Active location and recovery of unbalance problems in smart distribution networks

LI Yihong; PAN Yicheng; MA Meng; WANG Ping

doi:10.11959/j.issn.2096-3750.2025.00482

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Active location and recovery of unbalance problems in smart distribution networks

Internet of Energy Things | 更新时间：2025-09-12

- Active location and recovery of unbalance problems in smart distribution networks
- Chinese Journal on Internet of Things Vol. 9, Issue 2, Pages: 70-81(2025)
- 作者机构：
  
  1.北京大学软件与微电子学院，北京 102600
  2.北京大学计算机学院，北京 100871
  3.北京大学软件工程国家工程研究中心，北京 100871
  4.高可信软件技术教育部重点实验室（北京大学），北京 100871
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(92167104;62072006);Qiyuan Lab Innovation Fund(S20210201079);Foundation of Intelligent Parallel Technology Laboratory(2024JK15)
- DOI：10.11959/j.issn.2096-3750.2025.00482
  CLC： TM76
- Received：31 December 2024，
  
  Revised：2025-02-23，
  
  Published：10 June 2025
- 稿件说明：
移动端阅览
李一泓,潘宜城,马萌等.智能配电网不平衡问题的主动定位与调控恢复[J].物联网学报,2025,09(02):70-81.

LI Yihong,PAN Yicheng,MA Meng,et al.Active location and recovery of unbalance problems in smart distribution networks[J].Chinese Journal on Internet of Things,2025,09(02):70-81.
李一泓,潘宜城,马萌等.智能配电网不平衡问题的主动定位与调控恢复[J].物联网学报,2025,09(02):70-81. DOI： 10.11959/j.issn.2096-3750.2025.00482.

LI Yihong,PAN Yicheng,MA Meng,et al.Active location and recovery of unbalance problems in smart distribution networks[J].Chinese Journal on Internet of Things,2025,09(02):70-81. DOI： 10.11959/j.issn.2096-3750.2025.00482.

摘要

在新型电力系统快速发展与高比例分布式能源接入的背景下，配电网三相不平衡问题日益凸显，该问题不仅会带来额外损耗，而且会引发设备损毁、供电中断等安全隐患，严重威胁智能配电网的安全稳定运行。为此，提出了智能配电网不平衡问题的主动定位与调控恢复方法——PowerCause。首次将时间序列因果推断引入配电网异常分析领域，构建了“检测-定位-调控”的全流程解决方案。通过融合格兰杰因果检验与自适应区间检测算法，实现了无须预训练、不依赖物理拓扑的不平衡根因定位；基于OpenDSS搭建的主动调控系统集成了异常仿真、多维度指标采集与调控决策，形成“自感知-自诊断-自恢复”的闭环控制体系。仿真结果表明，该方法在根因定位精度和时间效率等各方面具有竞争力，并且对测量噪声、数据丢失误差等环境影响具有较好的鲁棒性。

Abstract

Under the rapid development of new power systems and the high penetration of distributed energy resources

three-phase unbalance issues in distribution networks have become increasingly prominent. This problem not only causes additional losses

but also triggers equipment damage and power supply interruptions

posing significant threats to the secure and stable operation of smart distribution networks. To this end

a method for active location and control recovery of unbalanced problems in smart distribution networks named PowerCause was proposed. The time-series causal inference was introduced into distribution network anomaly analysis

establishing a comprehensive "detection-localization-regulation" solution framework for the first time. By integrating Granger causality tests with adaptive interval detection algorithms

the method achieves unbalanced root cause localization without requiring pre-training or physical topology dependencies. The active regulation system built on OpenDSS incorporates anomaly simulation

multi-dimensional metric collection

and regulation decision-making

forming a closed-loop control system with self-perception

self-diagnosis

and self-recovery capabilities. Simulation results demonstrate the method's competitive performance in root cause localization accuracy and time efficiency

along with strong robustness against environmental disturbances such as measurement noise and data loss errors.

