Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks

Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Abdul Wadood and Herie Park
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Syed Basit Ali Bukhari: Department of Electrical Engineering, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Khawaja Khalid Mehmood: Department of Electrical Engineering, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Abdul Wadood: Department of Electrical Engineering, Kamra Campus, Air University, Kamra 43570, Pakistan
Herie Park: Department of Electrical Engineering, Dong-A University, Busan 49315, Korea

Energies, 2021, vol. 14, issue 18, 1-16

Abstract: This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands are combined, and various statistical features are calculated. Finally, the EWT-based features along with the three-phase voltage signals are input to the LSTM network to differentiate between non-islanding and islanding events. To assess the efficacy of the proposed IIDS, extensive simulations are performed on an IEC microgrid and an IEEE 34-node system. The simulation results verify the effectiveness of the proposed IIDS in terms of non-detection zone (NDZ), computational time, detection accuracy, and robustness against noisy measurement. Furthermore, comparisons with existing intelligent methods and different LSTM architectures demonstrate that the proposed IIDS offers higher reliability by significantly reducing the NDZ and stands robust against measurements uncertainty.

Keywords: empirical wavelet transform; islanding detection; long short-term memory network; non-detection zone (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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