Reconfiguration Method Research Articles

A Design Concept of an Intelligent Onboard Computer Network

The article suggests design principles of an advanced onboard computer network with an intelligent control system. It describes the main advantages of designing an onboard computer network based on fibre optics, which allows the implementation of an integrated intellectual system performing intelligent inference in emergency situations. The suggested principles significantly increase the reliability and fault tolerance of avionics suits, which, in turn, enhances flight safety. The suggested concept aims to solve a number of important problems including the design of a switchless computing environment, the development of the methods for dynamic reconfiguration of avionics suits with such an environment, and the implementation of a specialised multilevel intelligent avionics system within this environment.

A reconfigurable dynamic Bayesian network for digital twin modeling of structures with multiple damage modes

Dynamic Bayesian networks (DBNs) are commonly employed for structural digital twin modeling. At present, most researches only consider single damage mode tracking. It is not sufficient for a reusable spacecraft as various damage modes may occur during its service life. A reconfigurable DBN method is proposed in this paper. The structure of the DBN can be updated dynamically to describe the interactions between different damages. Two common damages (fatigue and bolt loosening) for a spacecraft structure are considered in a numerical example. The results show that the reconfigurable DBN can accurately predict the acceleration phenomenon of crack growth caused by bolt loosening while the DBN with time-invariant structure cannot, even with enough updates. The definition of interaction coefficients makes the reconfigurable DBN easy to track multiple damages and be extended to more complex problems. The method also has a good physical interpretability as the reconfiguration of DBN corresponds to a specific mechanism. Satisfactory predictions do not require precise knowledge of reconfiguration conditions, making the method more practical.

Network Reconfiguration for Searching Maximum Loading Capacity Radial Network: An Efficient Graph Theory-Based Machine Learning Approach

This study presents a unique method for network reconfiguration for Searching Maximum Loading Capacity Radial Network from an interconnected meshed network using continuation power flow. The network reconfiguration outcomes are also evaluated for various graph theory approaches presented in the literature. The combined graph theory and machine learning methods are used to determine the best sectional switch position. The system’s radiality is verified using graph theory. The recommended technique is tested on a 30-node mesh network with 41 sectional switches. The results demonstrate that the distribution system can manage a higher connected load using the suggested Bayesian-Ridge approach while lowering total active and reactive power losses.

Photovoltaics literature survey (No. 181)

Photovoltaics literature survey (No. 181)

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network: A cloud-edge collaboration architecture

With the construction of the power Internet of Things (IoT), communication between smart devices in urban distribution networks has been gradually moving towards high speed, high compatibility, and low latency, which provides reliable support for reconfiguration optimization in urban distribution networks. Thus, this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution. First, the multi-level dynamic reconfiguration method was discussed, which included feeder-, transformer-, and substation-levels. Subsequently, the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network. The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct “centralized training and decentralized execution” operation modes and improve the learning efficiency of the model. Thereafter, for a multi-agent system, this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy. In the offline learning phase, a Q-learning-based multi-agent conservative Q-learning (MACQL) algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase. In the online learning phase, a multi- agent deep deterministic policy gradient (MADDPG) algorithm based on policy gradients was proposed to explore the action space and update the experience pool. Finally, the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand.

Snap-interlock module system: Exploring an alternative architectural construction method

Responding to the rise of temporary architecture motivated by fast changing cultural and societal interests, construction methods must be adapted to meet the needs of reconfigurable systems. The prototype of Snap-Interlock Module System (SIMS) proposed in this study aims to integrate the simplicity of dry stacking as a primitive construction method through a coordinated joint system in order to increase material efficiency and structural integrity. This study explores a method of stacking blocks using unique interconnecting mechanisms without bonding agents to allow for reconfigurability. The considered unit of SIMS is configured to have four legs with integrated hooks on both top and bottom, allowing each block to snap into four adjacent blocks on either end. The centerpiece is designed such that each block can individually possess geometric versatility toward organic growth of the whole system. Larger assemblies of SIMS blocks can create full-scale structures without the use of bolting, welding, or other bonding agents. Finite element analysis demonstrates that the explored interlocking motion falls into the elastic range of the considered steel and confirms that structural integrity can be secured at the building scale as well. In order to test the proof-of-concept, 1:3 scaled Polylactic Acid (PLA) blocks are 3D printed and assembled into a 2.5 m tall portal frame, leading to a full-scale structural model executed with six full-scale steel blocks. The assembly and disassembly of both prototype structures are easily executed by a single individual. Despite the limitations of the chosen fabrication methods and material choices, the study promises diverse applications in the changing urban context and contributes to the broader sustainability of our built environment via an alternative and reconfigurable construction method.

