Showing papers on "Control reconfiguration published in 2019"

Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services

Abstract: Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.

Industrial Internet of Things Driven by SDN Platform for Smart Grid Resiliency

Abstract: Software-defined networking (SDN) is a key enabling technology of industrial Internet of Things (IIoT) that provides dynamic reconfiguration to improve data network robustness. In the context of smart grid infrastructure, the strong demand of seamless data transmission during critical events (e.g., failures or natural disturbances) seems to be fundamentally shifting energy attitude toward emerging technology. Therefore, SDN will play a vital role on energy revolution to enable flexible interfacing between smart utility domains and facilitate the integration of mix renewable energy resources to deliver efficient power of sustainable grid. In this regard, we propose a new SDN platform based on IIoT technology to support resiliency by reacting immediately whenever a failure occurs to recover smart grid networks using real-time monitoring techniques. We employ SDN controller to achieve multifunctionality control and optimization challenge by providing operators with real-time data monitoring to manage demand, resources, and increasing system reliability. Data processing will be used to manage resources at local network level by employing SDN switch segment, which is connected to SDN controller through IIoT aggregation node. Furthermore, we address different scenarios to control packet flows between switches on hub-to-hub basis using traffic indicators of the infrastructure layer, in addition to any other data from the application layer. Extensive experimental simulation is conducted to demonstrate the validation of the proposed platform model. The experimental results prove the innovative SDN-based IIoT solutions can improve grid reliability for enhancing smart grid resilience.

The development of an ontology for describing the capabilities of manufacturing resources

Abstract: Today’s highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model’s content and structure.

Reconfigurable Smart Factory for Drug Packing in Healthcare Industry 4.0

Abstract: Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.

Data-driven digital twin technology for optimized control in process systems.

Abstract: Due to the installation of various apparatus in process industries, both factors of complex structures and severe operating conditions could result in higher accident frequencies and maintenance challenges. Given the importance of security in process systems, this paper presents a data-driven digital twin system for automatic process applications by integrating virtual modeling, process monitoring, diagnosis, and optimized control into a cooperative architecture. For unknown model parameters, the adaptive system identification is proposed to model closed-loop virtual systems and residual signals with fault-free case data. Performance indices are improved to make the design of robust monitoring and diagnosis system to identify the apparatus status. Soft-sensor, parameterization control, and model-matching reconfiguration are ameliorated and incorporated into the optimized control configuration to guarantee stable and safe control performance under apparatus faults. The effectiveness and performance of the proposed digital twin system are evaluated by using different simulations on the Tennessee Eastman benchmark process in the presence of realistic fault scenarios.

A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding

Abstract: Resilience is an overarching concept that requires combined efforts from interdependent critical infrastructures to achieve. As the interdependence between the power system and the natural gas system grows, the roles of coordination in post-disaster repair will be unneglectable to improve the resilience of the two systems. In this paper, a combined repair crew dispatch problem for the interdependent power and natural gas systems is proposed. The repair schedule of the two systems is coordinated and co-optimized. Both power system topology reconfiguration and intentional DG islanding are modeled as operational measures to further improve the resilience of the interdependent systems. Case studies validate the effectiveness of the proposed method in reducing load shedding and repair duration, and prove that the interdependence has a significant impact on the repair sequence and crew coordination.

Distributed multi-robot formation control in dynamic environments

Abstract: This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation This ensures that the robots make progress and avoid collisions with static and moving obstacles In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots The method allows for reconfiguration Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics This approach is efficient and scalable with the number of robots We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human

PruneTrain: fast neural network training by dynamic sparse model reconfiguration

Abstract: State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resource intensive. Much research has been done on pruning or compressing these models to reduce the cost of inference, but little work has addressed the costs of training. We focus precisely on accelerating training. We propose PruneTrain, a cost-efficient mechanism that gradually reduces the training cost during training. PruneTrain uses a structured group-lasso regularization approach that drives the training optimization toward both high accuracy and small weight values. Small weights can then be periodically removed by reconfiguring the network model to a smaller one. By using a structured-pruning approach and additional reconfiguration techniques we introduce, the pruned model can still be efficiently processed on a GPU accelerator. Overall, PruneTrain achieves a reduction of 39% in the end-to-end training time of ResNet50 for ImageNet by reducing computation cost by 40% in FLOPs, memory accesses by 37% for memory bandwidth bound layers, and the inter-accelerator communication by 55%.

