Abstract: The increase of the penetration of renewable energy brings continuous challenges to the safe and stable operation of the power system. It becomes more and more difficult to analyze the system stability and control the stable operation of the power system by traditional methods. To solve this problem, a power system optimal operation framework with embedded security and stability constraints and an oblique regression tree and its ensemble algorithm for extracting power system security and stability rules are proposed. The algorithm first optimizes the oblique split coefficient to train a single oblique regression tree, then uses the boosting idea to integrate the oblique regression tree, and uses the regularization method to ensure the sparsity of the tree and enhance the interpretability of the algorithm. Compared with the black box model such as neural network, the proposed method can extract explicit security and stability rules, which lays a foundation for the optimal operation of the power system with embedded security and stability constraints. Finally, the static voltage stability problem is taken as an example to verify the effectiveness of the algorithm. The results show that the algorithm has good interpretability, strong representation ability and high ensemble efficiency.