Long-term Health Index Prediction for Power Asset Classes Based on Sequence Learning

Utility companies have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current or near-future asset health index based on the current condition data. For long-term preventative asset management, it is highly desirable to predict asset health indices in the next few years, especially for asset classes in which the assets share similar electrical and/or mechanical characteristics. This important problem has not been sufficiently addressed. This paper proposes a sequence learning based method to predict long-term health indices for power asset classes. A comprehensive data-driven method based on sequence learning is presented and solid tests are conducted based on real utility data. The proposed method revealed superior performance with comparison to other prediction methods.

[1]  C. Lee Giles,et al.  Sequence learning: from recognition and prediction to sequential decision making , 2001, IEEE Intelligent Systems.

[2]  Wenyuan Li,et al.  Incorporating a Condition Monitoring Based Aging Failure Model of a Circuit Breaker in Substation Reliability Assessment , 2015, IEEE Transactions on Power Systems.

[3]  R. D. Medina,et al.  Improved tool for power transformer health index analysis , 2017, 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON).

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  Siyuan Liu,et al.  Stock transaction prediction modeling and analysis based on LSTM , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[6]  Shie Mannor,et al.  The cross entropy method for classification , 2005, ICML.

[7]  W. Li,et al.  Incorporating Aging Failures in Power System Reliability Evaluation , 2002, IEEE Power Engineering Review.

[8]  Alexandre B. Nassif,et al.  Combining Modified Weibull Distribution Models for Power System Reliability Forecast , 2019, IEEE Transactions on Power Systems.

[9]  Zhixue Zhang,et al.  CMMS and its application in power systems , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  Rattanakorn Phadungthin,et al.  Condition assessment of the health index for power transformer , 2012, 2012 Power Engineering and Automation Conference.

[11]  Abdunnaser Younes,et al.  Feature Selection for Effective Health Index Diagnoses of Power Transformers , 2018, IEEE Transactions on Power Delivery.

[12]  Jeffrey J. Morrell,et al.  Wood Pole Maintenance Manual: 2012 Edition , 2012 .

[13]  Chaker El Amrani,et al.  Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks , 2016 .

[14]  Muhammad Buhari,et al.  Modelling of Ageing Distribution Cable for Replacement Planning , 2016, IEEE Transactions on Power Systems.

[15]  A. Hiranandani.  Calculation of cable ampacities including the effects of harmonics , 1998 .

[16]  Refat Atef Ghunem,et al.  A Techno-Economic Framework for Replacing Aged XLPE Cables in the Distribution Network , 2020, IEEE Transactions on Power Delivery.

[17]  Ali Ouni,et al.  Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † , 2018, Energies.

[18]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[19]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[20]  Bjoern H. Menze,et al.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data , 2009, BMC Bioinformatics.

[21]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[22]  Lipika Dey,et al.  A k-mean clustering algorithm for mixed numeric and categorical data , 2007, Data Knowl. Eng..