A combined covariance analysis and fuzzy logic controller-based protection methodology for discrimination of disturbance and asymmetrical/symmetrical events

This research introduces an innovative protection methodology designed to discriminate between disturbance events and asymmetrical/symmetrical faults within a microgrid (MG). The proposed methodology integrates a detection method utilizing covariance analysis of wavelet transform coupled with the sum of absolute differences approach and a fault classification method based on a fuzzy logic controller (FLC). The methodology leverages the advantages of covariance analysis, facilitating a straightforward demonstration of fault presence through the relationships among phases. In addition, a well-defined procedure effectively distinguishes between disturbance and asymmetrical/symmetrical events, accurately identifying the faulted phase(s) through FLC. The efficiency of the presented protection methodology is further enhanced when complemented with the identification of disturbance events. A comprehensive evaluation is conducted under diverse conditions, including variations in fault resistance, fault inception angle, and fault location. Furthermore, the methodology’s performance is validated through the consideration of fluctuating solar irradiance levels and source strength variations. To verify the robustness and applicability of the proposed protection methodology, various scenarios involving an unbalanced network model, nonlinear load, uncertain parameter variations, and measurement noise are systematically addressed. The methodology's effectiveness has been demonstrated through extensive evaluation on grid-connected MG with both radial and mesh topologies. Finally, the application of this protection methodology to islanded MG is discussed, exploring potential effects and highlighting the versatility of the proposed approach. This study improves MG protection strategies by emphasizing the adaptability and efficacy of the proposed methodology in a variety of operational scenarios.