The integration of phase change material(PCM)with building-integrated photovoltaic(BIPV)presents a compelling approach to enhance solar energy utilization and mitigate indoor thermal loads,contributing to energy-effic...The integration of phase change material(PCM)with building-integrated photovoltaic(BIPV)presents a compelling approach to enhance solar energy utilization and mitigate indoor thermal loads,contributing to energy-efficient and low-carbon building development.Traditional BIPV-PCM structures,however,struggle to balance PV efficiency and thermal insulation,particularly with varying PCM wall positions.To address this situation,this study introduces a novel double-PCM BIPV composite envelope(BIPV-dPCM).An experimentally validated dynamic heat transfer model was developed and used to perform a comparative simulation analysis with three reference systems to quantify the energy-saving potential of the BIPV-dPCM,focusing on PV output and wall insulation effectiveness metrics.Further dimensionless parametric analysis were carried out to investigate the systematic performance of the two PCMs at different relativities.In addition,the coupled working mechanism of the BIPV-dPCM system concerning the power generation performance and thermal insulation performance under transient variations is explored.It was found that the BIPV-dPCM showcases superior thermoelectric coupling performance compared to three alternative enclosures.Incorporating two PCMs significantly enhances electrical exergy efficiency by 11.66%and thermal exergy efficiency by 1.54%,surpassing other reference systems.The increase in PCM latent heat ratio has a limited effect on performance gain.Notably,as the PCM thickness ratio exceeds 1,the decline in P value decelerates,for every 0.5 increment in the g,the P value diminishes by merely 0.2%.The ideal h is identified between 1 and 1.5,with 1.5 being optimal for energy conservation objectives.Additionally,the self-sufficiency coefficient(SSC)of the BIPV-dPCM remains robust,sustaining a range of 55%to 65%over prolonged periods.This study offers novel perspectives and serves as a design reference for optimizing building energy systems and enhancing cooling efficiencies in subtropical climates.展开更多
Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation with...Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.展开更多
The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage viola...The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage violations that hinder the growth of rural distributed PV systems.Traditional voltage droop-based control methods regulate PV power output solely based on local voltage measurements at the point of PV connection.Due to a lack of global coordination and optimization,their efficiency is often subpar.This paper presents a centralized coordinated active/reactive power control strategy for PV inverters in rural LV distribution feeders with high PV penetration.The strategy optimizes residential PV inverter reactive and active power control to enhance voltage quality.It uses sensitivity coefficients derived from the inverse Jacobian matrix to assign adjustment weights to individual PV units and iteratively optimize their power outputs.The control sequence prioritizes reactive power increases;if the coefficients are below average or the inverters reach capacity,active power is curtailed until voltage issues are resolved.A simulation based on a real 37-node rural distribution network shows that the proposed method significantly reduces PV curtailment.Typical daily results indicate a curtailment rate of 1.47%,which is significantly lower than the 15.4%observed with the voltage droop-based control method.The total daily PV power output(measured every 15 min)increases from 5.55 to 6.41 MW,improving PV hosting capacity.展开更多
In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this pape...In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515010681)Fundamental Research Funds for the Central Universities(Grant No.21622417)Special Projects in Key Fields of Guangdong Universities(2022ZDZX1005).
文摘The integration of phase change material(PCM)with building-integrated photovoltaic(BIPV)presents a compelling approach to enhance solar energy utilization and mitigate indoor thermal loads,contributing to energy-efficient and low-carbon building development.Traditional BIPV-PCM structures,however,struggle to balance PV efficiency and thermal insulation,particularly with varying PCM wall positions.To address this situation,this study introduces a novel double-PCM BIPV composite envelope(BIPV-dPCM).An experimentally validated dynamic heat transfer model was developed and used to perform a comparative simulation analysis with three reference systems to quantify the energy-saving potential of the BIPV-dPCM,focusing on PV output and wall insulation effectiveness metrics.Further dimensionless parametric analysis were carried out to investigate the systematic performance of the two PCMs at different relativities.In addition,the coupled working mechanism of the BIPV-dPCM system concerning the power generation performance and thermal insulation performance under transient variations is explored.It was found that the BIPV-dPCM showcases superior thermoelectric coupling performance compared to three alternative enclosures.Incorporating two PCMs significantly enhances electrical exergy efficiency by 11.66%and thermal exergy efficiency by 1.54%,surpassing other reference systems.The increase in PCM latent heat ratio has a limited effect on performance gain.Notably,as the PCM thickness ratio exceeds 1,the decline in P value decelerates,for every 0.5 increment in the g,the P value diminishes by merely 0.2%.The ideal h is identified between 1 and 1.5,with 1.5 being optimal for energy conservation objectives.Additionally,the self-sufficiency coefficient(SSC)of the BIPV-dPCM remains robust,sustaining a range of 55%to 65%over prolonged periods.This study offers novel perspectives and serves as a design reference for optimizing building energy systems and enhancing cooling efficiencies in subtropical climates.
基金supported by National Natural Science Foundation of China(U1909201,62206062).
文摘Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.
基金supported by the Provincial Industrial Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.of China,grant number JC2024118.
文摘The dense integration of residential distributed photovoltaic(PV)systems into three-phase,four-wire low-voltage(LV)distribution networks results in reverse power flow and three-phase imbalance,leading to voltage violations that hinder the growth of rural distributed PV systems.Traditional voltage droop-based control methods regulate PV power output solely based on local voltage measurements at the point of PV connection.Due to a lack of global coordination and optimization,their efficiency is often subpar.This paper presents a centralized coordinated active/reactive power control strategy for PV inverters in rural LV distribution feeders with high PV penetration.The strategy optimizes residential PV inverter reactive and active power control to enhance voltage quality.It uses sensitivity coefficients derived from the inverse Jacobian matrix to assign adjustment weights to individual PV units and iteratively optimize their power outputs.The control sequence prioritizes reactive power increases;if the coefficients are below average or the inverters reach capacity,active power is curtailed until voltage issues are resolved.A simulation based on a real 37-node rural distribution network shows that the proposed method significantly reduces PV curtailment.Typical daily results indicate a curtailment rate of 1.47%,which is significantly lower than the 15.4%observed with the voltage droop-based control method.The total daily PV power output(measured every 15 min)increases from 5.55 to 6.41 MW,improving PV hosting capacity.
基金supported by the Gansu Provincial Department of Education Industry Support Plan Project(2025CYZC-018).
文摘In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.
