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Robust Allocation and Scheduling of Electric Parkings and Wind Resources in Distribution Networks Using Information Gap Decision Theory and improved flow direction algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2024年第1期2024卷

作者： Arabahmadi, Neda Ebrahimi, Reza Ghanbari, Mahmood Islamic Azad Univ Dept Elect Engn Gorgan Branch Gorgan Iran

This paper proposes a robust scheduling approach for electric parking lots (EPLs) integrated with battery storage and wind power sources in distribution networks, aiming to minimize the cost-to-revenue function. The method is based on information gap decision theory with a risk aversion strategy (IGDT-RAS) and takes into account uncertainties in network load and wind power. In deterministic scheduling, decision variables include the location and capacity of the EPLs and wind resources in the network, while in robust scheduling, the maximum uncertainty radius (UR) is determined using an improved flow direction optimization algorithm (IFDA), enhanced by an opposition learning strategy (OLS). The proposed method is applied to the 33- and 45-bus networks. The deterministic approach results in a lower cost-to-revenue ratio, reduced energy losses, and improved reliability compared to traditional FDA, whale optimization algorithm (WOA), and particle swarm optimizer. In robust scheduling, for the 33-bus network, the largest UR for load and wind power is 8.70% and 17.06%, respectively, while for the 45-bus network, it is 8.45% and 32.36%, respectively. The robustness of the network against the worst-case uncertainty scenario is demonstrated in the robust scheduling, and the superior performance of IGDT-RAS over Monte Carlo simulation (MCS) is confirmed in achieving a reliable cost level.

关键词： electric parking lots energy storage improved flow direction algorithm information gap decision theory robust scheduling wind energy resources

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Wireless channel corrupted image denoising using residual learning of adaptive wavelet with dilated deep convolutional neural network

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第3期18卷 2309-2321页

作者： Sreedhar, M. Aparna, K. Jawaharlal Nehru Technol Univ Anantapur Ananthapuramu Andhra Pradesh India Jawaharlal Nehru Technol Univ Anantapur JNTUA Coll Engn ECE Dept Constituent Coll Kalikiri Ananthapuramu Andhra Pradesh India

In digital image transmission, channel coding is essential for data integrity and accurate reception during digital communication. While minimizing the consequences of the channel, typical communication methods neglect the received context and content-related data. Many significant restrictions are applied to image transmission in digital communication. Due to the limited qualities of transferred data, image quality suffers across over wireless networks. Moreover, the transmission of digital images is a complicated task based on their shape, size, and bandwidth. Hence, it lacks flexibility and practicality in the real-world environment. Different image-denoising techniques are employed to decrease the noisy image channel effects. Thus, this research aims to analyze the images gathered during wireless channel communication to precisely eliminate the bugs and the impact of channel degradation. The image denoising is carried out during the wireless channel communication process at the receiver end. A novel deep Residual Learning of Adaptive Wavelet with Dilated Deep Convolutional Neural Network (RL-AWDDCNN) method is used to denoise the image more effectively. Hence, the residual images are generated based on the heuristic concept, where an improved flow direction algorithm (IFDA) is developed for optimizing the wavelet parameters with Dilated Deep Convolutional Neural Network. Throughout the result analysis, the designed method scored a 3037.233% peak signal-to-noise ratio (PSNR) rate and an 80.18675% structural similarity index measure (SSIM) rate. Thus, the developed denoising model's performance and the experimental results are analyzed and compared with various existing methods concerning the standard image quality measures.

关键词： Image denoising Dilated deep convolutional neural network improved flow direction algorithm Discrete wavelet transform Image decomposition

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