Voltage security assessment and control system using a hybrid intelligent method

This paper presents a new hybrid intelligent algorithm for assessing and enhancing voltage security. The problem is decomposed into two stages. Firstly, the security of current operating point is assessed by learning vector quantization (LVQ) network according to the pre-specified criterion. Throughout the paper, continuation power flow (CPF) is adopted as a tool for defining voltage security margin (VSM). If an insecure state is indicated by the LVQ, the power system operator is given the suggestion for appropriate control settings so as to maintain the specified security level. In the second stage, the optimal reactive power dispatch (ORPD) problem is formulated and VSM is considered as an additional constraint. To include VSM determined by CPF along the optimization process, feed-forward neural network (FFNN) is trained to learn and perform similarly to CPF to relieve the intensive computing requirement. Ant colony optimization (ACO) is applied to handle the VSM constrained ORPD problem. The proposed method was tested on IEEE 30-bus system and successful results were obtained.