Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis


  • Xiang Li Yunnan Power Grid Co.,Ltd



power outage research and judgment, data characteristics, noise analysis


INTRODUCTION: Power grid blackouts occur frequently, which significantly impacts social impact. Because these accidents are dynamic and random, predicting and evaluating them is challenging.

OBJECTIVES: To explore the complexity of the power grid itself, analyzes the critical changes of the self-organizing model during power grid fault, extracts the data characteristics related to the steady-state maintenance of abnormal systems, and puts forward an effective outage prediction model.

METHODS: Starting with cluster analysis, The authors can reduce data fluctuation and eliminate noise interference to optimize data. The evaluation indexes of initial fault occurrence possibility and fault propagation speed in the power grid are constructed.

RESULTS: The validation of the outage forecasting model has produced promising results, achieving 96.4% forecasting accuracy and a meager error rate. In addition, the evaluation index developed in this study accurately reflects the possibility and spread speed of power outage accidents.

CONCLUSION: The research proves the feasibility of establishing an outage prediction model based on the power grid system data characteristics. The model has high accuracy and reliability and is a valuable tool for power outage research and judgment.


Download data is not yet available.


Fan, M., Yang, Q., Guo, X.F., Liu, H., Xia J.L., Peng Y.W.. (2023). Prediction method of power outage in a distribution network for unbalanced data, Power System Protection and Control, 51(8): 96-106

Li, G.Q., Liu, D.G., Xiao, G.L., Zhang, B., Wang, G.W., Ren, H., Zhen, Z.. (2022).Risk Prediction of Node Outage in High Proportion New Energy Grid, Power System and Clean Energy, 38(10), 106-115.

Nan, D.L., Feng, C.Y., Cao, H., Wang, X., Li, Y.D.. (2021). Data-driven predictive model of distribution system blackout, Advanced Technology of Electrical Engineering and Energy, 40(12), 56-63.

Yu, Q., Qu, Y.Q., Shi, L.. (2018). Self-correlation Analysis of Power Grid Blackouts Based on Relative Value Method and Hurst Exponent." Automation of Electric Power Systems, 42(01), 55-60,124.

Hassani, H., Razavi-Far, R., & Saif, M.. (2022). Real-time out-of-step prediction control to prevent emerging blackouts in power systems: A reinforcement learning approach.Applied Energy,314, 118861. DOI:

Dong, M.. (2019). Combining unsupervised and supervised learning for asset class failure prediction in power systems. IEEE Transactions on Power Systems, 34(6), 5033-5043. DOI:

Corcoran, J., Davies, C. M.. (2018). Monitoring power-law creep using the failure forecast method. International Journal of Mechanical Sciences, 140, 179-188. DOI:

Dong, M., & Nassif, A. B.. (2018). I am combining modified Weibull distribution models for power system reliability forecast. IEEE Transactions on Power Systems, 34(2), 1610-1619. DOI:

Tang, Y., Li, F., Wang, Q., Xu, Y.. (2018). Hybrid method for power system transient stability prediction based on two‐stage computing resources. IET Generation, Transmission & Distribution, 12(8), 1697-1703. DOI:

Kamali, S., Amraee, T.. (2017). Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment. Applied Energy, 187, 50-61. DOI:

Salimian, M. R., Aghamohammadi, M. R.. (2017). A three-stage decision tree-based intelligent blackout predictor for power systems using brittleness indices.IEEE Transactions on Smart Grid, 9(5), 5123-5131. DOI:

Wetherill, R. R., Fromme, K.. (2016). Alcohol‐induced blackouts: A review of recent clinical research with practical implications and recommendations for future studies. Alcoholism: clinical and experimental research, 40(5), 922-935. DOI:

Gupta, S., Waghmare, S., Kazi, F., Wagh, S., Singh, N. (2016, March). Blackout risk analysis in intelligent grid WAMPAC system using KL divergence approach. In 2016 IEEE 6th International Conference on Power Systems (ICPS) (pp. 1-6). IEEE. DOI:

Wang, C., Grebogi, C., & Baptista, M. S.. (2016). Control and prediction for blackouts caused by frequency collapse in intelligent grids—chaos: An Interdisciplinary Journal of Nonlinear Science, 26(9), 093119. DOI:

Vasseur, J., Wadsworth, F. B., Lavallée, Y., Bell, A. F., Main, I. G., & Dingwell, D. B.. (2015). Heterogeneity: The key to failure forecasting. Scientific reports, 5(1), 13259.. DOI:

Gupta, S., Kazi, F., Wagh, S., & Kambli, R. (2015). Neural network-based early warning system for an emerging blackout in smart grid power networks. In Intelligent distributed computing(pp.173-183).SpringerInternationalPublishing. DOI:


Shiroei,M.,&Ranjbar,A.M.(2014).Supervisorypredictivecontrolofpowersystemloadfrequencycontrol.InternationalJournalofElectricalPower&EnergySystems,61,70-80. DOI:

Mcdermit,D.,Shipp,D.D.,Dionise,T.J.,Lorch,V..(2012).Mediumvoltageswitchingtransientinducedpotentialtransformerfailures;prediction,measurement,andpracticalsolutions.48thIEEEIndustrial&CommercialPowerSystemsConference.IEEE.Agarwal,Mridul,etal."Circuitfailurepredictionanditsapplicationtotransistoraging."25thIEEEVLSITestSymposium(VTS'07).IEEE,2007. DOI:

Agarwal,M.,Paul,B.C.,Ming,Z.,Mitra,S..(2007).CircuitFailurePredictionandItsApplicationtoTransistorAging.IEEEVlsiTestSymposium.IEEE. DOI:





How to Cite

Li X. Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis . EAI Endorsed Trans Energy Web [Internet]. 2023 Sep. 22 [cited 2024 May 26];10. Available from: