A kurtosis-ESPRIT algorithm for RealTime stability assessment in droop controlled microgrids
Abstract Although detailed analytical models for droop-controlled microgrids are available, they are computationally complex and do not consider real-time variations in microgrid parameters and operating conditions. This paper proposes Kurtosis-Estimation of Signal Parameters via Rotational Invarian...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84675-8 |
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| Summary: | Abstract Although detailed analytical models for droop-controlled microgrids are available, they are computationally complex and do not consider real-time variations in microgrid parameters and operating conditions. This paper proposes Kurtosis-Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) to identify the dominant modes in droop-controlled inverter-based microgrids (IBMGs) using local real-time measurements. In the proposed approach, a short-duration small disturbance is applied to the selected DG’s active power droop gain, and then, the system’s dominant modes are estimated from its local measurements. Additionally, a kurtosis measure is proposed as a quick measure to assess the estimation signal’s characteristics and evaluate the presence and prominence of significant modes within the signal. The effectiveness of the developed approach is validated via MATLAB/SIMULINK simulations. Four case studies were conducted to verify the robustness of the proposed algorithm as follows: under different values of active power droop gains, several variations of lines’ X/R ratios, various levels of noise, and under large load changes and topological disturbances. Besides, a controller-in-the-loop (CIL) experiment was conducted using OPAL-RT to provide a real-time validation of the results. The modes obtained from the proposed algorithm are validated against the analytically derived modes and the estimation accuracy is compared to the recent methods: Prony, Matrix Pencil, and Subspace Identification techniques. Results show higher estimation accuracy for the proposed approach with a robust performance in noisy environments, across varying load conditions, and under different network configurations. |
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| ISSN: | 2045-2322 |