This paper addresses the cybersecurity of hierarchical control of AC microgrids with distributed secondary control. The false data injection (FDI) cyberattack is assumed to alter the operating frequency of inverter‐based distributed generators (DGs) in an islanded microgrid. For the microgrids consisting of the grid‐forming inverters with the secondary control operating in a distributed manner, the attack on one DG deteriorates not only the corresponding DG but also the other DGs that receive the corrupted information via the distributed communication network. To this end, an FDI attack detection algorithm based on a combination of Gaussian process regression and one‐class support vector machine (OC‐SVM) anomaly detection is introduced. This algorithm is unsupervised in the sense that it does not require labelled abnormal data for training which is difficult to collect. The Gaussian process model predicts the response of the DG, and its prediction error and estimated variances provide input to an OC‐SVM anomaly detector. This algorithm returns enhanced detection performance than the standalone OC‐SVM. The proposed cyberattack detector is trained and tested with the data collected from a 4 DG microgrid test model and is validated in both simulation and hardware‐in‐the‐loop testbeds.
Deep Learning Based Multi-Label Attack Detection for Distributed Control of AC Microgrids
This paper presents a deep learning based multi-label attack detection approach for the distributed control in AC microgrids. The secondary control of AC microgrids is formulated as a constrained optimization problem with voltage and frequency as control variables which is then solved using a distributed primal-dual gradient algorithm. The normally distributed false data injection (FDI) attacks against the proposed distributed control are then designed for the distributed gener-ator's output voltage and active/reactive power measurements. In order to detect the presence of false measurements, a deep learning based attack detection strategy is further developed. The proposed attack detection is formulated as a multi-label classification problem to capture the inconsistency and co-occurrence dependencies in the power flow measurements due to the presence of FDI attacks. With this multi-label classification scheme, a single model is able to identify the presence of different attacks and load change simultaneously. Two different deep learning techniques are compared to design the attack detector, and the performance of the proposed distributed control and the attack detector is demonstrated through simulations on the modified IEEE 34-bus distribution test system.
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- NSF-PAR ID:
- 10318998
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
This paper presents a resilient control framework for distributed frequency and voltage control of AC microgrids under data manipulation attacks. In order for each distributed energy resource (DER) to detect any misbehavior on its neighboring DERs, an attack detection mechanism is first presented using a Kullback-Liebler (KL) divergence-based criterion. An attack mitigation technique is then proposed that utilizes the calculated KL divergence factors to determine trust values indicating the trustworthiness of the received information. Moreover, DERs continuously generate a self-belief factor and communicate it with their neighbors to inform them of the validity level of their own outgoing information. DERs incorporate their neighbors' self-belief and their own trust values in their control protocols to slow down and mitigate attacks. It is shown that the proposed cyber-secure control effectively distinguishes data manipulation attacks from legitimate events. The performance of proposed secure frequency and voltage control techniques is verified through the simulation of microgrid tests system implemented on IEEE 34-bus test feeder with six DERs.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SmartGridComm51999.2021.9631998
