Voltage collapse detection using Ant Colony Optimization for smart grid applications

Load demand levels in power networks are continuing to increase especially considering the penetration of Plug-in Hybrid Electric Vehicles, and as a result bus voltages are requiring more support from generators elsewhere in the network in order to maintain the voltage within specified limits. If the network is unable to support the increasing demand, bus voltage will begin to degrade until the point of voltage collapse which can lead to catastrophic network failure. Previous studies have shown that evolutionary computing techniques are effective methodologies for locating voltage collapse points. Ant Colony Optimization techniques allow for the optimization of many independent parameters simultaneously, the loading parameter for each bus is considered in this work. In this study, Ant Colony Optimization is applied to detect voltage collapse conditions in power networks, to obtain faster computing time with the future goal of providing online detection and prediction for use in smart grids. Two case studies are considered in this study to assess the performance of the proposed detection algorithm; the first case study includes 9-bus system while the other case study involves IEEE 118-bus system. Results obtained from both cases and conclusions drawn are also presented.

► Saddle-node bifurcations cause voltage collapse points due to increases in demand.
► Identification methods include the direct and continuation power flow methods.
► Ant Colony Optimization is used to determine maximum load levels and collapse points.
► Two cases, a 9-bus system and IEEE 118-bus system, are used to assess the algorithm.
► Identifies the weak system buses and the system parameters that drive collapse.