Fault Diagnosis Method of Intelligent Substation Based on Improved Association Rule Mining Algorithms

  • Li ChenEmail author
  • Liangyi Wang
  • Qian He
  • Hui Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)


With the digitalization and intellectualization of substations, the amount of state data collected by intelligent components is becoming larger and larger. Traditional data mining technology cannot meet the requirements of real-time data processing and application speed. For a typical smart substation, a fault diagnosis method based on improved association rule mining algorithm is proposed for transformer near-zone fault analysis. Firstly, an improved association rule data mining algorithm based on rough set is designed to extract information from massive data and diagnose faults. Then genetic algorithm is applied to improve association rule data mining algorithm to speed up data processing. Finally, simulation analysis is carried out to demonstrate the effectiveness and rapidity of the proposed method. The results show that the intelligent substation fault diagnosis method based on improved association rule mining algorithm has fast and powerful reduction ability in data processing. It can extract useful data from intelligent substation components to accurately judge fault information, reduce data scale and improve fault processing speed.


Intelligent substation Improved association rules Rough set Genetic algorithms Intelligent electronic device 



This work was supported by Science and Technology Project of State Grid Chongqing Electric Power Company in 2018. The project name is “Integrated Operational Support Technology of Intelligent Substations Based on Total Service Data” (No. 2018#35).


  1. 1.
    Liu F, Bie Z, Liu S et al (2018) Framework design transaction mechanism and key issues of energy internet market. Autom Electr Power Syst 42(13):108–117CrossRefGoogle Scholar
  2. 2.
    Glowacz A, Glowacz W, Glowacz Z et al (2018) Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement 113(7):1–9CrossRefGoogle Scholar
  3. 3.
    Gao C, Cao X, Yan H et al (2017) Energy management of data center and prospect for participation in demand side resource scheduling. Autom Electr Power Syst 41(23):1–7Google Scholar
  4. 4.
    Ming Z, Jiang Z, Feng K (2017) Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump. Mech Syst Signal Process 93(460):460–493Google Scholar
  5. 5.
    Peng H, Wang J, Ming J et al (2018) Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems. IEEE Trans Smart Grid 9(5):4777–4784CrossRefGoogle Scholar
  6. 6.
    Shi W, Zhu Y, Tian H et al (2017) An integrated data preprocessing framework based on apache spark for fault diagnosis of power grid equipment. J Sig Process Syst 86(2–3):221–236CrossRefGoogle Scholar
  7. 7.
    Torabi AJ, Meng JE, Xiang L et al (2017) Application of clustering methods for online tool condition monitoring and fault diagnosis in high-speed milling processes. IEEE Syst J 10(2):721–732CrossRefGoogle Scholar
  8. 8.
    Tyagi S, Panigrahi SK (2017) A hybrid genetic algorithm and back-propagation classifier for gearbox fault diagnosis. Appl Artif Intell 4:1–20CrossRefGoogle Scholar
  9. 9.
    Huang Z, Wang Z, Zhang H (2018) Multiple open-circuit fault diagnosis based on multistate data processing and subsection fluctuation analysis for photovoltaic inverter. IEEE Trans Instrum Meas 67(3):516–526CrossRefGoogle Scholar
  10. 10.
    Liu R, Yang B, Zio E et al (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Sig Process 108:33–47CrossRefGoogle Scholar
  11. 11.
    Ghoneim SSM (2018) Intelligent prediction of transformer faults and severities based on dissolved gas analysis integrated with thermodynamics theory. IET Sci Meas Technol 12(3):388–394CrossRefGoogle Scholar
  12. 12.
    Jiang S, Wang F, Shen L et al (2018) Local detrended fluctuation analysis for spectral red-edge parameters extraction. Nonlinear Dyn 93(3):995–1008CrossRefGoogle Scholar
  13. 13.
    Wu Y, Xiao Y, Hohn F et al (2018) Bad data detection using linear WLS and sampled values in digital substations. IEEE Trans Power Delivery 33(1):150–157CrossRefGoogle Scholar
  14. 14.
    Hong J, Liu CC (2019) Intelligent electronic devices with collaborative intrusion detection systems. IEEE Trans Smart Grid 10(1):271–281CrossRefGoogle Scholar
  15. 15.
    Chattopadhyay A, Ukil A, Jap D et al (2018) Toward threat of implementation attacks on substation security: Case study on fault detection and isolation. IEEE Trans Industr Inf 14(6):2442–2451CrossRefGoogle Scholar
  16. 16.
    Jiang Z, Li Z, Wu N et al (2018) A Petri net approach to fault diagnosis and restoration for power transmission systems to avoid the output interruption of substations. IEEE Syst J 12(3):2566–2576CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.State Grid Chongqing Electric Power CompanyChongqingChina

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