Towards a Data-Driven Approach to Ground-Fault Location in Distribution Power System using Artificial Neural Network

University essay from Linnéuniversitetet/Institutionen för fysik och elektroteknik (IFE)

Abstract: Motivated by the need for less polluting energy production, the recent increase in renewable electricity production is reshaping classical power systems. Initially unidirectional and constant power flow becomes multi-directional and dynamic. As one of the many consequences, classical power system fault location methods might become outdated.To this extent, the development of new methods as well as improvement of already existing methods is of great interest. Additionally, robust and fast means of fault location strengthen power system reliability by improving recovery time. Since most of the faults occur at the distribution level, a study of the main fault location methods in distribution power systems is first conducted. Relevant information about their respective advantages and drawbacks put into light the need to improve classical fault location methods or to develop new methods. The main objective of the thesis is to develop a prototype data-driven ground fault location method that aims to improve the robustness and accuracy offault location in the power system, as well as offer new solutions for fault location. An 11-bus 20 kV distribution power system with distributed generation is modeled to test the method. As a requirement for data-driven methods, the dataset is provided through simulation where time-domain three-phase voltages at the system substation during fault are generated. This data is then processed using dyadic discrete wavelet transform, a powerful signal processing method, to extract useful information of the signal, after what relevant features are found from the wavelet coefficients. To predict the location ofthe fault, neural networks are trained to find potential correlations between computed features and the distance of the fault from the substation. After testing and comparing different combinations of neural networks, results are analyzed, and eventually, challenges and potential improvements for further development and application of the method are introduced.

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