The integration of the Photovoltaic (PV) systems changes the nature of the power flow in the network and causes several problems such as voltage deviation which is considered the most important issue in electrical pow...
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The integration of the Photovoltaic (PV) systems changes the nature of the power flow in the network and causes several problems such as voltage deviation which is considered the most important issue in electrical power systems. In this work, the Augmented Grey Wolf optimization (AGWO) algorithm and advanced nonparametric models are proposed to mitigate the voltage deviation in the distribution network equipped with a PV farm. In the first stage of the work, the AGWO calculates the optimal value of reactive power for Static Synchronous Compensator (STATCOM) to relieve the voltage deviation. This stage is applied only in the offline mode due to the delay in AGWO's dynamic response caused by its iteration process in the computation. Therefore, in the second stage, the data set of AGWO is used to train the nonparametric models;Linear Regression (LR) and Support Vector Machine (SVM) to mitigate the voltage deviation quickly in the online mode. Jordanian Sabha Distribution Network (JSDN) equipped by PV farm is considered and modeled as a real case study to validate the proposed approach. The results showed the superior ability of the proposed integrated approach to handle the voltage deviation quickly and accurately.
The seismic displacement time history of a structure is very important for damage assessment. This paper proposes a displacement estimation method based on multisource measurement data. In this method, the structure i...
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The seismic displacement time history of a structure is very important for damage assessment. This paper proposes a displacement estimation method based on multisource measurement data. In this method, the structure is simplified to a mass-spring shear model, where a set of tunable parameters are assigned. The particle swarm optimization algorithm is used to search for the optimal values of the parameters, and the objective of the optimization problem is to minimize the difference between the measured roof response and that calculated by the model. A refined finite element model was established in ABAQUS, and the numerical calculation results were used to verify this proposed method. Results show that the estimation error of the maximum inter-story drift ratio among all floors is 8.9%, and the average error of the maximum displacement of each floor is 7.0%, representing a satisfactory estimation effectiveness. The source of the errors is discussed, and we find that this method is more suitable for low-rise buildings. Shaking table tests of a scaled four-story reinforced concrete (RC) frame were conducted, and the estimation error of the maximum inter-story drift ratio among all floors was 6.1%, indicating the feasibility of this method in practice.
This paper developed a new algorithm to predict dynamic thermal line rating to increase the capacity of transmission lines, which can enhance the capacity of wind power integrated to the grid and reduce the curtailmen...
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This paper developed a new algorithm to predict dynamic thermal line rating to increase the capacity of transmission lines, which can enhance the capacity of wind power integrated to the grid and reduce the curtailment. The proposed dynamic thermal line rating prediction model was trained by analyzing historical meteorological data and conductor physical parameters, and used deep learning with parameters optimized by an optimized algorithm. The prediction accuracy of the model is verified by Mean Absolute Error, R2 and comparison with other models. The simulation results show that the proposed prediction model has a good performance. The suggested dynamic thermal line rating algorithm, which bears resemblance to the actual value, boosts the static thermal line rating by varying degrees of 23% to 75% at different instances throughout the sample. At the same time, this paper designs an optimal power flow economic dispatch objective function. By comparing the economic dispatch of the power grid calculated by adding static thermal line rating and the prediction models, the method proposed in this paper can effectively increase the amount of wind power inte-gration and reduce power generation costs.
Network pruning extends a promising prospect to neural network compression. However, applications of existing methods to various vision tasks, e.g., human pose estimation, are limited by the parameter distribution dif...
