Power Network Research Articles

Fiber‐Integrated Diamond Quantum Sensor for High‐Voltage Current Measurements

AbstractIn power network systems, there is an urgent demand for highly accurate and miniaturized sensors, owing to their high safety level and limited installation space. Current sensors in high‐voltage grids are required to accommodate harsh environments and provide accurate measurements of several kiloamperes. Thus, this study proposed an integrated quantum diamond sensor to facilitate high‐accuracy, large‐dynamic‐range current measurements. The design incorporated optical fiber and directional microwave (MW) antennas to drive the diamond sensor, which significantly reduced the size and power consumption on the high‐voltage side. Remote‐control and demodulation systems are installed more than 10 m away from the low‐voltage side. The proposed approach achieved zero power consumption on the high‐voltage side and ensured efficient signal transmission. A passive diamond probe manufactured using microfabrication processes facilitated miniaturization and practical deployment. Through parameter optimization, a magnetic detection sensitivity of 4.86 nT·Hz−1/2 is achieved at a safe distance of 11 m, which can be further optimized to 0.77 nT·Hz−1/2 with enhanced MW power. This sensor achieved a current measurement error of ±0.4% in the 1000 A measurement range. Thus, this study provides a new solution for the application of diamond quantum sensors in power systems.

Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

Working in a relational way is everything: Perceptions of power and value in a drug policy-making network

BackgroundThe development of drug policies has been a major focus for policy-makers across North America in light of the ongoing public health emergency caused by the overdose crisis. In this context, the current study examined stakeholders’ experiences and perceptions of power and value in a drug policy-making process in a North American city using qualitative, questionnaire, and social network data.MethodsWe interviewed 18 people who participated in the development of a drug policy proposal between October 2021 and March 2022. They represented different groups and organizations, including government (n = 3), people who use drugs-led advocacy organizations (n = 5), other drug policy advocacy organizations (n = 5), research (n = 3) and police (n = 2). Most of them identified as men (n = 8) and white (n = 16), and their ages ranged between 30 and 80 years old (median = 50). Social network analysis questionnaires and semi-structured qualitative interviews were administered via Zoom. Social network data were analysed using igraph in R, and qualitative data were analysed using thematic analysis. The analyses explored perceptions of value and power within a drug policy-making network.ResultsThe policy-making network showed that connections could be found across participants from different groups, with government officials being the most central. Qualitative data showed that inclusion in the network and centrality did not necessarily translate into feeling powerful or valued. Many participants were dissatisfied with the process despite having structurally advantageous positions or self-reporting moderately high quantitative value scores. Participants who viewed themselves as more valued acknowledged many process shortcomings, but they also saw it as more balanced or fair than those who felt undervalued.ConclusionsWhile participation can make stakeholders and communities feel valued and empowered, our findings highlight that inclusion, position and diversity of connections in a drug policy-making network do not, in and of itself, guarantee these outcomes. Instead, policy-makers must provide transparent terms of reference guidelines and include highly skilled facilitators in policy discussions. This is particularly important in policy processes that involve historical power imbalances in the context of a pressing public health emergency.

A global assemblage of tax haven clusters: profit shifting, tax dodging and money laundering

