Dynamic Reconfiguration Research Articles

Dynamic Reconfiguration of Distribution Networks Considering the Real-time Topology Variation

Dynamic Reconfiguration of Distribution Networks Considering the Real-time Topology Variation

Research on Voltage Self-Calibration Sensing Technology Based on Measurement Circuit Topology Changes

In capacitance-coupled voltage-sensing technology, the degree of coupling capacitance is affected by the sensing area, relative position deviation, and other factors, and thus the measurement coefficient is often difficult to determine accurately and presents greater implementation difficulties in actual deployment. This paper proposes a dynamic reconfiguration based on the measurement circuit topology of the voltage sensor adaptive calibration method in order to measure voltage sensor gain in the process of automatic measuring. Firstly, the basic principle of voltage measurement is introduced, and the self-calibration method is proposed, considering the influence of the sensing area and the relative position error on the change in the coupling capacitance. On this basis, the influence of calibration accuracy on sensor structure parameters is analyzed using network sensitivity analysis, and the parameter selection principle is given, according to which the selection criterion of parameter optimization is formulated to complete the sensor design. By analyzing the coupling effect of the three-phase measurement, the installation method of the sensing structure is proposed. An experimental platform is built to test the accuracy of the voltage measurement of the sensor under laboratory conditions. The experimental results show that the maximum relative error of the voltage measurement amplitude is 2.24%. In order to verify the feasibility of the sensor technology designed, the measurement models that integrate communication, acquisition, and processing are installed on both ends of the circuit breaker wire, and the voltage waveform generated during the circuit breaker closing process is recorded in real time. The experimental results show that the sensor technology can accurately record the voltage waveform of the signal to be measured, and the feasibility of its application in switchgear equipment signal measurement is preliminarily verified by the results.

Reduction of losses in electric power distribution system–dynamic reconfiguration case study

Reduction of losses in electric power distribution system–dynamic reconfiguration case study

Conflict-mediation, consensus-building, and co-production: Three models of co-governance in China’s neighborhood regeneration

Existing literature on co-governance has extensively discussed how the state interacts with various actors beyond the government to promote urban development. In China, the discourse and practice of co-governance illustrate different trajectories from the West. As the urban development strategies in China shifted from property-led expansion to inner-city neighborhood regeneration, the importance of multiscalar participation in governing community affairs has been increasingly emphasized, moving beyond the government-market dichotomy and emphasizing negotiation and collaboration among the government, market participants, stakeholders, and the public. Based on practical experience and participatory action research in Guangdong province, we identify three models of co-governance in recent neighborhood regeneration practices: (1) the state-led co-creation model for conflict-mediation purposes, which primarily relies on local government and party organization to mediate inherited conflicts and rebuild local trust for promoting the regeneration agenda; (2) the professional-led place-making model for consensus-building purposes, which focuses on reshaping daily habits and beautifying public spaces to achieve consensus-based aspirations for neighborhood development; and (3) the co-production model, driven by society-community partnership, which reflects the emergence of new commons, encompassing dynamic reconfigurations on both socio-economic and institutional dimensions. Through case studies in Guangzhou and Foshan, specifically in the Enninglu, Keyuan, and Dongfeng neighborhoods, we illustrate how local governments enhance the inclusiveness and effectiveness of neighborhood governance by engaging various third-party actors, such as local enterprises, community planners, and residents’ self-help groups. Local governments are skillfully avoiding direct confrontation with residents by incorporating community planners and empowering social actors to promote co-production among multiscalar participants. By examining the emergence, functioning, and dilemmas of different co-governance models in China’s latest neighborhood regeneration practices, this study enriches the existing framework of the state-market-society landscape, demonstrating the plurality of the state’s governance intentions and highlighting the political space in which grassroots governance is constituted.

