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Corresponding Author

Mohamed El-madawy

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

Fault distance estimation in DC microgrids is a critical issue due to the growing adoption of DC-based distribution systems. Current methods face limitations like sensitivity to system parameters and high-resistance fault detection, necessitating improved accuracy. This study proposes a neural network approach to accurately locate fault distances in multi-terminal DC microgrids. Three different structures based on backpropagation algorithms are developed and trained to estimate fault distances with high precision. These structures can handle various fault scenarios, including different fault resistances and the presence of noise. Two of the structures can predict fault distances from one side locally, achieving low error rates of 0.3% for the source side and 0.6% for the load side. The third structure incorporates input variables from both sides, resulting in even more accurate predictions with an error rate of less than 0.15% for both terminals. A comparative analysis was performed to evaluate the proposed fault distance estimation structures in terms of error percentage, cost, fault resistance, and reliance on communication systems. The results demonstrated the superiority of the proposed structures in all aspects, emphasizing their effectiveness in improving the performance of the protection system.

Keywords

DC microgrids, fault location, machine learning approaches, neural network, Mean Square Error, Back propagation algorithm.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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