This study presents an experimental investigation of the impulse breakdown characteristics of natural ester oil, in its pure form, and as the base liquid for a nanofluid with Fe203 nanoparticles (0.050% w/w). A two-st...
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This article explores the topic involving children with autism, referencing what this condition is about, as well as the hypotheses for approaching the problem based on the literature reviewed. The proposed approach i...
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In recent years, there has been a tendency for people to resort to a more sedentary lifestyle, which has only been aggravated by the COVID-19 pandemic and the evermore common remote work policies. This lack of physica...
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Introducing all-electric aircraft (AEA) is a viable option to reduce greenhouse gas (GHG) emissions from the aviation sector. The wide-body AEA's electric power system needs higher voltage levels (a few kVs) than ...
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The formulations and approximations of the branch flow model for general(radial and mesh) power networks(General-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of th...
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The formulations and approximations of the branch flow model for general(radial and mesh) power networks(General-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of the exact General-BranchFlow model are listed. The six formats are mathematically equivalent with each other. Linear approximation and second-order cone programming(SOCP) are then used to derive the six formats of the convex General-BranchFlow model. The branch ampacity constraints considering the shunt conductance and capacitance of the transmission line Π-model are derived. The key foundation of deriving the ampacity constraints is the correct interpretation of the physical meaning of the transmission line Π-model. An exact linear expression of the ampacity constraints of the power loss variable is derived. The applications of the General-BranchFlow model in deriving twelve formats of the exact optimal power flow(OPF) model and twelve formats of the approximate OPF model are formulated and analyzed. Using the Julia programming language, the extensive numerical investigations of all formats of the OPF models show the accuracy and computational efficiency of the General-BranchFlow model. A penalty function based approximation gap reduction method is finally proposed and numerically validated to improve the AC-feasibility of the approximate General-BranchFlow model.
This paper introduces a methodology for parameterizing the DER A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The ...
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In coastal regions of China,offshore wind farm ex-pansion has spurred extensive research to reduce operational costs in power systems with high penetration of wind ***,frequent extreme weather conditions such as typho...
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In coastal regions of China,offshore wind farm ex-pansion has spurred extensive research to reduce operational costs in power systems with high penetration of wind ***,frequent extreme weather conditions such as typhoons pose substantial challenges to system stability and ***-vious research has intensively examined the steady-state opera-tions arising from typhoon-induced faults,with a limited em-phasis on the transient frequency dynamics inherent to such *** address this challenge,this paper proposes a frequen-cy-constrained unit commitment model that can promote ener-gy utilization and improve *** proposed model ana-lyzes uncertainties stemming from transmission line failures and offshore wind generation through typhoon *** types of power disturbances resulting from typhoon-in-duced wind farm cutoff and grid islanding events are *** addition,new frequency constraints are defined considering the changes in the topology of the power ***,the complex frequency nadir constraints are incorporated into a two-stage stochastic unit commitment model using the piece-wise ***,the proposed model is verified by nu-merical experiments,and the results demonstrate that the pro-posed model can effectively enhance system resilience under ty-phoons and improve frequency dynamic characteristics following fault disturbances.
The outputs of renewable energy sources(RESs)are inherently variable and uncertain,such as wind power(WP)and photovoltaic(PV).However,the outputs of various types of RESs in different regions are *** the capacity of R...
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The outputs of renewable energy sources(RESs)are inherently variable and uncertain,such as wind power(WP)and photovoltaic(PV).However,the outputs of various types of RESs in different regions are *** the capacity of RESs could be properly allocated during system planning,variability of the total output could be ***,system reliability and renewable energy(RE)consumption could be *** paper proposes an analytical model for optimal complementary capacity allocation of RESs to decrease variability of the total *** model considers the capacity ratio of RESs as decision variables and the coefficient of variation(CV)of the total output as the objective *** proposed approach transforms the single-level optimization model into a bilevel optimization model and derives an analytical equation that can directly calculate the optimal complementary capacity ratio(OCCR)of system *** studies on wind and solar farms in Xinjiang and Qinghai,China,are performed to verify the effectiveness of the proposed analytical allocation method.
In this paper, we utilize hyperspheres and regular n-simplexes and propose an approach to learning deep features equivariant under the transformations of nD reflections and rotations, encompassed by the powerful group...
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In this paper, we utilize hyperspheres and regular n-simplexes and propose an approach to learning deep features equivariant under the transformations of nD reflections and rotations, encompassed by the powerful group of O(n). Namely, we propose O(n)-equivariant neurons with spherical decision surfaces that generalize to any dimension n, which we call Deep Equivariant Hyperspheres. We demonstrate how to combine them in a network that directly operates on the basis of the input points and propose an invariant operator based on the relation between two points and a sphere, which as we show, turns out to be a Gram matrix. Using synthetic and real-world data in nD, we experimentally verify our theoretical contributions and find that our approach is superior to the competing methods for O(n)-equivariant benchmark datasets (classification and regression), demonstrating a favorable speed/performance trade-off. The code is available on GitHub. Copyright 2024 by the author(s)
Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This a...
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