Design and Implementation of a Modular Multilevel Series-Parallel Converter for Second-Life Battery Energy Storage Systems

Battery Energy Storage Systems (BESS) offer scalable energy storage solutions, especially valuable for remote, off-grid applications. However, traditional battery packs with fixed series-parallel configurations lack reconfigurability and are limited by the weakest cell, hindering their application for second-life batteries. The Modular Multilevel Series-Parallel Converter (MMSPC) addresses these limitations by enabling dynamic reconfiguration, optimizing cell balancing, and enhancing energy control. This paper experimentally evaluates a single-phase BESS based on the MMSPC with an output power equivalent to 2 kW and two battery units (155V), demonstrating stable output and reduced internal losses across varied battery parameters.

Reconfiguration of functional brain network organization and dynamics with changing cognitive demands in children with attention-deficit/hyperactivity disorder

Reconfiguration of functional brain network organization and dynamics with changing cognitive demands in children with attention-deficit/hyperactivity disorder

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging.

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

MADDPG-Based Active Distribution Network Dynamic Reconfiguration with Renewable Energy

MADDPG-Based Active Distribution Network Dynamic Reconfiguration with Renewable Energy

Robust network topology for unbalanced active distribution networks with uncertain injections

AbstractThis research paper introduces a comprehensive formulation for robust dynamic network reconfiguration (NR) of unbalanced active electric distribution networks (DNs). Network reconfiguration is a potent strategy to minimise active power loss in DN as it involves altering network topology through sectionalising (normally closed) and tie‐line switches (normally open). However, NR is usually a mixed integer NP‐hard non‐linear optimisation problem due to the discrete nature of the switches. Hence, including variable injection uncertainties (from generation or load) for an unbalanced active DN with all its attributes further poses a significant challenge in solving NR. The proposed formulation addresses these challenges in a robust optimisation (RO) framework to get a robust topology and power and voltage set points for dispatchable Distributed Generators (DGs). Also, Chance‐Constrained robust formulations are proposed to regulate the conservatism of RO. Numerical analyses demonstrate the impact of conservative robust NR on DG set points compared to the non‐robust NR method. Tests on a modified unbalanced IEEE 34‐bus system and comparison with previous formulations verify the efficacy of the proposed approach, showcasing its effectiveness.

Transcending Geography: The United Kingdom’s Accession to the Comprehensive and Progressive Trans-Pacific Partnership

ABSTRACT Immediately after Brexit, the UK started negotiations to join the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP). The decision is puzzling. First, Brexiteers lauded regaining sovereignty as a key benefit of Brexit, yet the UK Government sought accession to another trade grouping (albeit one far more restrained in regulation and supranationalism). Secondly, CPTPP is a Trans-Pacific project, the UK is not located in that geographic region. UK accession to CPTPP symbolises an operationalisation of the spatial imaginaries of a ‘free-trading’ ‘Global Britain’ that emerged during Brexit. It represents an illustrative example of a state reshaping its boundaries within a ‘space of flows’ where economic interactions and the rules that order these transcend national territories and geographic proximity. This paper draws on UK government and CPTPP documents related to the accession process to establish how the parties understood the process and the reconfiguration of spatial dynamics and redefinition of the TP part of CPTPP to include the UK. It pays particular attention to the development in the discourses of imaginaries of communities of rules and norms as being more significant than geographic considerations in determining the geographic reach of a regional integration initiative like CPTPP. The paper argues that it was this imaginary of a community of standards and free-trade values that justified the recasting the UK as ‘belonging’ in this regional grouping and enabled this new spatial projection for the UK and CPTPP.

A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

Quantum state processing through controllable synthetic temporal photonic lattices

AbstractQuantum walks on photonic platforms represent a physics-rich framework for quantum measurements, simulations and universal computing. Dynamic reconfigurability of photonic circuitry is key to controlling the walk and retrieving its full operation potential. Universal quantum processing schemes based on time-bin encoding in gated fibre loops have been proposed but not demonstrated yet, mainly due to gate inefficiencies. Here we present a scalable quantum processor based on the discrete-time quantum walk of time-bin-entangled photon pairs on synthetic temporal photonic lattices implemented on a coupled fibre-loop system. We utilize this scheme to path-optimize quantum state operations, including the generation of two- and four-level time-bin entanglement and the respective two-photon interference. The design of the programmable temporal photonic lattice enabled us to control the dynamic of the walk, leading to an increase in the coincidence counts and quantum interference measurements without recurring to post-selection. Our results show how temporal synthetic dimensions can pave the way towards efficient quantum information processing, including quantum phase estimation, Boson sampling and the realization of topological phases of matter for high-dimensional quantum systems in a cost-effective, scalable and robust fibre-based setup.

