Neural network based preventive control support system for power system stability enhancement

The authors propose an application of a newly developed neural network to the preventive control of a power system. The purpose of the proposed control is to improve the damping effect of the system on electromechanical modes by reallocating load to generators. Since the neural network has flexible learning capability the authors apply it to identify the complex and nonlinear relation between the damping effect and the distribution of generating power. The trained neural network acts as the support system which aids an operator in performing the generating reallocation for enhancing the system stability. Furthermore, the authors develop a new type of neural network which can deal with the equal constraints about the output layer in the error-back-propagation type of neural network because it is important for the generating reallocation to satisfy the equal constraint about the energy balance between generation and load