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In this paper, a new method for transient stability simulation is presented. The objective of this work is to exploit the maximum degree of parallelism that the problem presents. The transient stability problem can be seen as a coupled set of nonlinear algebraic and differential equations; by applying a discretization method such as the trapezoidal rule, the overall algebraicdifferential set of equations is transformed into an unique algebraic problem a t each time step. A solution that considers every time step, not in a sequential way, but concurrently, is suggested. The solution of this set of equations with a relaxation-type indirect method gives rise to a highly parallel algorithm. This parallelism consists of a parallelism in space (that is in the equations at each time step) and a parallelism in time. Another characteristic of the algorithm is that the time step can be changed between iterations using a nested iteration multigrid technique from a coarse time grid to the desired fine time grid to enhance the convergence of the algorithm. Also, this new method can handle all the typical dynamic models of realistic power system components. Test results are presented and compared with the sequential dishonest Newton algorithm for realistic power systems. KEY WORDSTrasient stability, parallel processing, dynamic simulation, parallel algorithm. assessment study of near term computer capabilities and their impact on power flow and stability simulation program". EPRI-TPS-77-749 , Final Report 2) H.

Machine learning algorithms have been widely used in power system transient stability evaluation. The combined application of data analysis and evaluation and neural network provides a new direction for power system transient stability analysis. After the actual power grid is running, there is obviously an imbalance between stable samples and unstable samples. The current deep learning network realizes the power system transient stability assessment method with too many redundant attributes, and the characteristics will inevitably be lost during the data transmission process. This leads to serious problems with the tendency of the training of the data-driven transient stability assessment model. The rough set theory algorithm is introduced to reduce the redundant attributes of power system transient data sets, which simplifies the difficulty of data training. At the same time, as the neural network deepens, the deep residual neural network model has a higher accuracy rate and effectively avoids the “gradient explosion” and “gradient dispersion” problems. Compared with the traditional neural network, it has better Evaluate performance.