Ensuring the stable operation of the interconnected dynamics of power systems with time‐varying and nonlinear elements is a complex matter, since it involves continuous adjustments among every part for a suitable and efficient performance. Small disturbances in the load variation procedure also routinely occur. Consequently, the controller parameters must be adjusted to the variable conditions. The nonlinear autoregressive model with exogenous input (NARX) neural network (NN) has been used in many nonlinear dynamic systems. This paper explores the NARX combined with a multiobjective optimization by using genetic algorithms (GAs) to damp local and interarea oscillation modes. The NN model is trained by using a historical database determined by the GA for several load levels. Subsequently, the model can change the stabilizer parameters in real time after the learning phase. This study is used to tune a power system stabilizer (PSS) in a two‐area four‐machine system. The results of extensive simulations indicate a substantial improvement of the GA‐NARX‐PSS design while maintaining a reasonable fault resilience of the synchronous machine for several operating loads.

中文翻译：

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具有外源输入（NARX）神经网络的非线性自回归网络，可抑制电力系统的振荡

确保具有时变和非线性元素的电力系统互连动力学的稳定运行是一件复杂的事情，因为它涉及到各个部分之间的连续调整，以实现合适和高效的性能。负载变化过程中的小干扰通常也会发生。因此，必须将控制器参数调整为可变条件。具有外源输入（NARX）神经网络（NN）的非线性自回归模型已用于许多非线性动力学系统中。本文探索了使用遗传算法（GA）抑制局部和区域间振荡模式的，结合多目标优化的NARX。NN模型是使用GA确定的几个负载水平的历史数据库进行训练的。后来，在学习阶段之后，该模型可以实时更改稳定器参数。本研究用于调整两区域四机系统中的电力系统稳定器（PSS）。大量仿真结果表明，GA-NARX-PSS设计得到了显着改进，同时在多个运行负载下都保持了同步电机的合理故障恢复能力。