Power Support Research Articles

Enhancing target benefits of power system stakeholders: A columns and constraint generation-based power-to-gas linked economic dispatch

Economic dispatch (ED) plays a crucial role in optimizing power plant output to minimize the cost of electricity production and ensure economic efficiency in complex power systems. However, with the growing emphasis on carbon neutrality policies and the increased use of renewable energy sources (RES), traditional ED models may lead to economic losses for independent power producers (IPP) and renewable energy providers (REP). To address this challenge, a new ED model is proposed in this study, which integrates power-to-gas (P2G) technology and takes into account the perspectives of all stakeholders, including independent system operator (ISO) IPP, and REP. The proposed approach is tested in the context of the cost based pool (CBP) market structure in S. Korea, where a mixed integer linear programming (MILP) problem is solved for each target benefit using columns and constraint generations (C&CG). By ensuring the target benefits of each stakeholder group, this improved ED model aims to encourage their participation in the power system and support economic efficiency. The simulation results of the P2G-linked ED proposed in this paper verify that as the capacity of the integrated P2G increases, there is an increase in the improvement rate of each target benefit for ISO, IPP, and REP.

Multi‐timescale synergistic planning for flexible regulation of thermal power to support wind‐photovoltaic‐storage

AbstractWith the proposed goal of 30•60 carbon peak and carbon neutrality, the proportion of new energy continues to increase, the proportion of conventional power supply capacity continues to decrease, and the flexibility demand of the existing power system continues to increase and the flexibility adjustment ability is insufficient. Therefore, a medium and long term planning method is proposed to flexibly adjust the multi‐time scale coordination of thermal power support wind and solar storage. Considering the long‐term investment decision and the short and medium term operation simulation, the flexible transformation cost and the penalty cost of insufficient flexibility of thermal power units are included in the planning objective, and a multi‐time scale coordinated planning model is established with wind power as the main power source and thermal power and hydrogen energy storage as the auxiliary power source. Through the multi‐stage cycle iteration of investment decision model, medium and long term production simulation and typical daily operation simulation, the flexible transformation resources of wind and solar fire storage and thermal power are optimized. Finally, a provincial power grid in northeast China is taken as an example to verify that hydrogen energy storage equipment assisting thermal power unit flexibility transformation can better support load fluctuation and large‐scale new energy access in new power systems.

Primary Frequency Regulation Strategy Based on Rotor Kinetic Energy of Double-Fed Induction Generator and Supercapacitor

To address the challenge of wind turbines meeting primary frequency regulation requirements, incorporating energy storage devices to handle most of the frequency regulation tasks would result in increased operational costs. When a wind turbine rotor accelerates, it deviates from the maximum power tracking point (MPPT), leading to reduced output while retaining significant rotational kinetic energy. Based on this characteristic, a primary frequency regulation strategy is proposed that coordinates the rotor kinetic energy of a double-fed induction generator (DFIG) with supercapacitors (SCs). Supercapacitors provide power support during low-frequency conditions, while accelerating the wind turbine rotor reduces output during high-frequency conditions. Additionally, continuous attention is given to subsequent frequency changes. In case of short-term, low-frequency conditions, stored kinetic energy is released for power support, establishing a mechanism for wind turbine kinetic energy recovery and release. This mechanism reduces charging and discharging requirements for supercapacitors, extends their service life, and considers both wind turbine frequency regulation requirements and economy. Finally, using MATLAB 2020/Simulink platform allows for the verification of the effectiveness and rationality of this proposed method.

Coordinated Q‐V/P‐f Control Strategy Using Virtual Power Factor for Autonomous AC Microgrid with PV Smart Inverters

Given the increasing interests in carbon neutrality due to climate change, photovoltaic (PV)‐based microgrid has become an important research topic worldwide. Typically, high PV penetration without proper control schemes causes power quality issues such as voltage or frequency violation, resulting in the limited PV hosting capacity. Reactive and active power support from the PV is effective to solve the issues, however, it is a critical challenge to efficiently contribute to the stabilizing controls under the inverter capacity constraint. Recent studies have applied smart inverters, equipped with fixed active and reactive power control reserves, for dynamic voltage and frequency support. In this paper, we proposed a control strategy in which the control reserves for voltage and frequency support are flexibly adjusted using virtual power factor (VPF) to improve the power quality with reducing active power curtailment. The effectiveness of the proposed Q‐V/P‐f control is verified through numerical simulations using a microgrid model developed in MATLAB/Simulink platform. It was observed through the simulation that the proposed method contributed to mitigating voltage and frequency fluctuation with satisfying the inverter capacity constraint. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Artificial Intelligence and Malicious Code Detection

