Two-phase neural network based estimation of degree of insecurity of power system
Increased loading and contingencies often lead to situations where the optimal power flow solution no longer remains within the secure region. In such situations there is a need of determining control actions to be taken quickly, as otherwise the system may become unstable. Hence it is important to quantify the degree of insecurity of the power system both in planning as well as at operational stages. The distance in parameter space between an insecure operating point and the closest point on feasible (secure) hyper-surface has been used as a measure of degree of insecurity. A method based on two-phase optimization neural network has been presented to compute the degree of insecurity and the voltages and angles at all the buses of the system corresponding to the closest secure point. Inclusion of security limits on power system variables assures a solution representing a secure system. When compared with conventional non-linear optimization techniques, the proposed neural network is superior, as it can be easily implemented using digital hardware and is highly suitable for real time implementation in energy management system. The proposed method has been tested on IEEE 30-bus test system and a practical 75-bus Indian system. The results achieved are compared with results from a conventional method. Insecurity arising due to increase in load and contingencies has been considered in this work.
two-phase neural network degree of insecurity closest secure point non-linear optimization constrained minimization euclidean norm