Reconfiguration Method Research Articles

Power generation maximization of distributed photovoltaic systems using dynamic topology reconfiguration

The ‘mismatch losses’ problem is commonly encountered in distributed photovoltaic (PV) power generation systems. It can directly reduce power generation. Hence, PV array reconfiguration techniques have become highly popular to minimize the mismatch losses. In this paper, a dynamical array reconfiguration method for Total-Cross-Ties (TCT) and Series–Parallel (SP) interconnected PV arrays is proposed. The method aims to improve the maximum power output generation of a distributed PV array in different mismatch conditions through a set of inverters and a switching matrix that is controlled by a dynamic and scalable reconfiguration optimization algorithm. The structures of the switching matrix for both TCT-based and SP-based PV arrays are designed to enable flexible alteration of the electrical connections between PV strings and inverters. Also, the proposed reconfiguration solution is scalable, because the size of the switching matrix deployed in the proposed solution is only determined by the numbers of the PV strings and the inverters, and is not related to the number of PV modules in a string. The performance of the proposed method is assessed for PV arrays with both TCT and SP interconnections in different mismatch conditions, including different partial shading and random PV module failure. The average optimization time for TCT and SP interconnected PV arrays is 0.02 and 3 s, respectively. The effectiveness of the proposed dynamical reconfiguration is confirmed, with the average maximum power generation improved by 8.56% for the TCT-based PV array and 6.43% for the SP-based PV array compared to a fixed topology scheme.

The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System

The main aim of this study was to develop a method for assessing the level of robustness of timetabled transport performance in rail transport. When the railway lines are supplied by DC networks, lower voltages are observed, and consequently, current values are often ten times higher than in AC networks. This is an operational problem, as high currents make it easier to overload the supply network. Based on a literature review, the authors show that the problem of running railway traffic when the capacity of the power supply network is limited (by the size of the permitted currents) is not well studied. The authors propose a method based on the Markov approach supplemented by classical theoretical vehicle traffic dynamics to improve the operational robustness of the rail transport system using DC power supply system. Each train run was parameterised in such a way that it is possible to determine the state that the train is in during the run, the transitions between states, and the determination of the probabilities of occurrence of such states. On the other hand, classical vehicle dynamics was used to assess the load generated by the train on the power grid. The proposed method—reduced to a function—was verified using a case study. The method of timetable reconfiguration proposed by the authors increased the operational robustness from 0.9454 to 0.9774.

Simulation Study of Network Reconfiguration and Load-balancing Method for the Xinjiang Astronomical Observatory Data Center

The Xinjiang Astronomical Observatory Data Center faces issues related to delay-affected services. As a result, these services cannot be implemented in a timely manner due to the overloading of transmission links. In this paper, the software-defined network technology is applied to the Xinjiang Astronomical Observatory Data Center Network (XAO-DCN). Specifically, a novel reconfiguration method is proposed to realise the software-defined Xinjiang Astronomical Observatory Data Center Network (SDXAO-DCN), and a network model is constructed. To overcome the congestion problem, a traffic load-balancing algorithm is designed for fast transmission of the service traffic by combining three factors: network structure, congestion level and transmission service. The proposed algorithm is compared with current commonly load-balancing algorithms which are used in data center to verify its efficiency. Simulation experiments show that the algorithm improved transmission performance and transmission quality for the SDXAO-DCN.

A Dynamic Reconfiguration Method based on a Deterministic Optimization Approach in Active Distribution Systems

This article proposes a method for dynamic distribution network reconfiguration (DDNR) with distributed generation for loss reduction. Advantages and conceptual principles of a deterministic optimization method are exposed, which allows the minimization of technical power losses in distribution systems based on a linear approximation of the loads. In this work is proposed the application of this approach for the modelling of distributed generation sources, obtaining a mathematical formulation that exploits mixed-integer nonlinear programming in solving the optimal reconfiguration problem. The DDNR method is implemented in an automatic tool integrating OpenDSS and GAMS software. IEEE69-Bus test system is considered to demonstrate the effectiveness of the technique. Dynamic results are obtained by implementing the optimal reconfiguration strategy, making the dynamic adjustments of the mathematical modelling in the face of variations in distributed generation and demand profiles. The results reveal schemes that range from one to twenty-four daily control actions.

