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Anti-Windup Active Power control for Wind Turbines Considering Loads Reduction

Anti-Windup Active Power Control for Wind Turbines Consideri...

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data driven control and learning systems (ddcls)

作者： Guozhong Wang Hao Luo Xuguo Jiao Zhibo Yang Xinxin Zhao School of Information and Control Engineering Qingdao University of Technology Qingdao P. R. China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the proportion of wind power in electricity contribution is increasing, and wind power generation technology has developed rapidly. How to enhance the stability of wind turbine systems and extend their service life are still challenging problems due to uncertain and intermittent characteristics of natural wind. In order to ensure that variable speed wind turbines track the active power instructions released by the power system, an improved anti-windup rotor speed control strategy is designed to switch different control strategies according to the wind turbine rotor speed value, with the aim of optimizing the mechanical loads of pitch systems. Firstly, in the low wind speed region, the maximum wind energy capture is achieved by optimal torque control. When the rotor speed increases to a critic value, torque control determined by the turbine's rated output power and rotor speed is adopted. In the high wind speed region, the pitch controller is used to adjust the wind energy capture coefficient to maintain the rotor speed at its rated value. Moreover, in order to solve the problem of integral saturation in traditional rotor speed control methods, this paper proposes a bounded constraints assisted Proportional-Integral (PI) pitch angle control scheme to optimize the operation of pitch system. this control method can effectively eliminates integral saturation, mitigate fatigue loads of wind turbines, reduces overshoot of rotor speed and ensures safe and stable operation of wind turbines. Finally, the proposed method is simulated on OpenFAST platform to verify its advancement.

关键词： Wind energy Wind speed Heuristic algorithms Velocity control Torque control Rotors Wind power generation

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Optimisation of Test Stimulus for Analogue Circuits Based on the AOA

Optimisation of Test Stimulus for Analogue Circuits Based on...

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data driven control and learning systems (ddcls)

作者： Gangqiang Li Gongye Yu Zhaoyang Li Hualiang Zhang Guangyao Hao Marine Design and Research Institute of China Shanghai China Science and Technology on Water Jet Propulsion Laboratory Shanghai China China Institute of Marine Technology & Economy Beiing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Test stimulus is the key to the detection for analog circuits of complex integrated electronic devices, in order to improve the accuracy of analogue circuits detection, this paper proposes an optimization method for analogue circuit test excitation based on the Arithmetic Optimization Algorithm (AOA), which consists of the AOA and the Wilkes statistic as the optimization objective. Initially, the amplitude-frequency characteristics of the circuits are acquired via Pspice simulations. Subsequently, the AOA algorithm establishes vector individuals based on these amplitude-frequency profiles of the circuits. the Wilkes statistics are then employed to compute the intraclass and interclass distances across various circuit states, encompassing normal variations. the AOA, recognized for its heuristic nature, robust optimization capabilities, swift computational speed, and global optimization pursuit, is utilized to optimize the test frequencies. Once the optimal test frequency is determined via the AOA, it is employed as the input for testing the circuit under evaluation. the time-domain output signal of the tested circuit is subsequently classified using a linear support vector machine (SVM) to ascertain the efficacy of the proposed optimization method. To validate the efficacy of this proposed method, a four-op-amp dual-secondary bandpass filter circuit is chosen for experimental verification. the results obtained from the experiments showcase that the test excitation derived from this method achieves an accuracy of 97.02%. this accuracy surpasses that achieved by commonly used impulse sources when utilizing a simpler classifier like the linear SVM. the experimental outcomes robustly demonstrate the effectiveness of this method in optimizing test excitations for analogue circuits.

关键词： Support vector machines learning systems Accuracy Optimization methods Analog circuits SPICE Vectors

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Audio Keyframe Extraction of Belt Conveyor Inspection Robot Based on Frequency Band Entropy and Pixel Significance

Audio Keyframe Extraction of Belt Conveyor Inspection Robot ...

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data driven control and learning systems (ddcls)

作者： Xiaoyu Yang Minghua Luo Chongquan Fang Ying She Chongqing Research Institute of China Coal Science and Technology Corporation Chongqing State Key Laboratory of Coal Mine Disaster Prevention and Control Chongqing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

As the main transportation equipment in coal mines, the safety and stability of belt conveyors are of great significance for the production efficiency and safety of coal mines. the traditional manual inspection method has problems such as low efficiency, poor real-time performance, and potential safety hazards. Fixed sensors have problems such as difficult deployment, difficult maintenance, and high cost. the emergence of belt conveyor inspection robots has provided a new mode for inspection methods. Unlike a fixed sensor with a single fixed mode of data, the data collected by the inspection robot during its movement is dynamic and time-varying. the traditional fixed sensor fault diagnosis methods often have poor results when directly applied to inspection robots. this article proposes an audio frame extraction technique based on frequency band entropy and pixel significance, based on the dynamic audio signals collected by a belt inspection robot and the comprehensive utilization of audio and image processing techniques. Frequency band entropy can extract faults or abnormal noise frames with concentrated audio energy, and image significance can extract faults or abnormal noise frames caused by slight continuous collisions. through experimental verification, this method can achieve the extraction of abnormal audio frames dynamically collected by inspection robots, and can extract audio near faulty rollers such as breakage, wear, rotor blockage, and bearing damage. the extraction of audio keyframes can provide basic data for subsequent roller fault localization and recognition, and provide research ideas for roller fault diagnosis methods based on inspection robot audio.

