Transfer learning for power system fault location using artificial neural networks
Corresponding Author(s) : Dimitrios Rakopoulos
Future Energy,
Vol. 4 No. 3 (2025): August 2025 Issue
Abstract
This paper investigates the application of transfer learning techniques to artificial neural networks (ANNs) for fault detection in power distribution systems, formulated as a classification problem. Comprehensive datasets are developed using multiple IEEE test feeders of varying complexity, including the 13-bus, 34-bus, 37-bus, and 123-bus test feeders. Various fault types are simulated across all three-phase buses in each system. Baseline performance is established by independently training ANNs on each feeder. Subsequently, knowledge learned from the complex 123-bus feeder is transferred to accelerate and improve fault location in simpler networks. The results demonstrate that transfer learning significantly improves both training efficiency and classification performance. Training convergence is accelerated by a factor of 1.68 to 2.56 across target feeders, corresponding to epoch reductions between 40.6% and 61.0%. Additionally, computational time is reduced by 24.0% to 49.5%, further enhancing the practical viability of the proposed approach. These findings suggest that transfer learning offers a powerful strategy to address data scarcity and computational challenges in fault location, enabling utilities to deploy accurate, efficient fault detection systems across diverse distribution networks with minimal retraining effort.
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- Majid Jamil, Sanjeev Kumar Sharma, and Rajveer Singh. Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus, 4(1):334, December 2015.
- Hamed Rezapour, Sadegh Jamali, and Alireza Bahmanyar. Review on artificial intelligence-based fault location methods in power distribution networks. Energies, 16(11):4636, 2023.
- Mohammad Pourahmadi-Nakhli and Ahmad A Safavi. Path characteristic frequencybased fault locating in radial distribution systems using wavelets and neural networks. IEEE Transactions on Power Delivery, 26(2):772–781, 2011.
- Ali Rafinia and Javad Moshtagh. A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ann and fls.
- International Journal of Electrical Power & Energy Systems, 55:261–274, 2014.
- Mohammad Dashtdar, Rahman Dashti, and Hamidreza Shaker. Distribution network fault section identification and fault location using artificial neural network. In 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), pages 273–278. IEEE, 2018.
- Yasin Aslan and Yılmaz Engin Yagan. Artificial neural-network-based fault location for˘ power distribution lines using the frequency spectra of fault data. Electrical Engineering, 99:301–311, 2017.
- Sayed Ahmad Mousavi Javadian, Amir Massoud Nasrabadi, Mahmoud Reza Haghifam, and Javad Rezvantalab. Determining fault’s type and accurate location in distribution systems with dg using mlp neural networks. In 2009 International Conference on Clean Electrical Power, pages 284–289. IEEE, 2009.
- Mustafa Bakkar, Santiago Bogarra, Francisco Corcoles, Ahmed Aboelhassan, Shuai´ Wang, and Jose Iglesias. Artificial intelligence-based protection for smart grids.´ Energies, 15(14):4933, 2022.
- GM Shafiullah, MA Abido, and Zeyad Al-Hamouz. Wavelet-based extreme learning machine for distribution grid fault location. IET Generation, Transmission & Distribution, 11(16):4256–4263, 2017.
- Kalpana Lout and Raj K Aggarwal. Current transients based phase selection and fault location in active distribution networks with spurs using artificial intelligence. In 2013 IEEE Power & Energy Society General Meeting, pages 1–5. IEEE, 2013.
- Zhidi Lin, Dongliang Duan, Qi Yang, Xuemin Hong, Xiang Cheng, Liuqing Yang, and Shuguang Cui. Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources. Energies, 13(1):275, January 2020.
- Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, and Mengchu Zhou. Transfer Learning for Fault Diagnosis of Transmission Lines, January 2022. arXiv:2201.08018 [cs].
- Ivan L. Degano, Leandro Fiaschetti, and Pablo A. Lotito. Location of faults based on´ deep learning with feature selection for meter placement in distribution power grids. International Journal of Emerging Electric Power Systems, 25(5):657–666, October 2024.
- Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, and Mengchu Zhou. Transfer learning for fault diagnosis of transmission lines. arXiv preprint arXiv:2201.08018, 2022.
- Yuantao Yao, Daochuan Ge, Jie Yu, and Min Xie. Model-based deep transfer learning method to fault detection and diagnosis in nuclear power plants. Frontiers in Energy Research, 10:823395, 2022.
- Supriya Asutkar and Siddharth Tallur. Deep transfer learning strategy for efficient domain generalisation in machine fault diagnosis. Scientific Reports, 13(1):6607, 2023.
- Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, 2 edition, November 2000.
- Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359–366, 1989.
- John S. Bridle. Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In Franc¸oise Fogelman Soulie´ and Jeanny Herault, editors,´ Neurocomputing, pages 227–236, Berlin, Heidelberg, 1990.
- Springer Berlin Heidelberg.
- Kevin P. Murphy. Probabilistic Machine Learning: An introduction. MIT Press, 2022.
- Stevo Bozinovski. Reminder of the first paper on transfer learning in neural networks, 1976. Informatica (Slovenia), 44(3), 2020.
