On-line probabilistic voltage security assessment using Radial Basis Function neural network

Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. In such environment, online voltage stability evaluation plays a significant role in secure operation of power systems. This paper presents a new approach for estimating voltage stability security assessment accounting uncertainties in the line parameters and control variables. Security index has been evaluated using Monte-Carlo simulation with & without consideration of unavailability of transmission lines, which is used to contingency selection and compared with well-established methods. Further, evaluation of probabilistic insecurity index (PISI) using cut-set method for single & double line outages at various loading conditions considering voltage stability limit. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power flow methodology. The applicability of PISI has been made on-line using radial basis function (RBF). Since voltage security assessment is possible on-line using RBFNN. Proposed algorithms have been implemented on IEEE 6-bus 7-line test systems.