Abstract
The advanced communication technology provides new monitoring and control strategies for smart grids. However, the application of information technology also increases the risk of malicious attacks. False data injection (FDI) is one kind of cyber attacks, which cannot be detected by bad data detection in state estimation. In this paper, a data-driven FDI attack detection framework of the smart grid with phasor measurement units (PMUs) is proposed. To enhance the detecting accuracy and efficiency, the multiple layer autoencoder algorithm is applied to abstract the hidden features of PMU measurements layer by layer in an unsupervised manner. Then, the features of the measurements and corresponding labels are taken as inputs to learn a softmax layer. Last, the autoencoder and softmax layer are stacked to form a FDI detection framework. The proposed method is applied on the IEEE 39-bus system, and the simulation results show that the FDI attacks can be detected with higher accuracy and computational efficiency compared with other artificial intelligence algorithms.
Highlights
Phasor measurement units (PMUs) can measure the voltage and current phasors directly with the help of global positioning system synchronization clock [1, 2]
The rapid developments of enhanced monitoring and information technology facilitate the malicious cyber attacks [3]. e large-scale integration of renewable energy resources poses a challenge for the security of the system operation due to inherent uncertainties of renewables [4,5,6]. e cyber attacks on the power system monitoring and data acquisition systems are the main objectives for attackers to seriously threaten the power system operating safety
A stacked autoencoder-based false data injection (FDI) attack detection framework is proposed, and it is applied on the IEEE 39-bus testing system under different conditions. e confusion matrix and 3 indexes are used to evaluate the performances of the detection methods. e simulation results show that the neuron numbers of encoders influence the detection performance
Summary
Phasor measurement units (PMUs) can measure the voltage and current phasors directly with the help of global positioning system synchronization clock [1, 2]. It is hard for the attacker to obtain the full acknowledgments of power systems Aiming at this problem, in [9], a FDI attack method is given based on only partial knowledge of the system topology and a subset of meter measurements. The deep learning is an effective method to detect the FDI attacks, some drawbacks, such as the heavy computation loads and bad generalization abilities with a huge amount of inputs, restrict the further applications. A stacked autoencoder-based FDI attack detection framework in the smart grid is proposed.
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