An online tripping index to improve microgrid dynamic stability based on estimating critical boundary area

Adaptive criteria of estimating power system separation times based on inter‐area signal

“…As it can been, in the case of fault scenario, considering four wind powers WP1-WP4, the system inter-area oscillation FIGURE 16 System oscillations in the presence of wind powers without activating ATI scheme FIGURE 17 ATI zones using GIS signals frequency is increased means reducing the system overall inertia. Also, it is revealed that, without performing the proposed ATI scheme, system experience an unstable condition which after a few inter-area cycles, overall instability is occurred.…”

“…Into the Step 3, some approaches with the aim of when to island are presented. In this category, researchers present different time aspects including decision tree [16], aggregated load model [17], sensitivity analysis [18], signal oscillatory pattern [19], clustering method [20], artificial intelligence techniques [21], smith predictor [22], residue analysis [23], inter-area rotor angle deviation [24] and the use of GIS signal [25] which are concentrated through estimating proper islanding times. After estimating proper splitting times, the next step is to identify the islanding location to split the system into stable islands.…”