Malicious software has become a critical cybersecurity issue due to the increasing number of threats it poses to devices and internet environments. Traditional static scanning techniques and behavior-based malware detection methods have limitations in meeting the new requirements in information security due to high false positives and false negatives. In this work, we propose a CNN convolutional neural network-based method for detecting malicious code. Practical operations were conducted using the Cuckoo sandbox system, and Python programs were utilized to preprocess analytical reports. This article presents the construction of a deep learning training model for CNN designed to identify malicious code. The model is compared with machine learning and general antivirus tools for comprehensive evaluation. Experimental verification shows that our proposed method exhibits greater advantages in comparison and achieved excellent detection results with higher feasibility. The research significance of this work is highlighted as malicious software has become a core issue in cybersecurity. The method proposed in this article provides powerful support for addressing new needs in information security. This paper emphasizes the importance of utilizing CNN-based methods in detecting malware, which can better address the limitations of traditional detection techniques. Overall, this work provides an effective solution to detect malicious code and addresses a critical cybersecurity issue.

Exploring the oxidative rancidity mechanism and changes in volatile flavors of watermelon seed kernels based on lipidomics

Watermelon seed kernels (WSK) are prone to oxidative rancidity, while their evaluation biomarkers and changes in volatile flavor are still unknown. The research tracked the changes in volatile compounds and lipid components before and after rancidity using HS-SPME-GC-O-MS and lipidomic techniques. The results showed the flavor of watermelon seed kernels changed significantly before and after rancidity, from mild aroma to rancidity. A total of 42 volatile compounds were detected via GC-O-MS, and a total of 220 lipid molecules were detected via lipidomic technology. 55 lipids with significant differences were screened via multivariate statistical analysis. Combining the above analysis, it found that glycerol phospholipid and glyceride pathways were the most important metabolic pathways and 1-Pentanol and styrene could be used as potential biomarkers to judge the rancidity process of watermelon seed kernels. The research could provide powerful technical support for the storage, transportation and freshness preservation of watermelon seed kernels.

Research on emergency DC power support coordinated control for hybrid multi-infeed HVDC system

Research on emergency DC power support coordinated control for hybrid multi-infeed HVDC system

Research on influencing factors of emergency power support for voltage source converter-based multi-terminal high-voltage direct current transmission system

Research on influencing factors of emergency power support for voltage source converter-based multi-terminal high-voltage direct current transmission system

Research on regional emergency DC power support strategy of VSC-MTDC transmission system

Research on regional emergency DC power support strategy of VSC-MTDC transmission system

How do household living conditions and gender-related decision-making influence child stunting in Rwanda? A population-based study.

Child stunting (chronic undernutrition) is a major public health concern in low- and middle-income countries. In Rwanda, an estimated 33% of children are affected. This study investigated the household living conditions and the impact of gender-related decision-making on child stunting. The findings contribute to ongoing discussion on this critical public health issue. In December 2021, a population-based cross-sectional study was conducted in Rwanda's Northern Province; 601 women with children aged 1-36 months were included. Stunting was assessed using low height-for-age criteria. The Multidimensional Poverty Index (MPI) was used to determine household socioeconomic status. Researcher-designed questionnaires evaluated gender-related factors such as social support and household decision-making. Multivariable logistic regression analysis identified risk factor patterns. Six hundred and one children were included in the study; 27.1% (n = 163) were diagnosed as stunted; there was a higher prevalence of stunting in boys (60.1%) than girls (39.9%; p<0.001). The MPI was 0.265 with no significant difference between households with stunted children (MPI, 0.263; 95% confidence interval [CI], 0.216-0.310) and non-stunted children (MPI, 0.265; 95% CI, 0.237-0.293). Most households reported a lack of adequate housing (78.9%), electricity (63.0%), good water sources (58.7%), and proper toilets (57.1%). Male-headed households dominated (92% vs. 8.0%; p = 0.018), and women often shared decision-making with their partners. However, 26.4% of women reported forced sexual intercourse within marriage (Odds Ratio [OR] 1.81; 95% CI, 1.15-2.85). Lack of support during illness ([OR], 1.93; 95% CI, 1.13-3.28) and absence of personal guidance (OR, 2.44; 95% CI, 1.41-4.26) were significantly associated with child stunting. Poverty contributes to child stunting in the Northern Province of Rwanda. Limited social support and women's lack of decision-making power in the household increase stunting rates. Interventions should empower women and address the broader social and economic context to promote both women's and children's health.