关键词： Fault diagnosis Location awareness Noise Inspection Belts Robot sensing systems Entropy

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A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network

A Hybrid MTS Anomaly Detection Method Based on Reconstructio...

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data driven control and learning systems (ddcls)

作者： X. Yuan Ke Ning K. Wei Yan-Lin He Qun-Xiong Zhu Ming-Qing Zhang Yi Luo Yang Zhang College of Information Science and Technology Beijing University of Chemical Technology Beijing P. R. China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing P. R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P. R. China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Effective anomaly detection of multivariate time series data is crucial for modern industrial applications. However, existing works usually ignore spatial and temporal dependencies of series. the data with complicated spatial-temporal feature put forward higher requirements for accurate detection of anomalies. In this paper, we propose an anomaly detection method for multivariate time series based on spatial-temporal network (STN). Firstly, the time series are reconstructed by Generative Adversarial Networks (GAN). the reconstruction block is used to amplify the influence of the abnormal state and prompt the anomalies more detectable, as poorly reconstructed data of abnormal state further worsen the quality of the final predicted value. Secondly, an adaptive learning graph structure layer is proposed. the graph structure is learned by adaptive method without any prior knowledge and is used for reconstructed data prediction. thirdly, we use STN to extract spatial and temporal dependencies of series. Especially, attention mechanism is introduced into graph convolution network to enhance the prediction performance by focusing on more important neighbor node message. Finally, our model is tested on 3 public datasets to evaluate performance. the results demonstrate that our method performs better than baselines.

关键词： learning systems Attention mechanisms Convolution Time series analysis Neural networks Focusing Generative adversarial networks

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Scheduling Optimization of Coal Washing Process Based on Two-Stage Fuzzy Heuristic Algorithm

Scheduling Optimization of Coal Washing Process Based on Two...

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data driven control and learning systems (ddcls)

作者： Hui Zhou Zhiyuan Wang Zhongyang Han Linqing Wang Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Coal washing and sorting plays a vital role for coal production. Reasonable scheduling of washing and sorting equipment, crushers and various coal silos in the coal washing and sorting process can effectively increase the total value of product coal and reduce the total energy consumption of equipment. In this paper, a multi-constrained nonlinear mixed integer programming model considering the start and stop of each equipment in the washing process is established to realize the specific regulation of the washing process. In order to overcome the problem such as local convergence when solving high-dimensional space problems, a two-stage fuzzy heuristic algorithm is proposed in this paper. In the first stage, a variety of population genetic algorithms based on fuzzy adaptive strategy control are designed for global search to establish an elite population bank to preserve high-quality individuals produced by various groups. In the second stage, an improved simulated annealing algorithm is used to perform a local search with a backtracking process on specific neighbors of individuals in the elite population bank, which can improve the stability of the algorithm and reduce the situation of falling into local convergence. then the real data of a Chinese coal mining company was used for experiments, where the proposed algorithm is compared with other commonly used methods. the experimental results demonstrate that the proposed algorithm can increase the total coal value of the washed product by at least 1.7% compared to other algorithms, and the average total number of starts and stops of the equipment is reduced by about 7.3%, which are effectively beneficial for the related enterprise.

关键词： Heuristic algorithms Sociology Coal Simulated annealing Production Scheduling Stability analysis

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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data driven control and learning systems (ddcls)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. this approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. the algorithm has been extensively evaluated on the developed AGV experimental platform. the experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression Visualization Laser radar Simultaneous localization and mapping conferences Robot vision systems Measurement uncertainty

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Capacity Configuration Model for Power Dispatch and Clean Energy Peak Shaving With Large-Scale Liquid Flow Batteries

Capacity Configuration Model for Power Dispatch and Clean En...

