Following the recent devastating blackouts in North America, UK and Italy, blackout prevention has attracted significant attention, though it is known as a notoriously difficult task. To prevent the blackout, it is essential to accurately predict the instable status of power network components. In the large-scale power network however, existing analysis tools fail to perform accurate and in-time prediction of component instability, because of the sophisticated structure of real-world power networks and the huge amount of system variables to be analyzed. To prevent the blackout, we need an accurate and efficient method that (a) can discover interesting features and patterns relevant to the blackout, from the highly complex structure and ten thousands of system variables of a power network, and (b) can give accurate and fast prediction of system instability whenever required, so that the network operator can take necessary actions in time. In this paper, we report our tool developed for power network instability prediction. The proposed method consists of two major stages. In the first stage,a novel type of patterns namely Local Correlation Network Pattern (LCNP) is mined from the structure and system variables of the power network. Correlation rules, which are useful for the network operator to locate potentially instable components, can be further generated from the LCNP. In the second stage, a kernel based network classification method is developed to predict the system instability. By testing on a real world power network (the New England system), we demonstrate that the proposed tool is effective in predicting system instability and thus highly useful for blackout prevention.