With the growing integration of converter-interfaced generating units in power systems, interactions between converters and grids have been observed across both low and highfrequency ranges, resulting from the diverse dynamics of these converters. Moreover, incorporating the complex dynamic model of converters, which involves a large number of state variables, increases the computational burden when using conventional methods to identify unstable and interaction modes. These modes are also influenced by the parameters of both converters and networks, necessitating numerous time-consuming re-computations of all eigenvalues for parametric variation analysis in large power systems. To address these challenges, this paper proposes a method that utilizes closely located open-loop modes and their displacement from open-loop to closed-loop modes to selectively identify interaction modes within power systems. The proposed method leverages the open-loop subsystem eigenvalues and the interconnections between state variables of different subsystems to determine the closed-loop eigenvalue using a second-order eigenvalue approximation, thereby avoiding the need for the calculation of all eigenvalues of the entire system. The interaction information is then used to obtain a reduced-order eigenvector approximation to improve the computation time as compared to the full-order system. The identified interaction modes and corresponding computation time using the proposed method are compared with the traditional modal participation method. The proposed method provides an efficient approach to identifying interactions between converters and the power system.