Bhuwan Pratap Singh
Department of Electrical Engineering, Manipal University Jaipur, Rajasthan, India.

Sunil Kumar Goyal
Department of Electrical Engineering, Manipal University Jaipur, Rajasthan, India.

Shahbaz Ahmed Siddiqui
Department of Mechatronics Engineering, Manipal University Jaipur, Rajasthan, India.

Divya Rishi Shrivastava
Department of Electrical Engineering, Manipal University Jaipur, Rajasthan, India.

Satyendra Singh
Faculty of Electrical Skills, Bhartiya Skill Development University Jaipur, India.

Majed A. Alotaibi
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia.

Hasmat Malik
Department of Electrical Power Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia. Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, Uttarakhand, India.

Fausto Pedro García Márquez
Ingenium Research Group, Universidad Castilla-La Mancha, Altagracia,13071, Ciudad Real, Spain.

Asyraf Afthanorhan
Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu, 21300, Terengganu, Malaysia.

DOI https://doi.org/10.33889/IJMEMS.2024.9.3.029

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Abstract

Effective microgrid control for system recovery and restoring normal operation necessitates fast event detection and implementation of remedial action (if need arises). However, fast and reliable event detection in microgrids is challenging because of low observability and inconsistencies in measurements. A novel technique is proposed in the present work for the real-time event detection and to identify the various emerging abnormalities in the microgrid. The continuous energy signature using TKEO (Teager-Kaiser Energy Operator) of the continuous varying voltage and frequency signal are extracted through μPMU. REII (Robust Event Identification Index) is constructed from these energy signatures and based on its abrupt post-event deviation from the nominal values an event is flagged in the proposed method. The proposed method is data–driven and only depends on the real-time inputs through μPMUs thus it automatically adapts the uncertainties associated with the intermittent sources of energy in the microgrid under different operating conditions. The traditional event detection techniques fail in identification of abnormalities for a microgrid connected to the transmission systems and equipped with multiple DERs such as PVDG, WG etc. To address this challenge, an integrated microgrid with multiple DERs viz. PVDG, WG and a SG (Synchronous Generator) is first developed in this work. The complexity of simultaneous operation of a static generator i.e. PVDG along with a rotor-based generator such as WG and SG is handled by the modeling the dynamic controllers of PVDG and WG for their frequency and voltage control. The simulation results depict the efficiency, accuracy and robustness of the proposed technique in terms of estimation time, event accuracy and applicability in all types of events. Moreover, the presented methodology is also compared with the four AI/ML based methods to highlight the superiority of the method.

Keywords- Distributed generation, Microgrid protection, Event detection, Event diagnosis, Teager-Kaiser energy operator.

Citation

Singh, B. P. Goyal, S. K. Siddiqui, S. A. Shrivastava, D. R. Singh, S., Alotaibi, M. A. Malik, H., García Márquez, F. P. & Afthanorhan, A. (2024). A Novel Approach for Enhanced Real-Time Event Diagnosis for Grid Connected Microgrid with Multiple Distributed Energy Resources (DERs). International Journal of Mathematical, Engineering and Management Sciences, 9(3), 557-580. https://doi.org/10.33889/IJMEMS.2024.9.3.029.