Coordinate Approach Research Articles

Adaptive Directional Overcurrent Relay Coordination in Microgrid Considering Optimal Full Setting Parameters

This article proposes a novel approach for the optimal coordination of adaptive directional overcurrent relay (ADOCR) for modernized active distribution system (ADS) with microgrid (MG) functions. The goal is to keep the coordinating circumstances while reducing the protective relay operation time, as much as possible. With the proposed round-off mixed-integer particle swarm optimization (ROMI-PSO), the setting parameters are not limited to only relay time multiplier setting (TMS) and pickup current setting (PS) but can include the discrete parameter as relay characteristic curve setting (CS). In the proposed framework, The CS can be both IEC and IEEE standard curves in the proposed framework. Four buses radial system and IEC-benchmark MG system were used in a number of simulation experiments. Comparing the suggested methodology to other current methods revealed that it is more effective at determining the minimum relay operating times and ensuring the proper operation of protections under various fault allocations and operating circumstances.

Artificial intelligence based optimal coordination of directional overcurrent relay in distribution systems considering vehicle to grid technology

Optimal coordination of overcurrent relays has become a major challenge in power distribution systems due to the increasing participation of distributed generation (DG) and electric vehicle (EV) charging stations, especially vehicle-to-grid (V2G) technology. This paper proposes a novel approach for optimal coordination of directional overcurrent relays (DOCRs) to achieve the minimum operating time and obtain optimal tuning in terms of time dial setting (TDS) and pick-up current (IP) considering V2G integration. The optimization was conducted using three different Artificial Intelligence (AI) techniques: grey wolf optimization (GWO), cuckoo search (CS), and Hunger Games Search (HGS), and was performed on two systems: IEEE 33-bus system and a local grid located in El-Shorouk City–district#8 (ShC-D8), Cairo, Egypt. The proposed methodology is constructed and validated on ETAP and MATLAB software packages. The simulation results demonstrate that the proposed optimization techniques provide high dynamic performance and accurate coordination of DOCRs. The results show that HGS achieves the minimum operating time of all overcurrent relays as compared with GWO and CS. The effect of V2G integration on the coordination methodology is also considered. The proposed approach provides a significant contribution to the field of power system protection and can help improve the reliability and efficiency of power distribution systems with V2G integration.

Synchronization transitions in Kuramoto networks with higher-mode interaction.

Synchronization is an omnipresent collective phenomenon in nature and technology, whose understanding is still elusive for real-world systems in particular. We study the synchronization transition in a phase oscillator system with two nonvanishing Fourier-modes in the interaction function, hence going beyond the Kuramoto paradigm. We show that the transition scenarios crucially depend on the interplay of the two coupling modes. We describe the multistability induced by the presence of a second coupling mode. By extending the collective coordinate approach, we describe the emergence of various states observed in the transition from incoherence to coherence. Remarkably, our analysis suggests that, in essence, the two-mode coupling gives rise to states characterized by two independent but interacting groups of oscillators. We believe that these findings will stimulate future research on dynamical systems, including complex interaction functions beyond the Kuramoto-type.

A cylindrical coordinates approach concerning azimuthal geophysical flows

A cylindrical coordinates approach concerning azimuthal geophysical flows

Massive Parallelization of Massive Sample-Size Survival Analysis

Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this article, we use Graphics Processing Units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time- and memory-efficient single-pass parallel scan algorithms for Cox proportional hazards models and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders of magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics. The above implementation is available in the open-source R package Cyclops.

RBDL: Robust block-Structured dictionary learning for block sparse representation

Dictionary learning methods have been extensively used in different types of image and signal processing tasks. In a number of applications, the collected data/signal may have a multi-subspace structure and be perturbed with outliers. These motivate the use of robust and block-sparse signal representations. In this paper, a new algorithm for learning a block-structured dictionary in the presence of outliers is proposed. It is based on α−divergence and has the advantage of tolerating the presence of outliers. A block coordinate descent approach is adopted to obtain simple closed-form solutions for both the sparse coding and dictionary update stages. Finally, experimental results illustrating the superiority of the proposed method over some state-of-the-art dictionary learning methods, are provided.

