A State-Observer-Based Protection Scheme for AC Microgrids with Recurrent Neural Network Assistance

Faisal Mumtaz, Haseeb Hassan Khan, Amad Zafar (), Muhammad Umair Ali () and Kashif Imran
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Faisal Mumtaz: USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Haseeb Hassan Khan: University School for Advanced Studies (IUSS), 98122 Pavia, Italy
Amad Zafar: Department of Intelligent Mechatronics, Sejong University, Seoul 05006, Republic of Korea
Muhammad Umair Ali: Department of Unmanned Vehicle, Sejong University, Seoul 05006, Republic of Korea
Kashif Imran: USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan

Energies, 2022, vol. 15, issue 22, 1-22

Abstract: The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab ® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy.

Keywords: artificial intelligence; fault detection; fault localization; high impedance faults; particle filter; recurrent neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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