This paper studies the vulnerability of deep reinforcement learning (DRL) models for power systems topology optimization under data perturbations and cyber-attack. DRL has recently solved many complex power system optimization problems. However, it has been practically proven that small perturbations of input data can lead to drastically different control decisions and induce danger. To evaluate and mitigate the security risks of DRL models in power systems, we propose a vulnerability assessment method for such DRL models under noisy data and cyber-attack. In specific, we assess the vulnerability of a DRL model in a way that perturbations are constructed to minimize the model’s performance. Besides, several vulnerability indices are proposed to identify the characteristics of perturbations that may cause malfunction of DRL. Simulations on the 14-bus system and the IEEE 118-bus system for topology optimization are carried out to validate the effectiveness of the proposed vulnerability assessment method. The results show that the performance of DRL models for power systems can be significantly degraded under cyber-attack and data perturbations, especially when a proposed vulnerability index has abnormal values.

中文翻译：

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电力系统拓扑优化深度强化学习模型漏洞评估

本文研究了深度强化学习 (DRL) 模型在数据扰动和网络攻击下用于电力系统拓扑优化的脆弱性。DRL 最近解决了许多复杂的电力系统优化问题。然而，实践证明，输入数据的微小扰动会导致截然不同的控制决策并引发危险。为了评估和减轻电力系统中 DRL 模型的安全风险，我们提出了一种在噪声数据和网络攻击下对此类 DRL 模型进行漏洞评估的方法。具体而言，我们以构建扰动以最小化模型性能的方式评估 DRL 模型的脆弱性。此外，还提出了几个脆弱性指标来识别可能导致 DRL 故障的扰动特征。对用于拓扑优化的 14 总线系统和 IEEE 118 总线系统进行了仿真，以验证所提出的漏洞评估方法的有效性。结果表明，电力系统 DRL 模型的性能在网络攻击和数据扰动下会显着降低，尤其是当提出的脆弱性指数具有异常值时。