We present a fast block direct solver for the unified dynamic simulations of power systems. This solver uses a novel Q-learning based method for task scheduling. Unified dynamic simulations of power systems represent a method in which the electric-mechanical transient, medium-term and long-term dynamic phenomena are organically united. Due to the high rank and large numbers in solving, fast solution of these equations is the key to speeding up the simulation. The sparse systems of simulation contain complex nested block structure, which could be used by the solver to speed up. For the scheduling of blocks and frontals in the solver, we use a learning based task-tree scheduling technique in the framework of Markov Decision Process. That is, we could learn optimal scheduling strategies by offline training on many sample matrices. Then for any systems, the solver would get optimal task partition and scheduling on the learned model. Our learning-based algorithm could help improve the performance of sparse solver, which has been verified in some numerical experiments. The simulation on some large power systems shows that our solver is 2-6 times faster than KLU, which is the state-of-the-art sparse solver for circuit simulation problems.
翻译:我们为动力系统的统一动态模拟提供了一个快速区块直接求解器。 这个求解器使用一种新的基于Q学习的方法来安排任务。 一个基于Q学习的方法来安排任务。 动力系统的统一动态模拟是电机瞬、 中期和长期动态现象有机地结合的一种方法。 由于在解答中级别高且数量大, 这些方程式的快速解决方案是加速模拟的关键。 模拟的稀疏系统包含复杂的嵌套区块结构, 可由求解器用来加快速度。 对于求解器中区块和前方的列表, 我们在Markov 决策程序的框架内使用基于学习的任务树调度技术。 也就是说, 我们可以通过在许多样本矩阵上进行离线培训来学习最佳的时间安排战略。 然后, 对于任何系统, 求解器都可以在学习模型上获得最佳的任务分配和调度。 我们的学习算法可以帮助改进稀薄的求解器的性能, 在某些数字实验中已经验证过。 一些大型电源系统的模拟显示, 我们的求解器速度比KLU快2-6倍, KLU是用于模拟的智能路的状态。