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6th International Conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Wu, Xiaogang Li, Zuxin Zhou, Xiaoqing Ji, Qingfeng Mao, Shuangshuang Ju, Xiaoming Lishui Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd Lishui China Zhenghao Group Technology Network Branch Lishui China School of Software Engineering East China Normal University Shanghai China Zhejiang Jierui Electric Power Technology Co. LTD Ningbo China

ISBN: (纸本)9781510668249

Accurate calculation of line loss in distribution networks can better guide the power system on how to optimize the power grid and how to carry out technical loss reduction work. In response to the problems of many electrical parameters, complicated steps and low accuracy of results required for the calculation of theoretical line loss values in traditional distribution networks, this paper proposes a distribution network line loss prediction method based on the light gradient boosting machine (LightGBM) model algorithm. The method uses machine learning models to model key electrical parameters and line loss values to automatically calculate the grid line loss. To address the difficulty of tuning the LightGBM model, we use a Bayesian optimization algorithm to adjust the model parameters. In this paper, grid data from the Kaggle data platform is used for analysis and validation, and the experimental results show that the model proposed in this paper has higher prediction accuracy than the traditional BP model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Machine learning

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Short-Term power Load Forecasting Based on CNN-BiLSTM-Attention Neural Network with ISSA Optimized 5

Short-Term Power Load Forecasting Based on CNN-BiLSTM-Attent...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Wei, Xiang Zhou, Yang Bai, Yaxin Li, Zihui Hu, Mingqi Liu, Qiuming State Grid Jiangxi Elettric Power co. Ltd Nanchang China Information and Communication Branch of State Grid Jiangxi Electric Power Co. Ltd. Nanchang China School of Software Engineering Jiangxi University of Science and Technology Nanchang China Advanced Copper Industry College Jiangxi University of Science and Technology Yingtan China

ISBN: (纸本)9798350355925

Accurate short-term electricity load forecasting is essential to ensure the safe, reliable, and economically efficient operation of power systems. However, traditional forecasting methods are increasingly inadequate to meet the demand for ever more precise load predictions. In response to this issue, a CNN-BILSTM-Attention neural network method optimized based on an improved Sparrow Search Algorithm is proposed for short-term electricity load forecasting. Firstly, the Sin chaotic mapping strategy is utilized to initialize the Sparrow population, enhancing the diversity of population positions. Additionally, the cosine optimization algorithm is introduced into the formula for updating the joiner's position. This aims to address the decrease in global search capability in the later iterations of the Sparrow algorithm, thereby enhancing the accuracy of the Sparrow search algorithm;Next, the ISSA algorithm is employed to optimize the CNN-BILSTM-Attention neural network. This helps avoid overly random initialization of the CNN-BILSTM-Attention neural network's initial weight thresholds, thus enhancing the stability of prediction results;Finally, load forecasting is conducted and compared with other algorithmic models for analysis. Additionally, for the WOA-CNN-BILSTM-Attention model, RMSE decreases by 1.832%, with a corresponding MAE reduction of 1.073%. Similarly, for the PSO-CNN-BILSTM-Attention model, RMSE decreases by 1.804% and MAE by 1.026%. The results of the case study demonstrate that the proposed model can effectively enhance prediction accuracy and is valid for short-term load forecasting. By accurately predicting the power demand, the reasonable distribution and optimal dispatch of power can be realized, and the power grid fluctuations caused by the load mutation can be reduced, so as to ensure the quality of power supply. © 2024 IEEE.

关键词： CNN-BILSTM-Attention ISSA Load Forecasting Sparrow Search Algorithm

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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EBFT: Efficient BFT Consensus through Shortcut Replies 21

EBFT: Efficient BFT Consensus through Shortcut Replies

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Lei, Qingsheng Xia, Yongjun Guo, Qitao Hao, Rui Dai, Xiaohai Dai, Weiqi Economic & Technology Research Institute State Grid Hubei Electric Power Company Limited China Hubei Central China Technology Development of Electric Power Company Limited China Software Institute Nanjing University China Huazhong University of Science and Technology School of Computer Science and Technology China Huazhong University of Science and Technology School of Cyber Science and Engineering China

ISBN: (纸本)9798350329223

As the core building block of blockchain technology, Byzantine Fault Tolerance (BFT) consensus has gathered renewed attention during the past decade. A large number of BFT consensus protocols have been proposed since its inception in the 1980s, the best-known of which is Practical Byzantine Fault Tolerance (PBFT). Generally speaking, PBFT consists of two sub-protocols: normal-case protocol and view-change protocol. However, it requires 7 δ for a client to commit a request in the normal case, where δ represents the actual network delay. If a request happens to go through the view change process, it will take additional 7 δ for a client to commit a request. To reduce the consensus latency, we propose a novel BFT consensus protocol named EBFT, which introduces the shortcut replies to the consensus design. With these shortcut replies, EBFT reduces the normal-case latency and view-change latency to 5 δ and 2 δ, respectively. Extensive experiments are conducted to evaluate EBFT, whose results demonstrate its effectiveness. © 2023 IEEE.

