Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240600039-6.doi: 10.11896/jsjkx.240600039

• Artificial Intelligence • Previous Articles     Next Articles

Self-matching Method of Virtual Terminals of Intelligent Stations Based on K-nearest Neighbor Weighting Algorithm

SHI Zhuopeng1, KONG Xiangmin1, WEI Jiahong1, SONG Xiaofan2   

  1. 1 State Grid Shanxi Electric Power Survey,Design and Research Institute Co.,Ltd.,Taiyuan 030000,China
    2 School of Electrical Engineering,Chongqing University,Chongqing 404100,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:SHI Zhuopeng,born in 1982,master,senior engineer.His main research interests include electrical engineering and electronics,and so on.
  • Supported by:
    State Grid(5200-202156079A).

Abstract: In order to solve the problems of frequent connection errors and repeated verification of the virtual terminal circuit of intelligent substation in engineering design,a self-matching method of virtual terminal of intelligent station based on K-nearest neighbor weighting algorithm is proposed.By decomposing the whole station virtual terminal matching problem of the intelligent substation into a typical interval and a single sending and receiving virtual terminal matching connection problem in a single intelligent electronic device(IED),introducing the format composition and connection of virtual terminals,to construct a mathematical analysis model.According to the distance measurement of the attribute connection between GOOSE and SV input and output virtual terminals between IED devices,the distance weight of the attributes is increased by the simulated annealing optimization method to improve the algorithm selection proximity,and the classification decision rule of the K-nearest neighbor algorithm is used to automatically match the corresponding imaginary terminal connection combinations.The accuracy and efficiency of the algorithm are verified by engineering test examples,which improves the connection accuracy of the invisible loop of the intelligent substation and ensures the safe and stable operation of the power grid.

Key words: K-nearest neighbor weighting, Virtual terminals, Self-matching, Simulated annealing, Intelligent substations, IED

CLC Number: 

