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7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Sharygin, Dmitriy Filatova, Galina Yablokov, Andrey Ivanovo State Power Engineering University (ISPU) Department of Electric Power Systems Automatic Control Ivanovo Russian Federation

ISBN: (纸本)9798331531836

The article is devoted to the development of new fault location algorithms based on emergency mode parameters at instantaneous values on overhead power transmission lines of 110 kV and above. Increasing the accuracy of fault location algorithms is an important and urgent task. The methodological and simulation errors of fault location are directly related to the configuration of the power transmission line. The presence of parallel power transmission lines, mutual induction or branch substations affects the accuracy of fault location. In this work, the authors modernize their own previously developed fault location algorithms based on instantaneous values for complex power transmission line configurations. The developed fault location algorithms do not require additional corrections or means for processing primary signals and can be successfully applied when using data from digital in-strumental transformers. The studies of the algorithms were carried out on the simulation models of the overhead power lines in the Simulink/MATLAB software package. The remote fault location of the damage was carried out according to the parameters of the emergency mode. The assignment of parameters for the simulation modeling is carried out using the Monte Carlo method with subsequent statistical processing of the results and the construction of a justified inspection zone. © 2025 IEEE.

关键词： fault location imitation model inspection zone instantaneous values measurement error mutual inductance terminal substation

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Evaluating the Non-participation Penalty in Renewable Energy Utilization to Supply a Portion of Electricity for large Industries in Iran 9

Evaluating the Non-participation Penalty in Renewable Energy...

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9th International Conference on technology and Energy Management, ICTEM 2024

作者： Najafi, Mahdi Kiani, Hossein Nazari, Mohammad Hassan Gharepetian, Gevork B. Hosseinian, S.H. Sarbazi, Jafar Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Research Department Tehran Iran

ISBN: (纸本)9798350329797

This paper, investigates the consequences of Non-Participation Penalty (NPP) imposed on large industries in Iran for their non-compliance with renewable energy usage. The study presents ten distinct configurations of a hybrid system incorporating grid, wind turbines (WTs), photovoltaic (PV), and diesel generator. HOMER Pro software was used to evaluate scenarios based on real data from a large industrial plant, analyzing the impact of variables on economic parameters, with a particular focus on NPC. According to the results, the integration of diesel generators in WT and PV combinations leads to reductions in both Net Present Cost (NPC) and Cost of Energy (COE) within the hybrid systems and using renewable energy is more cost-effective than accepting the fine and using only the grid until electricity prices increase by 5% annually. Also, its NPC is at least 4.62$ less. © 2024 IEEE.

关键词： Clean energy

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Integrating AUTOSAR End-to-End Communication Protection Library Inside Automotive Actuators 28

Integrating AUTOSAR End-to-End Communication Protection Libr...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Cǎpriţǎ, Horia V. Selişteanu, Dan Continental Automotive Systems Sibiu Romania University of Craiova Romania University of Craiova Department of Automatic Control and Electronics Craiova Romania

ISBN: (纸本)9798350339918

This paper explores new methods for increasing the safety level of the data transfer between electronic control units (ECUs) and actuators in automotive communication. It is proposed to introduce an end-to-end (E2E) module in automotive actuators to check the integrity of the data received through a standard automotive communication bus. The model contains hardware modules that implement the end-to-end communication checking (E2E Check) mechanism, as is defined by the Automotive Open System Architecture (AUTOSAR) standard. By integrating this module into automotive actuators, it is possible to connect them directly to the communication bus inside the vehicle, due to the possibility of verifying the integrity of the data received through the communication bus. The communication bus in this case could be (but not limited to) LIN (Local Interconnection Network), CAN (controller Area Network) or FlexRay. This paper describes the hardware model, design, and mapping of the E2E communication checking module in a Field Programmable Gate Array (FPGA) device. The proposed hardware model is fully configurable in terms of the structure of the received message from the communication bus, as is requested by the automotive E2E standard. The validation of the E2E communication checking module was done by using the data received from an E2E automotive sensor proposed by authors in previous works. Another validation method, presented in this paper, is to make a comparison of the status returned by the E2E communication checking module with the status provided by the AUTOSAR software E2E library, in context of several configurations of the received messages. © 2023 IEEE.

