Virtual collection for distributed photovoltaic data: Challenges, methodologies, and applications

L Ge, T Du, C Li, Y Li, J Yan, MU Rafiq - Energies, 2022 - mdpi.com
In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the
shortage of data monitoring devices and the difficulty of comprehensive coverage of …

A novel forecasting model for solar power generation by a deep learning framework with data preprocessing and postprocessing

QT Phan, YK Wu, QD Phan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Photovoltaic power has become one of the most popular forms of energy owing to the
growing consideration of environmental factors; however, solar power generation has …

Short-term solar power forecasting using xgboost with numerical weather prediction

QT Phan, YK Wu, QD Phan - 2021 IEEE International Future …, 2021 - ieeexplore.ieee.org
In recent years, solar photovoltaic (PV) generation becomes one of the most relevant
energies. However, the intermittent characteristics of solar generation create significant …

On combined PSO-SVM models in fault prediction of relay protection equipment

H Yuming, L Jiaohong, M Zhenguo, T Bing… - Circuits, Systems, and …, 2023 - Springer
Since there are no monitoring devices in relay protection equipment of substations, it takes
up a lot of manpower and material resources in operation and maintenance (O&M), and the …

[HTML][HTML] Status evaluation method for arrays in large-scale photovoltaic power stations based on extreme learning machine and k-means

L Liang, Z Duan, G Li, H Zhu, Y Shi, Q Cui, B Chen… - Energy Reports, 2021 - Elsevier
Large-scale photovoltaic (PV) power generation has developed rapidly, and its installed
capacity has reached 512 GW worldwide by the end of 2019. The status evaluation for …

Forecasting model of photovoltaic power based on KPCA-MCS-DCNN

H Gou, Y Ning - Computer Modeling in Engineering & Sciences, 2021 - ingentaconnect.com
Accurate photovoltaic (PV) power prediction can effectively help the power sector to make
rational energy planning and dispatching decisions, promote PV consumption, make full use …

Fault diagnosis method via one vs rest evidence classifier considering imprecise feature samples

X Xu, H Guo, Z Zhang, P Shi, W Huang, X Li… - Applied Soft …, 2024 - Elsevier
The key task of fault diagnosis is to establish the nonlinear mapping relationship between
fault feature sequences (FFSs) and fault modes (FMs). Therefore, it is usually necessary to …

Photovoltaic failure diagnosis using sequential probabilistic neural network model

H Zhu, SAZ Ahmed, MA Alfakih, MA Abdelbaky… - IEEE …, 2020 - ieeexplore.ieee.org
With increasing the installation of the photovoltaic modules, the different failures on
components of the system are dramatically increased and thus lead to a direct impact on …

Fault Pre-diagnosis, Type Identification and Degree Diagnosis Method of the Photovoltaic Array Based on the Current-Voltage Conversion

X Chen, M Jiang, K Ding, Z Yang… - … on Power Electronics, 2024 - ieeexplore.ieee.org
The study of fault diagnosis technology is of significant for the operation and maintenance of
photovoltaic (PV) power plants. A pre-diagnosis of PV array faults based on current-voltage …

Distributed Photovoltaic Generation Aggregation Approach Considering Distribution Network Topology

Y Tang, S Sun, B Zhao, C Ni, L Che, J Li - Energies, 2024 - mdpi.com
Distributed photovoltaics (DPVs) are widely distributed and the output is random, which
brings challenges to the safe operation of the distribution network, so the construction of …