Data mining for detection of sensitive buses and influential buses in a power system subjected to disturbances

Many kinds of major disturbances in the power system could lead to system load reduction. It is very challenging and useful for the system dispatcher to grasp the knowledge about whether some substations exist whose load reductions resulting from the disturbances are consistently more serious than others. In this paper, the data-mining technique is applied to a power system in Hong Kong to detect the substations most sensitive to the disturbances. Two indexes are defined to measure the severity of load reduction. By statistical analysis, the most sensitive substations can be discovered, which are confirmed to be the case by the experts working in the power system. Furthermore, based on the voltage-profile correlation analysis, the influential buses where the most effective voltage adjustment may be strategically applied to assist a sensitive bus to recover from the severe voltage fluctuation arising from the disturbance can be deduced.