关键词： Actuators

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Designing an Electronic Circuit of the Remotely Operated Underwater Vehicle Based on an Unified Element Base

Designing an Electronic Circuit of the Remotely Operated Und...

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2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2022

作者： Galtsov, German A. Sakovich, Sergey Yu. Department of Automatic Control Systems and Onboard Computer Facilities Saint Petersburg Russia

ISBN: (纸本)9781665409933

This article presents a project of a small-sized remotely operated underwater vehicle (ROV) being designed and developed at SMTU. Paper describes a functional scheme of the underwater vehicle, motion control units with the proposed standard components, propulsion and steering system, system of remote control, and possible ROV modifications. © 2022 IEEE.

关键词： Remote control

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Output Tracking for Uncertain Time-Delay systems via Robust Reinforcement Learning control

Output Tracking for Uncertain Time-Delay Systems via Robust ...

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第43届中国控制会议

作者： Zezhi Tang J Anthony Rossiter Xin Jin Benyuan Zhang George Panoutsos Department of Automatic Control and Systems Engineering University of Sheffield

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The composite optimal output tracking control problem of time-delay uncertain systems is investigated in this paper. To improve the robustness of the uncertain systems, a disturbance observer-based(DOB) compensation scheme is proposed to reduce the influence of the *** further enhance the control efficiency, we incorporate a Smith predictor into the control framework to precisely predict future states of the delayed system. Subsequently, a composite actor-critic reinforcement learning(RL) scheme is built for approximating both the ideal value function associated with the compensated system and the optimal control policy in real-time, by augmenting the system states with the output tracking error. To counteract the impact of the probing signal, concurrent learning is employed in this paper with the data collected from the known system *** results based on an experimental platform of slot die coating machines demonstrate the effectiveness of the proposed scheme.

关键词： Smith Output Tracking for Uncertain Time-Delay systems via Robust Reinforcement Learning control

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Tide Level Prediction Using NARX-based Recurrent Neural Networks 27

Tide Level Prediction Using NARX-based Recurrent Neural Netw...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Nikentari, Nerfita Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Tide level forecasting is one of major components in oceanography and marine research. The future prediction of tides is an important issue for human and environment. This study proposes new tide level prediction methods with multiple input multiple output (MIMO) approach based on nonlinear autoregressive with exogenous inputs (NARX) model and three types of recurrent neural networks (RNNs), namely, long short-term memory (LSTM), bi-directional long short-term memory (biLSTM) and gated recurrent unit (GRU) networks. We develop three types of NARX models: NARX-LSTM, NARX-biLSTM and NARX-GRU. We evaluate the three models and compare their performances with LSTM, biLSTM and GRU network models. It turns out that the proposed MIMO NARX based RNN models, especially NARX-LSTM and NARX-GRU have better performance in comparison with their RNN counterparts (i.e., LSTM, biLSTM, and GRU). The NARX-GRU model shows the best performance for one step and three step ahead prediction in terms of RMSE in each location and average RMSE, while NARX-LSTM performs better for two steps ahead prediction in terms average RMSE. The results also show that the mixture of RNN and NARX improves the performance of RNN model for all the four tasks at four locations in the UK (Wick, Workington, Ullapol and New Heaven) and for the three time horizons (i.e., 1, 2 and 3 steps ahead). © 2022 IEEE.

关键词： Forecasting

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of systems Science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Link Transmission Stability Detection Based on Deep Learning in Opportunistic Networks 5th

Link Transmission Stability Detection Based on Deep Learning...

