Network Reconfiguration Research Articles

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.

Resilience-oriented repair crew and network reconfiguration coordinated operational scheduling for post-event restoration

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC–NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid’s resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33-bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level.

Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.

Distribution Network Reconfiguration Based on an Improved Arithmetic Optimization Algorithm

Aiming to address the defects of the arithmetic optimization algorithm (AOA), such as easy fall into local optimums and slow convergence speed during the search process, an improved arithmetic optimization algorithm (IAOA) is proposed and applied to the study of distribution network reconfiguration. Firstly, a reconfiguration model is established to reduce network loss, and a cosine control factor is introduced to reconfigure the math optimization accelerated (MOA) function to coordinate the algorithm’s global exploration and local exploitation capabilities. Subsequently, a reverse differential evolution strategy is introduced to improve the overall diversity of the population and Weibull mutation is performed on the better-adapted individuals generated in each iteration to ensure the quality of the optimal individuals generated in each iteration and strengthen the algorithm’s ability to approach the optimal solution. The performance of the improved algorithm is also tested using eight basis functions. Finally, simulation analysis is carried out by taking the IEEE33 and IEEE69 node systems and a real power distribution system as examples; the results show that the proposed algorithm can help to reconfigure the system quickly, and the system node voltages and network losses were significantly improved after the reconfiguration.

Improving Reliability in Distribution Systems through Optimal Allocation of Distributed Generators, Network Reconfiguration and Capacitor Placement

Improving Reliability in Distribution Systems through Optimal Allocation of Distributed Generators, Network Reconfiguration and Capacitor Placement

Reconfigurable Cellulose Acetate‐Based Thermosets Cross‐Linked with Diboronic Acid

AbstractCellulose acetate (CA) has found broad applications for many years. However, the development of mechanically strengthened CA materials without losing the processibility in solution remains quite difficult. In this work, novel CA thermosets are reported that can be readily constructed by employing alkyl diboronic acid as a crosslinking agent, which has allowed convenient reconfiguration of CA networks without demanding catalysts or harsh processing conditions. Various properties including thermal stability, creeping resistance, rheological and mechanical strength have been successfully improved by using small amounts (1–5 wt%) of diboronic acid. The dynamic cross‐linked network based on boron chemistry has facilitated solution processing during three times of recycling. Control experiments have shown that alkyl diboronic acid can deliver superior mechanical strength to CA as compared to aryl diboronic acid or single‐arm alkyl boronic acid. Based on the importance of CA materials, this work should be attractive to the design of reprocessible CA thermosets with high performance.

Enhancing Distribution Network Efficiency with Andean Condor Algorithm-Driven Optimal Placement of Distributed Generation and Network Reconfiguration

This research introduces an innovative methodology for optimizing placement of Distributed Generations (DGs) within distribution networks, with primary focus on reducing power losses and enhancing energy efficiency. The surge in electric power demand has necessitated integration of DGs into distribution networks, bolstering system security. However, this integration brings about structural changes and alters system parameters, such as power fluctuations, voltage profiles and fault current levels. To address these complexities, this research contributes comprehensive approach that integrates Andean Condor Algorithm (ACA) and Network Reconfiguration (NR) to optimize DG placement. The proposed methodology aims to minimize both active and reactive power losses in distribution networks. MATLAB simulations using the 69 and 33 bus distribution networks are used to thoroughly validate the system’s efficacy. The suggested ACA achieves an active power loss of 120 KW and a reactive power loss of 28.66 KVAr for 33 bus system. In the same way, 69 bus system manages to achieve a 16 KVAr reactive power loss and a 92.3 KW active power loss. The results show that suggested methodology is both effective and better than popular CPSO method, which makes it valuable addition to the field of distributed generation optimization in power distribution networks.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

