Showing papers on "Control reconfiguration published in 2021"
TL;DR: Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique.
166 citations
TL;DR: The proposed test system is developed by modifying and updating the well-known 33 bus distribution system, and comprises both forms of balanced and unbalanced three-phase power systems, including new details on the integration of distributed and renewable generation units, reactive power compensation assets, reconfiguration infrastructures and appropriate datasets of load and renewablegeneration profiles for different case studies.
Abstract: The transformation of passive distribution systems to more active ones thanks to the increased penetration of distributed energy resources, such as dispersed generators, flexible demand, distributed storage, and electric vehicles, creates the necessity of an enhanced test system for distribution systems planning and operation studies. The value of the proposed test system, is that it provides an appropriate and comprehensive benchmark for future researches concerning distribution systems. The proposed test system is developed by modifying and updating the well-known 33 bus distribution system from Baran & Wu. It comprises both forms of balanced and unbalanced three-phase power systems, including new details on the integration of distributed and renewable generation units, reactive power compensation assets, reconfiguration infrastructures and appropriate datasets of load and renewable generation profiles for different case studies.
101 citations
TL;DR: In this article, a cloud-edge based dynamic reconfiguration to service workflow for mobile e-commerce environments is proposed, where the value and cost attributes of a service are considered, and a long short-term memory (LSTM) neural network is used to predict the stability of services.
Abstract: The emergence of mobile service composition meets the current needs for real-time eCommerce. However, the requirements for eCommerce, such as safety and timeliness, are becoming increasingly strict. Thus, the cloud-edge hybrid computing model has been introduced to accelerate information processing, especially in a mobile scenario. However, the mobile environment is characterized by limited resource storage and users who frequently move, and these characteristics strongly affect the reliability of service composition running in this environment. Consequently, applications are likely to fail if inappropriate services are invoked. To ensure that the composite service can operate normally, traditional dynamic reconfiguration methods tend to focus on cloud services scheduling. Unfortunately, most of these approaches cannot support timely responses to dynamic changes. In this article, the cloud-edge based dynamic reconfiguration to service workflow for mobile eCommerce environments is proposed. First, the service quality concept is extended. Specifically, the value and cost attributes of a service are considered. The value attribute is used to assess the stability of the service for some time to come, and the cost attribute is the cost of a service invocation. Second, a long short-term memory (LSTM) neural network is used to predict the stability of services, which is related to the calculation of the value attribute. Then, in view of the limited available equipment resources, a method for calculating the cost of calling a service is introduced. Third, candidate services are selected by considering both service stability and the cost of service invocation, thus yielding a dynamic reconfiguration scheme that is more suitable for the cloud-edge environment. Finally, a series of comparative experiments were carried out, and the experimental results prove that the method proposed in this article offers higher stability, less energy consumption, and more accurate service prediction.
93 citations
TL;DR: An outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling and has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.
91 citations
TL;DR: An improved equilibrium optimization algorithm (IEOA) combined with a proposed recycling strategy for configuring the power distribution networks with optimal allocation of multiple distributed generators for enhanced distribution system performance, quality and reliability is proposed.
88 citations
TL;DR: A comprehensive review on state-of-the-art photovoltaic array reconfiguration methods through a thoroughly investigation of 125 recently published papers makes a more exhaustive classification, in which sixty-four methods are thoroughly categorized into nine groups.
71 citations
TL;DR: The integrated optimization model is linearized to be a mixed-integer linear programming form, which can be effectively solved by off-the-shelf solvers such as Cplex and Gurobi and validated the effectiveness of the proposed model.
Abstract: This article proposes an integrated optimization model for unbalanced distribution system restoration after large-scale power outages caused by extreme events. The model can coordinate the control actions of multiple types of distributed energy resources (DERs), including dispatchable distributed generators (DGs), renewable DGs (mainly wind and solar), and energy storage systems (ESSs). The model also considers topology flexibility by forming dynamic islands through reconfiguration. Besides, the optimal dispatch model of repair crews and mobile emergency generators are also proposed to leverage the restoration capabilities of existing DERs installed in the distribution systems. The integrated optimization model is linearized to be a mixed-integer linear programming form, which can be effectively solved by off-the-shelf solvers such as Cplex and Gurobi. Numerical results on IEEE 123 and 8500 node test feeders validated the effectiveness of the proposed model and highlighted the necessity of coordinating various flexible resources.