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Network pruning extends a promising prospect to neural network compression. However, applications of existing methods to various vision tasks, e.g., human pose estimation, are limited by the parameter distribution difference, shortage of general pruning strategies and high pruning cost. As a complex and widely used backbone network, HRNet is an effective benchmark for studying these issues. We propose an adaptive Hard channel pruning method with an Adaptive Compression Rate to replace manual expe-rience in pruned model selection, called HACR. It first designs a hard criterion that takes gamma and fi to measure channel importance with the insight of the consistency relationship between the distribution adjustment of the channel and the final output distribution. Then, a partial pruning strategy is advanced for the complex structural network by only pruning channels without cross-layer integration and keeping the network infrastructure. Next, we adaptively predict an optimal compression rate based on the val-idation metric of unfine-tuned models, providing a reference for compression parameter selection. The processes of HACR are simplified to adaptive pruned model selection and fine-tuning with lower prun-ing cost. Furthermore, the numerical distribution of gamma and fi is used to extend HACR in common pruning task benchmarks, based on the a principle that lower level channels should be preferentially retained. We conduct HRNet pruning experiments on COCO2017 and MPII, and promote extension study on CIFAR-10 to verify the generality of strategy. The results show that HACR is effective on multiple tasks, e.g. HRNet-W48 reduces 58.2% Params and 50.1% FLOPS, and VGG-16 reduces 91.5% Params and 67.5% FLOPS with only 0.3% precision loss. (c) 2023 Elsevier B.V. All rights reserved.
The objective of utilizing mmWave/subTHz bands in next-generation wireless communications is to be achieved. Despite this, since reconfigurable intelligent surface (RIS)-assisted systems depend on the transmission cha...
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The objective of utilizing mmWave/subTHz bands in next-generation wireless communications is to be achieved. Despite this, since reconfigurable intelligent surface (RIS)-assisted systems depend on the transmission channel configuration, the system architecture design, and the methods used to derive channel state information (CSI) on a base station (BS) and RIS, channel estimation continues to be the main problem with these systems. This research proposes an innovative RIS-based and compressed sensing-based channel estimation technique for the internet of vehicles. To obtain the best phase shift matrix, the communication model must first be constructed, and the angle-of-arrival and departure are utilized. Channel estimation is then performed based on the perception matrix. The training overhead and complexity of the channel estimation are reduced by considering the position information of the vehicles in the optimal phase shift matrix. Simulation results show that the proposed algorithm exhibits better channel estimation and low complexity performance compared with existing algorithms.
"Mobile Ad Hoc Network (MANET)"is a self-configurable, self-repairing, self-maintaining, highly mobile, decentralized, and independent wireless network, which has the liberty to move from one to another plac...
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"Mobile Ad Hoc Network (MANET)"is a self-configurable, self-repairing, self-maintaining, highly mobile, decentralized, and independent wireless network, which has the liberty to move from one to another place. Such networks do not have any pre-existing infrastructure. The adoption of a smart environment in MANET requires new protocols to connect the gadgets to the internet. A smart environment with routing protocols should assure the following properties like connectivity among the nodes, "Quality of Service (QoS)", and fairness, both in access points and ad-hoc networks. Combination with the Internet of Things (IoT) and MANET generates a novel MANET-IoT system, which focuses on reducing the implementing costs of the network and providing better mobility for users. The necessity of these integrated networks is increasing in military operations, rescue operations, personal area networks, emergency rooms, and meeting rooms. Routing in MANETs is a not simple job and has projected a huge range of attention from researchers around the world. Thus, the intention of this task is a development of a security protocol in MANET for the IoT platform. For dealing with encryption and decryption strategies to handle MANET and IoT data, a new approach is suggested through the enhanced chaotic map. Here, three improved algorithms are implemented for proposing the optimized key management scheme under a chaotic map, which is the Modified Updating-based Harris Hawks optimization algorithm (MU-HHO), Mean Solution-based Averaging Sailfish Optimizer (MS-ASFO), Adaptive Basic Reproduction Rate-based Coronavirus Herd Immunity Optimizer (ABRR-CHIO). In the convergence evaluation, while taking the length of plain text as 40, ABRR-CHIO shows superior performance over other techniques at the 60th iteration, which is 96%, 95%, 93%, 96%, and 80% superior to HHO, SFO, CHIO, SA-SFO, and CHHSO. Finally, the performance evaluation is performed regarding "statistical analysis, convergence analysis, a
Due to climate change consequences, all Member States of the European Union signed an agreement with the goal of becoming the first society and economy with a neutral impact on the planet by 2050. The building sector ...