Assisting Residential Distribution Grids in Overcoming Large-Scale EV Preconditioning Load

The repercussion of increased electric vehicle (EV) charging demand is notable at the distribution grid especially during the cold morning, while users tend to precondition their vehicles before leaving their premises. Moreover, due to the price declination, a tendency of installing level 2 chargers in residential premises is anticipated, which should stimulate the appearance of a new peak to the residential load profile. Hence, multiple scenarios of preconditioning are simulated, and the corresponding network’s quality metrics (e.g., voltage level and power losses) are assessed to analyze the impact. And a remarkable consequence is observed. As a consequence, to mitigate the consequences and manage the new peak load, the optimal reconfiguration of network is implemented, and unfortunately, with a larger number of EVs, this technique fails to attain the minimum voltage level. Therefore, leveraging this high number of EVs, instead of relying on the network reconfiguration, power is assumed to be injected from idle EVs through vehicle-to-grid (V2G) energy transmission. An integer linear program is formed to schedule a set of EVs in participating in V2G, and the outcome indicates that V2G alone could not compensate for the disturbance in the network. Accordingly, a hybrid method of V2G and reconfiguration is proposed and evaluated to assist the network in handling the new peak load, and this hybrid solution reduces power losses in the network by 50% on average and maintains the voltage level above the operational threshold of 0.95 p.u.

Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network

Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2×2 experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array.

Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network

Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network.

Economic scheduling of mobile energy storage in distribution networks based on equivalent reconfiguration method

Compared with traditional stationary energy storage system (SESS), mobile energy storage system (MESS) has power transfer ability in both spatial and temporal dimensions. Thus, it can provide greater flexibility in power system auxiliary services. To simplify and clarify the optimal scheduling problem, a novel “virtual switch” indicator is defined to describe the scheduling of MESS and an equivalent reconfiguration method is proposed by analogy with distribution network reconfiguration. Based on the method proposed, the dynamic economic scheduling of MESS in distribution networks(DNs) can be formulated as a scenario-based stochastic optimization problem, where the uncertainties of traffic flow and renewable energy output are considered by generating scenarios using Monte Carlo simulation. The objective function is to maximize the profit of distribution network operator. The time and cost of MESS scheduling are transformed into those of virtual switch switching in distribution network reconfiguration. Therefore, the operation scheduling problem of a MESS is equivalent to a distribution network reconfiguration problem with a SESS and several virtual switches. In this way, a grid-traffic network coupling problem is simplified to a pure distribution network problem, which provides a novel way to solve the MESS scheduling problems. Finally, simulation results on the IEEE 33-bus distribution system and simple traffic network verify the validity and feasibility of the method proposed.

A polarization-sensitive array reconfiguration technique resorting to antenna selection for navigation interference mitigation

Polarization-sensitive array (PSA) processing works well in navigation system applications for interference mitigation. However, the fixed configuration of conventional PSA increases the computational burden of subsequent adaptive array processing and can even be inefficient and redundant in some cases. Furthermore, since the radio frequency (RF) front end is more costly than the antenna, it is urgent to exploit a PSA reconfiguration method that can lower the cost of array design and achieve sufficiently good anti-interference performance when employing fewer RF front ends and antennas. In this paper, a PSA reconfiguration technique maximizing the signal to interference plus noise ratio (SINR) is presented to solve the problem. Combining with adaptive processing, we derive the formula between the SINR and the polarization-spatial correlation coefficient (PSCC), thereby building the mathematical model with minimizing PSCC as the objective function. Then, the lower bound of the optimal PSCC, gained by the Lagrange Dual Relaxation method, is utilized to guide the selection of the antenna number. Finally, we adopt an iterative correlation measurement (ICM) approach to confirm the antenna element position of the optimal subarray with the minimum PSCC. Numerical simulations illustrate the effectiveness and correctness of the proposed technique and derived theoretical results.

