Three-Tier-Deep-Learning Model for Fault Classification in Power System using PMU Data: A Hybrid Meta-Heuristic Optimization
Keywords:
PMU Dataset, fault detection in power distribution system, ANN, Bi-LSTM and CNNAbstract
The process of categorising and recognising various sorts of defects that occur inside a power system is referred as fault classification. It involves classifying and identifying the faults that occur in the system like Short Circuit errors, Frequency Disturbance events, High Impedance faults and Phase-balance errors. To overcome these faults, we proposed a three-tier deep learning model using PMU Data. The proposed a methodology consist of Pre-Processing stage followed by Feature extraction and Feature Selection. In Pre-Processing phase, the data are collected from the PMU Dataset and then pre-processed via Missing Data Imputation, Outlier Detection and Handling and Data normalization via Z-score normalization. The pre-processed PMU data are used to extract the features using Higher-Order Statistical Moments and Multivariate Statistical Measures. The extracted optimal features are used to select the relevant features using the Hybrid meta-heuristic optimization model which is combination of Darts Game Optimization (DGO) and Monarch Butterfly Optimization (MBO). In this paper work a Three-Tier-Deep Learning-Based model is developed for fault diagnosis which combines Artificial Neural Network (ANN), Optimized Convolutional Neural Network (CNN) and Bi-LSTM. And finally, the model will be fine-tuned by adjusting hyper parameters, architecture, or the optimization process to enhance fault diagnosis accuracy. The proposed model is executed using PYTHON Platform.
Downloads
References
Ghaemi, A., Safari, A., Afsharirad, H. and Shayeghi, H., 2022. Accuracy enhances of fault classification and location in a smart distribution network based on stacked ensemble learning. Electric Power Systems Research, 205, p.107766.
Thomas, J.B. and Shihabudheen, K.V., 2023. Neural architecture search algorithm to optimize deep transformer model for fault detection in electrical power distribution systems. Engineering Applications of Artificial Intelligence, 120, p.105890.
Kurup, A.R., Summers, A., Bidram, A., Reno, M.J. and Martínez-Ramón, M., 2023. Ensemble models for circuit topology estimation, fault detection and classification in distribution systems. Sustainable Energy, Grids and Networks, 34, p.101017.
Zhu, K. and Pong, P.W., 2019. Fault classification of power distribution cables by detecting decaying DC components with magnetic sensing. IEEE Transactions on Instrumentation and Measurement, 69(5), pp.2016-2027.
Shadi, M.R., Ameli, M.T. and Azad, S., 2022. A real-time hierarchical framework for fault detection, classification, and location in power systems using PMUs data and deep learning. International Journal of Electrical Power & Energy Systems, 134, p.107399.
Xi, Y., Zhang, W., Zhou, F., Tang, X., Li, Z., Zeng, X. and Zhang, P., 2023. Transmission line fault detection and classification based on SA-MobileNetV3. Energy Reports, 9, pp.955-968.
Mishra, S., Swain, S.C., Biswal, T. and Routray, A., 2023. Tree Based Fault Classification in Underground Cable. Procedia Computer Science, 218, pp.524-531.
Carvalho, J.G.S., Almeida, A.R., Ferreira, D.D., dos Santos Jr, B.F., Vasconcelos, L.H.P. and de Oliveira Sobreira, D., 2022. High-impedance fault modeling and classification in power distribution networks. Electric Power Systems Research, 204, p.107676.
Shihabudheen, K.V. and Gupta, S., 2023. Detection of High Impedance Faults in Power Lines using Empirical Mode Decomposition with Intelligent Classification Techniques. Computers and Electrical Engineering, 109, p.108770.
Hong, L., Chen, Z., Wang, Y., Shahidehpour, M. and Wu, M., 2022. A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis. Energy Reports, 8, pp.9392-9401.
Sudha, B., Praveen, L.S. and Vadde, A., 2022. Classification of faults in distribution transformer using machine learning. Materials Today: Proceedings, 58, pp.616-622.
Pirmani, S.K. and Mahmud, M.A., 2023. Advances on fault detection techniques for resonant grounded power distribution networks in bushfire prone areas: Identification of faulty feeders, faulty phases, faulty sections, and fault locations. Electric Power Systems Research, 220, p.109265.
Gilanifar, M., Wang, H., Cordova, J., Ozguven, E.E., Strasser, T.I. and Arghandeh, R., 2021. Fault classification in power distribution systems based on limited labeled data using multi-task latent structure learning. Sustainable Cities and Society, 73, p.103094.
Rai, P., Londhe, N.D. and Raj, R., 2021. Fault classification in power system distribution network integrated with distributed generators using CNN. Electric Power Systems Research, 192, p.106914.
Zhang, J., He, Z.Y., Lin, S., Zhang, Y.B. and Qian, Q.Q., 2013. An ANFIS-based fault classification approach in power distribution system. International Journal of Electrical Power & Energy Systems, 49, pp.243-252.
Belagoune, S., Bali, N., Bakdi, A., Baadji, B. and Atif, K., 2021. Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement, 177, p.109330.
Goni, M.O.F., Nahiduzzaman, M., Anower, M.S., Rahman, M.M., Islam, M.R., Ahsan, M., Haider, J. and Shahjalal, M., 2023. Fast and accurate fault detection and classification in transmission lines using extreme learning machine. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 3, p.100107.
Rafique, F., Fu, L. and Mai, R., 2021. End to end machine learning for fault detection and classification in power transmission lines. Electric Power Systems Research, 199, p.107430.
Moloi, K., Ndlela, N.W. and Davidson, I.E., 2022. Fault Classification and Localization Scheme for Power Distribution Network. Applied Sciences, 12(23), p.11903.
Mo, H., Peng, Y., Wei, W., Xi, W. and Cai, T., 2022. SR-GNN Based Fault Classification and Location in Power Distribution Network. Energies, 16(1),
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