关键词

Keywords

references

BAIRAMI S , SALIMI M , MIRABBASI D . A novel method for maximum power point tracking of the grid-connected three-phase solar systems based on the PV current prediction [J ] . Chinese Journal of Electronics , 2023 , 32 ( 2 ): 353 - 364 .

YUE L , XU K , SUN J J , et al . Research on the source tracing method of three-phase unbalance based on equivalent circuit [C ] // Proceedings of the 2023 International Seminar on Computer Science and Engineering Technology (SCSET) . Piscataway : IEEE Press , 2023 : 687 - 689 .

MA K , FANG L R , KONG W W . Review of distribution network phase unbalance: scale, causes, consequences, solutions, and future research directions [J ] . CSEE Journal of Power and Energy Systems , 2020 , 6 ( 3 ): 479 - 488 .

SITI M W , NICOLAE D V , JIMOH A A , et al . Reconfiguration and load balancing in the LV and MV distribution networks for optimal performance [J ] . IEEE Transactions on Power Delivery , 2007 , 22 ( 4 ): 2534 - 2540 .

OCHOA L F , CIRIC R M , FELTRINA P , et al . Evaluation of distribution system losses due to load unbalance [C ] // Power System Computation Conference (PSCC) 2005 . Liege, Belgium . 2005 : 6 , 26 - 28 .

SALUSTIANO R , MARTINEZ M , NETO E . The unbalanced load cost on transformer losses at a distribution system [C ] // Proceedings of the 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013) . Institution of Engineering and Technology , 2013 : 0885 - 0885 .

吕琨璐 . 配电网三相不平衡的危害及改进措施 [J ] . 今日科苑 , 2013 ( 4 ): 121 .

LYU K L . Harm and improvement measures of three-phase imbalance in distribution network [J ] . Modern Science , 2013 ( 4 ): 121 .

GIRIGOUDAR K , ROALD L A . On the impact of different voltage unbalance metrics in distribution system optimization [J ] . Electric Power Systems Research , 2020 , 189 : 106656 .

BINA M T , KASHEFI A . Three-phase unbalance of distribution systems: complementary analysis and experimental case study [J ] . International Journal of Electrical Power & Energy Systems , 2011 , 33 ( 4 ): 817 - 826 .

WOOLLEY N C , MILANOVIC J V . Statistical estimation of the source and level of voltage unbalance in distribution networks [J ] . IEEE Transactions on Power Delivery , 2012 , 27 ( 3 ): 1450 - 1460 .

YAN R F , SAHA T K . Investigation of voltage imbalance due to distribution network unbalanced line configurations and load levels [J ] . IEEE Transactions on Power Systems , 2013 , 28 ( 2 ): 1829 - 1838 .

TORREZAN G F , OLESKOVICZ M , DA SILVA PESSOA A L . Analysis of voltage unbalance in a distribution system via OpenDSS software [C ] // Proceedings of the 2018 Simposio Brasileiro de Sistemas Eletricos (SBSE) . Piscataway : IEEE Press , 2018 : 1 - 5 .

WANG T L , YAN X H . Research on modeling and parameter identification of comprehensive load model of distribution network in industrial park [J ] . Chinese Journal of Electronics , 2024 , 33 ( 1 ): 264 - 273 .

LIN W M , OU T C . Unbalanced distribution network fault analysis with hybrid compensation [J ] . IET Generation Transmission & Distribution , 2011 , 5 ( 1 ): 92 - 100 .

SINGH U , SOLOMON R , ALIZADEH-MOUSAVI O . Monitoring-based localization of unbalances and root cause analysis in low-voltage distribution systems [J ] . IEEE Systems Journal , 2023 , 17 ( 3 ): 4177 - 4188 .

MASETTI C . Revision of European Standard EN 50160 on power quality: reasons and solutions [C ] // Proceedings of 14th International Conference on Harmonics and Quality of Power-ICHQP 2010 Piscataway . IEEE Press , 2010 : 1 - 7 .

LIU Y K , LI J , WU L . Coordinated optimal network reconfiguration and voltage regulator/DER control for unbalanced distribution systems [J ] . IEEE Transactions on Smart Grid , 2019 , 10 ( 3 ): 2912 - 2922 .