Nanoscale Control of the Metal–Insulator Transition at LaAlO3/KTaO3 Interfaces

Recent reports of superconductivity at KTaO3 (KTO) (110) and (111) interfaces have sparked intense interest due to the relatively high critical temperature as well as other properties that distinguish this system from the more extensively studied SrTiO3 (STO)-based heterostructures. Here, we report the reconfigurable creation of conducting structures at intrinsically insulating LaAlO3/KTO(110) and (111) interfaces. Devices are created using two distinct methods previously developed for STO-based heterostructures: (1) conductive atomic-force microscopy lithography and (2) ultralow-voltage electron-beam lithography. At low temperatures, KTO(110)-based devices show superconductivity that is tunable by an applied back gate. A one-dimensional nanowire device shows single-electron-transistor (SET) behavior. A KTO(111)-based device is metallic but does not become superconducting. These reconfigurable methods of creating nanoscale devices in KTO-based heterostructures offer new avenues for investigating mechanisms of superconductivity as well as development of quantum devices that incorporate strong spin-orbit interactions, superconducting behavior, and nanoscale dimensions.

Development of Reactive Power Allocation Method for Radial Structure Wind Farm Considering Multiple Connections

In recent years, the number of wind farms consisting of type 3 and type 4 wind turbines located within the distribution system has been growing rapidly. Wind turbines can be utilized as a continuous reactive power source to support the system voltage by taking advantage of their reactive power control capability. This paper aims to further develop the reactive power assignment strategy in order to minimize losses in wind farms described in the published paper. We introduce the method of reconfiguration and numbering to apply the algorithm to the wind farm structure and develop the previously-defined allocation ratio into two types of allocation ratios. The goal is to apply the loss minimization algorithm to a wind farm configuration with up to two wind turbines connected to one ring main unit (RMU). The proposed strategy reduces power loss and increases the real power flow in the wind farm by allocating reactive power to connected wind turbines taking into account the resistance value. The proposed allocation technique is validated in a Real Time Digital Simulator (RTDS)-based Hardware-in-the-loop Simulation (HILS) environment considering the Dongbok wind farm configuration in Jeju, South Korea. In the simulation, a Raspberry Pi acts as a wind farm controller sending a reactive power dispatch signal to each wind turbine via Modbus TCP/IP protocol. The simulation results mean that, applying the proposed algorithm, we can expect loss reduction effects in the wind farm.

A reconfigurable test method based on LFSR for 3D stacking integrated circuits

The specificity of the 3D integrated circuit and the changes in the design integration flow bring many new problems to the test process, such as reducing the controllability of the test and increasing the test cost. To address these problems, this paper proposes a low-power hybrid test scheme based on multiple reconfigurable linear feedback shift register(LFSRs) for 3D integrated circuit, which can reuse the test vector generator of the pre-binding test phase in the in-binding and post-binding test phase, reconfiguring a new test vector generator with lower test cost. Compared with the method of using the linear feedback shift register(LFSR) of each layer circuit independently as the test vector generator of the whole circuit in the in-binding test phase, the scheme in this paper can effectively utilize the original LFSR of each layer circuit and reduce the area overhead; the reconfigured 3D-LFSR can obtain better test data compression effect and lower test power consumption, improve test efficiency and reduce test time. Experiments have shown a 5% improvement in average test data compression, 53.1% and 9.6% reduction in average and peak power consumption respectively, 28% reduction in test area and 19.6% reduction in test time.

