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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Beikbabaei, Milad Alexander, Brady Venkataramanan, Ashwin Mehrizi-Sani, Ali The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University BlacksburgVA24061 United States

ISBN: (纸本)9781665464543

As the composition of the power grid evolves to integrate more renewable generation, its reliance on distributed energy resources (DER) is increasing. Existing DERs are often controlled with proportional integral (PI) controllers that, if not properly tuned or if system parameters change, exhibit sluggish performance or large overshoot. The use of set point automatic adjustment with correction-enabled (SPAACE) with a linear predictor improves the transient response of these DERs without the need to access the PI controller parameters. The limitation of the existing SPAACE method is the high sampling rate needed for improved performance, which is not always practical. This paper proposes the addition of a memory term to the SPAACE with a linear predictor. This memory term is the integral of the errors of previous samples, which adds another layer to the prediction to improve the response at lower sampling rates and further reduces the overshoot and settling time compared to the existing SPAACE method. Time-domain simulation studies are performed in PSCAD/EMTDC to show the effectiveness of the proposed controller. © 2024 IEEE.

关键词： Electromagnetic transients

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Real-Time Simulation of a Resilient Control Center for Inverter-Based Microgrids 50

Real-Time Simulation of a Resilient Control Center for Inver...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Beikbabaei, Milad Mehrizi-Sani, Ali The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

ISBN: (纸本)9781665464543

The number of installed remote terminal units (RTU) is on the rise, increasing the observability and control of the power system. RTUs enable sending data to and receiving data from a control center in the power system. A distribution grid control center runs distribution management system (DMS) algorithms, where the DMS takes control actions during transients and outages, such as tripping a circuit breaker and disconnecting a controllable load to increase the resiliency of the grid. Relying on communication-based devices makes the control center vulnerable to cyberattacks, and attackers can send falsified data to the control center to cause disturbances or power outages. Previous work has conducted research on developing ways to detect a cyberattack and ways to mitigate the adverse effects of the attack. This work studies false data injection (FDI) attacks on the DMS algorithm of a fully inverter-based microgrid in real time. The fully inverter-based microgrid is simulated using an RTDS, an amplifier, an electronic load, a server, a network switch, and a router. The DMS is integrated into the server codes and exchanges data with RTDS through TCP/IP protocols. Moreover, a recurrent neural network (RNN) algorithm is used to detect and mitigate the cyberattack. The effectiveness of the detection and mitigation algorithm is tested under various scenarios using the real-time testbed. © 2024 IEEE.

关键词： Electric circuit breakers

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Amini, Hossein Mehrizi-Sani, Ali The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

ISBN: (纸本)9781665464543

Power system operation needs to match generation to load demand. Accurate load forecasting helps operators maintain the power balance, reducing generation costs and preventing outages. This paper proposes an improved fuzzy clustering load forecasting method based on similar day and normalization approaches, especially for short-term power system load forecasting. Simulation results utilizing data from PJM, demonstrate a reasonable forecast error compared to PJM day-ahead hourly load forecasts. The superiority of this method is the simple calculations and accuracy based on the historical data. © 2024 IEEE.

关键词： Electric load forecasting

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Deep-recurrent-neural-network-based adaptive sliding mode control for a 6-DOF serial robot 22

Deep-recurrent-neural-network-based adaptive sliding mode co...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhu, Ningyu Xie, Wen-Fang Toker, Onur Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland United States

ISBN: (纸本)9798331527471

The efficient control of serial robots in the presence of dynamic uncertainties and external disturbances is significant in many industrial applications. In this work, an adaptive sliding mode control (ASMC) approach with deep recurrent neural network (DRNN) is proposed for a 6-degree-of-freedom (6-DOF) industrial serial robot in joint space. A model-based sliding mode controller is developed to maintain the strong robustness of the robotic system. A deep recurrent neural network is designed to estimate the lumped system uncertainties in the controller. It consists of a feedforward structure through two hidden layers and a feedback loop from the output layer to the input layer, which exhibits more powerful online learning ability and dynamic property than shallow feedforward neural networks. According to Lyapunov theorem, the adaptation laws of the neural network parameters are derived, and the stability of the controller can be guaranteed. Simulation results demonstrate the effectiveness and superiority of the DRNN-based ASMC strategy regarding estimation convergence speed and trajectory tracking accuracy. © 2024 IEEE.

关键词： Recurrent neural networks

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Mitigating False Data Injection Attacks on Inverter Set Points in a 100% Inverter-Based Microgrid

Mitigating False Data Injection Attacks on Inverter Set Poin...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Beikbabaei, Milad Montano, Mario Mehrizi-Sani, Ali Liu, Chen-Ching Virginia Polytechnic Institute and State University The Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350313604

The increasing number of 100% inverter-based microgrids is introducing new challenges in their control and cybersecurity. Previous work has studied the cyber vulnerabilities of microgrids;however, very few work has studied methods to mitigate and detect cyberattacks in a 100% inverter-based microgrid. Attackers can utilize communication-based devices in a microgrid to launch false data injection (FDI) attacks and cause voltage and frequency instability. This paper studies the effects of FDI attacks on the real and reactive power set points of inverter-based resources (IBR) in a 100% inverter-based microgrid. This work co-simulates a power system using PSCAD and a communication system using Python to study FDI attacks. The communication system is modeled as a first in first out (FIFO) queue model. A long short-Term memory (LSTM)based method is used to mitigate and detect ramp and bias FDI attacks. The proposed strategy is tested on a microgrid with four IBRs subject to different FDI attacks. © 2024 IEEE.