BAO G H , KE S K . Load transfer device for solving a three-phase unbalance problem under a low-voltage distribution network [J ] . Energies , 2019 , 12 ( 15 ): 2842 .

ASSAAD C K , DEVIJVER E , GAUSSIER E . Survey and evaluation of causal discovery methods for time series [J ] . Journal of Artificial Intelligence Research , 2022 , 73 : 767 - 819 .

KARL P . Contributions to the mathematical theory of evolution, Ⅲ: regression, heredity, and panmixia [J ] . Proceedings of the Royal Society of London , 1896 , 59 ( 1 ): 69 - 71 .

GRANGER C W J . Investigating causal relations by econometric models and cross-spectral methods [J ] . Econometrica , 1969 , 37 ( 3 ): 424 .

KANDULA S , MAHAJAN R , VERKAIK P , et al . Detailed diagnosis in enterprise networks [J ] . ACM SIGCOMM Computer Communication Review , 2009 , 39 ( 4 ) : 243 - 254

MARWEDE N , ROHR M , VAN HOORN A , et al . Automatic failure diagnosis support in distributed large-scale software systems based on timing behavior anomaly correlation [C ] // Proceedings of the 2009 13th European Conference on Software Maintenance and Reengineering . Piscataway : IEEE Press , 2009 : 47 - 58 .

ZHENG L C , CHEN Z Z , HE J R , et al . Multi-modal causal structure learning and root cause analysis [J ] . arXiv preprint , 2024 , arXiv: 2402.02357 .

HAMILTON W L , YING R , LESKOVEC J . Inductive representation learning on large graphs [J ] . arXiv preprint , 2017 , arXiv: 1706. 02216

LÖW S , MADRAS D , ZEMEL R , et al . Amortized causal discovery: learning to infer causal graphs from time-series data [J ] . arXiv preprint , 2020 , arXiv: 2006. 10833

LI M J , LI Z Y , YIN K L , et al . Causal inference-based root cause analysis for online service systems with intervention recognition [C ] // Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . New York : ACM , 2022 : 3230 - 3240 .

YAO L Y , CHU Z X , LI S , et al . A survey on causal inference [J ] . ACM Transactions on Knowledge Discovery from Data , 2021 , 15 ( 5 ): 1 - 46 .

DUGAN R C , MCDERMOTT T E . An open source platform for collaborating on smart grid research [C ] // Proceedings of the 2011 IEEE Power and Energy Society General Meeting . Piscataway : IEEE Press , 2011 : 1 - 7 .

KERSTING W H . Radial distribution test feeders [C ] // Proceedings of the 2001 IEEE Power Engineering Society Winter Meeting . Conference Proceedings . Piscataway : IEEE Press , 2001 : 908 - 912 .

ZAMANZADEH DARBAN Z , WEBB G I , PAN S R , et al . Deep learning for time series anomaly detection: a survey [J ] . ACM Computing Surveys , 2025 , 57 ( 1 ): 1 - 42 .

SIFFER A , FOUQUE P A , TERMIER A , et al . Anomaly detection in streams with extreme value theory [C ] // Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM , 2017 : 1067 - 1075 .

HAAN L , FERREIRA A . Extreme value theory: an introduction [M ] . New York : Springer , 2006 .

SPIRTES P , GLYMOUR C , SCHEINES R . Causation, prediction, and search [M ] . Cham : Springer New York , 1993 .

DUGAN R C , MONTENEGRO D . Reference guide: the open distribution system simulator (opendss) [J ] . Electric Power Research Institute , Inc, 2012 , 7 : 29 .

LIN W L , MA M , PAN D S , et al . FacGraph: frequent anomaly correlation graph mining for root cause diagnose in micro-service architecture [C ] // Proceedings of the 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) . Piscataway : IEEE Press , 2018 : 1 - 8 .

BROWN M , BISWAL M , BRAHMA S , et al . Characterizing and quantifying noise in PMU data [C ] // Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM) . Piscataway : IEEE Press , 2016 : 1 - 5 .

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