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8th International Conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Kumar, Swetha R Iniyal, U.S. Harshitha, V. Abinaya, M. Janani, J. Jayaprasanth, D. Psg College of Technology Department of Instrumentation and Control Systems Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665408165

In today's society, technological breakthroughs have led to the automation of most industries in order to ensure smooth operations and minimize human interaction. The automation has led to challenges in an important sector called Fault detection and diagnosis. Rather than univariate alerts, fault detection and diagnostics focus on abnormal situations, which is crucial for maintaining optimum operating conditions and anticipating chemical process hazards. Model-based and process history-based fault detection and diagnosis are the two types of fault detection and diagnosis, with the second being more commonly referred to as data-based. Model-based methods are often based on a fundamental grasp of the process's physics. Using a model-based approach, this paper tries to estimate and identify the types of problems in centrifugal pumps. The pump dynamics are estimated using a system identification approach in this paper. The outcomes of the model estimations are then summarized. Estimated and real values are compared while creating residues. Studies are carried out to look into the consequences in both faultless and faulty situations. © 2022 IEEE.

关键词： Fault detection

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Hybrid Switched-Inductor Switched-Capacitor High Step-Down Converter

Hybrid Switched-Inductor Switched-Capacitor High Step-Down C...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Stala, Robert Bhatnagar, Pallavee Mondzik, Andrzej Waradzyn, Zbigniew Pirog, Stanislaw Folmer, Szymon Gupta, Krishna Kumar Agh University of Krakow Department of Power Electronics and Energy Control Systems Kraków Poland National Institute of Technical Teachers' Training and Research Department of Electrical and Electronics Engineering and Education Bhopal India Thapar Institute of Engineering and Technology Electrical and Instrumentation Engineering Department Patiala India

ISBN: (纸本)9798350364446

This paper presents a hybrid DC-DC converter composed of switch-mode DC-DC converters and switched-capacitor circuits. The system has continuous voltage regulation, low voltage stress across switches, low inductor current, low input and output current ripple values, and high frequency of the output capacitor voltage ripples. Operation of the converter, its efficiency and loss distribution in transistors are demonstrated in the paper. © 2024 IEEE.

关键词： Capacitor bank

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Estimating Wind Energy Potential: Advanced Smart Hybrid VAWT Analysis in Banjarmasin, Indonesia 6

Estimating Wind Energy Potential: Advanced Smart Hybrid VAWT...

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6th International Conference on Power engineering and Renewable Energy, ICPERE 2024

作者： Irawan, Elysa Nensy Miyagawa, Mei Putra, Maulana Yamashita, Ken-Ichiro Fujita, Goro Functional Control Systems Shibaura Institute of Technology Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Indonesia Agency for Meteorology Climatology and Geophysics Centre for Instrumentation Calibration and Engineering Jakarta Indonesia Salesian Polytechnic Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798331504694

This study explores the potential of smart hybrid Savonius-Darrieus wind turbines to optimize wind energy capture in Indonesia, a country with significant wind resources. With solar and wind energy leading the renewable energy transition, this research focuses on enhancing wind turbine efficiency in regions with variable wind speeds. The hybrid Savonius-Darrieus turbine integrates the complementary strengths of both turbine types, employing a solenoid mechanism to switch between modes and a filtering method to stabilize operation. Our results demonstrate that this innovative design significantly reduces mechanical wear and operational instability by minimizing unnecessary switching events. Testing in Banjarmasin, Indonesia, the daily average wind energy potential for the smart hybrid and conventional hybrid turbines is calculated to be 6.1 kWh and 4.7 kWh, respectively. This condition revealed that the smart hybrid turbine generates approximately 30.89% more power than conventional hybrid turbines, highlighting its superior adaptability to fluctuating wind conditions. The turbine's ability to maintain efficient energy capture across a wide range of wind speeds underscores its potential for broader application in diverse environmental settings. © 2024 IEEE.

关键词： Windmill

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Design and Implementation of Biological Inspired Algorithm Coupled with Sliding Mode controller for Autonomous Cruise control System

Design and Implementation of Biological Inspired Algorithm C...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Gulzar, Muhammad Majid Ahmed, Muhammad Mughees Shakoor, Adnan Tahreem, Huma Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Department of Control & Instrumentation Engineering Dhahran31261 Saudi Arabia University of Central Punjab Department of Electrical Engineering Lahore Pakistan

ISBN: (纸本)9798350304602

In modern automobiles, driving assistance systems are extensively used for efficiency, safety and comfort. These systems are utilized for autonomous driving, cruise control, and obstacle avoidance. All of these systems consist of a large number of radars and sensors that generate rapid responses to control the speed or direction of a vehicle. The primary objective of this type of technology is to reduce accidents by reducing driver tension and enhancing travel comfort. The purpose of this study is to develop a controller for cruise control in vehicles that can precisely, rapidly, and efficiently attain the desired speed. In this study, biological inspired optimization tuned Sliding Mode controller is utilized which is more accurate and robust, and can help to adjust the parameters so that the cruise control system provides minimal error, stable results with the fewest disturbances possible. In order to demonstrate the superiority of the proposed controller, the results are subsequently compared to currently employed cutting-edge technologies for cruise control. © 2023 IEEE.

