Microgrid Fault Detection and Classification Based on the Boosting Ensemble Method With the Hilbert-Huang Transform

Empirical Evaluation of Microgrid Fault Identification Models from a Statistical Perspective

“…To illustrate the value of this novel control strategy, test microgrid systems with distributed energy resources are employed. In [13], microgrid defects are recognized and categorized using an intelligence-based ensemble. This strategy is suggested due to the dynamic nature of microgrids and the reliance on traditional fault diagnosis and protection on fault current level or impedance.…”

“…A CC D DC S JSP [1] MQR LQR HQR HQR LQR IBDG [2] HQR MQR HQR HQR MQR DT kNN [3] LQR HQR HQR VHQR LQR FDI [4] HQR MQR HQR HQR MQR LV DCMG [5] HQR HQR MQR HQR HQR HFL [6] MQR HQR MQR MQR MQR IIDG [7] HQR HQR HQR HQR MQR Hilbert [8] HQR MQR LQR HQR HQR Lyapunov [9] HQR HQR MQR MQR MQR LMIT [10] HQR HQR HQR HQR LQR ViM [11] MQR HQR MQR MQR HQR SMO [12] HQR HQR MQR HQR HQR HHT [13] HQR VHQR HQR HQR MQR CPL [14] HQR HQR HQR MQR MQR DA AFM [15] HQR HQR HQR HQR HQR KF [33] HQR MQR HQR HQR HQR DCCB [34] MQR HQR HQR VHQR HQR RS FL [35] HQR HQR HQR HQR MQR Based on this evaluation, it can be observed that DA AFM [15], PPFCPD [18], DWT ANN [20], ANN TMF [27], SCCL [31] showcase higher accuracy, thus can be used for applications where high precision of fault detection & mitigation is needed with low errors. Similarly, it can also be observed that JSP [1], IBDG [2], FDI [4], Hilbert [8], DCCB [19], DWT ANN [20], CFTC [26], ANN TMF [27], PFHO [30], SCCL [31], KF [33] showcase lower complexity, thus can be used small to medium scaled circuits, where computational power is limited, with lower number of faults.…”