Reconfiguration Method Research Articles

Reconfiguration and compensation design method for single-DoF closed-chain leg mechanism

Compared with a wheeled robot propelled by a rotary drive, a legged robot equipped with multiple motors on a single leg possesses superior adaptability, while at the cost of high energy consumption and less velocity. Inspired by the California mite, the design method of reconfigurable closed-chain leg mechanism with single degree of freedom (DoF) is proposed for effective striding and smooth propelling. Furthermore, the lower-mobility closed-chain leg mechanism is more appropriate for obtaining high frequency and fast movement with the merits of simple control system and rotary drive. Firstly, a reconfigurable design is performed in the swing phase for a high-knee motion intermittently driven by a walking motor through dynamic coupling. Secondly, its fluctuations in vertical displacement and horizontal speed are compensated through the contact outline curve and variable speed input in the supporting phase. Finally, the simulated performance is analyzed and compared, and a prototype is fabricated for fluctuating and obstacle-crossing experiments.

A Two-Stage Multi-Agent Deep Reinforcement Learning Method for Urban Distribution Network Reconfiguration Considering Switch Contribution

A Two-Stage Multi-Agent Deep Reinforcement Learning Method for Urban Distribution Network Reconfiguration Considering Switch Contribution

Topological valley waveguide state transport based on the nesting difference

Acoustic topological waveguide states are characterized by topologically protected transport. Existing acoustic topological valley-locked waveguide state transport structures can only realize single-scene acoustic field modulation due to the limitation of structural tunability, and it is difficult to realize width degree of freedom and reconfigurable transport paths of acoustic waves by adjusting the transport structure for complex acoustic field. Therefore, the transport structures become a conceptualized idea in terms of tunability. Our study introduces a nesting method in the design of the sonic crystal scatterer, which splits the scatterer structure into a removable outer shell and a core, and three topological phase transitions can be realized by only changing the nesting method of the sonic crystal shell, this approach constructs a fully reconfigurable topological waveguide. The results demonstrate that changing the nesting of the sonic crystals induces effective spatial inversion symmetry breaking, which leads to the valley topological phase transitionSimulations and experiments demonstrate that this transport structure has excellent robust transport properties with large inflection corners, and it is capable of realizing acoustic focusing, acoustic channeling, and acoustic logic gates. This fully reconfigurable sonic crystal design method can provide implications for the modulation of other classical waves.

Static reconfiguration method of photovoltaic array based on improved competence square

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

Deep learning-based post-disaster energy management and faster network reconfiguration method for improvement of restoration time

Natural disasters in the world often cause power outages. To improve post-disaster response and restoration time, a method for energy management and network reconfiguration in emergencies has been proposed, where energy storages systems (ESSs) in distribution networks are used to support the system balance immediately and then the network reconfiguration is accelerated based on deep learning techniques. First, when power failures occur, the outputs of ESSs are calculated optimally in terms of the proposed optimization model under the constraints of minimizing line losses. After the injections of ESSs in emergencies, the network reconfiguration is still needed due to the limitation of ESS capacities. To avoid calculations of power flow repeatedly, a deep-learning based reconfiguration method for distribution networks has been proposed, where the deep-learning model is trained offline and can output the parameters of power flow immediately when a state combination of distribution generators (DGs), loads and power lines, which is an effective way to accelerate the network reconfiguration. If many reconfiguration schemes are found, a decision-making method with preferences are used to select one according to different situations. Finally, cases are designed, and simulation results show that the calculation time of a reconfiguration schemes are reduced significantly by almost one hundred multiples.

Graph theory-enhanced integrated distribution network reconfiguration and distributed generation planning: A comparative techno-economic and environmental impacts analysis