A Resilience-Based Architecture for Joint Distributed Energy Resources Allocation and Hourly Network Reconfiguration

Abstract: As a result of the recent innovations in the deployment of plug-in electric vehicles (PEVs), this technology can play an important role as a distributed energy resource in supplying the system demand of the power systems of the future. This paper introduces a methodology for optimal coordinated allocation of wind farms (WFs), energy storage systems, and PEV's parking lots considering demand response programs and hourly distribution network reconfiguration in normal and severe contingency conditions. In the proposed methodology, the participation of different types of loads is also examined. The objective function is to minimize the total costs of purchased power from the upstream network and WFs, along with the costs of commercial/industrial loads flexibility and residential loads curtailment. To validate the performance of the proposed methodology, it is implemented on the well-known IEEE 33-bus distribution test system. The simulation results validate the feasibility and effectiveness of the proposed approach.

Distributed Secondary Control Strategy for Microgrid Operation with Dynamic Boundaries

Abstract: Microgrids (MGs) with dynamic boundaries, also known as dynamic MGs, are able to support critical loads without energization from utility and allow system topology variation upon request. Utilization of dynamic MGs can provide more flexible solutions toward distribution system restoration from natural disasters. This paper proposes a distributed secondary control strategy for dynamic MG operation under both static topology and topology variation. The proposed control strategy aims to guarantee seamless transitions during dynamic MG reconfiguration and proper power management among distributed generators (DGs) that are grouped dynamically. Smart switches (SSWs) are utilized to identify, process, and implement the reconfiguration request. The proposed control strategy provides a framework to coordinate the operation of multiple DGs in neighboring autonomous MGs and determine the operation status of SSWs, so that no transient over-shoots are introduced as SSWs operate and DGs are able to support the system power demand proportionally. Detailed controller designs are provided. Sufficient conditions under which the proposed controllers are exponentially stable are derived and the dynamic performance of the proposed controller are validated by comprehensive case studies in MATLAB/Simulink.

Coordinated flight control of miniature fixed-wing UAV swarms: methods and experiments

Abstract: In this paper, we present our recent advances in both theoretical methods and field experiments for the coordinated control of miniature fixed-wing unmanned aerial vehicle (UAV) swarms. We propose a multi-layered group-based architecture, which is modularized, mission-oriented, and can implement large-scale swarms. To accomplish the desired coordinated formation flight, we present a novel distributed coordinated-control scheme comprising a consensus-based circling rendezvous, a coordinated path-following control for the leader UAVs, and a leader-follower coordinated control for the follower UAVs. The current framework embeds a formation pattern reconfiguration technique. Moreover, we discuss two security solutions (inter-UAV collision avoidance and obstacle avoidance) in the swarm flight problem. The effectiveness of the proposed coordinated control methods was demonstrated in field experiments by deploying up to 21 fixed-wing UAVs.

On Robustness of Network Slicing for Next-Generation Mobile Networks

Abstract: Network slicing is a fundamental architectural technology for the fifth generation mobile network. It is challenging to design a robust end-to-end network slice spanning overall networks, where a slice is constituted by a set of virtual network functions (VNFs) and links. Bugs may accidentally occur in some VNFs, invalidating some slices, and triggering slice recovery processes. Besides, the traffic demands in each slice can be stochastic, and drastic changes of traffic demands may trigger slice reconfiguration. In this paper, we investigate robust network slicing mechanisms by addressing the slice recovery and reconfiguration in a unified framework. We first develop an optimal slice recovery mechanism for deterministic traffic demands. This optimal solution is used as a benchmark for evaluating other robust slicing algorithms. Then, we design an optimal joint slice recovery and reconfiguration algorithm for stochastic traffic demands by exploiting robust optimization. To tackle the slow convergence issue in the robust optimization algorithm, we propose a heuristic algorithm based on variable neighborhood search. Numerical results reveal that our proposed robust network slicing algorithms can provide adjustable tolerance of traffic uncertainties compared with the deterministic algorithm.

Reconfiguration in Network Slicing—Optimizing the Profit and Performance

Abstract: Network slicing enables diversified services to be accommodated by isolated slices in network function virtualization-enabled software-defined networks. To maintain satisfactory user experience and high profit for service providers in a dynamic environment, a slice may need to be reconfigured according to the varying traffic demand and resource availability. However, frequent reconfigurations incur certain cost and might cause service interruption. In this paper, we propose a hybrid slice reconfiguration (HSR) framework, where a fast slice reconfiguration (FSR) scheme reconfigures flows for individual slices at the time scale of flow arrival/departure, while a dimensioning slices with reconfiguration (DSR) scheme is occasionally performed to adjust allocated resources according to the time-varying traffic demand. In order to optimize the slice’s profit, i.e., the total utility minus the resource consumption and reconfiguration cost, we formulate the problems for FSR and DSR, which are difficult to solve due to the discontinuity and non-convexity of the reconfiguration cost function. Hence, we approximate the reconfiguration cost function with ${L} _{1}$ norm, which preserves the sparsity of the solution, thus facilitating restricting reconfigurations. Besides, we design an algorithm to schedule FSR and DSR, so that DSR is timely triggered according to the traffic dynamics and resource availability to improve the profit of slice. Furthermore, we extend HSR with a resource reservation mechanism, which reserves partial resources for near future traffic to reduce potential reconfigurations. Numerical results validate that our reconfiguration framework is effective in reducing reconfiguration overhead and achieving high profit for slices.