By determining the ideal combination of switch on/off status, distribution network reconfiguration (DNR), a critical operational problem in distribution systems, has been employed to increase system efficiency. Additionally, benefits such as improved voltage profiles and loss reduction through reactive power compensation are provided by placing capacitors in the distribution network optimally. Thus, this study formulates multi-objective DNR with optimal allocation of distributed generators (DGs), electric vehicles charging stations (EVCSs) and capacitors. Additionally, the demand response programme (DRP) is employed to enhance the distribution system’s operation performance. Losses and voltage variation, which are significant objectives for conventional distribution systems, are typically the focus of the DNR problem. Modern distribution systems’ security concerns caused by DGs that could jeopardize power system security have been all but ignored in terms of the issue of power system operation. As a result, the primary objective of this study is to address the DNR issue in order to increase reliability. This study specifies the energy not provided (ENS) in order to do this. This work uses a modified shuffled frog leaping algorithm (MSFLA), which benefits from a novel mutation technique, to reduce processing time and improve solution quality, in particular to avoid becoming stuck in local optima. To evaluate the efficiency and adaptability of the suggested method, it is applied to three distribution systems, comprising 33, 69, and 86-node test networks. After that, the outcomes are contrasted with those of various approaches. Power loss and ENS are reduced by around 45% and 5.5%, respectively, by executing the suggested strategy on a 33-node test system compared to the initial value before the DNR.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

An Improved Secondary Control Strategy for Dynamic Boundary Microgrids toward Resilient Distribution Systems

In order to achieve the flexible and efficient utilization of distributed energy resources, microgrids (MGs) can enhance the self-healing capability of distribution systems. Conventional primary droop control in microgrids exhibits deviations in voltage and frequency and lacks research on voltage–frequency control during network reconfiguration. Therefore, this paper investigates the control strategy of secondary control for voltage and frequency during the process of reconstructing distribution networks to operate in the form of microgrids after faults. Firstly, the mathematical model of three-phase voltage-source inverters with droop control is analyzed. Then, inspired by the generator’s secondary frequency control strategy, an improved secondary control method based on droop control is proposed, and the effectiveness of the improved droop control strategy is verified. Finally, simulation examples of distribution networks with various dynamic boundary topology changes are designed to simulate a relatively stable control result, validating the proposed strategy.

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

Distribution network reconfiguration utilizing the particle swarm optimization algorithm and exhaustive search methods

The load level for each period in the distribution network can be considered non-identical due to the increasing demand for loads and the bigger distribution network. The main problem in the transmission and distribution network system is power losses and voltage profiles, affecting the quality of service and operating costs. This study compares the reconfiguration of the network using exhaustive search techniques and particle swarm optimization (PSO) algorithms on the IEEE 33 bus distribution network system. The study’s results compare the study of power flow before and after network reconfiguration, which is a decrease in the value of power losses from 202.7 kW to 139.6 kW. Then voltage profile improved from 91.309% to 93.782%. The simulation results also found that this reconfiguration can improve the system voltage profile, which initially contained 21 buses outside the standard limits of IEEE Std 1159-1995 to 7 buses.

Application of Modified Flow Direction Algorithm for Network Reconfiguration with Distributed Generator Installation

Application of Modified Flow Direction Algorithm for Network Reconfiguration with Distributed Generator Installation

Correction to: Optimal reconfiguration of a smart distribution network in the presence of shunt capacitors

Correction to: Optimal reconfiguration of a smart distribution network in the presence of shunt capacitors

Self-healing radial distribution network reconfiguration based on deep reinforcement learning

Distribution network reconfiguration (DNR) has long been used to minimize losses in normal conditions and restore out-of-service areas in fault situations. However, the integration of renewable resources into power systems has made operating strategies more challenging, requiring fast and responsive approaches to handle abrupt interruptions. Traditional DNR methods rely on mathematical programming, heuristic and meta-heuristic methods that cannot generalize unseen generation and load profiles, and may require significant computational time to converge, posing an overrun risk for real-time execution. Recently, deep reinforcement learning (DRL) has been shown to be effective in optimizing DNR in normal conditions with calculation times of only a few milliseconds. However, previously proposed dynamic DNR approaches are not able to self-heal and restore in fault conditions, where timely restoration is critical for customer satisfaction. To address this gap, we propose a restorative dynamic distribution network reconfiguration (RDDNR) framework that enables continuous reconfiguration after faults, enabling the fast restoration of out-of-service areas. Our proposed framework was tested on two balanced test feeders and demonstrated competitiveness regardless of operating states. By leveraging RDDNR, distribution system operators can effectively restore service in a timely manner, improving overall grid resilience and customer satisfaction.

