The branch flow based optimal power flow(OPF) problem in radianlly operated distribution networks can be exactly relazed to a second order cone programming (SOCP) model without considering transformers. However, the introdution of nonlinear transformer models will make the OPF model non-convex. This paper presents an exact linearized transformer's OLTC model to keep the OPF model convex via binary expanstion scheme and big-M method. Validity of the proposed method is verified using IEEE 33-bus test system.
Thoroughly and accurately identifying various defects on concrete surfaces is crucial to ensure structural safety and prolong service life. However, in actual engineering inspections, the varying shapes and complexities of concrete structural defects challenge the insufficient robustness and generalization of mainstream models, often leading to misdetections and under-detections, which ultimately jeopardize structural safety. To overcome the disadvantages above, an efficient concrete defect detection model called YOLOv11-EMC (efficient multi-category concrete defect detection) is proposed. Firstly, ordinary convolution is substituted with a modified deformable convolution to efficiently extract irregular defect features, and the model’s robustness and generalization are significantly enhanced. Then, the C3k2module is integrated with a revised dynamic convolution module, which reduces unnecessary computations while enhancing flexibility and feature representation. Experiments show that, compared with Yolov11, Yolov11-EMC has improved precision, recall, mAP50, and F1 by 8.3%, 2.1%, 4.3%, and 3% respectively. Results of drone field tests show that Yolov11-EMC successfully lowers false and under-detections while simultaneously increasing detection accuracy, providing a superior methodology to tasks that require identifying tangible flaws in practical engineering applications.
In this paper, a fully distributed power flow algorithm is proposed. The method is composed of outer iteration and inner iteration. The outer iteration is identical to Newton's method, while the inner iteration solves the power flow correction equation using an exponentially fast converged distributed algorithm. The proposed algorithm does not need coordination infrastructure and each area in power systems only need to communicate less important information with neighbors. The numerical tests show that the method is able to converge with the same behavior as Newton's method.
Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks.
A comprehensive understanding of the heterogeneity of urbanization development at different levels and its influencing factors is crucial for promoting global urbanization and advancing China’s new urbanization. Using indicators related to urbanization development, a multidimensional index system was constructed based on five dimensions: population, economy, space, society, and ecology. Employing methods such as the Mann–Kendall test, Sen’s trend analysis, multiple linear regression, and spatial autocorrelation analysis, the spatiotemporal evolution characteristics of urbanization from 2000 to 2019 were analyzed comprehensively at national, economic zone, provincial, and prefectural city scales. The results indicate the following. (1) From 2000 to 2019, urbanization levels at all levels showed an overall upward trend, with the national urbanization rate increasing most rapidly at 5.39%. (2) Trend analysis reveals rapid and significant growth trends in urbanization at the national and economic zone scales, while urban-level changes exhibit greater diversity and spatiotemporal heterogeneity. (3) Spatial distribution patterns show that urbanization levels in the eastern coastal economic zones are significantly higher than those in the northeastern economic zones, highlighting pronounced regional disparities in development and agglomeration effects in economically advanced regions and provinces. (4) Regression analysis demonstrates that spatial urbanization significantly influences urbanization development in China, with urban infrastructure playing a crucial role across different levels.
This letter presents a mixed integer quadratic programming (MIQP) based topology identification model, which is suitable for radially operated distribution networks. This approach finds the topology configuration with weighted least square (WLS) of measurement residues. Validity of the proposed method is demonstrated using an IEEE 33-bus test network.
Optimal transmission network reconfiguration is an important means to improve the economy and reliability of power grid. However, the traditional optimal transmission switching only considers the transmission line switching strategy and some approximate power flow models are adopted to make it tractable, such as linearisation or convex relaxation ones. In this article, first, a comprehensive network reconfiguration model considering both the line switching strategy and the bus splitting strategy inside substations is established. This optimal network reconfiguration incorporating AC power flow equations is an intractable mixed integer non-linear programming problem. The model is first relaxed to a mixed integer second-order cone programming to get the initial solution and an extended feasibility recovery algorithm is developed to obtain the solution satisfying the AC power flow equations. Numerical tests show that the proposed method can reduce the total generation cost and guarantee the system security while the baseline methods cannot obtain feasible solution in most cases.
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