Data-Driven Self-Organization With Implicit Self-Coordination for Coverage and Capacity Optimization in Cellular Networks

Coverage and Capacity Optimization (CCO) and Inter-Cell Interference Coordination (ICIC) are two tightly coupled and conflicting Self-Organizing Network (SON) functions that are responsible for ensuring optimal coverage and capacity in any cellular network. While executing currently, these functions may modify the same RF and antenna parameters, resulting in severe performance deteriorations. In this context, a centralized optimization and coordination approach may be impractical considering the large sizes of network clusters and the dynamics involved between the several other defined SON use cases. In this work, an implicitly coordinated and scalable self-organizing architecture is followed such that when a carefully defined multi-objective utility function for CCO-ICIC joint optimization is optimized locally by each RAN node, a desired balance between the two conflicting network targets of coverage and capacity is ensured globally. Pareto analysis of three variants of the proposed Local Multi-Objective KPI (LMO KPI) has been conducted to implicitly coordinate the two SON functions in a distributed self-organized manner. In order to recommend appropriate network configurations dynamically to quickly adapt to altering network environments, two collaborative filtering-based Recommender Systems (RecSys), one using a Deep Autoencoder and another based on Singular Value Decomposition, have been employed along with a neural network regressor to improve recommendations for cold-start scenarios. The two proposed hybrid-RecSys-based SON coordination solutions, while adopting an appropriate Local Multi-Objective KPI (LMO KPI), outperform previous work in coverage by 36% and in capacity by around 2% while reducing power consumption by more than 50%. The study demonstrates that the definition of the LMO KPI is crucial to the performance of this approach. Altogether, the work shows that the adopted self-organization and implicit SON-coordination approach is not only feasible and performant but also scales well if implemented meticulously.

Emergent Magnetic Field and Nonzero Gyrovector of the Toroidal Magnetic Hopfion

Magnetic hopfions are localized magnetic solitons with a nonzero 3D topological charge (Hopf index). Herein, an analytical calculation of the magnetic hopfion gyrovector is presented and it is shown that it does not vanish even in an infinite sample. The calculation method is based on the concept of the emergent magnetic field. The particular case of the simplest nontrivial toroidal hopfion with the Hopf index in the cylindrical magnetic dot is considered and dependencies of the gyrovector components on the dot sizes are calculated. Nonzero hopfion gyrovector is important in any description of the hopfion dynamics within the collective coordinate Thiele's approach.

Distributed Optimal Coordination of a Virtual Power Plant with Residential Regenerative Electric Heating Systems

Renewable energy sources play a key role in the transition towards clean and affordable energy. However, grid integration of renewable energy sources faces many challenges due to its intermittent nature. The controllability of aggregated regenerative electric heating load provides a method for the consumption of renewable energy sources. Based on the concept of a virtual power plant (VPP), this paper considers the cooperative energy management of aggregated residential regenerative electric heating systems. First, considering physical constraints, network constraints, and user comfort, comprehensive modeling of a VPP is given to maximize its social benefits. In addition, this VPP is investigated as a participant in day-ahead energy and reserve markets. Then, to solve this problem, a distributed coordination approach based on an alternating direction method of multipliers (ADMM) is proposed, which can respect the independence of users and preserve their privacy. Finally, the simulation results illustrate the effectiveness of our algorithm.

Facilitating inter-municipal collaboration through mandated collaborative platform: evidence from regional environmental protection in China

ABSTRACT This article argues for the need to go beyond appreciating hierarchy and network as separate coordination processes. Rather it conceptualizes a mandated platform approach to facilitate inter-municipal collaboration with little collaboration experience and diverse interests. Based on a mandated regional platform in China, this article examines what an appropriate mandate is, and how network coordination evolves on this mandated platform. Practically, this study provides a potentially new coordination approach that integrates hierarchy and network. It also has important theoretical implications to enhance our understanding of hybrid coordination and collaborative governance in the public management field.