A review of PV array reconfiguration techniques for maximum power extraction under partial shading conditions

Partial shading is a significant obstacle to the effective use of photovoltaic (PV) systems because it reduces output power significantly. The output power reduction is not proportional to the shaded area but rather depends on the PV array configuration/reconfiguration and shadowing pattern. PV array reconfiguration is one of the most effective solutions for overcoming the adverse effects of partial shading in PV systems. The main objective of PV array reconfiguration strategies is to disperse shades consistently over the entire area of the PV array to reduce the adverse impact of partial shading situations. This paper provides an in-depth examination of the significant existing PV array reconfiguration methods used to address the problem of partial shading. The various methods are evaluated and compared based on their techniques, benefits, and drawbacks. This work is an interesting reference for researchers in the field and a simple guide for beginners on PV array reconfiguration.

Optimizing Solar Power Using Array Topology Reconfiguration With Regularized Deep Neural Networks

Reconfiguring photovoltaic (PV) array connections among different topologies such as series-parallel, bridge-link, honeycomb, or total-cross-tied is a popular strategy to mitigate impediments in power production caused by partial shading. Conventional approaches rely on either by-passing or replacing shaded modules with auxiliary panels through complex control mechanisms, optimization strategies, or simulator driven approaches to obtain the best topology. However, these solutions are not scalable and incur significant installation costs and computational overhead, motivating the need to develop ‘smart’ methods for topology reconfiguration. To this end, we propose a regularized neural network based algorithm that leverages panel-level sensor data to reconfigure the array to the topology that maximizes power output under arbitrary shading conditions. Based on our simulations that include wiring losses in different configurations, we observe power improvement of up to 11% through reconfiguration. The proposed algorithm can be easily integrated in any cyber-physical PV system with reconfiguration capabilities and is scalable.

Sequential Reconfiguration of Unbalanced Distribution Network with Soft Open Points Based on Deep Reinforcement Learning

With the large-scale distributed generations (DGs) being connected to distribution network (DN), the traditional day-ahead reconfiguration methods based on physical models are challenged to maintain the robustness and avoid voltage off-limits. To address these problems, this paper develops a deep re-inforcement learning method for the sequential reconfiguration with soft open points (SOPs) based on real-time data. A state-based decision model is first proposed by constructing a Marko decision process-based reconfiguration and SOP joint optimization model so that the decisions can be achieved in milliseconds. Then, a deep reinforcement learning joint framework including branching double deep <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> network (BDDQN) and multi-policy soft actor-critic (MPSAC) is proposed, which has significantly improved the learning efficiency of the decision model in multi-dimensional mixed-integer action space. And the influence of DG and load uncertainty on control results has been minimized by using the real-time status of the DN to make control decisions. The numerical simulations on the IEEE 34-bus and 123-bus systems demonstrate that the proposed method can effectively reduce the operation cost and solve the overvoltage problem caused by high ratio of photovoltaic (PV) integration.

Dynamic Adaptive Reconfiguration Method for Cloud Manufacturing Service Composition in Practical Multi-constraint Environment

Dynamic Adaptive Reconfiguration Method for Cloud Manufacturing Service Composition in Practical Multi-constraint Environment

A Flexible Sharding Blockchain Protocol Based on Cross-Shard Byzantine Fault Tolerance

Sharding technology is crucial to achieve decentralization, scalability, and security simultaneously. However, existing sharding blockchain schemes suffer from high cross-shard transaction processing latency, low parallelism, incomplete cross-shard views of shard members, centralized reconfiguration, high overhead of randomness generation, and lack of formalized protocol design and security proofs. This paper proposes a flexible sharding (FS) blockchain protocol. First, a cross-shard Byzantine fault tolerance (CSBFT) protocol is designed to cut down confirmation delays when processing cross-shard transactions. Second, we utilize multiple parallel CSBFT where each node acts not only as a leader but also as multiple ordinary members to break through the performance bottleneck caused by a leader’s bandwidth and computing power, improving the system parallelism. Third, a cross-shard transaction censorship attack is proposed, and a cross-shard view-change mechanism is designed to defend against it. Fourth, a secure and truly decentralized shard reconfiguration method combining proof-of-work, proof-of-possession, and intra-shard BFT is designed. Fifth, we utilize a formal protocol design method and give strict security proof for each protocol. Finally, we evaluate FS from both theoretical and practical perspectives. FS is proven to have lower communication and computation complexity and achieve considerable performance.

Enhancing the Output Power of the Partially Shaded PV Array Using the Corona Virus Optimization Algorithm

This paper proposes an electrical array reconfiguration system depending on the Coronavirus optimization algorithm (CVOA) for reducing the partial shading conditions and increasing its power. The increasing use of fossil fuels to generate energy is causing environmental issues. Therefore, human society moves toward renewable energies, such as wind energy, solar energy, and geothermal energy. Nowadays, solar energy plays a significant role in the energy crisis. In recent years, the photovoltaic (PV) system is used to gain solar energy. But, partial shading is the most significant and prominent one in changing the behavior of PVs. The impact of partial shading reduces the photovoltaic output power and it is overcome by the electrical array reconfiguration. The proposed approach generates a reconfigured array pattern, thus shading property spread over the array and growing the power output. In the reconfiguration method, the physical position of the modules remains constant, but their electric connections are modified depending on the irradiance level. The proposed CVOA approach has been experimented with under dissimilar shading conditions on 6 × 6 sizes of the array and analyzed with TCT and SuDoKu patterns. The efficiency of the CVOA algorithm is higher compared to those of the reconfiguration methods.

Design and implementation of P4 virtual switches and P4 virtual networks

This paper introduces the design and implementation of P4 virtual switches (VSWs) and P4 virtual networks (VNs). The proposed switch virtualization supports multiple VSWs embedded in a single P4 physical switch (PSW). Similar to a PSW, users use standard P4 language to program their VSWs. Given a set of VSWs and their target PSW, our hypervisor integrates all the VSW programs to generate configuration files that enable the target PSW to become a platform to realize switch virtualization. Users can define customized header types and metadata for their VSWs. In addition, our system supports live reconfiguration. Reconfiguring a VSW in a PSW will not interrupt the other VSWs in the PSW. Based on the proposed switch virtualization scheme, we present the way to share multiple VNs in a P4 physical network to provide multitenant services. A VN is composed of multiple VSWs and multiple virtual links (VL), and each VN is provisioned with guaranteed bandwidth for their VLs. Each tenant can control and manage its own VN—similar to a dedicated P4 network. In addition, each tenant can assign a priority to its traffic. The proposed network virtualization guarantees traffic isolation, preventing inter-VN interference. Results on an experimental network used for performance evaluation show that our system can successfully generate VSWs and VNs. The proposed hitless reconfiguration method can prevent service interruptions between VSWs during VSW reconfiguration. The experimental results also show that our network virtualization can accurately guarantee the provisioned bandwidths of VNs in a physical P4 network.

A 250 MS/s 65.8-dB SNDR SAR ADC With Cross-Phase Common-Mode Feedback and Reconfigurable Inverter-Based Preamplifier in 28-nm CMOS

This brief presents a 250 MS/s 12b successive-approximation-register (SAR) analog-to-digital converter (ADC) in 28 nm CMOS. The inverter-based preamplifier is introduced to suppress comparator noise. To improve the ADC robustness and speed, first, a cross-phase common mode feedback which explores the common mode voltage during the sampling phase, is proposed to address the PVT and input common mode voltage sensitivity of the inverter-based preamplifier. Second, a reconfigurable method is proposed to eliminate the long recovery time of the preamplifier at large input signals, as well as reduce the kickback noise at small input signals. Third, a PMOS pair with deliberate timing is introduced to accelerate the comparator regeneration without deteriorating its noise. The ADC employs 360 fF/side sampling capacitance and takes 2 ns conversion time to resolve 15 raw bits. Fabricated in 28 nm CMOS, the prototype ADC achieves 65.8 dB SNDR and 80.9 dB SFDR for a near-Nyquist input, occupies an active area of 0.01 mm2 and consumes a power of 4.7 mW, resulting in a Walden FOMW of 11.8 fJ/conv-step and a Schreier <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {FOM}}_{{\boldsymbol{S}}}$ </tex-math></inline-formula> of 170.0 dB, respectively. Last but not least, the ADC attains a rail-to-rail input CM range.

Unconstrained model and merge sorted serpentine arrangement method for dynamic reconfiguration of large PV arrays

• An unconstrained optimization model, and a heuristic method namely, Merge Sorted Serpentine Arrangement, are proposed for modelling and solving the problem of finding the optimal reconfiguration of a photovoltaic array to minimize partial shading losses. • The proposed unconstrained optimization model formulation enables the application of different meta-heuristic and heuristic optimization algorithms for solving the problem of finding the optimal reconfiguration of a photovoltaic array to minimize partial shading losses. • Extensive case studies show that the proposed method reduces average computation time by up to 80 % when compared with other methods. • The proposed method consistently reduces partial shading losses by up to 11 % under dynamic and static partial shading conditions due to its lower computation time as demonstrated by extensive case studies. • The main drawback of the proposed method is that it increases memory requirements by up 100 % when compared with other methods. The problem of finding the optimal interconnection between photovoltaic modules in a dynamic photovoltaic array to minimize partial shading losses is a Non-deterministic Polynomial time-hard (i.e., NP-hard) problem. Therefore, no known deterministic method can solve the problem in polynomial time. However, meta-heuristic and heuristic methods can provide solutions to this problem. In this paper, an unconstrained optimization model and a heuristic method, namely, Merge Sorted Serpentine Arrangement (MSA), are proposed for the first time for modelling and solving the problem. In the proposed method, the irradiance levels of the PV modules are accurately estimated using the double diode model and then sorted according to the merge sorting algorithm. After that, the PV modules are arranged in the PV array according to their sorted irradiance levels in a serpentine way using the proposed switching matrix. The proposed method has consistently resulted in lower computational time and partial shading losses when compared with the other methods in the literature, due to the application of the Merge Sorting Algorithm, which has an average time complexity of O (n log(n)). However, the proposed method has resulted in increased memory, switches, and sensor requirements when compared with some of the other methods in the literature.

Метод дистанційної діагностики, перепрограмування і реконфігурації вузлів вбудованої системи

The subject of study in this article is a method for remote diagnostics, reprogramming and reconfiguration of nodes of embedded and small-sized systems. The goal is to increase the reliability characteristics of systems built on the basis of microprocessor solutions. The task is: to consider the existing methods of communication of the MC set in the system with the possibility of specifying the requirements; consider and analyze the existing means of remote reprogramming of individual MCs as part of the target system; to consider methods of adding redundancy to the MC connection scheme for the possibility of reconfiguration; describe the elements of the method and sequence of development of systems that provide for the possibility of remote diagnostics, reprogramming and reconfiguration of their individual nodes. According to the tasks, the following results were obtained. The existing methods and equipment for communication of the MC set are analyzed. Possible technical solutions and theoretical foundations of intra-system reprogramming of system nodes based on MK are analyzed. The technical properties of one of the available industrial technologies, which contain crystals and development media, are presented, with the aim of finding properties that will allow expanding the capabilities of the created systems in terms of increasing reliability and energy efficiency. It is proposed to use a modified bootloader to organize the possibility of communication and reprogramming over one communication line. A set of steps is proposed for finding options for building a circuit with hardware redundancy. Conclusions. The idea of reprogramming and diagnostics of system nodes along one line using ready-made industrial components (COTS) based on microcontrollers and development tools is proposed, which makes it possible to make their development cheaper. The proposed elements of the method of remote diagnostics, reprogramming and reconfiguration of individual nodes of the built-in system based on MC. A practical example of the implementation of the concepts discussed in the article, including its reconfiguration, is presented; a diagram of the device before and after its reconfiguration is given. The practical significance of this research lies in the possibility of building and configuring a system from many separate nodes based on microcontrollers.

Plane Spiral OAM Mode-Group Orthogonal Multiplexing Communication Using Partial Arc Sampling Receiving Scheme

A variety of novel orbital angular momentum (OAM)-based communication or sensing systems have attracted much attention over the past decade, the superiority is brought about by nothing other than its orthogonality or vorticity. Plane spiral OAM (PSOAM) mode-group (MG) technique as a reconfigurable beamforming method can be used for building a multiple-in-multiple-out (MIMO) system to achieve the reduction in subchannel correlation, which benefits from the spiral phase distribution within the mainlobe. However, the demultiplexing process still depends on the MIMO algorithm, in which the receiver complexity is same as the conventional MIMO systems. In this article, a PSOAM MG orthogonal multiplexing communication link at the X-band using partial arc sampling receiving (PASR) scheme has been demonstrated experimentally. The MG channels have a good isolation of about 15 dB, and the existing performance can ensure the reliable 16-QAM wireless transmission. Besides, a real-time dual-channel video transmission experiment has been carried out to verify the channel isolation caused by MG’s orthogonality intuitively. More importantly, the demultiplexing procedure using the PASR scheme can be implemented by simple analog phase shifting operation with a lower receiver complexity compared with the conventional MIMO systems.

An improved equilibrium optimiser-based algorithm for dynamic network reconfiguration and renewable DG allocation under time-varying load and generation

This paper presents an improved equilibrium optimiser (IEO)-based method for dynamic hourly network reconfiguration (NR) with optimal allocation of dispatchable and non-dispatchable renewable distributed generator (DG) units in radial power distribution networks. In this work, solar- and wind-based DGs are considered non-dispatchable DG, while biomass DGs are considered dispatchable DG. The output of biomass DGs is adjusted according to the 24-hour load demand curve and that of solar and wind DG follow their hourly power generation. For this purpose, a multi-objective function is developed with consideration of hourly power loss minimisation and profit maximisation without violating the system’s operational constraints. The viability and efficacy of the proposed method are tested on IEEE 33-bus and 69-bus distribution systems for five distinct scenarios of network reconfiguration and renewable DG allocation. The analysis shows that the simultaneous NR and DG have a more substantial impact than considering the cases separately.

Unstructured Direct Ink Write 3D Printing of Functional Structures with Ambient Temperature Curing Dual‐Network Thermoset Ink

Fabrication of structures in unstructured environments is a promising field to expand the application spaces of additive manufacturing (AM). One potential application is to add new components directly onto existing structures. Herein, a versatile, reconfigurable direct ink writing (DIW) manufacturing method is developed in tandem with a two‐stage hybrid ink designed to fabricate high‐strength, self‐supporting parts in unconventional printing spaces such as underneath a build surface or horizontally. This two‐stage hybrid DIW ink combines a photopolymer and a tough epoxy resin. The photopolymer can cure rapidly to enable layer‐by‐layer printing of complex structures. It also possesses adequate adhesion to allow the fabrication of large volume structures on a diversity of substrates including acrylic, wood, glass, aluminum, and concrete. The epoxy component can cure after 72 h in ambient conditions with further increased adhesion strengths. The capabilities of the reconfigurable DIW extrusion nozzle method to print complex structures in inverted and horizontal environments are demonstrated. Finally, via addition of DIW‐deposited conductive paths, a functional 3D‐printed structure capable of in situ deformation monitoring is created. This work has the potential to be used for applications such as appending new parts to existing structures for increasing functionality, repair, and structure health monitoring.