In today's world, eco-friendly solutions are crucial for efficient power delivery and assessing their corresponding economic and environmental benefits is essential. As innovators, it is also imperative to continually improve on existing techniques to solve a problem. Evaluating the existing literature in this area of study, gaps of improving the optimization techniques by reducing the amount of infeasible configurations the reconfiguration procedure encounters was established, additionally, the need to utilize distributed generations that significantly reduce carbon footprint in the environment was also ascertained. Hence, this paper presents an effective integration method for the simultaneous reconfiguration of Radial Distribution Networks (RDNs) and Photovoltaic (PV) DGs allocation, considering the tripodal issues of cost, operational efficiency, and environmental sustainability. A modification of the adaptive mountain gazelle optimizer (AMGO) enhanced with graph theory is deployed for the optimization procedures. The crucial feature of the proposed approach is the reduction of unfeasible configurations throughout the optimization procedure toward satisfying the network's radiality constraints, achieving consistent convergence and reduced computation time. The technical benefits are active power loss minimization, voltage stability, and voltage profile improvement. The economic benefits are analyzed by estimating the purchased power, the associated cost of power losses, and the cost of DGs and switches over a planning period of 20 years. The consequent environmental benefits are analyzed in detail, highlighting the significant reduction in pollutant emissions. The proposed model was tested on the IEEE 33- and 69-bus RDN, considering several scenarios, including synchronous network reconfiguration and DG installations. From the results procured, the simultaneous network reconfiguration and DG allocation provided better outcomes, yielding minimum active power loss of 35.36 kW, minimum voltage of 0.9541 p.u., voltage stability index of 1.9936 p.u., total planning cost of $3.456 million, and emission of 1.744 million lb/hr, respectively, for the 33-bus systems. The corresponding value for the 69-bus system is 32.57 kW, 0.9832 p.u., 2.3847 p.u., $ 2.524 million, and 2.53 million lb/hr, respectively. The proposed model was compared with other reported techniques for performance validation, and its efficacy and superior performance was established.

Powered–coasting–powered multi-phase mission reconfiguration method for launch vehicles under power system failures

For the final stage of a launch vehicle with powered–coasting–powered multi-phase flight, this paper investigates the joint reconfiguration method to determine subsequent flight trajectory and degraded orbit after power system failure in the first powered phase. While power system failures due to mass–flow–rate drop have been thoroughly studied in the existing literature, this paper focuses on the more challenging specific impulse drop failures, which result in loss of total launcher energy. This paper aims to design a fault-tolerant guidance method with onboard potential and to analyze the survivability and characteristics of the launcher in the event of such a failure. In the problem modeling, the strong nonlinearity of the orbital elements as terminal constraints is eliminated by analyzing the orbital properties in the orbital coordinate system to establish the primal trajectory optimization problem. For this multi-phase trajectory optimization problem, a joint optimization algorithm for the degradation orbit and the flight trajectory, based on convex optimization and Radau pseudospectral method, is designed to achieve autonomous mission reconfiguration. The algorithm introduces the relaxation–penalization idea, where the terminal constraints are relaxed and penalized in the performance index, allowing the algorithm to adaptively form an optimal degradation circular or elliptical orbit based on the remaining capacity of the launch vehicle. Simulation experiments demonstrate the effectiveness and superiority of the mission reconfiguration algorithm. Finally, the paper presents the first analysis of the optimal allocation of flight time between the first powered phase, the coasting phase, and the second powered phase in a propellant depletion scenario following the launcher power system failure.

The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

Mission-oriented dynamic reconfiguration of airborne photovoltaic array based on multidisciplinary model

The solar-powered stratospheric airship is demonstrated overwhelming superiority because of affordable cost, recyclable material and low carbon emission. Progress has been made in the schemes of energy system consisting of curved photovoltaic array, storage, and energy management devices. However, research on various flight conditions and the resulting presence of non-uniform irradiance on the airborne solar array is rare. Moreover, the methods to reduce the power losses of on-board solar array are not investigated. This paper proposed a dynamic reconfiguration method to improve the output performance of on-board solar array in different mission conditions. The partial shading conditions of airborne and ground-based solar arrays are compared. The multiple traveling salesman problem with solar array is introduced. The Simulated annealing algorithm is adopted. The real-time optimization of solar array is obtained with the dynamic reconfiguration method according to the operating condition consisting of various parameters. The results show the proposed method effectively improves the output power of solar array compared with common total-cross-tied connections. The improvements of daily output energy of the airborne array are more than 13.7 %. The work provides a conducive reference for realizing the utilization prospect of solar energy system on aerial vehicles in a low carbon future.

Reinforcement learning-based multi-impulse rendezvous approach for satellite constellation reconfiguration

This paper presents a satellite constellation orbital reconfiguration method based on the intelligent multi-impulse rendezvous considering the impulse-magnitude limit. This problem is solved by two steps: the single-target rendezvous trajectory optimization and the multi-target rendezvous mission planning. For single target, the multi-impulse rendezvous problem is transformed into a Markov decision process and solved by a reinforcement learning algorithm. The rewards of the critic networks are used as estimation of fuel consumption, while the actions of the actor networks are used as initial values for further optimization. For multiple targets, an efficient Monte-Carlo tree search method is used to realize the global optimal solution of the satellite constellation reconfiguration problem. Finally, the numerical examples show that the proposed method can rapidly optimize the multi-impulse constellation reconfiguration problem.