67 citations
TL;DR: In this article, a magnetic dynamic polymer (MDP) composite composed of hard-magnetic microparticles in a dynamic polymer network with thermally responsive reversible linkages is reported, which permits functionalities including targeted welding for magnetic assisted assembly, magnetization reprogramming, and permanent structural reconfiguration.
Abstract: Shape-morphing magnetic soft materials, composed of magnetic particles in a soft polymer matrix, can transform shape reversibly, remotely, and rapidly, finding diverse applications in actuators, soft robotics, and biomedical devices. To achieve on-demand and sophisticated shape morphing, the manufacture of structures with complex geometry and magnetization distribution is highly desired. Here, a magnetic dynamic polymer (MDP) composite composed of hard-magnetic microparticles in a dynamic polymer network with thermally responsive reversible linkages, which permits functionalities including targeted welding for magnetic-assisted assembly, magnetization reprogramming, and permanent structural reconfiguration, is reported. These functions not only provide highly desirable structural and material programmability and reprogrammability but also enable the manufacturing of functional soft architected materials such as 3D kirigami with complex magnetization distribution. The welding of magnetic-assisted modular assembly can be further combined with magnetization reprogramming and permanent reshaping capabilities for programmable and reconfigurable architectures and morphing structures. The reported MDP are anticipated to provide a new paradigm for the design and manufacture of future multifunctional assemblies and reconfigurable morphing architectures and devices.
66 citations
TL;DR: In this paper, a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs) is proposed for routing and scheduling of mobile energy storage systems (MESSs) to achieve agile system response and recovery in facing the aftermath of high-impact low probability (HILP) incidents.
Abstract: With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs.
57 citations
TL;DR: In this paper, pre-programmed hydrogel crosslinks were embedded in different patterns within the alginate microstructures in an electric field using different electrode configurations, which enabled the shape-morphing and intelligence to be enhanced.
Abstract: Shape-morphing uses a single actuation source for complex-task-oriented multiple patterns generation, showing a more promising way than reconfiguration, especially for microrobots, where multiple actuators are typically hardly available. Environmental stimuli can induce additional causes of shape transformation to compensate the insufficient space for actuators and sensors, which enriches the shape-morphing and thereby enhances the function and intelligence as well. Here, making use of the ionic sensitivity of alginate hydrogel microstructures, we present a shape-morphing strategy for microrobotic end-effectors made from them to adapt to different physiochemical environments. Pre-programmed hydrogel crosslinks were embedded in different patterns within the alginate microstructures in an electric field using different electrode configurations. These microstructures were designed for accomplishing tasks such as targeting, releasing and sampling under the control of a magnetic field and environmental ionic stimuli. In addition to structural flexibility and environmental ion sensitivity, these end-effectors are also characterized by their complete biodegradability and versatile actuation modes. The latter includes global locomotion of the whole end-effector by self-trapping magnetic microspheres as a hitch-hiker and the local opening and closing of the jaws using encapsulated nanoparticles based on local ionic density or pH values. The versatility was demonstrated experimentally in both in vitro environments and ex vivo in a gastrointestinal tract. Global locomotion was programmable and the local opening and closing was achieved by changing the ionic density or pH values. This 'structural intelligence' will enable strategies for shape-morphing and functionalization, which have attracted growing interest for applications in minimally invasive medicine, soft robotics, and smart materials.
53 citations
TL;DR: A joint framework is proposed to integrate a novel incentive-based DR program and reconfiguration method in the EM problem of microgrid on a day-ahead time frame to minimize the fuel cost of conventional distributed generation and the cost of power purchased from the grid, while maximizing the profit for microgrid operator (MGO).
TL;DR: A new reconfiguration technique for PV panels using Genetic algorithm (GA) and two main reconfigurable steps based on a switching matrix is suggested, which proves the superiority of the proposed technique over other techniques for overcoming partial shading.
TL;DR: A novel effective optimization framework for the reconfiguration problem of modern DNs using the recent Harris hawks optimization (HHO) algorithm, which allows the decision maker to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements.
Abstract: Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain--without guarantees--the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time.
18 Jan 2021
TL;DR: A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network, and the effectiveness of the proposed method showed in reducing the voltage deviation and power loss of the distribution system.
Abstract: Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software.
TL;DR: This article investigates how to fulfill dynamic formation by distributively optimizing a team cost function by designing a decision unit for each agent to generate an implicit trajectory as a servo signal, based on which a control unit is designed with a displacement-gradient-based law to achieve the desired local solution.
Abstract: This article studies an optimal dynamic formation problem for heterogeneous affine nonlinear systems. The nonidenticality in agents and the requirement for dynamic spatial reconfiguration make it a challenging task to coordinate different types of agents to maintain an optimized formation shape. In an architecture of event-triggered decision and control, this article investigates how to fulfill dynamic formation by distributively optimizing a team cost function. The basic idea is to design a decision unit for each agent to generate an implicit trajectory as a servo signal, based on which a control unit is designed with a displacement-gradient-based law to achieve the desired local solution. Typical heterogeneous characteristics including different nonlinearities and nonidentical dimensions are dealt with in a unified framework. It is shown that with the proposed triggering mechanisms, the optimal dynamic formation problem can be solved by a distributed control law with only intermittent communication. In theory, the properties of convergence of trajectory tracking errors, optimality of the team solution, and Zeno-freeness of event-triggered mechanisms are proved. Two simulation examples are given to verify the proposed method.
TL;DR: In this article, a literature review and an analysis of the studies related to workforce reconfiguration strategies as a part of workforce planning for various production environments is provided, where five strategies are considered: the use of utility, temporary, walking, cross-trained workers, and bucket brigades.
Abstract: This paper provides a literature review and an analysis of the studies related to workforce reconfiguration strategies as a part of workforce planning for various production environments. The survey demonstrates that these strategies play a crucial role in the resilience and flexibility of manufacturing systems since they help industrial companies to quickly adapt to frequent changes in demand both in terms of volume and product mix. Five strategies are considered: the use of utility, temporary, walking, cross-trained workers, and bucket brigades. They are analyzed in the context of mixed and multi-model manual assembly lines, dedicated, cellular, flexible, and reconfigurable manufacturing systems. The review shows that most of the researches on these reconfiguration strategies focus on multi-or mixed-model assembly lines. At the same time, few studies consider workers team reconfiguration in flexible and reconfigurable manufacturing systems. Finally, this paper reveals several promising research directions in workforce reconfiguration planning, namely, the use of both machine and workforce reconfigurations, consideration of the ergonomic aspects, the combination of multiple workforce reconfiguration strategies, the study of workforce reconfiguration in human-robot collaborative systems, and the use of new technologies in human-machine industrial environments.
TL;DR: In this paper, an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation is proposed.
Abstract: Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem’s complexity, the proposed technique based on JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.
TL;DR: This work proposes a data driven approach to realize joint slice reconfiguration from 5GC to 5G RAN in real-time and shortens significantly the time of slice status update by mapping the resource status to a two-dimensional vector graph.
Abstract: Network slicing is a widely discussed technology for 5G broadcasting services, as it allows the operators to create end-to-end virtual slices across the 5G core (5GC) and the 5G radio access network (RAN). The joint network slicing is necessary for 5GC and 5G RAN in a bid to enhance the overall resource utilization and quality of service. Nevertheless, as the dimension of network resource and user demand increase, the computational complexity of slice status update goes up as well. To overcome this scalability problem, we propose a data driven approach to realize joint slice reconfiguration from 5GC to 5G RAN in real-time. Our design shortens significantly the time of slice status update by mapping the resource status to a two-dimensional vector graph. In addition, we also introduce a corresponding slice reconfiguration algorithm to balance the load of each slice in 5GC and 5G RAN. Simulation results justify that our proposed approach can greatly reduce the cost of slice status update and achieve desirable slice reconfiguration performance.
TL;DR: In this paper, an optimal framework for the resilience-oriented design (ROD) in distribution networks to protect these grids against extreme weather events such as earthquakes and floods is presented.
TL;DR: This work experimentally demonstrates an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip that works for various types of chip structures and is especially helpful to those that cannot be analytically decomposed and characterized.
