Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis
DOI:
https://doi.org/10.4108/ew.3949Keywords:
power outage research and judgment, data characteristics, noise analysisAbstract
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.
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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: https://doi.org/10.1016/j.apenergy.2022.118861
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: https://doi.org/10.1109/TPWRS.2019.2920915
Corcoran, J., Davies, C. M.. (2018). Monitoring power-law creep using the failure forecast method. International Journal of Mechanical Sciences, 140, 179-188. DOI: https://doi.org/10.1016/j.ijmecsci.2018.02.041
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: https://doi.org/10.1109/TPWRS.2018.2877743
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: https://doi.org/10.1049/iet-gtd.2017.1168
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: https://doi.org/10.1016/j.apenergy.2016.11.040
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: https://doi.org/10.1109/TSG.2017.2680600
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: https://doi.org/10.1111/acer.13051
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: https://doi.org/10.1109/ICPES.2016.7584069
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: https://doi.org/10.1063/1.4963764
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: https://doi.org/10.1038/srep13259
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: https://doi.org/10.1007/978-3-319-11227-5_16
Xinyao,L.,Huaqiang,L.,Yimiao,W.,Peiqing,L.,&Qiang,H.(2014,October).Cascadingfailuresforecastingresearchtopowergridbasedonself-organizedcriticality.In2014InternationalConferenceonPowerSystemTechnology(pp.820-825).IEEE..
Shiroei,M.,&Ranjbar,A.M.(2014).Supervisorypredictivecontrolofpowersystemloadfrequencycontrol.InternationalJournalofElectricalPower&EnergySystems,61,70-80. DOI: https://doi.org/10.1016/j.ijepes.2014.03.020
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: https://doi.org/10.1109/ICPS.2012.6229608
Agarwal,M.,Paul,B.C.,Ming,Z.,Mitra,S..(2007).CircuitFailurePredictionandItsApplicationtoTransistorAging.IEEEVlsiTestSymposium.IEEE. DOI: https://doi.org/10.1109/VTS.2007.22
Yu,Q.,Guo,J.B..(2006).StatisticsandSelf-organizedCriticalityCharactersofBlackoutsinChinaElectricPowerSystems,AutomationofElectricPowerSystems,30(2),16-21.
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