An adaptive two-step staircase static reconfiguration method for improving the power generation of PV array

Partial shading of solar photovoltaic (PV) panels can significantly affect the performance of solar PV arrays. Various reconfiguration techniques have been explored in recent years. Still, their applicability to actual PV power generation is controversial due to the number of electrical switches, physical locations, interconnections and complexity. This study proposes an adaptive two-step staircase (A2SS) static reconfiguration method. The technique is experimentally validated in several conditions and compared with the conventional TCT connection, single-step staircase (1SS) static reconfiguration method, Arrow soduku, modified odd–even–prime (MOEP) and two-step staircase(2SS) static reconfiguration method. For the eight shading cases of LN, LW, LD, Ran, Cen, Cor, CD, and Plus at SET#1, after reconfiguring the PV array using A2SS, the power has a significant improvement of 17.6%, 17.0%, 13.4%, 13.4%, 20.6%, 20.2%, 3.1%, and 0.82% than TCT. In the four shading cases of Lr. C, Lr. O, Lr. T, and Lr. U at SET#2, the power showed a significant improvement of 11.8%, 9.2%, 10.7%, and 15.8% compared to TCT. It also has the best performance in various reconfiguration techniques, which are mentioned. In addition, the A2SS reconfiguration method can be better applied to various sizes of PV arrays. By optimizing the shading distribution and adjusting the row irradiance deviation, the power stability of PV power generation is improved while maximizing energy efficiency.

A fault reconfiguration strategy based on logical structure and improved reinforcement learning for ship DC regional grid

Ship DC Regional Grid (SDRG) is an advanced power system that divides the power system into different regions, which can improve energy efficiency and stability. However, little research has been done on its fault reconfiguration. In the event of a fault, the switch is controlled by the fault reconfiguration technique to reconfigure the direction of the flow, which effectively improves the reliability of the power supply to the loads. A two-stage fault reconfiguration method based on Logical Structure (LS) and Improved Reinforcement Learning (IRL) is proposed, aiming to maximize the power load and minimize the number of switches. First, LS is utilized to design the load connectivity matrix based on the grid characteristics, and the grid topology is adjusted to achieve fast reconfiguration based on load prioritization. Further, IRL is employed to improve the reconfiguration speed and quality by establishing the optimal solution experience pool and improving the reward and punishment mechanism. The proposed method enables the selection of an appropriate reconfiguration strategy based on fault size, consequently improving convergence time and flexibility. Comparative experimental results in a four-region SDRG show that the proposed method can effectively solve the SDRG fault reconfiguration problem.

Backpropagation artificial neural network-based maximum power point tracking controller with image encryption inspired solar photovoltaic array reconfiguration

The enhancement of photovoltaic (PV) arrays through reconfiguration presents a promising avenue for increasing the global maximum power (GMP) and improving overall array performance. This enhancement is achieved by minimizing differences between rows, thereby reducing the computational load on Maximum Power Point Tracking (MPPT) systems. However, many existing reconfiguration methods face various challenges, including scalability issues, inadequate shading dispersion, distortion of array characteristics, emergence of multiple power peaks, increased mismatch, and more. In order to overcome these obstacles, this study presents a novel method for array reconfiguration that is modelled after the widely used Kolakoski Sequence Transform in picture encryption. The suggested approach is assessed in eight different scenarios with 9 × 9 and 5 × 5 PV arrays shaded differently. Its performance is compared against seven established techniques. Due to its intelligent reconfiguration aimed at minimizing shade dispersion, the suggested approach consistently outperforms alternative methods. It results in substantial improvements in GMP, enhancing it by 32.79%, 14.98%, 10.15%, and 4.13% for 9 × 9 arrays, and 37.10%, 14.36%, and 9.88% for 5 × 5 arrays across diverse conditions. Furthermore, this study comprehensively investigates three separate Artificial Neural Network algorithms, specifically the Levenberg-Marquardt (LMB), Scaled Conjugate Gradient, and Bayesian Regularization algorithms for MPPT. A Levenberg-Marquardt Backpropagation-based MPPT controller for a 250Wp standalone PV system is used to validate the effectiveness of the recommended configuration. This integrated approach, which combines reconfiguration and LMB-based MPPT utilizes only two sensors regardless of array size. It achieves accelerated convergence tracking within a short 0.13 s, displaying minimal steady-state oscillations.

