中国电机工程学报2024，Vol.44Issue(2)：620-630,中插15,12.DOI:10.13334/j.0258-8013.pcsee.222333
基于贝叶斯优化卷积神经网络的路面光伏阵列最大功率点电压预测方法
A Novel Maximum Power Point Voltage Forecasting Method for Pavement Photovoltaic Array Based on Bayesian Optimization Convolutional Neural Network
摘要
Abstract
The vehicle shadows formed by fast-moving vehicles on the pavement PV array have complex dynamic random distribution characteristics,which will cause the P-V curve of the pavement PV array to exhibit dynamic multi-peak characteristics and bring challenges to the maximum power point tracking(MPPT)control of the pavement PV array.Therefore,a maximum power point voltage forecasting method based on Bayesian optimization(BO)convolutional neural network(CNN)is proposed.The images of environmental information of the pavement PV array are input into the maximum power point voltage forecasting model based on CNN for learning,and then this model is used for predicting the maximum power point operating voltage of the pavement PV array.Finally,simulation and experimental results show that this predicting model has good adaptability and can accurately predict the maximum power point operating voltage of the pavement PV array under different vehicle shadow conditions,especially in greatly improving the forecasting speed of the maximum power point voltage,which lays a foundation for MPPT control of pavement PV array under the shadows of dynamic random vehicles.关键词
动态随机车辆阴影/路面光伏阵列/贝叶斯优化/卷积神经网络/图像信息/最大功率点电压预测Key words
dynamic random vehicle shadows/pavement PV array/Bayesian optimization/convolutional neural network(CNN)/image shadow/maximum power point voltage forecasting分类
信息技术与安全科学引用本文复制引用
毛明轩,冯心营,陈思宇,王立宁..基于贝叶斯优化卷积神经网络的路面光伏阵列最大功率点电压预测方法[J].中国电机工程学报,2024,44(2):620-630,中插15,12.基金项目
国家自然科学基金项目(52107177). Project Supported by National Natural Science Foundation of China(52107177). （52107177）
