物理学报
物理學報
물이학보
2015年
8期
088801-1-088801-7
,共1页
赵志刚%张纯杰%苟向锋%桑虎堂
趙誌剛%張純傑%茍嚮鋒%桑虎堂
조지강%장순걸%구향봉%상호당
太阳电池温度%热模型%支持向量机%粒子群优化算法
太暘電池溫度%熱模型%支持嚮量機%粒子群優化算法
태양전지온도%열모형%지지향량궤%입자군우화산법
solar cell temperature%thermal model%support vector machines%particle swarm optimization
建立通用而精确的太阳电池热模型对光伏系统的建模、输出功率与转换效率的损失分析至关重要.基于复杂的太阳电池温度机理,分别研究了太阳电池温度的稳态热模型(steady state thermal model, SSTM)和支持向量机(support vector machines, SVM)方法建立的精确预测热模型.首先,基于空气温度、太阳辐射强度、风速3个最主要因素与太阳电池温度的近似线性关系,在已有SSTM的基础上,建立并校正了太阳电池的SSTM并采用差分进化算法提取模型的未知参数.其次,为提高SVM的模型预测精度,采用粒子群优化(particle swarm optimization, PSO)算法对SVM的核参数和惩罚因子进行动态寻优,在确定输入/输出样本集并划分训练集和测试集的基础上,建立了基于粒子群优化支持向量机(PSO-SVM)的太阳电池温度精确预测热模型.最后,搭建实验平台,在实验操作过程中减弱空气湿度、太阳入射角和热迟滞效应等因素对太阳电池温度的耦合.通过实验对比表明,建立的预测热模型性能可靠、全面、简洁,其参数寻优算法优于遗传算法和交叉校验法,模型预测精度优于反向传播神经网络(back propagation neural network)和SSTM.
建立通用而精確的太暘電池熱模型對光伏繫統的建模、輸齣功率與轉換效率的損失分析至關重要.基于複雜的太暘電池溫度機理,分彆研究瞭太暘電池溫度的穩態熱模型(steady state thermal model, SSTM)和支持嚮量機(support vector machines, SVM)方法建立的精確預測熱模型.首先,基于空氣溫度、太暘輻射彊度、風速3箇最主要因素與太暘電池溫度的近似線性關繫,在已有SSTM的基礎上,建立併校正瞭太暘電池的SSTM併採用差分進化算法提取模型的未知參數.其次,為提高SVM的模型預測精度,採用粒子群優化(particle swarm optimization, PSO)算法對SVM的覈參數和懲罰因子進行動態尋優,在確定輸入/輸齣樣本集併劃分訓練集和測試集的基礎上,建立瞭基于粒子群優化支持嚮量機(PSO-SVM)的太暘電池溫度精確預測熱模型.最後,搭建實驗平檯,在實驗操作過程中減弱空氣濕度、太暘入射角和熱遲滯效應等因素對太暘電池溫度的耦閤.通過實驗對比錶明,建立的預測熱模型性能可靠、全麵、簡潔,其參數尋優算法優于遺傳算法和交扠校驗法,模型預測精度優于反嚮傳播神經網絡(back propagation neural network)和SSTM.
건립통용이정학적태양전지열모형대광복계통적건모、수출공솔여전환효솔적손실분석지관중요.기우복잡적태양전지온도궤리,분별연구료태양전지온도적은태열모형(steady state thermal model, SSTM)화지지향량궤(support vector machines, SVM)방법건립적정학예측열모형.수선,기우공기온도、태양복사강도、풍속3개최주요인소여태양전지온도적근사선성관계,재이유SSTM적기출상,건립병교정료태양전지적SSTM병채용차분진화산법제취모형적미지삼수.기차,위제고SVM적모형예측정도,채용입자군우화(particle swarm optimization, PSO)산법대SVM적핵삼수화징벌인자진행동태심우,재학정수입/수출양본집병화분훈련집화측시집적기출상,건립료기우입자군우화지지향량궤(PSO-SVM)적태양전지온도정학예측열모형.최후,탑건실험평태,재실험조작과정중감약공기습도、태양입사각화열지체효응등인소대태양전지온도적우합.통과실험대비표명,건립적예측열모형성능가고、전면、간길,기삼수심우산법우우유전산법화교차교험법,모형예측정도우우반향전파신경망락(back propagation neural network)화SSTM.
Establishing a general and precise solar cell temperature model is of crucial importance for photovoltaic system modeling, the loss analysis of output power, and conversion e?ciency. According to the complex mechanism of solar cell temperature, in this paper we study the steady state thermal model (SSTM) of solar cell temperature and accurate prediction model of method of support vector machine (SVM). Firstly, based on the approximate linear relationship among air temperature, solar radiation intensity, wind speed and solar cell temperature, the polynomial model of solar cell temperature is established and the unknown parameters of the model are extracted with the improved differential evolution algorithm. Secondly, in order to improve the accuracy of SVM prediction model, the particle swarm opti-mization algorithm is adopted to optimize the parameters (including kernel parameter g and penalty factor C from the radial basis function kernel) of SVM. After the input/output sample set is determined and the training set and test set are classified, a prediction model of solar cell temperature based on particle swarm optimization support vector machine is established. Finally, experimental acquisition platform is built to reduce the influences of air humidity, solar incidence angle, and thermal hysteresis effects on PV cell temperature. Through contrasting experiments, it is shown that the established fitting of the SSTM is better than the models given in other literature, and the prediction model is reliable, comprehensive and simple. The selected parameter optimization algorithm is superior to genetic algorithm and cross-validation method established on the optimization performance, and the accuracy of prediction model is superior to the prediction performance of back propagation neural network and identified SSTM.