  • TP311.13
[1]ZENG F X,SHI Z Y,XIU L M,et al.Intelligent MatchingBased Automatic Connection Technology of Smart Substation Virtual Circuit[J].Modern Electronics Technique,2018,41(19):146-150.
[2]WANG W Q,HU Y,ZHAO N,et al.Automatic ConnectionMethod of Virtual Terminators Based on Optimization Model of Distance Weight Vectors[J].Power System Technology,2018,42(1):346-352.
[3]FAN W D,FENG X W,DONG J X,et al.Automatic Matching Method of a virtual Terminal in Intelligent Substation Based on Semantic Similarity of Historical Data[J].Power System Protection and Control,2020,48(17):179-186.
[4]WANG P L,SONG X L,WANG H Y,et al.Aided Automatic Design System of Virtual Terminals in Intelligent Substation[J].Power System Protection and Control,2020,48(5):151-157.
[5]HAN W,JIANG S,MA W D,et al.Research and Application ofVirtual Circuit Visualization Technology in Smart Substation Based on the Short Address Associated Data Identification[J].Journal of Electric Power Science and Technology,2018,33(4):95-101.
[6]DAI Z H,QIU X Q,GENG H X,et al.An Approach to Judge Veracity of Virtual Terminal Connection in Smart Substations Based on Similarity Matching[J].Journal of North China Electric Power University,2021,48(3):32-38,56.
[7]YE.Y B,LI D C,XIE M,et al.Research on Automatic Calibration Technology of Virtual Circuit in Smart Substation for Novel Power System[J].Electrical Measurement & Instrumentation,2022,59(7):91-99.
[8]LI J S,TONG X Y,ZHANG W,et al.Analysis Method forDifference of Similar SCD Files from the Perspective of Big Data[J].Automation of Electric Power Systems,2021,45(4):164-171.
[9]ZHONG W,LV F P,LIAO X J,et al.Internal Comparison of Intelligent Substation SCD Files Based on Clustering[J].Electrical Measurement & Instrumentation,2022,59(6):83-89.
[10]YANG G F,XIE B,ZENG Z A,et al.Research on Cross-interval Maintenance Technology of Intelligence Substation[J].Power System Protection and Control,2019,47(2):175-181.
[11]LIU.H J,GAO X,DU L Y,et al.Modular Design of Intelligent Substation SCD File Management and Control System[J].PowerSystem Protection and Control,2019,47(3):154-159.
[12]WANG G L,CHEN W,TANG M S,et al.Desingn and Development of SCD File Control System Based on Correlation Inerval Decoupling[J].Power System Protection and Control,2019,47(11):157-164.
[13]YUAN M X,YU Y,TONG X Y,et al.Difference CheckingMethod and Software of the ICD/CID and SCD in Smart Substation[J].Power System Protection and Control,2019,47(11):118-124.
[14]WANG X Y,LV F P,ZHONG W,et al.SCD File Based Au-tomatical Generation Method for Primary Connection of Smart Substation[J].Automation of Electric Power Systems,2019,43(24):119-125.
[15]XU Y,SHAN Y C.Visualization of Secondary Circuit of Smart Substation Based on SCD File[C]//Proceedings of the CSU-EPSA.2023:69-76.
[16]FAN L D,FENG J H,WANG X Y.Method for Automatically Generating Primary Connection Based on SCD File Clustering Analysis[J].Computer simulation,2022,39(10):84-88,98.
[17]ASADI M A,SAMET H,GHANBARI T.k-NN based fault detection and classification methods for power transmission systems[J].Protection and Control of Modern Power Systems,2017,2(4):359-369.
[18]LÜ X,CHENG X,TANG Y.Short-term power load forecasting based on balanced KNN[C]//IOP Conference Series:Materials Science and Engineering.IOP Publishing,2018.
[19]LIU P Y,YANG C Y,WU J,et al.Hybrid features based K-means clustering algorithm for use in electricity customer load pattern analysis[C]//2018 37th Chinese Control Conference(CCC).Wuhan,China,2018:25-27.
[20]LIU J,SUN G X,BIN S.Knowledge Graph Embedding Model with the Nearest Neighbors Based on Improved KNN[J].Complex Systems and Complexity Science,2024,21(2):30-37.
[21]SHANG L,GUAN W G,GONG R X.Improved localization algorithm based on clustering optimization and adaptive KNN*[J].Transducerand Microsystem Technologies,2023,42(3):136-139.
[1] LI Jiawei , DENG Yuandan, CHEN Bo. Domain UML Model Automatic Construction Based on Fine-tuning Qwen2 [J]. Computer Science, 2025, 52(6A): 240900155-4.
[2] SHI Kun, LI Decang, MENG Yanbing, LIU Yatong. Study on Regional Cold Chain Multimodal Transport Routes Considering Multiple Tasks [J]. Computer Science, 2025, 52(6A): 240600160-6.
[3] LIN Haonan, TAN Hongye, FENG Huimin. Clinical Findings Recognition and Yin & Yang Status Inference Based on Doctor-Patient Dialogue [J]. Computer Science, 2024, 51(11A): 231000084-7.
[4] HU Shaoru, WANG Juanwei, WANG Shengyuan. Implementation of Retargeting CompCert Trusted Compiler for Loongson Processors [J]. Computer Science, 2024, 51(11A): 240200115-9.
[5] ZHANG Chang'en, CHENG Qing, SI Yuehang, HUANG Jincai. Large-scale Innovation Competition Evaluation Scheme Based on Multi-stage Evaluation [J]. Computer Science, 2024, 51(10): 86-93.
[6] MIAO Kuan, LI Chongshou. Optimization Algorithms for Job Shop Scheduling Problems Based on Correction Mechanisms and Reinforcement Learning [J]. Computer Science, 2023, 50(6): 274-282.
[7] ZHANG Qi, PAN Ke, ZHU Kai. Method for Identifying Active Module Based on Gene Prioritization [J]. Computer Science, 2023, 50(11A): 221200113-8.
[8] SHA Yuji, WANG Xin, HE Yanxiao, ZHONG Xueyan, FANG Yu. Mining and Application of Frequent Patterns with Counting Quantifiers [J]. Computer Science, 2023, 50(11A): 230100041-12.
[9] LI Tao, WANG Hairui, ZHU Guifu. Detection of Farmland Change Based on Unified Attention Fusion Network [J]. Computer Science, 2023, 50(11A): 221100060-6.
[10] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[11] HU Cong, HE Xiao-hui, SHAO Fa-ming, ZHANG Yan-wu, LU Guan-lin, WANG Jin-kang. Traffic Sign Detection Based on MSERs and SVM [J]. Computer Science, 2022, 49(6A): 325-330.
[12] LIU Yun, DONG Shou-jie. Acceleration Algorithm of Multi-channel Video Image Stitching Based on CUDA Kernel Function [J]. Computer Science, 2022, 49(6A): 441-446.
[13] SHI Dian-xi, LIU Cong, SHE Fu-jiang, ZHANG Yong-jun. Cooperation Localization Method Based on Location Confidence of Multi-UAV in GPS-deniedEnvironment [J]. Computer Science, 2022, 49(4): 302-311.
[14] SHI Dian-xi, SU Ya-qian-wen, LI Ning, SUN Yi-xuan, ZHANG Yong-jun. Multi-UAV Cooperative Exploring for Large Unknown Indoor Environment Based on Behavior Tree [J]. Computer Science, 2022, 49(11A): 210900083-11.
[15] WU Xiao-wen, ZHENG Qiao-xian, XU Xin-qiang. Improved Ant Colony Algorithm for Solving Multi-objective Unilateral Assembly Line Balancing Problem [J]. Computer Science, 2022, 49(11A): 210900165-5.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!