Optimal allocation of controllable power factor distributed generation with network reconfiguration on electric vehicle charging station loaded distribution network

This paper presents a hybrid optimisation technique to solve the problem of optimal allocation of controllable power factor Distributed Generators (DGs) together with network reconfiguration on Electric Vehicle Charging Stations (EVCSs)-loaded distribution networks. The suggested hybrid optimisation strategy combines the Artificial Gorilla Troops Optimizer (GTO) algorithm with genetic operators to improve convergence characteristics and prevent premature convergence to local optima. The objective is to minimise power loss, bus voltage deviation and voltage stability index reduction, which are formulated as a multi-objective function. The effectiveness of the proposed approach is evaluated on 69 and 118-bus distribution networks. Additionally, a comparative analysis is performed to evaluate the efficacy of the suggested approach in comparison with standard GTO and other well-known optimisation algorithms. The results demonstrate that simultaneous optimal placement of controllable power factor DGs with network reconfiguration demonstrates superior effectiveness in mitigating the adverse impact of EVCSs on distribution networks compared to optimisation of fixed-power factor DG placement with network reconfiguration. Furthermore, the hybrid optimisation approach outperforms other applied algorithms in terms of solution quality.

A two-stage risk-based framework for dynamic configuration of a renewable-based distribution system considering demand response programs and hydrogen storage systems

Distribution feeder reconfiguration (DFR) in distribution systems can enhance operating conditions by reducing losses and improving voltage indices. This paper introduces a dual-stage risk-based framework that concurrently tackles day-ahead reconfiguration and microgrid scheduling within the distribution network. To control the negative aspects of uncertainties, the proposed framework integrates energy storage systems (ESS/HSS) and demand response programs (DRPs) at the network level, enhancing adaptability. The initial stage of the proposed model employs the AC power flow model, utilizing loss and voltage deviation functions as objective benchmarks for network reconfiguration. The scheduling is meticulously executed per interval, deriving optimized structures under diverse scenarios with the aid of a case reduction technique (CRT) to streamline solutions. The ultimate solution employs the grey wolf optimization (GWO) algorithm and CPLEX solver in the first and second stages respectively. Outcomes from applying this approach to the adjusted 118-bus network manifest improved operational conditions and voltage quality through reconfiguration. Impressively, the integration of ESS/HSS and DRPs yields a substantial 22.34% reduction in total operating costs, a conclusion substantiated by the numerical findings. Furthermore, by leveraging the CRT, a remarkable 56.17% reduction in problem-solution time is achieved.

Bicontinuous vitrimer heterogels with wide-span switchable stiffness-gated iontronic coordination.

Currently, it remains challenging to balance intrinsic stiffness with programmability in most vitrimers. Simultaneously, coordinating materials with gel-like iontronic properties for intrinsic ion transmission while maintaining vitrimer programmable features remains underexplored. Here, we introduce a phase-engineering strategy to fabricate bicontinuous vitrimer heterogel (VHG) materials. Such VHGs exhibited high mechanical strength, with an elastic modulus of up to 116 MPa, a high strain performance exceeding 1000%, and a switchable stiffness ratio surpassing 5 × 103. Moreover, highly programmable reprocessing and shape memory morphing were realized owing to the ion liquid-enhanced VHG network reconfiguration. Derived from the ion transmission pathway in the ILgel, which responded to the wide-span switchable mechanics, the VHG iontronics had a unique bidirectional stiffness-gated piezoresistivity, coordinating both positive and negative piezoresistive properties. Our findings indicate that the VHG system can act as a foundational material in various promising applications, including smart sensors, soft machines, and bioelectronics.