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Energy Management in the Microgrid and Its Optimal Planning for Supplying Wireless Charging Electric Vehicle
The ongoing research work on electric vehicles (EVs) as well as the growing concern around the world to ensure a pollution-free environment is sure to lead to a significant increase in the number of EVs in the near future. The electrification of automobiles is an inevitable trend of future development. However, the growth of EVs relies on several elements: autonomy, the charging practice and infrastructure, the price, and the high amount of energy needed for supplying EV. This tendency impacts several points in transportation such as the road infrastructure and electrical power network. The aim of this article is the integration of new energy power sources as a part of the microgrid (MG) to supply EV with dynamic wireless charging. The main goal is to establish an energy management strategy reducing the running cost. The purpose is suggested for two kinds of operation mode: relying only on the MG (island mode) or relying on the MG and the large grid (grid-connected). The optimization problem is solved on the basis of the particle swarm optimization (PSO) algorithm. We could note that the stability of the microgrid in the off-grid mode is better, when the load is close to the output power of the distributed power supply. Through the coordination and cooperation of the battery output and the other two distributed power generation units, the microgrid can achieve its autonomy and maximize the economy of the system operation. Thanks to our methodology, a better revenue and an enhanced flexible dispatching of the system were met in the grid-connected mode as well.
Multilevel Thresholding for Image Segmentation Using Mean Gradient
Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically impossible to implement these algorithms in a low-capacity hardware application where computational power and memory utilization are of great concern. In this study, a simple yet effective and noniterative global and bilevel thresholding technique is proposed. It uses the concept of image gradient vector to binarize or segment the image into three clusters. In addition, a parametric preprocessing approach is also proposed that can be used in image restoration applications. Evidences from the experiments from both visual and standard evaluation metrics show that the proposed methods perform exceptionally well. The proposed global thresholding outperforms the formidable Otsu thresholding technique.
Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking
Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. However, due to the large number of existing deep features, it is difficult to deploy it on mobile devices to achieve real-time tracking speed. This paper presents a target-aware deep feature compression for power intelligent inspection tracking. First, a negative balance loss function is designed to mine channel features suitable for the current inspection scene by shrinking the contribution of pure background negative samples and enhancing the impact of difficult negative samples. Based on this, the deep feature compression model is combined with Siamese tracking framework to achieve real-time and robust tracking. Finally, we evaluate the proposed method on real application scenarios and general data to prove the practicability of the proposed method.
Integrated Planning of MV/LV Distribution Systems with DG Using Single Solution-Based Metaheuristics with a Novel Neighborhood Search Method Based on the Zbus Matrix
This paper presents a new methodology for the optimal integrated planning of medium- and low-voltage distribution systems, considering the location and sizing of distributed generation. The integrated problem is formulated as a mixed-integer nonlinear model and, to solve it, two well-known optimization algorithms (simulated annealing and iterated local search) are used. The intensification and diversification processes are usually the bottleneck of metaheuristic techniques for solving complex problems. To overcome such complexity, a new neighborhood search method based on the Zbus matrix (NSZM) is proposed, to explore the solution space more efficiently and effectively for both algorithms. The proposed methodology is validated and tested on a real distribution system taken from the literature. The results obtained are better than those reported in the literature. To verify the efficiency of the new NSZM method, the Wilcoxon signed ranks test is used to measure the performance behavior of the NSZM method in the two optimization algorithms used. The numerical results demonstrate that the NSZM method enhances both algorithms equally.
Filtering Algorithm for Positioning Accuracy of the Logistics Tracking System Based on the 3D Virtual Warehousing Logistics Demonstration System
Under the background of the global integrated supply chain, the work of logistics is more and more complicated. Warehouse management is now an important part of logistics. The optimization of the logistics tracking system in the building material market proves that the tracking result of the system is highly reliable. The system has the advantages of small size, low cost, accurate positioning, real-time convergence, and high performance.
Techno-Economic Comparisons of HVAC and Simultaneous AC-DC Transmission
Electric power consumption has been increasing rapidly across the globe; this increase specially accelerated in the last decade. Consequently, existing transmission lines are becoming overloaded beyond their power transfer capability. The inadequacy of the transmission lines has contributed to power interruptions and instability of the power system. Construction of new transmission lines can be a solution to mitigate these problems. However, to build the whole structure of a new transmission line, a huge investment is required. Besides this, environmental concerns would create further barriers that delay the accomplishment of the project. Therefore, the effective and quickest solution to tackle this problem is enhancing the power transfer capability and stability margin of existing transmission lines. This paper studied the techno-economic feasibility of converting an existing HVAC line into a simultaneous AC-DC power transmission system to enhance power transfer capability as well as to improve power system stability. Using the proposed method, the loadability of Tana Beles to Addis Ababa 400 kV, 476.2 km AC line has increased to more than double which is from 1091.66 MW to 2196.85 MW. The active power loss and corona loss evaluation of the two systems ensured that simultaneous AC-DC system is more efficient than HVAC system. It is also shown that the instability can be effectively improved by simultaneous AC-DC power transmission with fast DC power modulation. From the economic point of view, rather than constructing new HVAC line, converting existing HVAC line into simultaneous AC-DC transmission system has a price reduction of about 107,984,968.56 USD or 32.46% of the new HVAC line cost. Considering a 35-year project life cost analysis, it is observed that the life cycle cost of the simultaneous AC-DC transmission system is about 29.2% lower than the life cycle cost of a new 400 kV HVAC line. Thus, the designed simultaneous AC-DC power transmission system has better technical performance and also is less costly than constructing a new HVAC line.