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Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1959-1970页

作者： Zhihao Yang Anupam Trivedi Ming Ni Haoming Liu Dipti Srinivasan IEEE College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China Department of Electrical&Computer Engineering National University of SingaporeSingapore

This paper proposes a distribution locational marginal pricing(DLMP) based bi-level Stackelberg game framework between the internet service company(ISC) and distribution system operator(DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads(IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

关键词： Bi-level optimization congestion management data center demand response distribution locational marginal pricing(DLMP) robust optimization

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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Hybrid Recommender System Using Systolic Tree for Pattern Mining

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computer Systems Science & engineering 2023年第2期44卷 1251-1262页

作者： S.Rajalakshmi K.R.Santha Department of Computer Science Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia

A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of *** work will present the implementation of sequence pattern mining for recommender systems within the domain of *** work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender *** feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target *** will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy *** work pre-sents a new hybrid recommender system based on optimized feature selection and systolic *** features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)*** systolic tree is used for pattern mining,and based on this,the recommendations are *** proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes conﬁrmed the efﬁciency of the *** was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativeﬁltering,the precision of 0.89 was achieved.

关键词： Recommender systems hybrid recommender systems frequent pattern mining collaborativeﬁltering systolic tree river formation dynamics particle swarm optimization

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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Active Voltage Balancing of Series-Connected SiC MOSFETs During Body-Diode Turn-Off Using an Active Gate Driver

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IEEE Transactions on Power Electronics 2025年第8期40卷 10609-10622页

作者： Rai, Ajay Ganesan, P. Hatua, Kamalesh Bhattacharya, Subhashish Department of Electrical Engineering Indian Institute of Technology Madras Chennai India Centre for Development of Advanced Computing (C-DAC) Thiruvananthapuram India Department of Electrical and Computer Engineering NC State University Raleigh NC USA

The series connection of fast-switching SiC MOSFETs presents a promising approach to enhance the efficiency, compactness, and simplicity of Medium Voltage (MV) power converters. However, managing voltage imbalances during the turn-off of MOSFETs and their body diodes introduces significant challenges. While existing literature covers passive and active voltage balancing strategies for switch turn-off (e.g., MOSFETs/IGBTs), the issue of uneven voltage distribution during body diode turn-off remains largely unexplored. This gap limits the widespread adoption of series-connected devices in MV power converters, as achieving reliable performance with minimal additional components and low switching losses remains difficult. This article presents an Active Voltage Balancing technique for handling body diode turn-off events by regulating the gate charge of parallel MOSFETs during voltage rise transients in the closedloop using Active Gate Drivers (AGD). This approach ensures uniform voltage distribution among the body diodes of seriesconnected MOSFETs. A comprehensive analysis and modelling of gate charge control during voltage transients is presented. The proposed method is experimentally validated on two seriesconnected SiC MOSFETs operating in buck mode (DC-DC) and half-bridge inverter mode (DC-AC) at a 1.5 kV DC bus voltage, with switching frequencies up to 40 kHz. © 1986-2012 IEEE.

关键词： Diodes

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 34-40页

作者： Joice G.Philip Jaesung Jung Ahmet Onen the Department of Energy Systems Research Ajou UniversitySuwonSouth Korea the Department of Electrical and Electronic Engineering Abdullah Gul UniversityKayseri38080Turkey the Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscatOman

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)*** phasors from PMUs are preprocessed to check for the presence of *** the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida *** superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest *** show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

关键词： Empirical wavelet transform(EWT) sub-synchronous oscillation Prony-based method Yoshida algorithm variational mode decomposition phasor measurement unit(PMU)

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FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features 38

FUG: Feature-Universal Graph Contrastive Pre-training for Gr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jitao Jin, Di Ge, Meng Shan, Lianze Wang, Xin He, Dongxiao Feng, Zhiyong College of Intelligence and Computing Tianjin University China Department of Electrical and Computer Engineering National University of Singapore Singapore

Graph Neural Networks (GNNs), known for their effective graph encoding, are extensively used across various fields. Graph self-supervised pre-training, which trains GNN encoders without manual labels to generate high-quality graph representations, has garnered widespread attention. However, due to the inherent complex characteristics in graphs, GNNs encoders pre-trained on one dataset struggle to directly adapt to others that have different node feature shapes. This typically necessitates either model rebuilding or data alignment. The former results in non-transferability as each dataset need to rebuild a new model, while the latter brings serious knowledge loss since it forces features into a uniform shape by preprocessing such as Principal Component Analysis (PCA). To address this challenge, we propose a new Feature-Universal Graph contrastive pre-training strategy (FUG) that naturally avoids the need for model rebuilding and data reshaping. Specifically, inspired by discussions in existing work on the relationship between contrastive Learning and PCA, we conducted a theoretical analysis and discovered that PCA's optimization objective is a special case of that in contrastive Learning. We designed an encoder with contrastive constraints to emulate PCA's generation of basis transformation matrix, which is utilized to losslessly adapt features in different datasets. Furthermore, we introduced a global uniformity constraint to replace negative sampling, reducing the time complexity from O(n2) to O(n), and by explicitly defining positive samples, FUG avoids the substantial memory requirements of data augmentation. In cross domain experiments, FUG has a performance close to the re-trained new models. The source code is available at: https://***/hedongxiao-tju/FUG. © 2024 Neural information processing systems foundation. All rights reserved.

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A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

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International Journal of Energy Technology and Policy 2024年第3-4期19卷 363-376页

作者： Tong, Wei Huang, Qi-Ping School of Computer Engineering Anhui Wenda Information Engineering College Hefei Anhui231201 China Electrical Engineering Department Anhui Electrical Engineering Professional Technique College Hefei Anhui230051 China

Different malfunctions may arise during the operation of power equipment, impacting the quality and dependability of the power supply. Conventional monitoring techniques face challenges, prompting the introduction of a novel power equipment monitoring and fault localisation method based on an enhanced ant colony algorithm. This approach entails gathering operational signals from the power equipment and amalgamating singular value decomposition with particle filtering methods to oversee the equipment's condition. Through enhancements to the pheromone configuration and update approach of the ant colony algorithm, a fault localisation assessment function is formulated to achieve precise fault localisation. Empirical findings have illustrated that this method has the capability to promptly monitor equipment status and attain fault localisation accuracy surpassing 90%. © 2024 Inderscience Enterprises Ltd.

关键词： Ant colony optimization

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A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 953-962页

作者： Kunjie Tang Shufeng Dong Yonghua Song College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao SARChina IEEE

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first *** that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is *** with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are ***,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power *** experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark ***,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional ***,in some cases,where conventional methods diverge,these LSI methods can still converge.

关键词： Fixed-point theory integrated transmission and distribution networks least-squares-based iterative method master-slave-splitting method optimal power flow power flow

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