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Due to climate change consequences, all Member States of the European Union signed an agreement with the goal of becoming the first society and economy with a neutral impact on the planet by 2050. The building sector is one of the highest energy consumers, using 33% of global energy production. Given the global increase for energy demand, implementing energy flexibility strategies is crucial for a better integration of renewable energy sources and a reduction of consumption peaks arising from the electrification of energy demand. The work described in this paper aims to develop an optimization algorithm to use the existing aggregated energy flexibility in office buildings to reduce both the electric energy costs of each office, considering the tariffs applied at each moment and the total power peak, aiming to reduce the entire building's cost of the contracted power, considering the Portuguese context. The obtained results conclude that it is possible to reduce both the costs associated with electric energy consumption and contracted power. Nevertheless, since the cost of contracted power has a lower impact on the overall energy bill, it is more beneficial to focus only on the reduction of costs associated with electric energy consumption in the considered case study.
The aim of this paper is to design an optimal membership function (MF)-based fuzzy PI (proportional integral) controller to control the core power of a nuclear reactor. The molten salt breeder reactor (MSBR) is extens...
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The aim of this paper is to design an optimal membership function (MF)-based fuzzy PI (proportional integral) controller to control the core power of a nuclear reactor. The molten salt breeder reactor (MSBR) is extensively utilized for studying primary power management of nuclear reactors. Many challenges are associated with such systems, including mathematical modelling errors, parametric uncertainties, and external disturbances. Moreover, the liquid fuel used in MSBR systems makes it more challenging to design a suitable controller for the core relative power control of such systems. The conventional PI controller may not perform efficiently under such uncertainties in the MSBR system. Different types of perturbations can be handled by a fuzzy controller, if the coordination among fuzzy rules and membership functions of fuzzy variables is done accurately. The idea of this work is to optimize the settings (range and scale) of fuzzy MFs in a fuzzy base controller. The fuzzy controller will not be used directly in the system;rather, it will be used to tune a PI controller for the system where proper expert knowledge may not be available. A nonlinear dynamic acceleration coefficients -based class topper optimization (NDAC-CTO) algorithm is also developed to optimize the fuzzy membership functions. For load tracking with step disruption, the power control of the MSBR core is investigated. With the proposed controller, a significant improvement of 70 to 80 % in the settling time of the power profile of the MSBR system is achieved in comparison with the existing results.
In the digital era, information security becomes a challenging process that can be miti-gated by the utilization of cryptography and steganography techniques. Earlier studies on steganog-raphy have the risk of exposin...
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In the digital era, information security becomes a challenging process that can be miti-gated by the utilization of cryptography and steganography techniques. Earlier studies on steganog-raphy have the risk of exposing confidential data by an anonymous user. For resolving, the limitations related to the existing algorithms, one of the efficient solutions in encryption-based steganography. Encryption techniques act as an important part in protect actual data from illegal access. This study focuses on the design of Bald Eagle Search Optimal Pixel Selection with Chaotic Encryption (BESOPS-CE) based image steganography technique. The presented BESOPS-CE tech-nique effectively hides the secret image in its encrypted version to the cover image. For accomplish-ing this, the BESOPS-CE technique employs a BES for optimal pixel selection (OPS) procedure. Besides, chaotic encryption was executed for encrypting the secret image, which is then embedded to choose pixel points of the cover image. Finally, embedding and extraction processes are carried out. The inclusion of the encryption process aids in accomplishing an added layer of security. A comprehensive simulation study was used to report on the BESOPS-CE approach's increased per-formance, and the results are examined from many angles. A thorough comparative analysis revealed that the BESOPS-CE model outperformed more contemporary methods.& COPY;THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).
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