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All Journal Civil Engineering Journal International Journal of Electrical, Energy and Power System Engineering (IJEEPSE)
Li, Bin
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Detection and Classification of Physical and Electrical Fault in PV Array System by Random Forest-Based Approach SYED, Sikandar Shah; Li, Bin; Zheng, Anqi
International Journal of Electrical, Energy and Power System Engineering Vol. 7 No. 2 (2024): The International Journal of Electrical, Energy and Power System Engineering (I
Publisher : Electrical Engineering Department, Faculty of Engineering, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ijeepse.7.2.67-84

Abstract

The importance of solar photovoltaic (PV) systems has increased over the past ten years due to the solar PV industry's explosive growth. To ensure the reliable, safe, and efficient operation of residential PV systems, fault detection is crucial. Early classification of faults can improve PV system performance and reduce damage and energy loss. Many recent studies have focused on classifying and detecting PV faults but most of them are limited to specific reasons like Real-world data can be restricted, unbalanced, or include noise, all of which may decrease the effectiveness of ML models. This paper proposes a method for identifying and classifying both physical and electrical faults in the PV array system applying a machine learning (Random Forest) model to that is trained using a synthetic photovoltaic training database. Make use of a synthetic PV database opens the door to a more precise, effective, and scalable PV system by addressing the limitations of real-world data. MATLAB is used to create a synthetic database while scikit-learn tool in Jupyter Notebook is used to train an ML model are the two main steps in this paper. The performance of the proposed model is compared with the existing ML model and achieves the most effective algorithm offering higher accuracy in detection of 98.6% and classification accuracy is 94.2% for both physical and electrical faults after being successfully tested on real-world datasets and trained on historical data from the PV array system (PV Database).
Mechanical Characteristics of Prestressed Concrete Cylinder Pipe Strengthened by EPS and CFRP Liner Zhai, Kejie; Dang, Mingzhe; Zhang, Yi; Chen, Qiang; Li, Bin; Wang, Niannian; Du, Xueming; Cui, Penglu
Civil Engineering Journal Vol. 11 No. 6 (2025): June
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-06-06

Abstract

Prestressed concrete cylinder pipe (PCCP) has been applied in many large-scale hydraulic engineering projects around the world. And the prestressed wire breakage is the most common form of PCCP damage. Traditional carbon fiber reinforced polymer (CFRP) liner techniques fail to fully exploit the tensile performance of CFRP. Therefore, the method of using EPS cushion and CFRP liner to strengthen the PCCP with broken wire is proposed in this study. To clarify the effect of the proposed method, a finite element three-dimensional model is established and validated using experimental data. Subsequently, the effects of EPS thickness, CFRP thickness, and wire breakage ratio on the stress-strain response of the PCCP are analyzed. Based on different failure modes of the pipe, the influence of EPS and CFRP thickness on the internal pressure bearing capacity is discussed. The study reveals that the synergistic action of the EPS cushion can effectively enhance the internal pressure bearing capacity of the PCCP. As the thickness of EPS cushion and CFRP increases, the bearing capacity almost linearly increases. Under the influence of internal pressure, visible cracks first appear in the concrete core, followed by yielding of the steel cylinder, and finally the steel wire stress reaches its ultimate strength.
Co-Authors Cui, Penglu Dang, Mingzhe Du, Xueming Qiang Chen SYED, Sikandar Shah Wang, Niannian Yi Zhang Zhai, Kejie Zheng, Anqi