Showing papers on "Control reconfiguration published in 2018"

Particle Swarm Optimization Based Solar PV Array Reconfiguration of the Maximum Power Extraction Under Partial Shading Conditions

Abstract: For large photovoltaic power generation plants, number of panels are interconnected in series and parallel to form a photovoltaic (PV) array. In this configuration, partial shade will result in decrease in power output and introduce multiple peaks in the P–V curve. As a consequence, the modules in the array will deliver different row currents. Therefore, to maximize the power extraction from PV array, the panels need to be reconfigured for row current difference minimization. Row current minimization via Su Do Ku game theory do physical relocation of panels may cause laborious work and lengthy interconnecting ties. Hence, in this paper, an alternative to physical relocation based on particle swarm optimization (PSO) connected modules is proposed. In this method, the physical location of the modules remains unchanged, while its electrical connections are altered. Extensive simulations with different shade patterns are carried out and thorough analysis with the help of I–V , P–V curves is carried out to support the usefulness of the proposed method. The effectiveness of proposed PSO technique is evaluated via performance analysis based on energy saving and income generation. Further, a comprehensive comparison of various electrical array reconfiguration based is performed at the last to examine the suitability of proposed array reconfiguration method.

Power Distribution System Outage Management With Co-Optimization of Repairs, Reconfiguration, and DG Dispatch

Abstract: This paper proposes a two-stage method for the outage management of power distribution systems. The first stage is to cluster repair tasks of damaged power generation and delivery components based on their distances from the depots (central crew stations) and the availability of resources, to improve the computational efficiency in solving outage management problems for large distribution systems. The second stage is to co-optimize the repair, reconfiguration, and distributed generation dispatch to maximize the picked-up loads and minimize the repair time. The distribution system repair and restoration problem is formulated as a mixed integer linear program, considering constraints of system operation and routing repair crews. Crews are dispatched considering equipment resources, traveling time, and repair time. The proposed method is tested on modified IEEE 34 and 123-bus distribution test systems with multiple damages. The results demonstrate the advantages of co-optimizing repair and restoration.

Deep Feature Pyramid Reconfiguration for Object Detection

Abstract: State-of-the-art object detectors usually learn multi-scale representations to get better results by employing feature pyramids. However, the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process. Finally, we propose a novel reconfiguration architecture to combine low-level representations with high-level semantic features in a highly-nonlinear yet efficient way. In particular, our architecture which consists of global attention and local reconfigurations, is able to gather task-oriented features across different spatial locations and scales, globally and locally. Both the global attention and local reconfiguration are lightweight, in-place, and end-to-end trainable. Using this method in the basic SSD system, our models achieve consistent and significant boosts compared with the original model and its other variations, without losing real-time processing speed.

Introduction to Reconfiguration

Abstract: Reconfiguration is concerned with relationships among solutions to a problem instance, where the reconfiguration of one solution to another is a sequence of steps such that each step produces an intermediate feasible solution. The solution space can be represented as a reconfiguration graph, where two vertices representing solutions are adjacent if one can be formed from the other in a single step. Work in the area encompasses both structural questions (Is the reconfiguration graph connected?) and algorithmic ones (How can one find the shortest sequence of steps between two solutions?) This survey discusses techniques, results, and future directions in the area.

Reconfigurable photoactuator through synergistic use of photochemical and photothermal effects.

Abstract: A reconfigurable actuator is a stimuli-responsive structure that can be programmed to adapt different shapes under identical stimulus. Reconfigurable actuators that function without control circuitry and are fueled remotely are in great demand to devise adaptive soft robotic devices. Yet, obtaining fast and reliable reconfiguration remains a grand challenge. Here we report a facile fabrication pathway towards reconfigurability, through synergistic use of photochemical and photothermal responses in light-active liquid crystal polymer networks. We utilize azobenzene photoisomerization to locally control the cis-isomer content and to program the actuator response, while subsequent photothermal stimulus actuates the structure, leading to shape morphing. We demonstrate six different shapes reconfigured from one single actuator under identical illumination conditions, and a light-fueled smart gripper that can be commanded to either grip and release or grip and hold an object after ceasing the illumination. We anticipate this work to enable all-optical control over actuator performance, paving way towards reprogrammable soft micro-robotics.