Real-Time Reconfigurable Radio Frequency Arbitrary-Waveform Generation via Temporal Pulse Shaping with a DPMZM and Multi-Tone Inputs

Benefitting from a large bandwidth and compact configuration, a time-domain pulse-shaping (TPS) system provides possibilities for generating broadband radio frequency (RF) arbitrary waveforms based on the Fourier transform relationship between the input–output waveform pair. However, limited by the relatively low sampling rate and bit resolution of an electronic arbitrary-waveform generator (EAWG), the diversity and fidelity of the output waveform as well as its reconfiguration rate are constrained. To remove the EAWG’s limitation and realize dynamic real-time reconfiguration of RF waveforms, we propose and demonstrate a novel approach of RF arbitrary-waveform generation based on an improved TPS system with an integrated dual parallel Mach–Zehnder modulator (DPMZM) and multi-tone inputs. By appropriately adjusting the DC bias voltages of DPMZM and the power values, as well as the center frequencies of the multi-tone inputs, any desired RF arbitrary waveform can be generated and reconfigured in real time. Proof-of-concept experiments on the generation of different user-defined waveforms with a sampling rate up to 27 GSa/s have been successfully carried out. Furthermore, the impact of modulation modes and higher-order dispersion on waveform fidelity is also discussed in detail.

Shape-Morphing in Oxide Ceramic Kirigami Nanomembranes.

Interfacial strain engineering in ferroic nanomembranes can broaden the scope of ferroic nanomembrane assembly as well as facilitate the engineering of multiferroic-based devices with enhanced functionalities. Geometrical engineering in these material systems enables the realization of 3-D architectures with unconventional physical properties. Here, 3-D multiferroic architectures are introduced by incorporating barium titanate (BaTiO3, BTO) and cobalt ferrite (CoFe2O4, CFO) bilayer nanomembranes. Using photolithography and substrate etching techniques, complex 3-D microarchitectures including helices, arcs, and kirigami-inspired frames are developed. These 3-D architectures exhibit remarkable mechanical deformation capabilities, which can be attributed to the superelastic behavior of the membranes and geometric configurations. It is also demonstrated that dynamic shape reconfiguration of these nanomembrane architectures under electron beam exposure showcases their potential as electrically actuated microgrippers and for other micromechanical applications. This research highlights the versatility and promise of multi-dimensional ferroic nanomembrane architectures in the fields of micro actuation, soft robotics, and adaptive structures, paving the way for incorporating these architectures into stimulus-responsive materials and devices.

Robust Characterization of Photonic Integrated Circuits

AbstractPhotonic integrated circuits (PICs) offer ultra‐broad optical bandwidths that enable unprecedented data throughputs for signal processing applications. Dynamic reconfigurability enables compensation of fabrication flaws and fluctuating external environments, tuning for adaptive equalization and training of optical neural networks. The initial step in PIC reconfiguration entails measuring its dynamic performance, often described by its frequency response. While measuring the amplitude response is straightforward, e.g. using a tunable laser and optical power meter, measuring the phase response presents challenges due to various factors, including phase variations in test connections and instrumentation limitations. To address these challenges, a universal and robust characterization technique is proposed, which uses an on‐chip reference path coupled to the signal processing core (SPC), with a delay gap larger than the total delay across the signal processing paths. A Fourier transform of the chip's power response reveals the SPC's impulse response. The method is more robust against low reference‐path power and imprecise delays. Experiments using a finite‐impulse‐response (FIR) structure demonstrate rapid SPC training, overcoming thermal crosstalk and device imperfections. This approach offers a promising solution for PIC characterization, facilitating expedited physical parameter training for advanced applications in communications and optical neural networks.