In this paper we explore a selection of tax haven clusters in the US and Europe as diverse but recognisable species of some fifty of the phenomena worldwide that have been seldom studied from an economic geography or spatial planning viewpoint. We treat the objects of study as being almost perfect expressions of the classical, canonical cluster configuration. They are: spatial agglomeration, untraded as well as market dependencies, small and medium-sized enterprise status, associative or agentic relationships, interaction with large firms inside or outside the cluster and knowledge acquisition, advisory, consultancy and knowledge translation services from research and innovation intermediaries - public and/or private. However, we think their characteristics demand further recognition of the interactive and often innovative communication networks by means of which these cluster incumbents interact globally. This is because they are set within a worldwide system that can be conceptualised as an exemplar of the well-known construct known as an ‘assemblage’ (Deleuze and Guattari, 2004a). Unlike more recent treatments from De Landa (2019) and others, we try to concentrate on the motive power of the ‘agentic’ impulses of ‘desire’ that motivate human action (e.g. regarding ‘God, Gold or Glory’ as commonly-rehearsed motivators for colonialism of various kinds). It has been more common to use ‘assemblage theory’ to analyse the interlocking ‘machinic’ elements of complex systems like power networks or laboratory interactions. To unlock the mysteries of system interlocking in our selected tax havens, which comprise Wilmington, Delaware (US), Ireland (EU) and Gibraltar (former EU, now British Overseas Territory) – post-Brexit - occupying a ‘quantum’ space ‘in which both the colonial and post-colonial find expression’ (Perera, 2021). Methodologically, we seek ‘pattern recognition’ to identify the lineaments that explain these clusters and the ‘agentic’ actors that motivate them.

DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks

Neoantigens, derived from tumor-specific mutations, play a crucial role in eliciting anti-tumor immune responses and have emerged as promising targets for personalized cancer immunotherapy. Accurately identifying neoantigens from a vast pool of potential candidates is crucial for developing effective therapeutic strategies. This study presents a novel deep learning model that leverages the power of protein language models (PLMs) and multi-window scanning convolutional neural networks (CNNs) to predict neoantigens from normal tumor antigens with high accuracy. In this study, we present DeepNeoAG, a novel framework combines the global sequence-level information captured by a pre-trained PLM with the local sequence-based information features extracted by a multi-window scanning CNN, enabling a comprehensive representation of the protein's mutational landscape. We demonstrate the superior performance of DeepNeoAG compared to existing methods and highlight its potential to accelerate the development of personalized cancer immunotherapies.

Conjunctive optimal operation of water and power networks

Water distribution systems (WDSs) are designed to convey water from sources to consumers. Their operation is a main concern for engineers, researchers, and practitioners and is subject to demand, pressure, and quality constraints. Pumping stations require power to pump water and keep system pressure at a desired level. On the other hand, power is generally supplied through power grids (PGs), which require optimal operation while satisfying operational constraints, such as generation limits, power consumption, and voltage constraints. Since the two infrastructure systems are interconnected, decision-makers could benefit from a holistic approach that would allow solving the two operational optimization problems together as one conjunctive problem. This paper presents the full mathematical formulation of the conjunctive optimization problem, including a novel modelling approach for the operation of a variable speed pump, which does not include integer variables for pump status, thus allowing to solve the model as a non-linear programming (NLP) problem. The formulation is applied to two illustrative case studies, and the results are compared to the optimal operation of the independent WDS. The inclusion of the PG in the optimization problem is observed clearly in the results and influences them quite significantly. WDS operation is shown adjust to the PG constraints, and the application of the conjunctive model results in a cost reduction rate of more than 10 % for both case studies.

Association of a DASH diet and magnetoencephalography in dementia-free adults with different risk levels of Alzheimer's disease.

This study explored how adherence to the DASH diet relates to electrophysiological measures in individuals at varying Alzheimer's disease (AD) risk due to family history (FH). Therewere 179 dementia-free subjects. DASH index was calculated, and participants were classified into different DASH adherence groups. Tertiles of relative alpha power in default mode network (DMN) regions were calculated. Multivariate logistic regression models were used to examine the association. Lower DASH adherence was associated with decreased odds of higher relative alpha power in the DMN, observed across the entire sample and specifically among those without a FH of AD. Logistic regression models indicated that participants with poorer DASH adherence had a reduced likelihood of elevated DMN alpha power, potentially influenced by vascular and amyloid-beta mechanisms. These findings underscore the dietary pattern's potential role in neural activity modulation, particularly in individuals not genetically predisposed to AD.