On-Demand Reconfiguration of Nanomaterials: When Electronics Meets Ionics

Abstract: Rapid advances in the semiconductor industry, driven largely by device scaling, are now approaching fundamental physical limits and face severe power, performance, and cost constraints. Multifunctional materials and devices may lead to a paradigm shift toward new, intelligent, and efficient computing systems, and are being extensively studied. Herein examines how, by controlling the internal ion distribution in a solid-state film, a material's chemical composition and physical properties can be reversibly reconfigured using an applied electric field, at room temperature and after device fabrication. Reconfigurability is observed in a wide range of materials, including commonly used dielectric films, and has led to the development of new device concepts such as resistive random-access memory. Physical reconfigurability further allows memory and logic operations to be merged in the same device for efficient in-memory computing and neuromorphic computing systems. By directly changing the chemical composition of the material, coupled electrical, optical, and magnetic effects can also be obtained. A survey of recent fundamental material and device studies that reveal the dynamic ionic processes is included, along with discussions on systematic modeling efforts, device and material challenges, and future research directions.

Optimal Planning of Distributed Energy Storage Systems in Active Distribution Networks Embedding Grid Reconfiguration

Abstract: In this paper, we present a procedure for the optimal siting and sizing of energy storage systems (ESSs) owned, and directly controlled by network operators of active distribution networks. The peculiarity of the proposed planning procedure consists in embedding the grid reconfiguration. We use a recently proposed conditionally exact convex optimal power flow (OPF) as the core of the optimization model. We appropriately model the objective function to include both technical and economic aspects, while keeping the exactness of the relaxed convex OPF. In particular, the proposed procedure accounts for the minimization of: voltage-magnitude deviations, feeders’/lines’ congestion, cost of supplying loads, and investment costs related to the ESSs. In addition, the seasonal configurations of the grid are determined based on 1) network security constraints, and 2) the minimum resistive losses. The stochasticity of loads and renewable productions are also taken into account. We suitably modeled the ESSs to consider their ability to support the network by both active and reactive powers. Two test cases are used to demonstrate, and quantify, the capabilities of the proposed procedure for providing optimal and feasible solutions.

Multiband Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks

Abstract: The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive fifth generation (5G) network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multiband cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multiband approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multiband approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality-of-service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for cross-layer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

Multi-step self-guided pathways for shape-changing metamaterials.

Abstract: Multi-step pathways—which consist of a sequence of reconfigurations of a structure—are central to the functionality of various natural and artificial systems. Such pathways execute autonomously in self-guided processes such as protein folding1 and self-assembly2,3,4,5, but have previously required external control to execute in macroscale mechanical systems, provided by, for example, actuators in robotics6,7,8,9 or manual folding in origami8,10,11,12. Here we demonstrate shape-changing, macroscale mechanical metamaterials that undergo self-guided, multi-step reconfiguration in response to global uniform compression. We avoid the need for external control by using metamaterials that are made purely of passive components. The design of the metamaterials combines nonlinear mechanical elements with a multimodal architecture that enables a sequence of topological reconfigurations caused by the formation of internal self-contacts between the elements of the metamaterial. We realize the metamaterials by using computer-controlled water-jet cutting of flexible materials, and show that the multi-step pathway and final configuration can be controlled by rational design of the nonlinear mechanical elements. We also demonstrate that the self-contacts suppress errors in the pathway. Finally, we create hierarchical architectures to extend the number of distinct reconfiguration steps. Our work establishes general principles for designing mechanical pathways, opening up new avenues for self-folding media11,12, pluripotent materials9,13 and pliable devices14 in areas such as stretchable electronics and soft robotics15.

Observer-Based Adaptive Fault-Tolerant Tracking Control of Nonlinear Nonstrict-Feedback Systems

Abstract: This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of “explosion of complexity”. Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.