关键词： Long short-term memory

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Approximately Gaussian Replicator Flows: Nonconvex Optimization as a Nash-Convergent Evolutionary Game 63

Approximately Gaussian Replicator Flows: Nonconvex Optimizat...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anderson, Brendon G. Pfrommer, Samuel Sojoudi, Somayeh California Polytechnic State University Department of Mechanical Engineering United States University of California Department of Electrical Engineering and Computer Sciences Berkeley United States

ISBN: (纸本)9798350316339

This work leverages tools from evolutionary game theory to solve unconstrained nonconvex optimization problems. Specifically, we lift such a problem to an optimization over probability measures, whose minimizers exactly correspond to the Nash equilibria of a particular population game. To algorithmically solve for such Nash equilibria, we introduce approximately Gaussian replicator flows (AGRFs) as a tractable alternative to simulating the corresponding infinite-dimensional replicator dynamics. Our proposed AGRF dynamics can be integrated using off-the-shelf ODE solvers when considering objectives with closed-form integrals against a Gaussian measure. We theoretically analyze AGRF dynamics by explicitly characterizing their trajectories and stability on quadratic objective functions, in addition to analyzing their descent properties. Our methods are supported by illustrative experiments on a range of canonical nonconvex optimization benchmark functions. © 2024 IEEE.

关键词： Nash equilibrium

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Overlay CR-NOMA Assisted Intelligent Transportation System Networks with Imperfect SIC and CEEs

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Chinese Journal of Electronics 2023年第6期32卷 1258-1270页

作者： LI Xingwang GAO Xuesong LIU Yingting HUANG Gaojian ZENG Ming QIAO Dawei Physics and Electronic Information Engineering Henan Polytechnic University School of Electronic and Information Engineering Lanzhou Jiaotong University State Grid Gansu Electric Power Research Institute School of Electronic Engineering Beijing University of Posts and Telecommunications Department of Electrical and Computer Engineering Laval University School of Emergency Management Henan Polytechnic University

With the development of the mobile communication and intelligent information technologies,the intelligent transportation systems driven by the sixth generation(6G) has many opportunities to achieve ultralow latency and higher data transmission rate. Nonetheless, it also faces the great challenges of spectral resource shortage and large-scale connection. To solve the above problems, non-orthogonal multiple access(NOMA) and cognitive radio(CR) technologies have been proposed. In this regard, we study the reliable and ergodic performance of CR-NOMA assisted intelligent transportation system networks in the presence of imperfect successive interference cancellation(SIC) and non-ideal channel state information. Specifically, the analytical expressions of the outage probability(OP) and ergodic sum rate(ESR) are derived through a string of calculations. In order to gain more insights, the asymptotic expressions for OP and ESR at high signal-to-noise ratio(SNR) regimes are discussed. We verify the accuracy of the analysis by Monte Carlo simulations, and the results show: i) Imperfect SIC and channel estimation errors(CEEs) have negative impacts on the OP and ESR; ii) The OP decreases with the SNR increasing until convergence to a fixed constant at high SNR regions; iii) The ESR increases with increasing SNR and there exists a ceiling in the high SNR region.

关键词： NOMA Analytical models Interference cancellation Spectral efficiency System performance Channel estimation Reliability

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Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network

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computers, Materials & Continua 2023年第7期76卷 1213-1247页

作者： Muhammad Rizwan Sana Ul Haq Noor Gul Muhammad Asif Syed Muslim Shah Tariqullah Jan Naveed Ahmad Department of Electronics University of PeshawarPeshawar25120Pakistan Department of Electronics Engineering Korea Polytechnic UniversitySiheungKorea Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science Prince Sultan UniversityRiyadh11586Saudi Arabia

Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time *** addition,the performance of a model decreases as the subject’s distance from the camera *** study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition *** 20BN-Jester dataset was used to train the model for six gesture *** achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the *** being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition *** the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the ***,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,*** observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.

关键词： 3D separable CNN computational complexity hand gesture recognition human-computer interaction

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Artificial Neural Network-based Simplified Model Predictive Control for Enhancing Voltage Source Inverters: CMV Reduction and Weighting Factor Optimization 50

Artificial Neural Network-based Simplified Model Predictive ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Shokrani, Mohammad Imanlou, Arash Behkam, Reza Nazari-Heris, Morteza Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Polytechnic Tehran Iran Department of Engineering East Carolina University GreenvilleNC United States

ISBN: (纸本)9781665464543

Controlling voltage source inverters (VSIs) is crucial for ensuring the efficient operation of inverter-based systems. Model predictive control (MPC), notably finite control set MPC (FCS-MPC), is increasingly recognized as an effective control strategy within power electronics, particularly for addressing multi-objective control problems. This article proposes a novel approach for controlling voltage source inverters using a simplified FCS-MPC scheme with common-mode voltage (CMV) reduction, limited switching frequency, and optimized weighting factors (WFs). The proposed method simplifies the MPC controller by replacing the computational burden of current predictions with a single voltage reference estimation, thereby reducing computational complexity. Moreover, offline optimization based on the non-dominated sorting genetic algorithm II (NSGA-II) is employed to determine optimized weighting factors for the cost function, as well as enhance the controller's performance. For further alleviation of computational burden, the controller is imitated using an artificial neural network (ANN) trained offline with data labeled by the original FCS-MPC algorithm. A simulation study is presented to control both conventional two-level VSI (2L-VSI) and three-level neutral point clamped (3L-NPC) inverters to demonstrate the effectiveness and validity of the proposed method. The results underline that the ANN imitator of simplified MPC reduces computational complexity while maintaining performance comparable to optimal control. © 2024 IEEE.

关键词： Cost functions

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