7690 Acute hormonal signaling-induced 3D genome organization controls transcriptional dynamics during adipocyte thermogenesis

Abstract Disclosure: Y. Zhang: None. R. Zheng: None. T. Tsuji: None. Y. Xing: None. C. Wang: None. J. Darcy: None. K. Chen: None. Y. Tseng: None. Brown adipose tissue (BAT) maintains body temperature by dissipating energy as heat, presenting a promising target against obesity and its associated metabolic disorders. When exposed to cold, the β3-adrenergic receptor (β3-AR) in brown adipocytes is stimulated, initiating a sequential signaling cascade and activating a thermogenic gene program essential for heat generation. Despite remarkable progress has been made towards understanding the engagement of transcriptional and epigenetic regulators during thermogenesis in response to β3-AR stimulation, the precise impact of three-dimensional (3D) genome organization, an increasingly important modulator of gene expression, on the activation of thermogenic gene program remains elusive. In this study, we employed high-resolution Micro-C in murine brown adipocytes to interrogate the rapidity of 3D genome reorganization in response to β3-AR hormonal signaling and its impact on transcriptional dynamics during thermogenesis. Our findings unveiled substantial rewiring of chromatin loops within 4 hours of β3-AR stimulation. These dynamic changes in chromatin loops, particularly enhancer-promoter (E-P) interactions, were coupled with gene activation during thermogenesis. Furthermore, we uncovered the mechanisms by which β3-AR hormonal signaling mediates 3D chromatin reorganization to regulate the thermogenic gene program. We found that β3-AR hormonal signaling induced the phosphorylation of p18Hamlet, a subunit of the SRCAP complex, facilitating the deposition of the histone variant H2A.Z into regulatory DNA regions. This nucleosome incorporation of H2A.Z further mediated nucleosome rearrangement, favoring the formation of nucleosome-depleted regions (NDRs) and enhancing the accessibility of regulatory DNA regions. As a result, H2A.Z chromatin incorporation facilitated interactions between cis-regulatory regions and gene promoters, such as E-P interactions, thereby activating thermogenic gene expression in response to β3-AR stimulation. Loss of H2A.Z disrupted loop dynamics, impeding gene activation and BAT thermogenic capacity. The conserved role of H2A.Z in human thermogenesis was validated, and we identified adiposity-related SNPs in human homologous loop anchors that could potentially be targeted for treating obesity. Taken together, our results unveil a previously underappreciated aspect of acute hormonal signaling-induced chromatin dynamics in orchestrating the thermogenic program in brown adipocytes. These findings not only enhance our understanding of molecular regulation of thermogenesis but also shed light on the role of rapid and dynamic chromatin reconfiguration in regulating the cellular response to external stimuli. Our study bridges a critical gap between 3D genome organization and gene activation in the thermogenic program mediated by acute β3-AR hormonal signaling. Presentation: 6/2/2024

Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources

The integration of renewable energy sources into smart distribution grids poses substantial challenges in maintaining grid stability, efficiency, and reliability due to their inherent variability and intermittency. This study addresses these challenges by proposing a novel two-level optimization model aimed at enhancing operational efficiency and robustness in smart distribution grids. The model synergistically integrates renewable energy sources, energy storage systems, electric vehicles, and demand-side management through a dynamic reconfiguration approach. It employs a robust optimization framework combined with a two-stage second-order cone optimization model to manage real-time operations and strategic grid reconfiguration. Key findings from simulations on the IEEE 33 and 69-bus networks underscore the model’s effectiveness. In the 33-bus system, implementing the demand response program led to a significant reduction in power losses, from 0.64 MW to 0.52 MW, and improved voltage stability, with the minimum voltage increasing from 0.970 to 0.980 p.u. Similarly, in the 69-bus system, power losses decreased from 0.85 MW to 0.79 MW, and voltage stability improved, with the minimum voltage rising from 0.962 to 0.972 p.u. The model also demonstrated reduced energy procurement needs, showcasing its impact on enhancing grid efficiency and reliability. These results highlight the model’s potential for advancing smart grid management strategies, offering significant improvements in operational performance and stability under varying demand conditions.

