AGA在太阳能电池阵寿命预测中的应用
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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.
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.
引文
[1]盛志东.卫星太阳能电池阵的效能及可靠性分析与评估[D].合肥:中国科学技术大学,2017.
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[11]王小平,曹立明.遗传算法理论应用与软件实现[M].西安:西安交通大学出版社,2001:83-105.
[12]陈树,张继中.基于自适应均值粒子群算法的SVM参数优化方法[J].测控技术,2018,37(4):6-10.
[1]刘正高,栾家辉,吴雷,等.基于混合自回归滑动平均模型的卫星太阳能电池阵寿命预测方法:201310159799. 4[P]. 2013-09-11.
[3]张岩,魏强,邢杰,等.GEO轨道卫星单结砷化镓电池阵在轨衰减特性分析[J].太阳能学报,2015,36(3):546-550.
[4] Peng M,Wang W W,Wu J,et al. Study on attenuation factor of Si solar array for satellite in sun synchronous orbit[J].Spacecraft Engineering,2011,20(5):61-67.
[5] Letin V A. Optical,radiation and thermal cycling losses of power Si solar array returned from orbital station"Mir"after10.5 years of operation[J]. Solar Engineering,2004(2):3-12.
[6]高慧,程宗毛.ARIMA模型在卫星的可靠性评估中的应[J].杭州电子科技大学学报(自然科学版),2016,36(5):77-81.
[7]郭建辉,罗广文.太阳能电池阵的功率预测[J].科学技术与工程,2014,14(14):220-223.
[8] Meng J R,Feng J,Sun Q,et al. Degradation model of the orbiting current for GaInP/GaAs/Ge triple-junction solar cells used on satellite[J]. Solar Energy,2015,122:464-471.
[9]井元良,孙海涛,雷英俊.太阳同步轨道卫星太阳能电池阵在轨特性分析[J].航天器工程,2013,22(5):61-66.
[10]孙权,冯静,潘正强,等.一种基于电流衰减的太阳能电池寿命预测方法:201510275771.6[P]. 2015-05-27.
[11]王小平,曹立明.遗传算法理论应用与软件实现[M].西安:西安交通大学出版社,2001:83-105.
[12]陈树,张继中.基于自适应均值粒子群算法的SVM参数优化方法[J].测控技术,2018,37(4):6-10.