Hydrophobic, Ultrastable Cuδ+ for Robust CO2 Electroreduction to C2 Products at Ampere-Current Levels.

Copper (Cu) is the only known material that can efficiently electrocatalyze CO2 to value-added multicarbon products. Owing to the instability of the Cuδ+ state and microscopic structure in reactions, Cu catalysts are still facing big challenges with low selectivity and poor durability, particularly at high current densities. Herein, we report a rational one-step surface coordination approach for the synthesis of Cu dendrites with an ultrastable Cuδ+ state and hydrophobicity (Cu CF), even after exposure to air for over 6 months. As a result, Cu CF exhibited a C2 FE of 90.6% at a partial current density of 453.3 mA cm-2 in a flow cell. A 400 h stable electrolysis at 800 mA and even a ground-breaking stable operation at a large industrial current of 10 A were achieved in the membrane electrode assembly (MEA) form. We further demonstrated a continuous production of C2H5OH solution with 90% relative purity at 600 mA over 50 h in a solid-electrolyte reactor. Spectroscopy and computation results suggested that Cu(II) carboxylate coordination species formed on the surface of Cu CF, which ensured the stability of the Cuδ+ state and hydrophobicity. As a result, rich active sites and a stable three-phase interface on the catalyst surface were achieved, along with the optimized *CO adsorption strength and adsorption configuration. The mixed *CO adsorption configurations on Cu CF made the *CO dimerization process easier, which promoted the conversion of CO2 to C2 products. This work provides a promising paradigm for the design and development of Cu-based catalysts with ultrahigh stability under industrial current densities.

Metal-Organic Framework Nanoparticles with Universal Dispersibility through Crown Ether Surface Coordination for Phase-Transfer Catalysis and Separation Membranes.

Dispersing metal-organic framework (MOF) solids in stable colloids is crucial for their availability and processibility. Herein, we report a crown ether surface coordination approach for functionalizing the surface-exposed metal sites of MOF particles with amphiphilic carboxylated crown ether (CEC). The surface-bound crown ethers significantly improve MOF solvation without compromising the accessible voids. We demonstrate that CEC-coated MOFs exhibit exceptional colloidal dispersibility and stability in 11 distinct solvents and six polymer matrices with a wide range of polarities. The MOF-CEC can be instantaneously suspended in immiscible two-phase solvents as an effective phase-transfer catalyst and can form various uniform membranes with enhanced adsorption and separation performance, illustrating the effectiveness of crown ether coating.

Metal‐Organic Framework Nanoparticles with Universal Dispersibility through Crown Ether Surface Coordination for Phase‐Transfer Catalysis and Separation Membranes

AbstractDispersing metal‐organic framework (MOF) solids in stable colloids is crucial for their availability and processibility. Herein, we report a crown ether surface coordination approach for functionalizing the surface‐exposed metal sites of MOF particles with amphiphilic carboxylated crown ether (CEC). The surface‐bound crown ethers significantly improve MOF solvation without compromising the accessible voids. We demonstrate that CEC‐coated MOFs exhibit exceptional colloidal dispersibility and stability in 11 distinct solvents and six polymer matrices with a wide range of polarities. The MOF‐CEC can be instantaneously suspended in immiscible two‐phase solvents as an effective phase‐transfer catalyst and can form various uniform membranes with enhanced adsorption and separation performance, which highlights the effectiveness of crown ether coating.

Combined knowledge transfer and adaptive coordinate systems approach for evolutionary bilevel optimization