Energy Management of AC–DC Hybrid Distribution Systems Considering Network Reconfiguration

Abstract: This paper proposes a two-stage energy management scheme (EMS) for AC–DC hybrid smart distribution systems (DSs). The proposed EMS is formulated as a multi-objective optimization problem to minimize the DS operation costs and energy losses. The proposed EMS is achieved in two stages. In the first stage, a network reconfiguration algorithm determines the optimal day-ahead reconfiguration schedule for a hybrid DS. In the second stage, a real-time optimal power flow algorithm determines the real-time operational schedule of the energy resources. This paper also introduces a new linearized power flow model for AC–DC hybrid DSs. This new model facilitates the formulation of the first-stage algorithm as a mixed-integer linear programming problem and the formulation of the second-stage algorithm as a linear programming problem. The proposed two-stage EMS was tested on a case study of a hybrid DS that included different types of loads and distributed generators. The results demonstrate the efficacy of the proposed EMS: the optimal day-ahead reconfiguration schedule was successfully obtained in the first stage, and the proper and optimal real-time operation was achieved in the second stage.

Fault Diagnosis and System Reconfiguration Strategy of a Single-Phase Three-Level Neutral-Point-Clamped Cascaded Inverter

Abstract: This paper presents a fault diagnosis method and system reconfiguration strategy of a three-level neutral-point-clamped cascaded inverter (3L-NPC-CI) to enhance the reliability of power supply in an electrified railway. First, the neural network (NN) is employed to perform an open-circuit fault diagnosis and identify fault switches. Then, system reconfiguration strategy is proposed to perform a single-phase 3L-NPC-CI reconfiguration. Through the analysis of the operation status of 3L-NPC, there are eight fault modes in a single-bridge arm, including the single-switch fault and double-switch fault. By the feature analysis for the output arm voltage harmonic of normal and eight fault modes, seven harmonic parameters are selected as fault feature vectors. Meanwhile, a three-layer NN is constructed, and the seven feature vectors are the input layer of the NN. By training algorithm, the fault switches location can be identified accurately about one modulation period. Then, the fault module is bypassed and the 3L-NPC-CI system is stable by reconfiguring the modulation strategy of other normal modular. Simulation and experimental results are given to validate the proposed fault diagnosis and system reconfiguration strategy. The proposed method is able to improve robustness and reliability of 3L-NPC-CI in the traction power supply system.

Bi-level network reconfiguration model to improve the resilience of distribution systems against extreme weather events

Abstract: When a natural disaster occurs in a distribution network, a widespread power interruption may occur for a few days or weeks. This study presents a bi-level optimisation-based model for reconfiguration of the distribution network to improve the resilience of electricity distribution network against severe weather events such as storm and hurricane with the aim of minimising the cost of load outage. To achieve this, a model is first presented for evaluating the vulnerability of distribution network poles to estimate the damages imposed by the threat. Then, in the first level, according to the forecasting of possible failed lines and based on the predicted wind speed before the storm, a network reconfiguration strategy is employed to minimise the expected cost of load outage. In the second level, a new reconfiguration is carried out to restore the system loads and minimise the cost of load outage after the storm. The proposed model is then applied to a standard 33-bus radial distribution system using the GAMS software. The simulation results demonstrate the effectiveness of the proposed model in increasing network resilience and highlight the importance of network reconfiguration in the face of extreme natural disasters.

Enhancement of Distribution System Restoration Capability in Single/Multiple Faults by Using Microgrids as a Resiliency Resource

Abstract: Natural disasters such as hurricanes and major floods result in extensive interruptions and lack of service to end-use customers in electrical distribution systems. Robust systems immune to extreme abnormal events are uneconomical and hard to achieve, so, fast and reliable solutions to restore the service after major outages are preferable. Rerouting the supply to critical loads and deploying microgrids are common solutions. In this paper, a sophisticated solution based on a combination of reconfiguration and application of microgrids is proposed to enhance the restoration capability of the distribution system using spanning tree search strategy. The coordinated plan would maximally restore the critical loads by using microgrids as emergency sources even when utility power is unavailable. The objectives are minimizing the number of switching operations, total system losses and out-of-service loads. A heuristic approach is proposed to simplify the graph of the distribution network in order to reduce the computational complexity. Instead of removing all degree one/two vertices, a threshold for simplification is applied to avoid establishment of zones with high loads that could not be restored by microgrids. The proposed method is simulated on a four-feeder, 1069-bus unbalanced test system with four microgrids to demonstrate its effectiveness.

Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space

Abstract: This paper proposes an alternative to solve the distribution network reconfiguration (DNR) problem, aiming real power losses' minimization. For being a problem that has complexity for its solution, approximate techniques are adequate for solving it. Here, the proposition is a technique based on the firefly metaheuristic, named selective firefly algorithm, where the positioning of these insects is compressed in a selective range of values. The algorithm is applied to the DNR, and all its implementation and adequacy to the problem studied are presented. To define the search space, the methodology presented initially considers a set of candidate switches for opening based on the studied systems' mesh analysis. To reduce these possibilities, a refinement through a load flow analysis criterion (LFAC) is proposed. This LFAC considers the real power losses on each branch for a configuration with all switches closed, then, selecting possible switches to elimination from the set previously established. To demonstrate the behavior and the viability of the LFAC, it was initially applied on a 5 buses' and 7 branches' system. Also, to avoid getting stuck on results that may be considered not good, a disturbance resetting the population is set to occur every time a counter reaches a pre-defined number of times that the best solution does not change. Results found for simulations with 33, 70, and 84 buses are presented and comparisons with selective particle swarm optimization (SPSO) and selective bat algorithm (SBAT) are made.

Adequate Topology for Efficient Energy Resources Utilization of Active Distribution Networks Equipped With Soft Open Points

Abstract: Active distribution networks concerned with providing efficient control technologies for large-scale integration of distributed generation (DG) units into the distribution systems. This research proposes a methodology for distribution networks reconfiguration by controlling number, sharing, size, and location of DG units. Also, the soft-open points (SOPs) are added instead of the tie line switches. The SOPs are benefited with its high capability in controlling active/reactive power flow to enhance transmission system performance. The main target of this work is to increase the efficiency of energy utilization through minimizing the power system losses and improving system voltage profile while preserving all system constraints within permissible limits with reliable and flexible networks in normal and abnormal conditions with a suitable penetration level of DG. A modified particle swarm optimizer is developed to find the best system configuration, size, and placement of DG units as well as the size and allocation of SOPs. Research methodology is tested on two standards: the IEEE 33-node and 69-node distribution networks under different operating cases. This paper compares the obtained results with those in the literature to prove the capabilities of the proposed work. Finally, the suggested work pursues the optimal number of DG units with their appropriate penetration levels and selects the most convenient location of SOPs for adequate network reconfiguration.

Multi-objective planning model for multi-phase distribution system under uncertainty considering reconfiguration

Abstract: In this study, multi-objective particle swarm optimisation (MOPSO) and preference order (PO) ranking-based multi-objective planning model is presented for placement and sizing of wind and solar-based distributed generators (DGs), and capacitors in the multi-phase distribution network, under uncertainty considering network reconfiguration. Uncertainty in solar irradiance, wind speed, and load are considered using Monte Carlo simulation (MCS) with suitable probabilistic models. The planning problem is formulated considering different scenarios generated using MCS. For objective function formulation with varying demand and generation conditions, a dynamic load generation model is developed. A priority vector is proposed for DG and capacitor placement using the analytic hierarchy process to reduce the search space and computational time. The key benefits of the proposed DG placement algorithm are that it gives a single solution that is nearly optimal for all the possible network topologies and it works well for both unbalanced and balanced conditions. The proposed technique has been applied to IEEE 34-bus and IEEE 123-bus systems. The result shows a significant reduction in power losses, current unbalancing and improvement in reliability after the placement of DGs and capacitors.

Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation

Abstract: Network reconfiguration is an effective strategy for different purposes of distribution systems (DSs), e.g., resilience enhancement. In particular, DS automation, distributed generation integration and microgrid (MG) technology development, etc., are empowering much more flexible reconfiguration and operation of the system, e.g., DSs or MGs with flexible boundaries. However, the formulation of DS reconfiguration-related optimization problems to include those new flexibilities is non-trivial, especially for the issue of topology, which has to be radial. That is, existing methods of formulating radiality constraints can cause underutilization of DS flexibilities. Thus, this work proposes a new method for radiality constraints formulation fully enabling the topological and some other related flexibilities of DSs, so that the reconfiguration-related optimization problems can have extended feasibility and enhanced optimality. Graph-theoretic supports are provided to certify its theoretical validity. As integer variables are involved, we also analyze the tightness and compactness issues. The proposed radiality constraints are specifically applied to post-disaster MG formation, which is involved in many DS resilience-oriented service restoration and/or infrastructure recovery problems. The resulting new MG formation model, which allows more flexible merge and/or separation of sub-grids, etc., establishes superiority over the models in the literature. Case studies are conducted on two test systems.