Research on microbial data modeling and disease prediction by fusion and integration of deep learning

Abstract The dynamics of microbial communities are closely related to human health. Deep learning techniques have shown great potential in bioinformatics by demonstrating powerful data analysis capabilities in areas such as microbial data modeling and disease Prediction since recent years. Our paper explores new approaches combining deep learning techniques with microbial data to generate more precise predictive models for early diagnosis and treatment. This study is dedicated to developing a method for modeling microbial data and predicting diseases that incorporates deep learning. Multiple deep learning models combine data from microbial communities to improve prediction accuracy through an integrated learning strategy. Experiments were conducted on two primary disease datasets: Inflammatory Bowel Disease (IBD) and Colorectal Cancer (Colorectal). This research method’s AUC values were 0.897 and 0.876, which is an improvement compared to traditional machine learning methods. A new perspective on the study of disease mechanisms was provided by identifying highly correlated microbial markers through feature selection analysis. This study can effectively enhance the use of microbial data in disease prediction by integrating deep learning models, which provides powerful technical support for future clinical diagnosis and treatment.

Design of Multi-Competitors Winner-Take-All Neural Networks Based on DNA Strand Displacement for Molecular Pattern Recognition

DNA strand displacement technology (DSDT) provides flexible and powerful technical support for DNA molecular computing. DNA-based neural networks with Winner-Take-All (WTA) strategy has a great potential for nonlinear calculation. However, so far it has been limited to achieving the simultaneous competition of two competitors. Optimizing the calculation model and reducing system response time to recognize complex and functional molecular patterns remains a huge challenge. Here a novel neural network with WTA strategy based on DSDT was constructed, which allowed three competitors to participate in the competition at the same time. Firstly, the feasibility of the three-competitor WTA neural network was proved by 9-bit pattern recognition. Then the three-competitors WTA neural network was further extended to larger scale pattern recognition, which successfully recognized 64-bit letters A, B, and C and 100-bit handwritten digits 0, 2, and 4, respectively. Simulations showed that when recognizing the same target patterns with same number bits, compared with two-competitors WTA neural network, the three-competitors WTA network only used down to two-thirds DNA strands, and the system response time was reduced by more than ten times. This paper demonstrated the efficient recognition ability of the three-competitor WTA neural network, which is expected to be used to identify more complex information.

An RFID-Powered Multisensing Fusion Industrial IoT System for Food Quality Assessment and Sensing

The development in Internet of Things (IoT) have empowered revolution in almost all walks of life. Although substantial effort has been made to bring IoT in manufacturing, there is still technical challenges of providing solutions of real-time pervasive multi-sensing, quality evaluation and boundary-less information sharing, which put people at risk from deteriorated food products and food adulteration. This investigation presents an Industrial IoT-based system for food product quality assessment and prediction. This research completes the real-time food quality assessment via radio frequency identification-based multi-sensor fusion for the first time. Also, a novel concept of shelf-life prediction is proposed. The RFID-powered sensors provide a new idea for nondestructive food product and environment sensing. A five-layer architecture considering sensing, controlling, communication, interfaces, and data analysis is highlighted. A novel RFID metadata structure is first proposed to achieve multi-dimensional information traceability and global data sharing. Machine learning-based multi-sensor fusion is proposed to provide accurate quality assessment and prediction. The proposed system is implemented as a smart shelf system to demonstrate its feasibility and advantages. The system is of great significance in improving food safety, reducing food waste and providing powerful information support for food manufacturing line and supply chain management.

A multi-channel electrochemical biosensor based on polyadenine tetrahedra for the detection of multiple drug resistance genes.

