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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水库计算遇到智能电网：使用延迟反馈网络的攻击检测

介绍了一种利用水库计算技术检测风力发电机对智能电网的攻击的新方法。RC是神经形态计算领域中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均显示出卓越的性能。与现有的攻击检测方法（例如MLP，支持向量机（SVM）和常规状态向量估计（SVE））相比，结合时间编码，DFN和多层感知器（MLP）作为输出读出层，可以提高性能。就智能电网中的攻击检测而言。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。基于平均10 000次仿真的精度指标，所提出的基于RC的系统的攻击检测率高于99％。