Reconfigurable Tolerant Control of Uncertain Mechanical Systems With Actuator Faults: A Sliding Mode Observer-Based Approach

Abstract: This paper studies a key issue of developing reconfigurable fault-tolerant control to retain a nominal feedback controller and simultaneously handles actuator faults and system uncertainty, while the closed-loop system is stabilized with all control objectives achieved. A theoretical architecture of a reconfigurable control design is presented for a class of uncertain mechanical systems by using an observer technique. As a stepping stone, a nonlinear observer-based estimation mechanism is designed to reconstruct uncertain dynamics and actuator faults with the estimation error converging to zero within finite time. A reconfigurable control effort is then synthesized from the reconstructed knowledge. This control power operates as a compensation control part, and it is added to the nominal control part to accommodate system uncertainties and actuator faults. It is proved that the overall system resulted from the developed control framework has the same control performance of the nominal closed-loop system, including certain system dynamics and the nominal control effort. The effectiveness of the scheme is validated on a serial robotic manipulator.

FPGA Dynamic and Partial Reconfiguration: A Survey of Architectures, Methods, and Applications

Abstract: Dynamic and partial reconfiguration are key differentiating capabilities of field programmable gate arrays (FPGAs). While they have been studied extensively in academic literature, they find limited use in deployed systems. We review FPGA reconfiguration, looking at architectures built for the purpose, and the properties of modern commercial architectures. We then investigate design flows and identify the key challenges in making reconfigurable FPGA systems easier to design. Finally, we look at applications where reconfiguration has found use, as well as proposing new areas where this capability places FPGAs in a unique position for adoption.

Robust Sensors-Fault-Tolerance With Sliding Mode Estimation and Control for PMSM Drives

Abstract: In general, permanent magnet synchronous motor (PMSM) drives require four sensors (one position, one dc-link voltage, and at least two current sensors) to obtain good dynamic control performance. If an unpredictable fault occurs in any of these sensors, the performance of the drive deteriorates or even becomes unstable. Most of the existing works are limited to fault diagnosis of one or two sensors due to complexity. Therefore, to provide a continuous drive operation regardless of any of the sensor faults, an advanced fault-tolerant control (FTC) scheme that comprises of higher order sliding mode (HOSM) based observers and controllers is proposed. Two HOSM observers and one Luenberger observer are designed to generate the respective residuals and provide the detection of all sensor faults. Moreover, HOSM controllers are developed to ensure finite-time convergence of the error trajectories after the fault reconfiguration. The proposed FTC scheme reduces the existing chattering phenomenon with good performance in terms of convergence speed and steady-state error. Evaluation results on a three-phase PMSM are presented to validate the effectiveness of the proposed FTC approach.

Online Resource Allocation, Content Placement and Request Routing for Cost-Efficient Edge Caching in Cloud Radio Access Networks

Abstract: In this paper, we advocate edge caching in cloud radio access networks (C-RAN) to facilitate the ever-increasing mobile multimedia services. In our framework, central offices will cooperatively allocate cloud resources to cache popular contents and satisfy user requests for those contents, so as to minimize the system costs in terms of storage, VM reconfiguration, content access latency, and content migration. However, this joint resource allocation, content placement and request routing, is nontrivial, since it needs to be continuously adjusted to accommodate system dynamics, such as user movement and content slashdot effect, while taking into account the time-correlated adjustment costs for VM reconfiguration and content migration. To this end, we build a comprehensive model to capture the key components of edge caching in C-RAN and formulate a joint optimization problem, aiming at minimizing the system costs over time and meanwhile satisfying the time-varying user requests and respecting various practical constraints (e.g., storage and bandwidth). Then, we propose a novel online approximation algorithm by resorting to the regularization, rounding, and decomposition technique, which can be proved to have a parameterized competitive ratio with a polynomial running time. Extensive trace-driven simulations corroborate the efficiency, flexibility, and lightweight of our proposed online algorithm; for instance, it achieves an empirical competitive ratio around 2 – 4 and gains over 30% improvement compared with many state-of-the-art algorithms in various system settings.