- Claudia Ehrig, Benedikt Sonnleitner, Ursula Neumann, Catherine Cleophas, and Germain Forestier. The impact of data set similarity and diversity on transfer learning success in time series forecasting, 2024.
- Resources – IEEE PES Test Feeder. - https://cmte.ieee.org/pes-testfeeders/resources/
- OpenDSS - https://www.epri.com/pages/sa/opendss
- Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Yee Whye Teh and Mike Titterington, editors, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, volume 9 of Proceedings of Machine Learning Research, pages 249–256, Chia Laguna Resort, Sardinia, Italy, 13–15 May 2010. PMLR.
References
Majid Jamil, Sanjeev Kumar Sharma, and Rajveer Singh. Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus, 4(1):334, December 2015.
Hamed Rezapour, Sadegh Jamali, and Alireza Bahmanyar. Review on artificial intelligence-based fault location methods in power distribution networks. Energies, 16(11):4636, 2023.
Mohammad Pourahmadi-Nakhli and Ahmad A Safavi. Path characteristic frequencybased fault locating in radial distribution systems using wavelets and neural networks. IEEE Transactions on Power Delivery, 26(2):772–781, 2011.
Ali Rafinia and Javad Moshtagh. A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ann and fls.
International Journal of Electrical Power & Energy Systems, 55:261–274, 2014.
Mohammad Dashtdar, Rahman Dashti, and Hamidreza Shaker. Distribution network fault section identification and fault location using artificial neural network. In 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), pages 273–278. IEEE, 2018.
Yasin Aslan and Yılmaz Engin Yagan. Artificial neural-network-based fault location for˘ power distribution lines using the frequency spectra of fault data. Electrical Engineering, 99:301–311, 2017.
Sayed Ahmad Mousavi Javadian, Amir Massoud Nasrabadi, Mahmoud Reza Haghifam, and Javad Rezvantalab. Determining fault’s type and accurate location in distribution systems with dg using mlp neural networks. In 2009 International Conference on Clean Electrical Power, pages 284–289. IEEE, 2009.
Mustafa Bakkar, Santiago Bogarra, Francisco Corcoles, Ahmed Aboelhassan, Shuai´ Wang, and Jose Iglesias. Artificial intelligence-based protection for smart grids.´ Energies, 15(14):4933, 2022.
GM Shafiullah, MA Abido, and Zeyad Al-Hamouz. Wavelet-based extreme learning machine for distribution grid fault location. IET Generation, Transmission & Distribution, 11(16):4256–4263, 2017.
Kalpana Lout and Raj K Aggarwal. Current transients based phase selection and fault location in active distribution networks with spurs using artificial intelligence. In 2013 IEEE Power & Energy Society General Meeting, pages 1–5. IEEE, 2013.
Zhidi Lin, Dongliang Duan, Qi Yang, Xuemin Hong, Xiang Cheng, Liuqing Yang, and Shuguang Cui. Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources. Energies, 13(1):275, January 2020.
Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, and Mengchu Zhou. Transfer Learning for Fault Diagnosis of Transmission Lines, January 2022. arXiv:2201.08018 [cs].
Ivan L. Degano, Leandro Fiaschetti, and Pablo A. Lotito. Location of faults based on´ deep learning with feature selection for meter placement in distribution power grids. International Journal of Emerging Electric Power Systems, 25(5):657–666, October 2024.
Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, and Mengchu Zhou. Transfer learning for fault diagnosis of transmission lines. arXiv preprint arXiv:2201.08018, 2022.
Yuantao Yao, Daochuan Ge, Jie Yu, and Min Xie. Model-based deep transfer learning method to fault detection and diagnosis in nuclear power plants. Frontiers in Energy Research, 10:823395, 2022.
Supriya Asutkar and Siddharth Tallur. Deep transfer learning strategy for efficient domain generalisation in machine fault diagnosis. Scientific Reports, 13(1):6607, 2023.
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, 2 edition, November 2000.
Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359–366, 1989.
John S. Bridle. Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In Franc¸oise Fogelman Soulie´ and Jeanny Herault, editors,´ Neurocomputing, pages 227–236, Berlin, Heidelberg, 1990.
Springer Berlin Heidelberg.
Kevin P. Murphy. Probabilistic Machine Learning: An introduction. MIT Press, 2022.
Stevo Bozinovski. Reminder of the first paper on transfer learning in neural networks, 1976. Informatica (Slovenia), 44(3), 2020.
Claudia Ehrig, Benedikt Sonnleitner, Ursula Neumann, Catherine Cleophas, and Germain Forestier. The impact of data set similarity and diversity on transfer learning success in time series forecasting, 2024.
Resources – IEEE PES Test Feeder. - https://cmte.ieee.org/pes-testfeeders/resources/
OpenDSS - https://www.epri.com/pages/sa/opendss
Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Yee Whye Teh and Mike Titterington, editors, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, volume 9 of Proceedings of Machine Learning Research, pages 249–256, Chia Laguna Resort, Sardinia, Italy, 13–15 May 2010. PMLR.