基于布谷鸟算法的光伏组件故障诊断模型优化

光伏组件是光伏发电系统中重要的组成部分。为了分析光伏组件在运行过程中出现的故障情况,建立布谷鸟搜索算法优化反向传播(BP)神经网络光伏组件故障诊断模型,并使用布谷鸟搜索算法寻找BP神经网络中的阈值和权值,降低网络对初始值的敏感度,避免网络陷入局部最小,实现模型分类效果的优化。对比结果显示,该模型能够准确有效地识别光伏组件的故障类型。相对于其他算法,优化的故障诊断模型具有更高的精确度,证明了该模型的有效性和可行性。

Optimization of fault diagnosis model for photovoltaic module based on cuckoo algorithm

Photovoltaic modules are important parts of photovoltaic power generation systems.In order to analyze the fault conditions of the PV module during operation,a cuckoo search algorithm was proposed to optimize the BP neural network PV module fault diagnosis model.In order to reduce the sensitivity of networks to the initial values and avoid the networks falling into the local minimum,the cuckoo search algorithm was implemented to find the threshold and weight in the BP neural network,thus the model classification effect was optimized.The comparison test results show that the algorithm can accurately and effectively identify the fault type of PV modules.Compared with other algorithms,the optimized fault diagnosis model has higher accuracy,which proves the validity and feasibility of the model.