Static reconfiguration method of photovoltaic array based on improved competence square

Partial shading can reduce the power output of a photovoltaic (PV) array due to mismatch losses. Therefore, various static and dynamic reconfiguration techniques have been proposed to address this problem. Although dynamic reconfiguration methods are fast and flexible, they face challenges such as complex hardware circuits and high costs. In contrast, static reconfiguration methods simplify control complexity, improve system stability, and significantly reduce costs. Among the existing static reconfiguration techniques, the competence square (CS) method, which changes the physical positions of PV panels without modifying the total cross tied (TCT) based electrical connections, has been introduced to enhance power generation. However, this arrangement faces drawbacks due to the lack of effective chromatic dispersion and insufficient power enhancement under shading conditions. This paper proposes an improved competence square (ICS) reconfiguration method to further improve the power output. The ICS method first reconfigures the PV array using the CS method, then applies the lo shu (LS) method for secondary reconfiguration to determine the optimal connections. This method is compared with TCT, CS and improved northern goshawk optimization (INGO) methods. Additionally, the performance of the proposed method is evaluated against various existing photovoltaic array configurations by comparing the global maximum power point (GMPP), fill factor (FF), mismatch loss (ML), and power enhancement (PE). The experiment shows that under shading conditions of square shading, rectangle shading, trapezoid shading, triangle shading, L-shaped shading, cross shading, discontinuous shading, and irregular shading, reconfiguring PV arrays using ICS consistently achieves the highest power improvement. Compared to TCT, the power output increases significantly by 16.6%, 2.1%, 18.0%, 16.0%, 19.1%, 5.0%, 4.0%, and 3.0%, respectively. Comparison results validate the proposed ICS method in enhancing the global maximum power under shaded conditions.

Towards fault-tolerance of IMA with safe dynamic reconfiguration

Integrated Modular Avionics (IMA) is essential to modern avionics. It increases the possibilities for reuse of software and hardware resources by system integrators, through the use of standardized communication interfaces and operating system services. Meanwhile, the safety requirements of DO-297 dictate that the system architecture must prevent common cause failures and that a single failure cannot disable any critical function. As a result, critical functions have to be allocated redundantly to additional resources at integration-time. In the spirit of IMA, it may be desirable to pool together these resources so that they can be allocated to any critical function at run-time. For this, a way to redefine the communication between individual allocations of functions is necessary. In this paper, we demonstrate and evaluate a prototypical implementation of a message router that allows us to dynamically reconfigure the communication between the allocated functions, using only standardized communication interfaces and operating system services of ARINC 653. We discuss the safety implications of such an approach and how it may be possible to mitigate them, evaluate the feasibility of our approach using a combination of end-to-end delay measurements and on-target tracing, and verify our assumptions about the individual factors contributing to the end-to-end delay using a discrete event simulation. We find that the approach is feasible, but the usefulness for critical functions is limited by the communications overhead from routing the messages, insufficient real-time guarantees of standardized operating system services, and missing global time synchronization.

Topology, dynamics, and control of a muscle-architected soft arm

Muscular hydrostats, such as octopus arms or elephant trunks, lack bones entirely, endowing them with exceptional dexterity and reconfigurability. Key to their unmatched ability to control nearly infinite degrees of freedom is the architecture into which muscle fibers are weaved. Their arrangement is, effectively, the instantiation of a sophisticated mechanical program that mediates, and likely facilitates, the control and realization of complex, dynamic morphological reconfigurations. Here, by combining medical imaging, biomechanical data, live behavioral experiments, and numerical simulations, an octopus-inspired arm made of [Formula: see text]200 continuous muscle groups is synthesized, exposing "mechanically intelligent" design and control principles broadly pertinent to dynamics and robotics. Such principles are mathematically understood in terms of storage, transport, and conversion of topological quantities, effected into complex 3D motions via simple muscle activation templates. These are in turn composed into higher-level control strategies that, compounded by the arm's compliance, are demonstrated across challenging manipulation tasks, revealing surprising simplicity and robustness.