Reducing the number of lower-level function evaluations (LFEs) is crucial to designing a bilevel evolutionary algorithm (BLEA) for bilevel optimization. Direct neighbor solution transfer (DST) is regularly used in BLEA because it can improve the lower-level search efficiency and reduce the number of LFEs. But this method still faces the challenge of misleading searches to make the algorithm converge to the wrong optimum. This paper proposes a bilevel improved multi-operator differential evolution algorithm (BL-IMODE) for bilevel optimization, which uses the knowledge transfer and adaptive coordinate systems approach. Specifically, this paper proposes a lower-level search knowledge transfer (LLS-KT) and a lower-level (LL) optimal solution distribution information transfer (LLO-DIT) mechanism to implement knowledge transfer. In LLS-KT, transferring LL solutions to the LL optimizer is similar to DST, but the LLS-KT transfers multiple solutions and thus has less possibility of misleading searches than DST. Moreover, the LLO-DIT transfers the LL optimal solution distribution information to the LL optimizer for generating promising offspring solutions. Since this method does not directly transfer LL solutions, it avoids misleading searches. Additionally, the adaptive coordinate systems (ACS) approach is used in two-level optimizers to suit different fitness landscapes and further reduce the number of LFEs. In ACS, three differential evolution mutation operators are produced in two coordinate systems. An adaptive strategy is used to select the suitable differential evolution mutation operator to generate promising offspring solutions in the Eigen coordinate or original coordinate system. Experimental studies on three test suites benchmark problems for comparison with seven state-of-the-art algorithms are conducted. The results show BL-IMODE has a significant advantage over the comparative algorithms. Finally, BL-IMODE is applied to tackle the inverse optimal control problem.

An Efficient LP-Based Approach for Spatial-Temporal Coordination of Electric Vehicles in Electricity-Transportation Nexus

A growing integration of electric vehicles (EV) and fast charging stations (FCS) has paved the way for the emergence of transportation and power distribution networks. However, previous work on the coordination of EV in the transportation system mainly focuses on static traffic assignment (STA) models which are unable to characterize the temporal evolution of EV flows on traffic links. Meanwhile, limited prior effort in dynamic traffic assignment (DTA) employs a static FCS model which is unable to describe the temporal evolution of charging and discharging (C&D) behavior of EV flows, while respecting the state-of-charge (SoC) related operating constraints, hindering full exploitation of EV flexibility potentials. To bridge the knowledge gap, this paper proposes a novel DTA model to optimize the spatial-temporal distribution of EV flows on roads and at FCSs, taking into account their flexible C&D options and SoC-related constraints, through the solution of only a single linear program. Case studies validate the effectiveness of the proposed DTA model by benchmarking its performance against traditional STA models, and corroborate the core benefits from the proposed spatial-temporal coordination of EV C&D demand in the power distribution network in terms of peak demand reduction, improved RES absorption, reduced carbon emission as well as more efficient network congestion management.

Platoon Cooperation Across Carriers: From System Architecture to Coordination

Truck platooning is a well-studied technology that has the potential to reduce both the environmental impact and operational costs of trucks. The technology has matured over the last 20 years, and the commercial rollout of platooning is approaching. Cooperation across carriers is essential for the viability of platooning; otherwise, many platooning opportunities are lost. We first present a cross-carrier platooning system architecture in which many carriers cooperate in forming platoons through a platoon-hailing service. Then, we present a cross-carrier platoon coordination approach in which each carrier optimizes its platooning plans according to the predicted plans of other carriers. A profit-sharing mechanism to even out the platooning profit in each platoon is embedded in the platoon coordination approach. Finally, a simulation study over the Swedish road network is performed to evaluate the potential of platooning under realistic conditions. The simulation study shows that the energy consumption of trucks in Sweden can be reduced by 5.4% due to platooning and that cooperation across carriers is essential to achieve significant platooning benefits.

Path planning for graded concrete element fabrication

AbstractThe technology of functionally graded concrete (FGC) is a new methodology in the field of concrete construction, striving for mass savings by adjusting the elements interior design. A promising approach herein is meso-gradation, where concrete hollow spheres are placed inside the formwork before casting the element; this allows up to 50% mass savings without a loss in load-bearing capacity, whilst also ensuring recyclability compared to e.g. bubble decks. In order to prevent damage/displacement of the spheres during automated fabrication, the extruded concrete flow must avoid the spheres, whilst neatly covering the elements area in order to prevent cavities. Both requirements formulate a complex path planning problem that must be solved in order to achieve automated fabrication. In this paper, we propose a method for solving this problem, which is based on theoretical findings on Hamiltonian triangulations. Our approach is based on the idea that the elements area is triangulated, such that all sphere centers are corners of triangles. For each triangle, a smooth path can be planned straightforwardly on a consideration of the geometry, such that the global path is made of a sequence of local ones. This necessitates finding a triangulation that is hamiltonian, i.e. a sequence where all triangles are visited exactly once. To this end, we first present a new class of triangulations and proof their hamiltonicity, followed by an algorithm that generates such triangulations on certain FGC element geometries. This is followed by the local path planning problem, whose special structure with start/end tangential and curvature constraints facilitates the use of a polar coordinate approach.

