中文摘要：

为了使光伏发电系统时刻工作在最大功率点处，构建了非对称基神经网络跟踪光伏最大功率点的方法，给出了该方法的具体实现步骤．依据光伏发电因素对发电效率的影响程度不同，构建了模糊因素隶属函数，计算出影响因素的模糊权值，并将该权值融入到非对称基神经网络结构的构建中．通过固定基宽的径向基函数方法、传统的径向基函数方法以及文中方法，并采用4种数量的样本训练网络，通过网络训练时间及标准差进行对比，可得采用180个样本训练网络的精度最高，且文中方法获得网络的精度高于其他方法至少1个数量级以上．使用这种神经网络时刻识别光伏系统的工作参数，能使光伏系统通过滑动变阻器在任一时刻均能让内外电阻完金匹配，从而保证该系统时刻工作在最大功率点处．

英文摘要：

To ensure that the photovoltaic power generation system always works at its maximum power point, a method for photovoltaic maximum power point tracking by the neural network based on asymmetric basis is proposed, and its concrete implementation steps are given. Fuzzy factor membership functions are built according to the influences of photovoltaic power generation factors on the power generation efficiency, and the fuzzy weights of the influencing factors are calculated, with these weights infused into the building of the neural network based on fuzzy asymmetric basis. The network is trained by using methods of fixed basis width RBF, traditional RBF and the method proposed in this paper with four kinds of quantities of samples, and the comparison in terms of the network training time and the standard deviation indicates that the accuracy of the network with 180 samples is the highest, at least an order of magrtitude higher than other that of methods. By determining the working parameters of the photovoltaic system in real time by using this neural network, the photovoltaic system can make the internal and external resistances completely match at every moment through the slide rheostat, thus ensuring that the system always works at the maximum power point.