State-of-Charge Estimation of Reconfigurable Supercapacitor

Abstract A large body of research has been done on state-of-charge (SOC) estimate of supercapacitors in the literature; most of the works have been on SOC estimation of individual supercapacitor units. Nonetheless, supercapacitors are usually linked to balancing circuits in real-world applications in order to remove inter-cell imbalances. The cells’ system dynamics shift to a new mode when the balancing circuit is turned on, making the SOC calculation techniques that are currently in use inappropriate. Our proposal in this research is a Kalman filtering-based reconfigurable supercapacitor balancing system SOC estimate method. By fusing a reconfigurable balancing circuit with the equivalent circuit model of supercapacitors, we create a linear switching systems model. The switching system’s discrete-time form is shown, and its observability is examined. With filter gains dynamically modified according to the system mode, a switching Kalman filter is intended to estimate the SOC of the switched system. The accuracy of SOC estimation is greatly improved by the suggested method when supercapacitors are in the balancing mode, as confirmed by the simulation results.

Optically controllable deformation and phase change in VO2/Si3N4/Au hybrid nanostructures with polarization selectivity

Optically spatial displacement and material modification hold great potential for the appealing applications in nanofabrication and reconfiguration of functional optical devices. Here, we propose and demonstrate a scheme to achieve simultaneous deformation and phase change in vanadium dioxide (VO2)/Si3N4/Au hybrid nanostructures by laser stimuli. Low triggering threshold and significant deformation characteristics of VO2, based on controllable phase transition, are demonstrated in microscale cantilevers. The plasmonic properties of the nanostructure array are further utilized to achieve a polarization-selective dynamic response. The persistence of deformation and dynamical optical modulation are further demonstrated. Such high-precision fabrication methods and non-contact reconfiguration methods are useful for future applications in dynamic optical manipulation.

A dynamic reconfiguration model and method for load balancing in the snow-shaped distribution network

The snow-shaped distribution network (SDN) is a cable distribution network composed of eight (or six) 10 kV feeders from four (or three) substations in a regular connection. Compared with the traditional 10 kV distribution network, SDN can support a wide range of load transfer among six or eight feeders. Aiming at the problem of load spatial-temporal unbalanced condition caused by the integration of distributed generators (DGs) and different load types in different feeders, this paper proposes a dynamic reconfiguration strategy for load balancing in SDN considering DGs and energy storage system (ESS). Firstly, the basic structure of SDN is analyzed and the power flow model for its dynamic reconfiguration is developed. Secondly, the dynamic reconfiguration optimization model for load balancing in SDN considering DGs and ESS is proposed to utilize the load transfer capability to mitigate the load unbalanced condition and reduce active power loss. Thirdly, the original non-convex model is converted into a mixed-integer second-order cone programming (MISOCP) model by applying the second-order cone relaxation and the big-M method, which is solved by CPLEX solver. Finally, the effectiveness of the proposed model and method are verified by an actual case in Tianjin and IEEE 33-node system. The analysis results show that the proposed method can significantly alleviate the load unbalanced spatial-temporal distribution and improve the economic efficiency by regulating the operation of SDN including ESS optimization and dynamic reconfiguration.

Аналіз технологій реконфігурації систем інтернету речей на рівні програмних модулів та завантажувачів

