Dynamic security constrained rescheduling using stability sensitivities by neural network as a preventive tool

In this paper, a novel approach for generation rescheduling as a preventive control for enhancing dynamic security using neural network is presented.. Critical clearing time (CCT) associated with each fault including the effect of system controllers and limitation, is adopted as dynamic security criteria. A Dynamic Security Analyzer Neural Network (DSANN) is trained to estimate CCTs associated with different system faults. For each given operating point, DSANN evaluate system CCTs by using steady state pre fault operating condition as input pattern. The most interesting feature of the proposed neural network application is evaluation of sensitivity of CCT with respect to generation pattern. These sensitivities are derived from the information stored in the weighting factors of trained DSANN. The sensitivity of CCT is used as a guideline for selecting the most effective pair of generators to reschedule their MW generation in the process of generation rescheduling aimed security enhancement. The proposed method has been demonstrated on the IEEE-39 bus system with promising results for enhancing dynamic security by generation rescheduling using sensitivity characteristic of neural network.