Abstract: Recent advances in silicon photonic chips have made huge progress in optical computing owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural networks serves a...
TL;DR: Simulation results demonstrate that the proposed swarm reinforcement learning (SRL) can obtain a larger total benefit than genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA), harris hawks optimizer (HHO), butterfly optimization algorithms (BOA), and Q-learning, in which the benefit increment can reach from 2.12% ( against PSO) to 10.62% (against Q- learning).
TL;DR: The proposed Salp Swarm Algorithm (SSA) is based on the salps swarming behavior in oceans when navigating and foraging and aims at minimisation of power loss and voltage deviation in the distribution network.
Abstract: Recent studies show that the majority of the researchers have focused on either Distributed Generation (DG) allocation or network reconfiguration for enhancing distribution network performance. How...
TL;DR: An improved fault-tolerant control strategy is proposed for CHB-based static synchronous compensator (STATCOM) under SM faults that possesses the ability of cluster voltage balancing, which is an important issue for the STATCOM application.
Abstract: Fault-tolerant operation ability is of great importance for stable operation of cascaded H-bridge (CHB) converters, under open-circuit (OC) or short-circuit (SC) switch failures in submodule (SM). In this article, an improved fault-tolerant control strategy is proposed for CHB-based static synchronous compensator (STATCOM) under SM faults. First of all, compared with the conventional fault-tolerant method of directly bypassing the faulty SMs, the proposed fault-tolerant method takes advantage of the healthy switches of the faulty SMs, where they are still able to generate either positive or negative voltage level. As a result, more output voltage levels can be generated, and it raises the attainable balanced line-to-line voltage, especially when different fault types exist at the same time. Then, based on the specific condition of OC fault or SC fault, when the output voltage reference of the faulty phase reaches its limit, the references of the other two healthy phases are redistributed to generate the desired line-to-line voltage. With the reconfiguration of modulation waves, the attainable balanced line-to-line voltage can be further improved. In addition, the proposed fault-tolerant method possesses the ability of cluster voltage balancing, which is an important issue for the STATCOM application. Simulation and experimental results validate the effectiveness of the proposed fault-tolerant method.
TL;DR: This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems and touches on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell.
Abstract: This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.
TL;DR: A redesigned convolutional neural network was used to predict short-term wind power, and the proposed methods were trained and tested based on a dataset of a real wind farm in China and showed good performance and effectively improve the accuracy of short- term wind power prediction.
TL;DR: In this article, a modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration associated with the allocation of distributed generators (DGs) for simultaneous DNR.
Abstract: A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the...
TL;DR: The proposed scheme utilizes the switching-frequency-based harmonic component for fault detection and localization, and a postfault restoration and control strategy is also proposed to ensure equal current sharing among the remaining healthy modules within their maximum current rating and minimize the input current ripple in the PV panel.
Abstract: To utilize the solar photovoltaic (PV) energy efficiently, dc–dc converters are widely used in both grid-connected and stand-alone systems. Among the various topologies, interleaved dc–dc boost converter offers the benefit of modularity, high power density, and high efficiency along with reduced input current ripple to the PV panel, thereby improving its power extraction efficiency. Open-circuit faults in any of the semiconductor switches of interleaved boost converter could lead to unequal loading on the healthy phases and increase in ripple current that reduces the extraction efficiency of the PV system. To address this issue, a new fault detection and localization scheme is proposed in this article. The proposed scheme utilizes the switching-frequency-based harmonic component for fault detection and localization. Once the fault is localized, a postfault restoration and control strategy is also proposed to ensure equal current sharing among the remaining healthy modules within their maximum current rating and minimize the input current ripple in the PV panel. Detailed simulations are carried out to show the effectiveness of the proposed approach. A laboratory prototype of the interleaved converter is built to validate the proposed approach and experimental test results are provided.
TL;DR: A comprehensive review of the major existing PV array reconfiguration approaches which are used to overcome the problem of partial shading is presented in this paper, where different approaches are evaluated and compared according to their techniques, advantages and drawbacks.
TL;DR: This research provides a basic concept for self-organized reconfiguration management and discusses the Cyber-Physical Production Systems and some of their potentials regarding the reconfigurations issues.