The subject of study and research in this article are the technology of programming and interaction of nodes, as well as individual small-sized components of embedded Internet of Things (IoT) systems. The goal is to increase the flexibility of IoT system components by simplifying the reconfiguration process of individual nodes and components. The task is: to analyze existing approaches to building configurable systems, analyze and describe existing solutions that implement joint approaches; to analyze the tools for implementing the selected approaches; to describe the method of building a living system based on the analysis approach; and to provide a technical example of the proposed programming method. According to the tasks, the following results were obtained. Possible ways of reconfiguration of IoT components are analyzed. Methods of reconfiguration at the network level and partial node reconfiguration are considered. Methods to ensure the flexibility of IoT components are analyzed and classified. A comparative analysis of the summary parameters of the loaders was carried out. The possibility of using software components as immutable blocks of code to modify the microcontroller program as a method of reconfiguration was analyzed. The idea of using variable software modules to represent the microcontroller program at a higher level of abstraction with a modified bootloader is thus proposed. Elements of a method for reprogramming variable software modules of microcontrollers as middleware are proposed. Conclusions. According to a review of the existing types of systems, they were classified according to the level of organization of their reconfigured parts. According to the obtained preliminary classifications, some architectural solutions were considered from the point of view of improving operational characteristics. The main advantages for each type of organization of the reconfigurable parts of the systems are highlighted. An analysis of practical solutions regarding the modification of the built-in components of microcontrollers for managing and updating their firmware was conducted. The practical significance of this study is the proposal of a method of programming with variable software modules for microcontrollers and the obtained results of a competitive analysis of methods of modifying components and loader characteristics.

Maximum power extraction of partially shaded solar photovoltaic array using reconfiguration method

ABSTRACT This article presents a reconfiguration strategy, named the Cheetah Optimization Algorithm (COA), for maximizing power generation in partially shaded solar photovoltaic (PSPV) arrays. The proposed method’s originality aims to mitigate the negative impacts of partial shading, improve the output power of the PV system, and prevent thermal failure of shaded solar panels. The strategy combines efficient maximum power point tracking techniques with suitable photovoltaic system design topologies. The COA is done on the MATLAB platform and is analyzed with the existing methods such as the Wild Horse Optimizer (WHO), Salp Swarm Algorithm and Heap-based Optimizer. The outcome shows the superior performance of the COA in achieving the extraction of maximum power compared to the other methods. Additionally, by comparing the two patterns of partial shading, it is shown that the COA extracts more power than the existing methods and gets to the global power value faster. The COA is compared with existing methods, including WHO, SSA, and HBO. In terms of accuracy, the proposed system achieves the highest value of 0.93, surpassing the accuracy of HBO, WHO, and SSA, which stand at 0.8, 0.9, and 0.9, respectively. The specificity, precision and recall are also reflect the superiority of the proposed system with the values of 0.87, 0.9, and 0.86, respectively, outperforming the existing algorithms in most categories.

Reconfigurable Chiral Spintronic THz Emitters

AbstractCollective spin arrangements manifest diverse spin textures, encompassing ferromagnetism, antiferromagnetism, and chiral vortices. However, mapping these spin textures in ultrathin magnetic multilayers has remained elusive. A reconfigurable chiral spintronic terahertz emission method is introduced through investigations on a model system of synthetic antiferromagnet and the spin information of individual ferromagnet (FM) layers is extracted. Upon femtosecond photoexcitation of the synthetic antiferromagnet (FM1/Ru/FM2), the ferromagnets generate a pair of linearly polarized ultrafast spin currents, which, after relaxation at the FM/Ru interface, emit corresponding THz pulses. The Ruderman–Kittel–Kasuya–Yosida interactions between the two FMs give rise to magnetic‐field‐controlled spin textures causing a spin relaxation imbalance at the interfaces and induce a phase shift between the orthogonal components of the emitted terahertz fields, consequently reconfiguring the polarization of the emitted terahertz field from linear to circular. This approach offers a novel means of investigating electronic and magnetic states in ultrathin spintronic heterostructures, low‐dimensional quantum materials, topological insulators, and Weyl semimetals. Furthermore, it enables the exploration of spin textures, including skyrmions, merons, and solitons.

Optimization method of dynamic reconfiguration in virtual power plants

With the continuous expansion of distributed energy resources (DERs), virtual power plants (VPPs) have emerged as an efficient solution for their aggregation and management. This study proposes a dynamic reconfiguration method for VPPs, addressing the problem of optimally aggregating DERs within a VPP. To enhance DER participation, an innovative membership rewards mechanism is introduced. Furthermore, we develop a method for DER feature extraction and classification to deeply understand their performance. We also assess DERs' willingness to participate, which is the precondition for effective aggregation. The core of our method is an optimized strategy for multi-stage dynamic planning of VPPs, designed to accommodate the dynamic evolution of DERs and electricity markets conditions, and formulated as a two-layer model to offer optimal solutions for DERs aggregation. Numerical simulations validate the superiority of our method in economic benefits and operational efficiency of VPP, highlighting our contributions to enhance the feasibility and efficiency of VPP aggregation through multi-stage planning, dynamic adaptability, and strategic incentives.