Optimal resilience-based restoration plan for disrupted interdependent infrastructure networks under uncertainty

The functioning of vital services in a society depends heavily on the proper and continuous operation of critical infrastructure networks, including water, transportation, power, natural gas, and telecommunication. Such networks are essential for maintaining the economy, security, and overall quality of life. However, since these critical networks are interdependent; hence, they are susceptible to various types of disruptions caused by natural disasters, failures, or malicious activities, leading to varying degrees of performance impacts that could directly affect people's daily lives. Thus, a disruption to one network can quickly spread to others, resulting in significant impacts on daily life. Therefore, restoring such disrupted networks in a timely manner is crucial to ensure a prompt recovery and minimize the impact on daily life. In this work, we consider the problem of restoring a set of interdependent infrastructure networks following a disruptive event. We study two essential factors that can accelerate the recovery process: (i) the restoration facilities location, which are considered as dispatching points for restoration crews, and (ii) the routes of the restoration crews within the interdependent networks. Accordingly, we formulate two optimization models using mixed integer programming. The first model, restoration facility location model, finds the optimal location for restoration facilities within interdependent infrastructure networks considering uncertainty in disruptions. The second model, restoration crew routing model, determine the optimal routes for restoration crews within the interdependent infrastructure networks considering: (i) travelled distance, (ii) restoration time required for disrupted network components, and (iii) different importance of network components. The two optimization models are solved through generated interdependent power and water networks to demonstrate their effectiveness.

Design of voice command recognition chip based on heterogeneous acceleration

Abstract Speech, as one of the earliest forms of communication used by humans, can effectively convey information. However, the current deep neural network models for speech recognition are generally large in scale and can only be deployed in the cloud, which imposes high deployment environment requirements and power consumption, thereby limiting their implementation on embedded devices. In the context of end-to-end speech recognition, a series of challenges are encountered, including power consumption constraints, computing power limitations, network dependencies, privacy protection, bandwidth restrictions, and communication delays. To address these issues, this paper proposes the design of an end-to-end voice command recognition chip based on deep neural networks specifically for recognizing voice commands in specific scenarios. This chip achieves low power consumption and minimal delay in recognition. Additionally, we introduce a weighted, overloadable chip architecture to enable seamless scene migration, ultimately aiming to resolve the aforementioned challenges.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

Optimal Power Flow Calculation Based on Improved Crossover Algorithm

Abstract Aiming at the optimization problem of power flow distribution in power system, this paper puts forward a method for calculating the optimal power flow of power network based on the improved crisscross algorithm. The fast convergence crisscross optimization algorithm (FCSO) is based on the traditional crisscross optimization algorithm (CSO) to improve the efficiency and accuracy of solving the Optimal Power Flow, OPF) problem. Based on the original double crossover operator, the improved algorithm puts forward a brand-new operator-central crossover operator, which alternates with transverse crossover operator according to certain rules, In order to improve the quality of single search and accelerate convergence, each individual in the population performs crossover operation with the current optimal individual in turn, and then performs competition operator to selectively approach the current global optimal individual. Finally, combined with experimental verification, the optimal power flow calculation of reactive power optimization based on FSCO has obvious practical effects in improving network voltage quality and reducing line loss, The average voltage increase ratio of 220kV bus and 110kV bus is 0.676% and 0.855% respectively, and the loss reduction rate reaches 3.51%, Compared with CSO, the convergence and calculation speed of FSCO proposed in this paper is faster, the convergence curve is smoother, and the average time consumption is increased to 59.53% compared with CSO, In the case of obtaining the same precision solution, the optimization time is obviously shortened, the timeliness of the optimal solution in regulation operation is effectively improved, and the problem of reactive power optimization in power system can be well solved.

