Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation-based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder, is designed to properly extract the nonlinear and nonstationary features in electric load data. These features are then applied to improve the accuracy for electric load forecasting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.

中文翻译：

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基于深度学习的交流智能电网抵御稀疏网络攻击的区间状态估计


由于电力基础设施老化，传统电网正在向智能电网现代化，智能电网能够实现消费者和公用事业之间的双向通信，因此更容易受到网络攻击。然而，由于攻击成本的原因，从对手的角度来看，从一种操作场景到另一种操作场景，攻击策略可能会有很大差异，这在以前的研究中没有考虑到。因此，本文提出了基于场景的完整和不完整网络信息的智能电网两阶段稀疏网络攻击模型。然后，为了有效检测已建立的网络攻击，创新性地开发了基于区间状态估计的防御机制。在该机制中，每个状态变量的下限和上限被建模为双重优化问题，旨在最大化系统变量的变化区间。最后，设计了一种典型的深度学习，即堆叠式自动编码器，以正确提取电力负荷数据中的非线性和非平稳特征。然后应用这些功能来提高电力负荷预测的准确性，从而使状态变量的宽度更窄。预测误差的不确定性被建模为参数高斯分布。通过对各种 IEEE 基准的综合测试，证明了所提出的网络攻击模型和防御机制的有效性。