Interdisciplinary integration is a superior method to improve the optimization algorithm. In this paper, control theory and optimization are combined, and the optimization algorithm is regarded as a control process. Based on the premise of optimal control, the state equation corresponding to Lagrange Algorithm is established with the Karush-Kuhn-Tucker (KKT) conditions as the objective. As an optimal control method, Linear Quadratic Regulator (LQR) is utilized to control the calculation process, and an innovative LQR-Lagrange Algorithm is proposed. The Lyapunov stability criterion is applied to analyze the convergence, and it is proved that the proposed LQR-Lagrange Algorithm is bound to converge as long as the parameter matrices and are positive definite. The analysis indicates that the influence of parameters in LQR-Lagrange Algorithm on the calculation speed is monotonic, and the elements in and has no effect on the convergence. Therefore, the proposed algorithm has a monotonic and user-friendly parameter tuning strategy. The significance and advantages of interdisciplinary integration with control theory and optimization are discussed in the end.

An online updated data-driven linear power flow (LPF) model based on regression learning is proposed in this paper. We obtain a quadratic power flow model through regression learning first, and then derive the normal and incremental forms of LPF models by Taylor expansion. The parameters of LPF model are updated online, which improves the generalization ability. After only one initial regression learning, the proposed data-driven LPF model avoids model retraining when updated. The new parameter of the proposed model is simply calculated according to the real-time measurement data. Therefore, the LPF model we proposed is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies the superiority of the proposed method.