Critical Clearing Time (CCT) estimation in the real-time condition is considered a challenging computing problem. It could be founded by calculating the Critical Clearing Angle (CCA) using Equal Area Criterion (EAC) then by time domain simulation, the CCT could be determined. This method is used for off-line simulation and is not adequate for real-time condition due to the wide operating range of the power systems. Also, the CCT could be estimated in real time directly by energy function, but this method did not achieve the exact accuracy. This paper focuses on the creation of a technique for estimating CCT, based on Adaptive Neuro Fuzzy Inference System (ANFIS) and SynchroPhasors Measurement Units (SPMUs), for transient stability assessment and control in real-time under any operating conditions. The method is applied on Single Machine to Infinite Bus (SMIB) test system. Comprehensive comparisons between the CCT from proposed ANFIS and proposed Artificial Neural Network (ANN) and the exact Time Domain EAC (TDEAC) are presented to show the effectiveness and validity of the proposed ANFIS for the real-time condition. |
