A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.

中文翻译：

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水库计算与智能电网的结合：使用延迟反馈网络进行攻击检测


本文介绍了一种利用水库计算（RC）对含风力发电机的智能电网进行攻击检测的新方法。 RC 是神经形态计算领域内的一种节能计算范例，RC 的延迟反馈网络 (DFN) 实现在许多分类任务中表现出了卓越的性能。时间编码、DFN 和多层感知器 (MLP) 的组合作为输出读出层被证明可以比现有的攻击检测方法（例如 MLP、支持向量机 (SVM) 和传统状态向量估计 (SVE)）带来性能改进在智能电网中的攻击检测方面。在处理不同的变量（例如攻击幅度、攻击类型以及智能电网中受损测量的数量）时，所提出的算法被证明比 MLP 和 SVE 更稳健。根据 10000 次模拟的平均准确度指标，所提出的基于 RC 的系统的攻击检测率高于 99%。