Research on the mechanism and extinguishing technology of substation fire

This article utilizes the combustion characteristics of combustible materials to investigate the fire mechanisms and extinguishing technologies in substations. Experimental and simulation analyses have revealed patterns in heat release rates, flame heights, flame temperatures, flame resistance, and particulate ash. A modular solution employing a novel hot aerosol fire suppression system has been introduced. This system integrates independently manufactured alarm devices, initiation devices, sensors, and fire suppression equipment, offering either semi-automatic or fully automatic functionality. It serves both targeted fire suppression and complete immersion scenarios. By developing an integrated fire suppression and database alert system, enhanced intelligent fire protection has been attained, thereby advancing the sophistication of substation fire safety and the digitization of power networks.

Angle stability improvement using optimised proportional integral derivative with filter controller

Load inconsistency has disrupted the power system, causing rotor angle fluctuation that leads to angle instability in the system. This research suggests an innovative proportional integral derivative with filter (PIDF)-based thyristor-controlled series compensator (TCSC) controller that utilise an evolutionary programming sine cosine algorithm (EPSCA) for hybrid optimisation to increase the angle stability of the power system. The challenge of the PIDF-TCSC design is transformed into an optimal control problem with respect to performance indices, such as the maximum imaginary part of system eigenvalues, damping ratio and damping factor, where another multi-objective function is utilised to determine the best stabiliser settings. Eigenvalue analysis is used to conduct the stability study in a linearised paradigm of the single-machine infinite-bus (SMIB) network. The resilience of the PIDF controller was tested using a SMIB power network under various operating circumstances. Simulation results are used to evaluate the system's effectiveness with the proposed optimised PIDF-TCSC controller to that of the system using the proportional integral derivative, proportional integral, and base case PIDF-TCSC approaches. The research findings demonstrate the efficacy of EPSCA in implementing PIDF-TCSC motif and its excellent resilient performance for improving power system stability as related to other strategies in various situations.

Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective

The growth of global data is increasing exponentially, leading to a greater demand for computing power. To address this requirement, expanding computing power from the cloud to the edge is essential. However, this transformation presents two significant challenges: how to share computing resources more efficiently and how to optimize resource allocation. To tackle these challenges, we propose a three-layer Computing Power Network (CPN) framework that focuses on implementing the collaborative allocation of computing nodes and user tasks. We formulate the resource allocation problem in CPN as a double auction game and use an experience-weighted attraction algorithm that enables participants to adjust bidding strategies based on environmental interactions. We implemented a prototype of our proposed CPN framework and conducted extensive experiments to verify our algorithm’s convergence and evaluate the benefits obtained by buyers (users) and sellers (computing nodes) from the perspective of transaction prices, rewards, and average pricing. The comprehensive experimental results demonstrate the effectiveness of our proposed method. Compared with state-of-the-art pricing strategies, our approach achieves a 20% increase in convergence speed and an 88% increase in overall returns. Furthermore, it also exhibits a 2.5% increase in deal prices and a substantial 83% rise in the income of individual users. These outcomes convincingly prove the superiority of our method in achieving better convergence, improving overall returns, and benefiting both buyers and sellers in the CPN resource auction market.

Online Monitoring System of Electromechanical Transient Simulation Data of Distribution Network Based on Edge Computing

The current distribution network is developing toward the direction of information and intelligent distribution Internet of Things (IoT). In order to explore the online monitoring system for transient electromechanical simulation of distribution networks, this paper is based on the equivalent model of generator sets. Firstly, it describes the relevant theoretical knowledge of edge computing, designs the distribution IoT based on edge computing, and briefly introduces its network architecture. Secondly, based on the discrete-time domain equivalent model of generators, the electromechanical transient simulation distribution network is constructed by introducing the machine network division of the power network. Finally, the Western System Coordinating Council (WSCC)-3 units and 9 nodes are taken as an example, and simulation experiments are conducted under two fault simulation conditions to verify the effectiveness of the simulation model. The results show that under the two fault simulation conditions, the results of equivalent model simulation of generator terminal voltage and relative power angle change are like those of the Power System Analysis Software Package (PSASP). The changing trend of the two is similar and stable. After the PSASP results are stabilized at a value, the simulation results fluctuate extremely up and down the value. For example, under fault condition 1, the changing trend of the relative power angle of the No. 2 generator is first fluctuating and then becomes stable. After violent fluctuation, the relative power angle tends to be stable from about 20s to -12.35°. The result of PSASP software is stable at -12.35°. The transient electromechanical simulation of the generator equivalent model can provide some ideas for the online monitoring system of the distribution network.