Multi-objective coordinative scheduling of system with wind power considering the regulating characteristics of energy-intensive load

Energy-intensive load (EIL) has excellent regulating potential in the utilization of wind power: it not only offers high flexibility and large power capacity but also features significant price sensitivity. Without considering the production tasks and the regulating characteristic of energy-intensive load, the current scheduling approach is difficult to implement. A novel multi-objective coordinative scheduling approach is proposed in this paper for wind-power utilization considering the regulating models of energy-intensive load to guarantee the benefits of both power system and energy-intensive load, which can greatly improve the willingness of energy-intensive load to participate in scheduling. Firstly, A general model of regulating characteristics for energy-intensive load is given based on production process. Next, based on this modeling, a coordinative scheduling model is established with three layer. Layer A determines the total tasks of load side and generating side with the goal of minimizing the wind curtailment. Layer B and layer C make decomposition optimization to thermal power units and load control units with the goal of minimizing the generation cost and load-adjustable cost, respectively. Finally, a case study to verify the modeling is presented. The study shows that the proposed method can significantly improve the utilization of wind power at lower cost on the basis of ensuring the production requirements and benefits of energy-intensive load.

The Behavioral Inhibition System as a Mediator between Child Sexual Abuse and Perceived Pornography Addiction in Adulthood

Worldwide, an estimated 12.7% of children have experienced child sexual abuse (CSA). CSA can lead to mental health, emotional, and behavioral consequences that often persist into adulthood such as depression, post-traumatic stress disorder, suicidal ideation, and maladaptive sexual functioning (e.g. sexual avoidance, compulsivity). Research suggests that CSA may lead to unhealthy pornography use, a form of maladaptive sexual functioning, in adulthood, though the psychological pathways governing this relationship are unexplored. In this study, 182 United States-based individuals (mean age 36.637 [SD = 11.333] years, 51.648% men) were surveyed using Amazon MTurk regarding CSA history and perceived cyberpornography addiction. Participants also completed the Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ). We found that individuals who experienced CSA reported higher perceived cyberpornography addiction scores. Serial mediation model results indicated that the behavioral inhibition system (BIS) mediated the relationship between CSA and perceived cyberpornography addiction. The BIS is a dimension of the RST-PQ which resolves conflicts between the behavioral activation system (coordinates approach behaviors) and the flight-fight-freeze system (coordinates retreat behaviors) and is associated with trait anxiety. These findings contribute to the literature on the costs of CSA and identifies BIS sensitivity as a psychological system that can be targeted in therapeutic settings to reduce maladaptive engagement with pornography by CSA survivors.

Robust Resource Allocation Design for Secure IRS-Aided WPCN

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless powered communication networks (WPCN). In particular, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, the passive beamforming at the IRS, and the transmit power of each wireless-powered device (WD) and jammer node (JN). We formulate a non-convex optimization problem for the robust resource allocation design taking into account the secrecy rate requirement of the WDs and the power budgets for both the WDs and the JNs. To handle this intractable problem, we propose a computationally efficient iterative suboptimal algorithm exploiting the block coordinate descent approach, the successive convex approximation, and the penalty method, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Also, we reveal that the optimal energy beamforming matrices are rank-one sharing the same spatial direction. Simulation results unveil that the proposed scheme is able to dramatically reduce the HBS transmit power over various baseline schemes adopting existing solutions. Besides, our results show the superiority of introducing IRS for secure communication in wireless-powered systems.