An Adaptive Approach for PEVs Charging Management and Reconfiguration of Electrical Distribution System Penetrated by Renewables

Abstract: An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs’ drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.

Low-carbon transition via system reconfiguration? A socio-technical whole system analysis of passenger mobility in Great Britain (1990–2016)

Abstract: Low-carbon transitions in whole systems (in energy, mobility, agro-food) are an important, yet understudied topic in socio-technical transition research. To address this topic, the paper builds on the Multi-Level Perspective, but stretches it to address developments in multiple regimes and multiple niche-innovations. This ‘zooming out’ strategy changes the conceptualisation of transition dynamics from bottom-up disruption (driven by singular niche-innovations) to gradual system reconfiguration, which represents a more distributed, multi-source view of change that includes cumulative incremental regime change, shifts in relative sizes of regimes, regime alignments, component substitution, and symbiotic adoption. To illustrate the reconfiguration approach and empirically explore the topic of whole system change, the paper investigates unfolding trajectories in UK passenger mobility. This analysis, which addresses developments in auto-mobility, train, bus and cycling regimes and five niche-innovations (biofuels, electric vehicles, smart cards, compact cities, home working), aims to assess if and how the mobility system is reconfiguring in low-carbon directions. It also aims to provide an interpretive assessment of the 12.7% decrease in domestic transport-related CO2-emissions between 2007 and 2013. This decrease is attributed to reduced auto-mobility (due to the financial-economic crisis), incremental engine efficiency improvements in new cars, some modal shift from cars to trains, and biofuels. Radical niche-innovations (smart cards, compact cities, electric vehicles) did not (yet) greatly contribute to emission reductions. CO2-emissions increased again since 2014, which suggests that further low-carbon transitions require deeper system reconfiguration.

Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration

Abstract: With growing penetration of renewable distributed generations (DGs) in distribution systems, effective integration of DGs has become a major concern. Distribution system dynamic reconfiguration (DSDR), which relies on real-time operations of remote-controlled switches, is potentially an efficient strategy receiving inadequate attention. Moreover, in most DSDR-related publications, normally all switches are assumed remotely controllable, which is not practical. Here we borrow the concept of critical switches to denote the switches that are most effective in accommodating DGs by DSDR. In this regard, the problem of identifying critical switches is not well investigated, although in several related publications, selected switches are assumed remote-controlled based on experience. In this work, we study the application of DSDR for DG integration. Critical switches, which optimally enable intra-day DSDR to minimize DG curtailments, are identified by limiting the number of switches to be operated and the switch-type-dependent operation constraints. Considering uncertainties of loads and DGs, the problem is formulated as a two-stage robust optimization model solved by a nested column-and-constraint generation algorithm. Illustrative cases show that DG curtailments can be significantly reduced by a small number of critical switches that operate only several times in intra-day DSDR. The proposed method can be used to provide insights for switch allocation, maintenance, and operation.

Stochastic Resilient Post-Hurricane Power System Recovery Based on Mobile Emergency Resources and Reconfigurable Networked Microgrids

Abstract: This paper develops an effective two-stage stochastic post-hurricane recovery framework to improve networked microgrid resilience using mobile emergency resources (MERs) and a proposed reconfiguration strategy. In the first stage, network reconfiguration actively alters the local power flow path and provides opportunities for restoring critical loads, thus reducing the energy not supplied to electric consumers. The optimal schedule determined in the first stage problem is also used to determine the islanded loads that need MERs for restoration. In the second stage, truck-mounted MERs will deliver power to islanded loads, observing the shortest path and post-hurricane transportation infrastructure constraints. Dijkstra’s algorithm is used to produce the shortest path and avoid possible out-of-service roads. In order to model the uncertainties of the problem, a stochastic framework based on unscented transform is employed. The proposed problem is formulated as a two-stage stochastic single-objective optimization problem maximizing system resilience. Simulation results on a test networked microgrid demonstrate the effectiveness and satisfying performance of the proposed model.

