A new dynamic security enhancement method via genetic algorithms integrated with neural network based tools

In this paper, a new dynamic security assessment and generation rescheduling method utilizing genetic algorithms (GAs) which are integrated with probabilistic neural networks (PNNs) and adaptive neuro fuzzy inference systems (ANFISs) is proposed for the preventive control of large power systems against transient instabilities. By the proposed approach, PNNs are employed in a feasible manner to calculate the security regions accurately during the assessment and control. The security constrained generation rescheduling is implemented through a GA which optimizes the total fuel cost or the generation shifting during the preventive control. The steady-state solutions of the variables required for the GA are smoothly performed by the use of an ANFIS. The proposed methods are demonstrated on the 17-generator 163-bus Iowa power system and on the 50-generator 145-bus IEEE test system successfully and the effectiveness of the approaches is discussed.

► We propose a new dynamic security enhancement method for transient stability of power systems.
► Security constrained generation rescheduling is optimized through a genetic algorithm.
► Power flow computations and time-domain simulations slow down the optimization.
► Replacing them by the proposed neural network based tools increases the optimization speed significantly.