4-D Gesture Sensing Using Reconfigurable Virtual Array Based on a 60-GHz FMCW MIMO Radar Sensor

A novel 4-D gesture sensing technique using reconfigurable virtual array method with a 60-GHz frequency-modulated continuous wave (FMCW) radar is presented. The 4-D sensing includes the 3-D spatial positioning and the 1-D motion tracking, which combines detection in both spatial and temporal domains. The proposed technique can be used for gesture sensing for both macro gestures such as handwriting letters and numbers, hand drawing different patterns in space and micro gestures such as the fingers’ actions of virtual slider, finger tap, and finger wave. Moreover, this work presents a method to reconfigure the working modes of different transmitting and receiving channels in the multiple input and multiple output (MIMO) architecture, which effectively increases the sensing range of the radar sensor and the signal-to-noise ratio (SNR) of the beat signal. For various gestures at different distances or in different environments, the proposed technique can adaptively achieve the best detection by reconstructing the virtual array of the MIMO radar. A series of experiments are carried out in both microwave anechoic chamber and office environments to validate the proposed technique. The experimental results show that the proposed technique performs well in sensing a variety of both macro gestures andmillimeter-level micro gestures. Compared with the conventional MIMO method, the proposed technique increases the sensing range by 3.5 times, and the SNR is also greatly improved by 12 dB when the radar performs gesture sensing at the distance of 0.65 m.

An ANN based switching network for optimally selected photovoltaic array with battery and supercapacitor to mitigate the effect of intermittent solar irradiance

ABSTRACT Power fluctuation at the PV panel output is caused by variation of input irradiance. Several optimization techniques have been used for selecting the Global Maximum Power Point (GMPP) from different Local Maximum Power Point (LMPP) that are caused due to variable irradiance. Reconfiguring the PV array by various combination based on the available irradiance is used to overcome the effects of partial shading. In these methods, system complexity increases and efficiency reduces as the size of PV array increases. Hence, in this paper Artificial Neural Network (ANN) based switching network of optimum number of PV panel with battery and supercapacitor is formed. Genetic Algorithm (GA) is used to optimize the number of PV panels, battery and supercapacitor. Battery and supercapacitors are used as energy back up to support the load during low power generation from PV panels. Size of supercapacitors is calculated to support the system during instant power fluctuation caused either due to sudden increase in load or unexpected variation in irradiance. Power enhancement of optimized PV array is performed by ANN based switching and numerically analyzed, and compared with other reconfiguration methods. Optimization is carried out for a system having 245 kW load which led to 1281 number of PV panels, 161 number of batteries and 8 supercapacitors for minimum cost of energy sources. Results of optimization are used for switching of 1281 PV array to form 16 × 81 strings. Out of which 16 × 9 strings are selected for Month Ahead Scheduling (MAS) and string of 16 × 1 PV panels for Real Time Scheduling (RTS). Considering variations at the input irradiance while load is constant. For the selected value of irradiance 16 × 45 strings remain ON which is selected as the base value.

Improved Security Approach Based on AES Algorithm for LST Retrieval Using Satellite Imagery in Radiation-Tolerant FPGAs

Transmission of sensitive data in space missions and particularly in satellite remote sensing to the ground station is exposed to multiple threats impacting the confidentiality of data, access unauthorized to the satellite system, in addition, the space environment causes several threats that can affect the hardware of satellites. This paper describes an improved approach to implement a secure Land Surface Temperature-Split Windows (LST-SW) algorithm based on the Advanced Encryption Standard using the Reconfigurable Dynamic Method for application on-board earth observation satellites implemented on radiation-tolerant Virtex-4QV FPGA. The experimental results showed that the proposed hardware secure implementation of the LST-SW algorithm using Xilinx Virtex-4QV FPGA achieves higher throughput of 907.644 Mbps sufficient for satellite remote sensing mission. Moreover, the suggested implementation consumes 4089 Slices and 4 BRAMs. Finally, the authors use security measurement analyses to verify the safety and performance of the proposed encryption LST-SW module.