关键词： Fault tolerance

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Cost-Aware Flexibility Evaluation for Microgrids

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Journal of Modern power Systems and Clean Energy 2024年第3期13卷 1003-1013页

作者： Manijeh Alipour Gevork B. Gharehpetian Roya Ahmadiahangar Argo Rosin Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Power Engineering & Mechatronics Tallinn University of Technology Tallinn Estonia

As the penetration of intermittent renewable energy resources in microgrids (MGs) continues to grow globally, optimal operation management becomes increasingly crucial due to the variability of these sources. One potential solution to this challenge is the use of demand response (DR) programs, which are practical and relatively low-cost options. However, ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables. Additionally, in real-world operational decision-making problems, there is a simultaneous optimization of multiple objectives, including the maximization of system flexibility and the minimization of system cost. This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid, which are subject to volatile market prices. The model is based on the feasibility analysis of the uncertain space of wind power generation and load, and it also investigates the level of inflexibility present in the system. The impact of the DR program on the flexibility of MGs is quantified through a case study. The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems.

关键词： Costs Uncertainty Indexes Security Microgrids Wind power generation Vectors Load modeling Energy storage Aerospace electronics

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ekpo, Raphael Henshaw Chukwudi Osamor, Victor Azeta, Ambrose A. Akindeji, Kayode Orelaja, Adeyinka Elizabeth Covenant University Department of Computer and Information Sciences Km 10 Idiroko Road Ogun State Ota Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Durban University of Technology Faculty of Engineering and the Built Environment Department of Electrical Power Engineering South Africa Department of Computer Science and Quantitative Methods ClarksvilleTN United States

ISBN: (纸本)9798350358155

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers' model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset. © 2024 IEEE.

关键词： Federated learning

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SHWD-YOLOv8: An enhanced YOLOv8 detector for resource-efficient safety helmet wear identification in substation 4

SHWD-YOLOv8: An enhanced YOLOv8 detector for resource-effici...

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4th International Conference on Computer technology, Information engineering, and Electron Materials, CTIEEM 2024

作者： Liu, Fengqi Gao, Zhendong Lv, Xiaofei Gao, Hui Zhao, Wenjun Henan Jiuyu Enpai Electric Power Technology Co. Ltd. Zhengzhou 450001 China Management Engineering School Henan University of Engineering Zhengzhou 451191 China School of Software Henan University of Engineering Zhengzhou 451191 China

ISBN: (纸本)9781510689299

To address the high deployment costs caused by the existing safety helmet detection algorithm for platform resources, a helmet-wearing detector based on improved YOLOv8 for substation environments, named SHWD-YOLOv8, further improves the detection performance. By combining MobileNetv3 with YOLOv8 and incorporating an efficient enhanced feature pyramid network along with a parameter-free attention mechanism (SimAM), the algorithm significantly reduces the number of parameters and computational load of the backbone network. The improved feature pyramid network enhances the network's sensitivity to important features, while SimAM reduces the model's parameter count and improves the detection accuracy for small safety helmets. The SHWD-YOLOv8 algorithm is characterized by its low computational requirements and minimal parameter count, posing shallow demands on the execution platform's resources. The optimized model size is reduced to 4.65MB, greatly lowering the costs of network deployment and application, and expanding its application scope in real scenarios. Future research will continue to improve the model's precision and explore the possibilities of deployment on hardware devices, making the safety helmet detection system more convenient and efficient in practical applications. © 2025 SPIE.

关键词： Helmet-Wearing Detection Multi-Scale SimAM Substation YOLOv8

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Analysis and fault location of single-pole ground fault in LVDC ungrounded system 4

Analysis and fault location of single-pole ground fault in L...

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4th International Conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024

作者： Li, Guiyu Wang, Jianhua Guo, Yiyun Jin, Xiaokuan Fan, Tao Fan, Yuanliang Huang, Xinghua School of Electrical Engineering Southeast University Jiangsu Nanjing210096 China College of Software Engineering Southeast University Jiangsu Suzhou215125 China State Grid Fujian Electric Power Research Institute Fujian Fuzhou350007 China Fujian Provincial Enterprise Key Laboratory of High Reliable Electric Power Distribution Technology Fujian Fuzhou350007 China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

This paper establishes the single-pole ground fault equivalent circuit of the LVDC ungrounded system, theoretically derives the single-pole ground fault characteristics of the ungrounded system, and obtains the voltage and current dynamic characteristic equations after the single-pole ground fault equivalent circuit. Based on the fault characteristics, a global harmonic signal injection method for active fault identification is proposed to extract the harmonic voltage and current signals after the fault, and use the impedance characteristics to locate the fault. This article not only gives the fault detection process, but also describes the harmonic signal injection method and selection principle in detail. Finally, a simulation model was built in MATLAB/Simulink for verification. © 2024 SPIE.

关键词： Poles

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