A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing

Abstract: Software-Defined Networking (SDN) opened up new opportunities in networking with its concept of the segregated control plane from the data-forwarding hardware, which enables the network to be programmable, adjustable, and reconfigurable dynamically. These characteristics can bring numerous benefits to cloud computing, where dynamic changes and reconfiguration are necessary with its on-demand usage pattern. Although researchers have studied utilizing SDN in cloud computing, gaps still exist and need to be explored further. In this article, we propose a taxonomy to depict different aspects of SDN-enabled cloud computing and explain each element in details. The detailed survey of studies utilizing SDN for cloud computing is presented with focus on data center power optimization, traffic engineering, network virtualization, and security. We also present various simulation and empirical evaluation methods that have been developed for SDN-enabled clouds. Finally, we analyze the gap in current research and propose future directions.

Short-Circuit Fault Management in DC Electric Ship Propulsion System: Protection Requirements, Review of Existing Technologies and Future Research Trends

Abstract: Ease of integration of the variable speed diesel generators resulting in substantial reduction of the fuel consumption is the key motivation for the development of the dc shipboard power system (SPS). One of the impediments to the widespread adoption of the dc SPS, however, has been the lack of comprehensive fault management strategies during the short-circuit faults. Such strategies comprise of fault detection, fault isolation, and reconfiguration of dc SPS. In the existing literature, all these aspects of fault management are dealt independently and mostly assuming ideal conditions. All the strategies are of utmost importance and it is needed to study them under a common framework which is the aim of this paper. This paper starts with a brief discussion on the characteristics of dc SPS along with recent modeling techniques. Subsequently, this paper describes the short-circuit fault studies, fault characteristics, and protection requirements. Finally, this paper outlines the working principle, advantages, and limitations of the fault detection, isolation, and reconfiguration strategies developed for the dc power system and analyzes their suitability to the dc SPS. This paper is concluded by identifying the future research trends needed for the development of the short-circuit fault management strategies of dc SPS for critical marine missions.

CASOA: An Architecture for Agent-Based Manufacturing System in the Context of Industry 4.0

Abstract: The fourth industrial revolution involves the advanced topics, such as industrial Internet of Things, cyber-physical system and smart manufacturing that address increasing demands for mass customized manufacturing. The agent-based manufacturing is a highly distributed control paradigm that can cope with these challenges well. This paper gives an overview of agent-based architectures for manufacturing systems. Besides, a cloud-assisted self-organized architecture is presented by comprising smart agents and cloud to communicate and negotiate through networks. Ontological representations of knowledge base are constructed to provide the information basis for decision-making of agents, which enables dynamic reconfiguration among agents in a collaborative way to achieve agility and flexibility. Furthermore, the agents’ interaction behavior is modeled to structure the agents hierarchically to reduce the complexity, because the interactions among agents in distributed system are difficult to understand and predict. The experimental results show that the presented architecture can be easily deployed to build smart manufacturing system and can improve the adaptiveness and robustness of the manufacturing system when dealing with mixed multi-product tasks.

Multi-Stage Planning of Active Distribution Networks Considering the Co-Optimization of Operation Strategies

Abstract: This paper presents a multi-stage and multi-load-scenario active distribution network (ADN) expansion planning model. The proposed model considers the applications of new distributed generation (DG) and construction of feeders at the planning level, and the utilization of DG supply and topology reconfiguration of distribution network including microgrid at the operation level. The proposed co-optimization model incorporates both investment and operation costs of ADN in the objective function. The ADN operation at each time stage is divided into several scenarios based on the load forecast data, in which the optimal reconfiguration of the ADN topology and the power output of DG units are calculated. The co-optimization considers investment decisions at each planning stage and operation strategies at each ADN loading scenario. The benefits of introducing DG and network construction as planning options, and DG supply and network reconfiguration as operation strategies, are discussed. The effectiveness of the proposed co-optimization of investment-operation is demonstrated using the numerical results.