This study presents a coordinated model free adaptive iterative learningcontroller (MFAILC) for multiple high-speed trains (MHSTs) subjected to random false data injection attacks (FDIA). Firstly, the nonlinear train...
详细信息
While many promising data-driven power system transient stability assessment (TSA) studies have been recently reported, very few of them further propose efficient data-driven solutions for follow-up control actions, e...
详细信息
While many promising data-driven power system transient stability assessment (TSA) studies have been recently reported, very few of them further propose efficient data-driven solutions for follow-up control actions, e.g., generator tripping, against potential instability. To address this inadequacy, this work develops an integrated data-driven transient stability monitoring and enhancement (TSMAE) approach that can reliably and efficiently handle various emergency situations in real time. First, by introducing the emerging spatial-temporal synchronous graph convolutional network (STSGCN), wide-area spatial-temporal features w.r.t. system stability are sufficiently learned to reliably implement online TSA. Then, to handle impending instability in a tractable manner, remedial actions are quickly taken based on intelligent critical generator identification (CGI). Specifically, with the help of the STSGCN again, the potential effects of tripping individual generators on system stabilization are efficiently predicted from the spatial-temporal perspective. Based upon that, the most critical generators for tripping are adaptively selected to enhance system stability. Numerical test results on a realistic provincial power grid of China illustrate the efficacy of the proposed TSMAE approach.
The paper extends our previous contributions about datadrivencontrol from the theory and practice in aircraft control system respectively, while achieving the combination of theory and practice. After feeding back t...
详细信息
In this research, a battery control method is proposed to handle the repetitive but nonidentical daily state-of-charge (SoC) profiles of electric vehicle (EV) batteries. The proposed method employs an iterative learni...
详细信息
In this research, a battery control method is proposed to handle the repetitive but nonidentical daily state-of-charge (SoC) profiles of electric vehicle (EV) batteries. The proposed method employs an iterative learningcontrol (ILC) framework having a quadratic performance index with iteration-varying weighting matrices. This results in iteration-varying ILC control gains to better cope with iteration-varying SoC profiles. Moreover, input constraints representing the limits on the ranges of the charge and discharge currents are considered, leading to an iteration-varying constrained convex optimization problem. This optimization problem is solved to obtain the ILC control input update via resolving its Lagrange dual problem. Next, a data-driven method based on the dynamic mode decomposition (DMD) approach is proposed to predict the SoC profile in the next weekday based on the SoC profiles in the current and previous weekdays. The predicted SoC profile is then served as the reference for the ILC tracking controller. Finally, the proposed methods are verified through extensive numerical simulations for a synthetic case and for a realistic, benchmark driving pattern. In the simulations, different ways of selecting the iteration-varying weighting matrices are introduced and their control performances are compared. It is also shown that the proposed ILC control design outperforms conventional P-type and adaptive ILC controllers as well as the classical proportional-integral-derivative controller on the tracking of the SoC profile based on the considered realistic driving pattern.
The improved YOLOX-S algorithm is proposed for the detection of small helmet targets based on an improved YOLOX-S algorithm for the detection of helmets worn by relevant personnel in hazardous scenarios in substations...
详细信息
ISBN:
(纸本)9798350321050
The improved YOLOX-S algorithm is proposed for the detection of small helmet targets based on an improved YOLOX-S algorithm for the detection of helmets worn by relevant personnel in hazardous scenarios in substations. First, the ECA attention mechanism is introduced into the CSPLayer structure in YOLOX-S to direct the model to pay more attention to channel features of small target information and enhance the model's ability to utilize useful features. Secondly, the addition of the ConvNext Block module after the three feature layers of the backbone feature extraction network to enhance the model's ability to exploit useful features. Finally, the weighted feature fusion mechanism of BiFPN is introduced in the enhanced feature extraction network by changing the original concat to BiFPN_concat, adding learnable weights to each input feature to learn the importance of different input features, distinguishing the importance of different features in the feature fusion process, and better focusing on the target information to be detected. The experimental results show that the mAP of the improved algorithm is 92.65%, which is an average accuracy improvement of 2.55% over the original YOLOX-S algorithm and meets the practical requirements.
An variational mode decomposition (VMD) has been applied in the field of harmonic detection, but the error will be large if the decomposition parameters are set artificially. To improve the accuracy of VMD in inter-ha...
