The development of high-definition (HD) maps has enabled predictive cruise control (PCC) systems to access additional road and traffic information. This study provides a novel control scheme of PCC, which utilizes HD map information. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control, and the derivation and implementation of the fast solver are discussed. Then, a novel shiftmap is proposed to define the different working regions to allow the application of the proposed PCC system. The use of the real-time HD map is discussed, and the proposed control scheme is evaluated through simulation and experimental tests. The total fuel-savings rates obtained with the PCC system and factory-installed ACC system over a 370 km route were compared. An average fuel-savings rate of as high as 8.73% can be obtained by the proposed PCC system.
Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.
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