Multi-Lane Unsignalized Intersection Cooperation With Flexible Lane Direction Based on Multi-Vehicle Formation Control

Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation considers fixed lane direction, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume and the proportion of vehicles with different turning expectation may change with time, fixed lane direction may lead to inefficiency at intersections. This paper proposes a multi-lane unsignalized intersection cooperation method that considers flexible lane direction. The two-dimensional distribution of vehicles is calculated and vehicles that are not in conflict are scheduled to pass the intersection simultaneously. The formation reconfiguration method is utilized to achieve collision-free longitudinal and lateral position adjustment of vehicles. Simulations are conducted at different input traffic volumes and turning proportion of incoming vehicles, and the results indicate that our method outperformances the fixed-lane-direction unsignalized cooperation method and the signalized method.

A new contour balancing rearrangement technique to enhance the solar PV array power under a realistic partial shading condition

Under partial shading conditions, the solar photovoltaic (PV) array receives an irregular pattern of solar light intensity which results in the creation of multiple peaks in the power vs voltage (P-V) and current vs voltage (I-V) plot and ultimately leads to hot spots creation and reduced power generation. Therefore, in this paper, redistribution of the shadow effect is assessed using a newly proposed reconfiguration method for the PV array called Contour Balancing Rearrangement (CBR) method. The proposed CBR method is based on the “Smash n′ Throw” approach which is applied to reconfigure the total-cross-tied (TCT) topology of the PV array that redistributes the shading pattern in such a way that the average irradiance of each row is almost the same. Further, a novel pragmatic dynamic cloud model is formulated to realize the partial shading condition as in realistic form at a particular location of the earth’s surface. For a more realistic analysis, the movement and acceleration of moving clouds are taken into account along with the coordinates of the place of interest for more accurate values of solar irradiation. Furthermore, different comparative studies are also carried out among the proposed and existing methods of various PV array reconfiguration methods to show the efficacy.

Boosting Alkaline Hydrogen Evolution on Stoichiometric Molybdenum Carbonitride via an Interstitial Vacancy‐Elimination Strategy

AbstractAn interstitial vacancy on molybdenum nitride has been determined as a negative factor towards the alkaline hydrogen evolution reaction (HER) by reason of upraising the d orbitals of Mo. Nevertheless, investigations aiming to eliminate the vacancies are rarely reported. Here, an interstitial reconfiguration method for the design of stoichiometric molybdenum carbonitride (Mo2CN) is proposed, in which the vacancies are fulfilled by lattice carbon. Multiple fine structural analyses alongside with the theoretical calculations indicate that beyond lower the d orbitals of Mo by the hybridization of additive p‐d orbitals, lattice carbon also behaves as the extra active center with exceptional H adsorption/desorption energy. Mo2CN reveals an adorable overpotential of −84 mV at a current density of 10 mA cm−2 with a long‐term electrochemical stability by accompanying the nitrogen‐doped carbon substrate. It is anticipated that the vacancy‐eliminating concept will provide a constructive entry point for the rational design of electro‐catalysts and beyond.

Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System

Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes.

Static and Dynamic Reconfiguration Strategies for Reducing Partial Shading Effects in Photovoltaic Array: A Comprehensive Review

The output power generated by a photovoltaic (PV) cell decreases remarkably because of the irregular irradiance pattern. Thus, the overall system performance is degraded. The decrease in the power output is not dependent on the shaded area but is rather dependent on the array configuration and the shading pattern. To eliminate partial shadings, many strategies have been documented in the state‐of‐the‐art literature. These strategies may not improve the maximum output power to the required level but definitely do so to a certain extent. Thus, to minimize these losses, a promising reconfiguration approach is needed, i.e., reconfiguring the PV modules inside the PV array to improve the maximum power output. These techniques are broadly divided into two categories, viz., static and dynamic reconfiguration methods. The advanced PV array reconfiguration solutions to improve global power output under mismatch and partial shading scenarios are presented in this article. According to the results of the review article studied in this research, dynamic approaches are costlier as compared to static reconfiguration strategies, but are more effective to reduce the partial shading impact in comparison to the static techniques.