For improving the transient control of power grids, an alternative fast acting feature is offered by the vast application of energy storage systems (ESS). However, Denial of Service (DoS) attacks can interfere with ESS, significantly hindering the performance of the transient control. In the present study, a framework of a non-zero sum difference game between a transient controller and a DoS attacker was established to determine an optimal control strategy of ESS in DoS attacks. In order to solve the aforementioned game, an input convex neural networks (ICNNs) based adaptive dynamic programming (ADP) scheme was proposed. Regarding the DoS attacker and the transient controller, two long term strategy utility functions are approximated by ICNNs, thereby ensuring the existence of the Nash equilibrium (NE) of the game without requiring a small sampling period or linearity of power system models. Through neural training to reach the NE from the framework of the game, an approximated optimal controller is derived. The simulations of the 9-bus and 30-bus system experiments validate the effectiveness of the proposed transient control scheme against DoS attacks. Further, increased performance of the ICNNs based ADP in multiple sampling periods and the nonlinearity of the power grids are illustrated.

为了改善电网的瞬态控制，储能系统 (ESS) 的广泛应用提供了另一种快速作用功能。但是，拒绝服务 (DoS) 攻击会干扰 ESS，从而显着阻碍瞬态控制的性能。在本研究中，建立了瞬态控制器和 DoS 攻击者之间的非零和差异博弈框架，以确定 DoS 攻击中 ESS 的最佳控制策略。为了解决上述问题，提出了一种基于输入凸神经网络（ICNNs）的自适应动态规划（ADP）方案。对于 DoS 攻击者和瞬态控制器，ICNN 近似了两个长期策略效用函数，从而确保博弈的纳什均衡 (NE) 的存在，而不需要小采样周期或电力系统模型的线性度。通过神经训练从游戏的框架到达NE，推导出一个近似的最优控制器。9 总线和 30 总线系统实验的仿真验证了所提出的瞬态控制方案对 DoS 攻击的有效性。此外，还说明了基于 ICNN 的 ADP 在多个采样周期中提高的性能以及电网的非线性。9 总线和 30 总线系统实验的仿真验证了所提出的瞬态控制方案对 DoS 攻击的有效性。此外，还说明了基于 ICNN 的 ADP 在多个采样周期中提高的性能以及电网的非线性。9 总线和 30 总线系统实验的仿真验证了所提出的瞬态控制方案对 DoS 攻击的有效性。此外，还说明了基于 ICNN 的 ADP 在多个采样周期中提高的性能以及电网的非线性。