 100084,China;Shanghai Supore Instruments Co.,Ltd.,Shanghai 200444,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院上海市现代光学系统重点实验室,上海200093 [2]国家卫星气象中心,北京100081 [3]北京东方振动和噪声技术研究所,北京100084 [4]上海雄博精密仪器股份有限公司,上海200444

出　　处：《测控技术》2019年第2期46-49,55,共5页Measurement & Control Technology

基　　金：国家自然科学基金资助(41075019);上海市宝山区科委产学研项目(bkw2015130)

摘　　要：针对在轨卫星的GaInP/GaAs/Ge三结太阳能电池阵输出电流衰减模型中参数辨识精度低和后期误差大且实时性不高的问题,提出采用遗传算法(GA)对太阳能电池阵电流衰减模型进行参数辨识。针对GA过早收敛及易陷入局部最优解的缺点,提出了基于自适应交叉变异算子和线性变换适应度函数的自适应遗传算法(AGA)进行参数辨识。然后把模型预测的输出电流与遥感测得的输出电流进行比较。实验结果表明,运用AGA和广义最小二乘法(GLS)得到的均方根误差(RMSE)分别为1.427×10^(-4)和1.337×10^(-3),误差平方和(SSE)分别为2.0571×10^(-6)和1.92×10^(-3),AGA辨识后的模型预测更加准确,对卫星运行后期电源的管理具有实际意义。In order to solve the problems of low accuracy of parameter identification and large late error and low real time performance of GaInP/GaAs/Ge triple junction solar cell array in orbit satellite,a genetic algorithm (GA)is proposed to identify the parameters of the solar cell array current attenuation model.In order to overcome the shortcoming that GA prematurely converges and falls into local optimal solution,a adaptive genetic algorithm (AGA)based on adaptive crossover and mutation operator and linear transformation fitness function was proposed for parameter identification.Then,the predicted output current was compared to the sensed output current to verify the accuracy of the predicted output current.Experimental results show that the root mean square error (RMSE)of AGA and generalized least squares (GLS)is respectively 1.427×10^-4 and 1.337×10^-3,sum of error squares (SSE)is respectively 2.0571×10^-6 and 1.92×10^-3.The model prediction after AGA identification is more accurate.It is of practical significance to manage the power supply in the later stage of satellite operation.

关 键 词：太阳能电池阵列 自适应遗传算法 寿命预测 电流衰减模型 参数辨识 

分 类 号：TM914.4[电气工程—电力电子与电力传动] TP18[自动化与计算机技术—控制理论与控制工程]