Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. This letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. For comparison, we have evaluated classic data-driven methods, including $K$-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

中文翻译：

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基于多层长短期记忆网络的太阳能发电场数据完整性攻击检测与诊断

光伏 (PV) 系统正变得越来越容易受到网络威胁。为了应对这一新出现的问题，开发网络安全电力电子转换器受到了 IEEE 电力电子学会越来越多的关注，该协会最近发起了一项网络物理安全计划。这封信提出了一种基于深度序列学习的诊断解决方案，用于针对智能电网中光伏系统的数据完整性攻击，包括 dc-dc 和 dc-ac 转换器。多层长短期记忆网络用于利用来自光伏系统中电流和电压传感器的时间序列电波形数据。所提出的方法已在具有广泛定量分析的光伏智能电网基准模型中进行了评估。为了比较，我们评估了经典的数据驱动方法，包括 $K$-最近邻、决策树、支持向量机、人工神经网络和卷积神经网络。比较结果验证了所提出的光伏系统各种数据完整性攻击检测和诊断方法的性能。