Antimicrobial resistance poses a serious threat to human health due to the high morbidity and mortality caused by drug-resistant microbial infections. Therefore, the development of rapid, sensitive and selective identification methods is key to improving the survival rate of patients. In this paper, a sandwich-type electrochemical DNA biosensor based on a polyadenine-DNA tetrahedron probe was constructed. The key experimental conditions were optimized, including the length of polyadenine, the concentration of the polyadenine DNA tetrahedron, the concentration of the signal probe and the hybridization time. At the same time, poly-avidin-HRP80 was used to enhance the electrochemical detection signal. Finally, excellent biosensor performance was achieved, and the detection limit for the synthetic DNA target was as low as 1 fM. In addition, we verified the practicability of the system by analyzing E. coli with the MCR-1 plasmid and realized multi-channel detection of the drug resistance genes MCR-1, blaNDM, blaKPC and blaOXA. With the ideal electrochemical interface, the polyA-based biosensor exhibits excellent stability, which provides powerful technical support for the rapid detection of antibiotic-resistant strains in the field.

A study on muti-strategy predator algorithm for passenger traffic prediction with big data

Abstract In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.

Evaluation of a multicomponent infrastructural and app-based intervention to increase children's active school travel: A multiple methods study in Tāmaki Makaurau/Auckland, Aotearoa/New Zealand

Power Up Schools (Power Up) was a temporary intervention involving a range of activities aimed at increasing children's active school travel (AST) with a focus on physical and social aspects of schools and their surrounding environs. We evaluated the impact of Power Up on children's AST and how multiple stakeholders (children, school representatives, parents, residents) perceived the overall intervention and its components. This was a multiple methods evaluation study with four primary (elementary) schools in Auckland and their wider communities. The study involved longitudinal data collection (daily travel modes reported by children), repeat cross-sectional surveys with parents, single time-point cross-sectional surveys with residents, post intervention focus groups with children and parents, and post intervention interviews with school representatives. Findings showed evidence for increased AST; general support for Power Up by children, parents, and school representatives; mixed support by residents for some components; the importance of considering diverse needs and preferences in future interventions; the need for more comprehensive communication across stakeholders; and the value of streamlining processes for schools and parents. Comprehensive approaches that are embedded within, and accepted by, the wider school community are recommended for sustainable and effective AST interventions.

One Hop Routing Optimization Approach Using Machine Learning

Every data packet must pass through a few intermediate nodes to reach its destination. Among other reasons, tremendous growth in internet devices encourages those intermediate nodes to drop the data packets. Optimizing the data packet route is an effective solution to deal with packet loss. Advanced machine learning approaches have been identified as a powerful support tool for routing optimization in node networks. Cloud computing has kept pace with the continuously developing hardware infrastructure. Improved connection, processing power, and memory units enable real-time machine learning. This paper proposes and evaluates an approach for optimizing the packet path, by one hop, for intermediate nodes as a backup called Cloud Acknowledgement Scheme (CAS). It offers information on the transmission trend and the tendencies of certain adjacent nodes or groups of neighboring nodes in a network. The proposed CAS has been validated via a series of machine learning experiments using real-world node data.

Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Rapid Active Power Support by Regional PV Aggregation Considering Physical Topological Correlation and Communication Delay

Rapid Active Power Support by Regional PV Aggregation Considering Physical Topological Correlation and Communication Delay

Carbon-Aware Operation of Resilient Vertical Farms in Active Distribution Networks

Emerging utilization of Electric Vehicles (EVs) in Distribution Networks (DNs) decreases the power quality in the DN. The main quest of this research is to find out Can EVs and Photovoltaic (PV) systems support the DN and mitigate the voltage magnitude violations due to utilizing high penetration level EV? This article proposes a novel volt/VAR optimization (VVO) model by leveraging the reactive power support of EVs and PVs as Distributed Energy Resources (DERs) for the DN. The proposed VVO model contains the second-order conic relaxed form of the AC power flow equations. Thus, the presented VVO problem is a Mixed-Integer Second-Order Cone Programming (MISOCP) problem. To obtain the ability to support the DN, Smart Inverters (SIs) are utilized to connect EVs and PV systems to the DN. Thus, to add the PV and EV systems to the convexified form of the VVO problem, the P-Q characteristics and the model of SIs working at different operating points are presented based on experimental results. The proposed model of SIs allows the participation of DERs in the reactive power compensation to mitigate voltage issues of DN due to utilizing high penetration level EVs. Besides, the proposed model of SIs enables DERs to increase their dispatchability. The performance of the proposed model is examined in the case studies using the IEEE 33-bus system and the 123-bus system. It is illustrated in the case studies that the solar dispatchability increases by 12% when the proposed volt/VAR model is utilized for IEEE 33-bus system. Besides, it is illustrated in the case studies that the voltage profile of buses remains in the desired operating range when the proposed model is leveraged, even if the penetration level of EVs increased to 40%.