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data driven control and learning systems (ddcls)

作者： Dianyang Li Dai Cui Nan Xiao Jiaze Li Huijie Jiang Feiran Yang Power Dispatching Control Center State Grid Liaoning Electric Power Co. LTD Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the rapid development of renewable energy has brought us closer to achieving carbon peak and carbon neutrality goals, but it has also exacerbated the challenge of load fluctuation in power grids. To enhance grid stability and security, it is essential to implement effective peak shaving measures. Relying solely on various types of power generation to complement each other has limitations. In this paper, we propose a model and method for configuring the capacity requirements of large-scale liquid flow batteries involved in grid peak shaving, within the context of increasing demand for renewable energy integration. Our model utilizes an optimization approach with the objective of minimizing the operational costs of the peak shaving system. the model not only considers the technical constraint index of the system, but also innovatively introduces the reliability constraint of the probability of insufficient peak regulation and the reliability constraint of the mean value of insufficient peak regulation, and forms the optimal configuration model of peak regulation capacity of large-capacity flow battery assisted power system. the model takes the 24-hour load time series and wind power time series as inputs, employs the YALMIP toolbox, and utilizes the CPLEX solver to optimize the capacity and power requirements for large-scale liquid flow batteries assisting in renewable energy dispatch. A simulation analysis is conducted using a case study with a 24-hour load time series and wind power time series as inputs to evaluate the proposed model.

关键词： Green energy Costs Time series analysis Fluid flow Power system stability Wind power generation Reliability engineering

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Analysis Pipeline for High-Dimensional Neuromechanical Model Improvement 13th

Analysis Pipeline for High-Dimensional Neuromechanical Model...

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13th International conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Fernandez, Camila J. McManus, Jeffrey M. Li, Yanjun Bennington, Michael J. Quinn, Roger D. Chiel, Hillel J. Webster-Wood, Victoria A. Department of Mechanical Carnegie Mellon University PittsburghPA15232 United States Department of Biomedical Engineering Carnegie Mellon University PittsburghPA15232 United States McGowan Institute for Regenerative Medicine Carnegie Mellon University PittsburghPA15232 United States Department of Biology Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Mechanical Engineering Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Neurosciences Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Biomedical Engineering Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States

ISBN: (纸本)9783031725968

To capture and understand animal behavior, engineers and biologists seek to develop biologically accurate neuromechanical models of muscle dynamics and neural control. However, demand-driven enhancement of complex neuromechanics, such as the multifunctional Aplysia californica feeding apparatus, can be challenging due to the multidimensional biomechanical and neural models involved. We propose an analysis pipeline that enables reinforcement learning (RL) to classify which aspects of an engineered neuromechanical model can accurately capture animal behavior. As an example, prioritizing where demand-driven enhancement of a biomechanical and neural model is needed, the neural model of a published neuromechanical model of Aplysia swallowing during feeding was replaced with an RL controller and their performances were compared and correlated with in vivo swallowing behavior. By comparing the performance of the neural model and the learned model to in vivo animal behavior, we can pinpoint areas for improvement. the analysis pipeline identified that the neuromechanical model confidently captured force performance with no significant difference from animal swallowing force behavior. It most usefully also indicated that the biomechanical model will need to be improved in future iterations to better capture motor neuron activity. Future work should explore the accuracy of the RL-enabled analysis pipeline with a more advanced biomechanical model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Emotion-driven Intelligent Product Design Based on Deep learning

Emotion-Driven Intelligent Product Design Based on Deep Lear...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Mengmeng Zhang Polus International College Chengdu China

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

this research investigates the integrated application of deep learning technologies in the field of emotion-driven interior soft decoration product design. through comprehensive online surveys conducted with over 1000 participants and coupled with biometric feedback from 300 individuals, this study offers a multi-faceted analysis of user emotions during interactions with interior soft decoration products. the employment of a finely-tuned Convolutional Neural Network (CNN) model facilitated the extraction of key emotional design elements, indicating that around 520 of the surveyed participants were satisfied with certain interior soft decoration designs, while a minority of 80 expressed dissatisfaction. Moreover, the biometric data revealed a significant correlation between increased heart rates (exceeding 85 beats per minute) and intense emotional responses. the integration of biometric and questionnaire textual data suggested that users indeed experienced strong positive emotions in interactions with specific soft decoration products. the study also addresses potential challenges, including possible biases in data collection and the inherent limitations of deep learning models. the findings underscore the significant potential of deep learning in capturing the complex patterns of user emotions, providing valuable insights for the future design of emotion-centric interior soft decoration products. through this interdisciplinary approach, the study charts a promising direction for the advancement of artistic disciplines such as interior design and soft decoration product design, emphasizing the importance of understanding and incorporating user emotion in the design process.

关键词： Deep learning Surveys Knowledge engineering Heart rate Biometrics (access control) Biological system modeling Neural networks

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Blockchain and Machine learning for data-driven Insights in Consumer Behavior Analytics

Blockchain and Machine Learning for Data-Driven Insights in ...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Bhupender Singh Rawat Nitin thapliyal Sunil Kumar Pal Nikhil Mishra Rohan Verma College of Smart Computing COER University Roorkee Department of Computer Science & Engineering Graphic Era Hill University Dehradun Department of Computer Science & Application Axis Colleges Kanpur Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. this comparative study shows how neural networks can handle complex data and make good predictions. these networks will be increasingly accessible in daily life.

关键词： Consumer behavior Machine learning algorithms Sensitivity analysis Neural networks Decision making data protection Machine learning

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