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5th EAI International Conference on Multimedia technology and Enhanced Learning, ICMTEL 2023

作者： Ren, Jun Wang, Ruidong Jia, Huichen Li, Yingchen Pei, Pei North Automatic Control Technology Institute Taiyuan030000 China Department of Foreign Languages Changchun University of Finance and Economics Changchun130200 China

ISBN: (纸本)9783031505768

In order to solve the low throughput and high delay problems of traditional link transmission stability detection methods, a detection method of link transmission stability in opportunistic networks based on deep learning is proposed. Establish the network link blocking model. Considering the impact of path delay, analyze the network link information to adjust the hierarchical structure, divide the link data into data blocks, and complete the construction of the link model. According to the link transmission data, the ground point coordinates of the network links in the area are obtained. Under the constraint of link carrying capacity, obtain the barcode sent by network transmission. Calculate the number of packets sent by the network source during congestion, extract network level features using deep learning algorithm, select the number of network layers, set hidden layer nodes, implement network training according to the learning rate, achieve the construction of classification prediction model, and complete the link transmission stability detection. The experimental results show that the proposed link transmission stability detection method can effectively improve the throughput of opportunistic network links and reduce the communication delay of opportunistic networks. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Stability

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Fault Diagnosis Method for Rotating Machinery Based on Attention Adversarial Transfer Networks Under Variable Operating Conditions 14

Fault Diagnosis Method for Rotating Machinery Based on Atten...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Gao, Tianyu Yang, Cheng Yang, Jingli Department of Automatic Test and Control Harbin Institute of Technology Harbin China China Institute of Marine Technology & Economy Beijing China

ISBN: (纸本)9798350301359

Recently, deep learning-based fault diagnosis methods for rotating machinery have achieved promising classification performance under the independent and identical distribution of training and test data. However, the complex and variable operating conditions, including bearing speed, working load, and load torque, of rotating machinery in practical applications lead to the problem of inconsistent distribution between training and testing data. To address the above issue, an attentional adversarial transfer network is developed to improve the accuracy of rotating machinery fault diagnosis under variable operating conditions by using adversarial transfer learning techniques. First, a feature extractor based on self-normalized convolutional neural networks (SCNN) is constructed to provide effective features for different domains. A lightweight attention layer is further designed to reinforce the structural information in the features while reducing the redundant disturbances. Then, a discriminator based on probability calculation is employed to perform adversarial learning with the feature extractor so as to achieve the alignment of feature distribution between different domains. Finally, an efficient loss function is designed to drive efficient adversarial transfer learning for the whole model. To verify the effectiveness and superiority of the proposal, the Paderborn University (PU) bearing dataset is selected for fault diagnosis experiments, where proposed method achieves an average cross-domain fault diagnosis accuracy of 88.36%. The experimental results indicate that this approach can implement accurate fault identification for rotating machinery under variable operating conditions. © 2023 IEEE.

关键词： adversarial transfer learning attention mechanism fault diagnosis rotating machinery

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A Novel Cutting Fluids Performance Assurance System for CNC Machine Tools

A Novel Cutting Fluids Performance Assurance System for CNC ...

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2024 International automatic control Conference, CACS 2024

作者： Chou, You-Syuan Chen, Yi-Shiang Chiang, Fu-Yu Yang, Chen-Yen Shi, Zhe-Xun Lin, Yu-Sheng Lin, Shang-Chih Su, Shun-Feng Huang, Yennun Feng Chia University Institute of Automatic Control Engineering Taichung407 Taiwan Feng Chia University Department of Automatic Control Engineering Taichung407 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

ISBN: (纸本)9798350354904

In the CNC machine tool industry, cutting fluid is a crucial component of the machining process. Nevertheless, as the duration of cutting fluid use increases, its performance progressively deteriorates, presenting a significant challenge that necessitates resolution. This study introduces an innovative intelligent monitoring system designed to optimize the preparation of cutting fluid in CNC machine tools. The aim is to extend the fluid's service life, mitigate tool wear, and address issues arising from fluid degradation. The system integrates a heating plate, sensors, pumping motors, and embedded systems to monitor and manage the cutting fluid effectively. Testing was conducted using a customdesigned container, and data analysis identified the optimal supply strategy, thereby extending the usable lifespan of the cutting fluid. This system enhances production longevity and allows for the configuration of containers with appropriate capacities based on specific requirements, ensuring consistent and high-quality production. © 2024 IEEE.

关键词： Cutting fluids

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