Multi-objective Optimization of Power Networks Integrating Electric Vehicles and Wind Energy

Multi-objective Optimization of Power Networks Integrating Electric Vehicles and Wind Energy

Improving grid reliability with grid-scale Battery Energy Storage Systems (BESS)

The modern electric power system is stable because generation and demand are balanced in real-time. To provide grid managers the leeway to maintain this balance, grid-scale energy storage devices are seeing increased deployment. Another existing technique to achieve a stable and reliable power system today is integrating renewable energies with a battery energy storage system (BESS). Integrating grid-scale BESS to improve grid dependability is crucial since renewable energy sources, which may be somewhat unpredictable, are increasingly being integrated into existing power networks. With its massive electrical energy storage and distribution capabilities, BESS contributes to the grid's ability to balance supply and demand. The BESS helps maintain grid stability by storing energy that is not used during peak hours. This energy comes mostly from renewable sources like solar and wind and is then sent back to the system when the demand is highest. Primary function of BESS includes energy storage and time-shifting, regulation of frequency, voltage support, and enhancement of grid reliability. Development in battery technologies and controls has made them cheaper and inevitable for future power networks. The functions and elements of BESS, the types of electrochemical batteries, the implications of their degradation, and their applications for grid optimization and reliability are discussed in this research. In conclusion, BESS is a significant enabler of grid modernization, resilience, and the evolution of the energy system.

Coding Dynamics of the Striatal Networks During Learning.

The rat dorsomedial (DMS) and dorsolateral striatum (DLS), equivalent to caudate nucleus and putamen in primates, are required for goal-directed and habit behaviour, respectively. However, it is still unclear whether and how this functional dichotomy emerges in the course of learning. In this study, we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous spatial alternation task, from the acquisition to optimized performance. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of simultaneously recorded neurons, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMSand DLS neural networks across learning stages. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite these major differences, the decoding power of both networks increases during learning. These results suggest that DMS and DLS neural networks gradually reorganize in different ways in order to progressively increase their control over the behavioral performance.

Surface Roughness Prediction in Additive Manufacturing: Presenting the Power of Neural Networks Compared to Linear Regression

This paper investigates surface roughness prediction in additive manufacturing through a comprehensive comparative analysis of linear regression (LR) and neural network (NN) models. Employing [Formula: see text]-means clustering, we identify four distinct clusters within the experimental data, each closely associated with specific 3D printing parameters. Within each cluster, we explore the optimal combination of factors that contribute to surface roughness and power efficiency. The main objective focuses on predicting a target variable, with an emphasis on evaluating model performance via key metrics such as [Formula: see text]-squared ([Formula: see text]2), adjusted [Formula: see text]-squared, predicted [Formula: see text]-squared, mean squared error (MSE), and correlation coefficient ([Formula: see text]). Our study’s results illuminate the robust predictive capabilities of both LR and NN models. However, it becomes evident that the Neural Network model outperforms Linear Regression. It exhibits excellent performance metrics, characterized by higher [Formula: see text]2and correlation values, reduced MSE, and greater resilience to outliers. This pronounced disparity underscores the Neural Network model’s exceptional suitability for tasks requiring precise predictions and the identification of nonlinear patterns, particularly in the field of surface roughness prediction in additive manufacturing. These findings emphasize the key role of advanced machine learning techniques, illustrated by neural networks, in achieving precision within similar domains.