详细信息
ISBN:
(纸本)9798350321050
An variational mode decomposition (VMD) has been applied in the field of harmonic detection, but the error will be large if the decomposition parameters are set artificially. To improve the accuracy of VMD in inter-harmonics detection, we need to determine the number of wolves, maximum number of iterations, convergence factor and other parameters, and then select component sample entropy function as the fitness function of the Grey Wolf algorithm. The variational mode decomposition can be utilized to extract the harmonic signal and choose a minimum envelope entropy weight as the best component. The Fourier transform is used to obtain the amplitude and frequency information of interharmonic signals. The simulation results show that the proposed method can effectively optimize the parameters and reduce the VMD decomposition error. Compared with empirical mode decomposition (EMD), complementary ensemble empirical mode decomposition (CEEMD) and empirical wavelet transform (EWT), the VMD with optimized parameters can significantly improve the accuracy of interharmonic detection and improve the accurate trace of accident source.
The grid connection of renewable energy poses challenges to the coordinated control of coal-fired power generation systems. Model uncertainty makes model-driven methods less effective due to the lack of adaptive capab...
详细信息
The grid connection of renewable energy poses challenges to the coordinated control of coal-fired power generation systems. Model uncertainty makes model-driven methods less effective due to the lack of adaptive capability. Large inertia of thermal process leads to local aggregation of state information, and the direct grafting reinforcement learning methods will affect the learning efficiency due to insufficient data utilization. To this end, this article proposes dual-prioritized experience replay value distribution deep deterministic policy gradient (DPER-VDP3G) algorithm. Value distribution is introduced to reflect the influence of model uncertainty on the evaluation of coordinated control policy, thus improving the accuracy of prediction cost function. The DPER is designed to reduce the nonuniform sampling bias and remove redundant data to enhance sample diversity. Comparative experiments demonstrate the advantages of the proposed method for improving network training efficiency, ameliorating load tracking accuracy and speed, and reducing energy consumption.
The development and applications of digital twin-driven Decision Support systems (DSS) are discussed. In order to combine the advantages and features of model-driven, data-driven, knowledge-driven, documentation-drive...
详细信息
Navigation through crowded intersections is a challenge for autonomous vehicles, where uncertainty arises from interaction with other road users, encountering new scenes and weathers, etc. Recent end-to-end autonomous...
详细信息
ISBN:
(纸本)9798350377712;9798350377705
Navigation through crowded intersections is a challenge for autonomous vehicles, where uncertainty arises from interaction with other road users, encountering new scenes and weathers, etc. Recent end-to-end autonomous control deep models learned from human drivers have shown promising driving performance, whereas they are not as transparent and safe as traditional rule-based systems. When facing situations that they are unfamiliar with or uncertain about, the deep models' predictions could be unsafe and untrustworthy. Without the ability to identify these situations and issue warnings beforehand, cascading errors of deep models may result in catastrophes. Therefore, this work combines the strengths of both data-driven and traditional rule-based approaches to achieve better driving quality and safety. We propose a heterogeneity uncertainty quantification method based on imitation learning, where both data and model uncertainties of the lateral and longitudinal control tasks are quantified. We also propose a policy deployment strategy where a safety indicator is developed upon estimated uncertainty to bridge the data-driven performance layer and the rule-based fallback layer. We learned from human driving demonstrations and conducted extensive closed-loop tests. Results demonstrate the effectiveness and importance of the proposed uncertainty quantification method and policy deployment strategy.
Brain-computer interface (BCI) technology establishes communication between the brain and external devices by decoding EEG signals. BCI technology based on motor imagery (MI) has great application potential. There are...
详细信息
ISBN:
(纸本)9798350321050
Brain-computer interface (BCI) technology establishes communication between the brain and external devices by decoding EEG signals. BCI technology based on motor imagery (MI) has great application potential. There are many different methods to extract motor intention from electroencephalogram (EEG) based on motor imagery (MI).These methods rely on extracting the unique features of EEG in the process of imaginary movement, which directly affect the performance of neural decoding algorithm of BCI. Convolutional neural network (CNN) shows outstanding advantages in automatic extraction of image features. In this paper, an image representation method based on the EEG is proposed as the input of the network. Then, a CNN and a CNN based on Channel Attention Mechanism (CAM) are built as the classifier, convolution layers and activation functions of different sizes are validated. The performance of the method is evaluated. A CNN framework based on CAM, which contained three convolution layers (3-L) is better than the other state-of-the-art approaches. The accuracy on dataset IV from BCI competition II reaches 72.6%.
暂无评论