随着社会的高速发展和对能源需求量的急速上升，国家能源结构从传统能源向新 能源的方向转变。光伏发电作为国家大力推崇的新能源项目，其主要是通过光伏组件 吸收太阳光照产生电能，实现对能量的转化。光伏组件表面积灰的产生会影响光伏组 件工作效率，因此开展光伏组件表面积灰的研究与积灰对光伏组件的影响研究，将对 大力发展光伏发电具有很重要的研究意义和价值。论文就以实现对光伏组件表面积灰 预测和分析积灰密度与组件功率之间的影响关系为研究目的，通过进行建立积灰预测 模型和搭建积灰状态下光伏组件功率监测平台来开展论文的研究工作，研究工作如下：

  首先，对光伏组件表面积灰成因进行分析，确定影响光伏组件产生积灰因素为搭 建预测模型输入量选取提供理论依据；与此同时，分析光伏组件表面产生灰尘堆积后 对其产生的影响，进一步展开分析了积灰对组件产生遮挡效应、温度效应和腐蚀效应 的原因；经过综合对比分析后，选定支持向量机（Support Vector Machine，SVM）作 为进行光伏组件积灰预测的模型并对 SVM 模型进行搭建。

  其次，为了提高 SVM 模型的预测精度提出采用麻雀算法（Sparrow Search Algorithm，SSA）对 SVM 中关键参数惩罚因子和核函数宽度进行寻优。针对标准 SSA 算法的种群初始化过程中个体集聚现象、麻雀种群比例固定和容易陷入局部收敛的问 题，通过引入 Logistic-tent混沌映射、发现者-加入者自适应调整策略和差分变异算子进 行针对性的改进，得到改进算法 GSSA。为了验证改进后的 GSSA 算法的寻优效果，使 用 2 种类别的 6 个不同函数进行算法求解性能的测试，并与常用的粒子群算法、灰狼算 法以及标准麻雀算法进行对比。最终的测试结果显示，改进的 GSSA 算法在对函数的 求解速度和求解精度上均优于另外3种算法，证明了改进后算法的优越性，并将改进算 法 GSSA 与 SVM 模型进行结合后搭建 GSSA-SVM 积灰预测模型。

  最后，通过实测采集的积灰数据和使用 LabVIEW 软件搭建上位机监测平台获取的 不同积灰状态下光伏组件的功率输出数据，分别进行对搭建的 GSSA-SVM 积灰模型的 预测效果检验与不同积灰密度与光伏组件功率衰减率之间影响关系的分析。测试结果 表明，在所提 3 种评价指标的表现上提出的 GSSA-SVM 实测效果最好，其平均绝对百 分比误差、均方根误差、平均绝对误差分别为 4.643、0.011、0.0064；此外，通过对积灰组件的功率进行分析后，发现在工作环境一致的情况下，两者之间为非线性的正相 关关系，光伏组件功率衰减率伴随着积灰密度的增大出现增高。

  With the rapid development of society and the rapid increase in energy demand, the national energy structure has changed from traditional energy to new energy. As a new energy project highly praised by the country, photovoltaic power generation mainly absorbs solar light through photovoltaic modules to generate electric energy and realize energy conversion. The generation of ash on the surface area of photovoltaic modules will affect the working efficiency of photovoltaic modules. Therefore, the research on the ash on the surface area of photovoltaic modules and the influence of ash deposition on photovoltaic modules will be of great significance and value for the development of photovoltaic power generation. The purpose of this paper is to realize the prediction of ash deposition on the surface area of photovoltaic modules and analyze the influence relationship between ash deposition density and module power. The research work of this paper is carried out by establishing the ash deposition prediction model and building the power monitoring platform of photovoltaic modules under the condition of ash deposition. The research work is as follows:

  Firstly, the causes of ash deposition on the surface area of photovoltaic modules are analyzed, and the factors affecting the ash deposition of photovoltaic modules are determined to provide a theoretical basis for the selection of the input amount of the prediction model. At the same time, the influence of dust accumulation on the surface of photovoltaic modules is analyzed, and the causes of shielding effect, temperature effect and corrosion effect of dust accumulation on modules are further analyzed. After comprehensive comparative analysis, Support Vector Machine(SVM) is selected as the model for predicting the ash deposition of photovoltaic modules and the SVM model is built.

  Secondly, in order to improve the prediction accuracy of the SVM model, Sparrow Search Algorithm (SSA) is proposed to optimize the penalty factor and kernel function width of the key parameters in the SVM. Aiming at the problems of individual agglomeration, fixed proportion of sparrow population and easy to fall into local convergence in the process of population initialization of standard SSA algorithm, the improved algorithm GSSA is obtained by introducing Logistic-tent chaotic mapping, discoverer-joiner adaptive adjustment strategy and differential mutation operator. In order to verify the optimization effect of the improved GSSA algorithm, six different functions of two categories are used to test the performance of the algorithm, and compared with the commonly used particle swarm optimization algorithm, grey wolf algorithm and standard sparrow algorithm. The final test results show that the improved GSSA algorithm is superior to the other three algorithms in the solution speed and accuracy of the function, which proves the superiority of the improved algorithm, and combines the improved algorithm GSSA with the SVM model to build the GSSA-SVM ash accumulation prediction model.

  Finally, through the measured ash deposition data and the power output data of photovoltaic modules under different ash deposition conditions obtained by using LabVIEW software to build the host computer monitoring platform, the prediction effect test of the built GSSA-SVM ash deposition model and the analysis of the relationship between different ash deposition density and the power attenuation rate of photovoltaic modules are carried out respectively. The test results show that the GSSA-SVM proposed in the performance of the three evaluation indexes is the best, and its average absolute percentage error, root mean square error and average absolute error are 4.643, 0.011 and 0.0064, respectively. In addition, through the analysis of the power of the ash deposition module, it is found that there is a non-linear positive correlation between the two in the case of the same working environment, and the power attenuation rate of the photovoltaic module increases with the increase of the ash deposition density.

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