为研究太阳能PV/T热电联供系统的性能和针对太阳能PV/T系统复杂的能量平衡方程,搭建了太阳能PV/T系统试验台,同时建立了基于改进灰狼优化的BP神经网络(back propagation neural network model based on improved grey wolf algorithm,IG...为研究太阳能PV/T热电联供系统的性能和针对太阳能PV/T系统复杂的能量平衡方程,搭建了太阳能PV/T系统试验台,同时建立了基于改进灰狼优化的BP神经网络(back propagation neural network model based on improved grey wolf algorithm,IGWO-BP)预测模型,在晴朗天气下进行试验,并采用该模型对系统电功率以及蓄热水箱内水温进行预测。结果显示,晴朗日系统的电效率8.7%~12.2%、热效率51.7%;预测结果与BP神经网络预测模型、基于粒子群优化的BP神经网络(back propagation neural network based on particle swarm optimization,PSO-BP)预测模型和卷积神经网络(convolutional neural network,CNN)预测模型预测结果进行比较,结果显示IGWO-BP预测模型电效率预测模型的绝对百分比误差(mean absolute percentage error,MAPE)、决定系数(determination coefficient,R^(2))、均方根误差(root mean square error,RMSE)、效率因子(efficient factor,EF)和Pearson相关系数(pearson related coefficient,r)分别为4.5E-05、0.99、0.24、0.99和1.00,在储热罐温度预测中,上述指标分别为8.90E-04、0.98、0.07、0.98、0.99,均优于其他预测模型,IGWO-BP神经网络预测模型具有更好的预测性能。研究结果可为太阳能PV/T热电联供系统性能预测与优化控制提供参考。展开更多
Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual conta...Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.展开更多
面对居民日益增长的生活热水和电能需求,光伏/光热(photovoltaic/thermal,PV/T)技术的应用可以降低建筑运行时的能源消耗。本文介绍了一种太阳能PV/T光储直驱热电联产(combined heat and power,CHP)系统,为了减少系统运行过程中的能量损...面对居民日益增长的生活热水和电能需求,光伏/光热(photovoltaic/thermal,PV/T)技术的应用可以降低建筑运行时的能源消耗。本文介绍了一种太阳能PV/T光储直驱热电联产(combined heat and power,CHP)系统,为了减少系统运行过程中的能量损失,采用直流压缩机和储能电池,并在兰州地区对系统的运行性能开展了实验测试。研究结果表明,PV/T系统的光伏板温度相比传统PV组件温度平均降低12.26℃,平均发电效率相对提升8.1%。在将24.4~27.2℃的水加热到50.1~50.7℃的过程中,平均性能系数(coefficient of performance,COP)可达到5.48,相比传统空气源热泵热水器提高82.1%~106.8%。平均集热效率和综合效率分别为37.30%和71.24%,PV/T系统的发电量和耗电量分别为3.33kWh和1.69kWh,发电量相比PV系统提高5.7%。太阳能PV/T光储直驱热电联产系统可以减少建筑部门的能源消耗,并提升PV/T系统的发电效率和综合效率,在晴天条件下可以实现离网运行。展开更多
文摘为研究太阳能PV/T热电联供系统的性能和针对太阳能PV/T系统复杂的能量平衡方程,搭建了太阳能PV/T系统试验台,同时建立了基于改进灰狼优化的BP神经网络(back propagation neural network model based on improved grey wolf algorithm,IGWO-BP)预测模型,在晴朗天气下进行试验,并采用该模型对系统电功率以及蓄热水箱内水温进行预测。结果显示,晴朗日系统的电效率8.7%~12.2%、热效率51.7%;预测结果与BP神经网络预测模型、基于粒子群优化的BP神经网络(back propagation neural network based on particle swarm optimization,PSO-BP)预测模型和卷积神经网络(convolutional neural network,CNN)预测模型预测结果进行比较,结果显示IGWO-BP预测模型电效率预测模型的绝对百分比误差(mean absolute percentage error,MAPE)、决定系数(determination coefficient,R^(2))、均方根误差(root mean square error,RMSE)、效率因子(efficient factor,EF)和Pearson相关系数(pearson related coefficient,r)分别为4.5E-05、0.99、0.24、0.99和1.00,在储热罐温度预测中,上述指标分别为8.90E-04、0.98、0.07、0.98、0.99,均优于其他预测模型,IGWO-BP神经网络预测模型具有更好的预测性能。研究结果可为太阳能PV/T热电联供系统性能预测与优化控制提供参考。
文摘Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.
文摘面对居民日益增长的生活热水和电能需求,光伏/光热(photovoltaic/thermal,PV/T)技术的应用可以降低建筑运行时的能源消耗。本文介绍了一种太阳能PV/T光储直驱热电联产(combined heat and power,CHP)系统,为了减少系统运行过程中的能量损失,采用直流压缩机和储能电池,并在兰州地区对系统的运行性能开展了实验测试。研究结果表明,PV/T系统的光伏板温度相比传统PV组件温度平均降低12.26℃,平均发电效率相对提升8.1%。在将24.4~27.2℃的水加热到50.1~50.7℃的过程中,平均性能系数(coefficient of performance,COP)可达到5.48,相比传统空气源热泵热水器提高82.1%~106.8%。平均集热效率和综合效率分别为37.30%和71.24%,PV/T系统的发电量和耗电量分别为3.33kWh和1.69kWh,发电量相比PV系统提高5.7%。太阳能PV/T光储直驱热电联产系统可以减少建筑部门的能源消耗,并提升PV/T系统的发电效率和综合效率,在晴天条件下可以实现离网运行。
文摘针对传统太阳能光伏光热PV/T双源热泵存在的热力性能差、能量损耗大等问题,提出一种光伏直驱PV/T双源热泵制热水系统(太阳能+空气源),并对系统进行实验研究。结果表明,在室外平均环境温度27.9℃、平均太阳辐射强度691.1 W/m2的夏天户外实验工况下,系统运行约4 h,将250 L 26.5℃的水加热到目标温度55℃,热泵平均COP为8.83。实验期间,PV/T光伏组件的平均温度比同样工况下的纯参比光伏组件温度降低9.8℃,光电性能相对提高17.53%。