关键词： Autonomous vehicles

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Modeling of an external force estimator for an end-effector of a robot by neural networks

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Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2023年第8期46卷 895-904页

作者： Junplod, Goragod Kornmaneesang, Woraphrut Chen, Shyh-Leh Wongsa, Sarawan Department of Control Systems and Instrumentation Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan Department of Mechanical Engineering National Chung Cheng University Chiayi Taiwan Advanced Institute of Manufacturing with High-Tech Innovations National Chung Cheng University Chiayi Taiwan

This paper proposes a method to estimate external forces at the tip of a robot end-effector by using a neural network model. In order to avoid the use of an expensive force sensor in the training purpose, the proposed method implements the indirect training method by including the inverse dynamic model of the robot manipulator to the training algorithm with available information from a default robot system. In this method, the robot dynamics equations are necessary for the training, therefore a disturbance observer is adopted to deal with the existing uncertainties and errors. The performance of the proposed estimation method is evaluated through experiments of a 5-DOF robotic experimental platform, comparing to another existing estimation method using recurrent neural network with a type-1 disturbance observer for the external force estimation. The estimation results show that the behavior of the estimated external forces strongly correlates with the applied external forces and the proposed method is superior to the other method. © 2023 The Chinese Institute of Engineers.

关键词： Recurrent neural networks

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Deep learning framework for wind speed prediction in Saudi Arabia

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Neural Computing and Applications 2025年第5期37卷 3685-3701页

作者： Alabdulhadi, Arwa Ahmed Rehman, Shafiqur Ali, Amjad Shafiullah, Md Physics Department College of Science and Humanities Imam Abdulrahman Bin Faisal University Jubail3065 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory, through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Clean energy

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Model-Free Predictive control in Anaerobic Digestion Wastewater Treatment

Model-Free Predictive Control in Anaerobic Digestion Wastewa...

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SICE International Symposium on control systems (ISCS)

作者： Jiravit Pratvittaya Department of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9784907764852

ISBN: (纸本)9798331533502

Anaerobic digestion is a wastewater treatment method that utilizes bacterial decomposition to convert organic matter into biogas, which provides both environmental and economic benefits. However, controlling biogas production in anaerobic digestion systems poses significant challenges due to the complexity and non-linearity of the digestion process. This study explores the application of model-free predictive control for anaerobic digestion systems, utilizing only inputoutput data without using mathematical models as in conventional control approaches. Previous research has demonstrated the applicability of this approach for nonlinear systems, particularly in multi-input, multi-output nonlinear systems. In this study extends the methodology to controlling in methane production of anaerobic digestion systems, evaluating its effectiveness through numerical simulations.

关键词： Methane Production Predictive models Numerical simulation Mathematical models Biogas Numerical models Wastewater treatment Nonlinear systems Predictive control

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Hilbert Transform Based Combined 1d and 2d Cnn Framework for Power Quality Disturbance Classification

SSRN

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SSRN 2024年

作者： Savaridass, M. Pravin Esakkirajan, S. Department of Electronics and Instrumentation Engineering Bannari Amman Institute of Technology Sathyamanglam India Department of Instrumentation and Control Systems Engineering PSG College of Technology Coimbatore India

Power quality disturbances (PQDs), primarily induced by nonlinear loads in electric power systems, adversely affect signal quality, resulting in decreased overall efficiency. Accurate identification and classification of PQDs helps to manage power quality problems by making necessary corrective measures. This research introduces a combined convolutional neural network (CNN) method that combines a 1D CNN with a 2D CNN to address the PQD classification challenge. The features are extracted from the envelope 1D signal and 2D image generated using Hilbert Transform (HT). The proposed approach is used to classify 16 PQDs, including single and combined disturbances, under various noise levels. The combined 1D and 2D CNN method utilizes the VGG-19 architecture for classification, and the HT based signal processing method outperforms conventional signal processing techniques such as STFT and CWT, with accuracies of 99.60%, 98.91%, and 95.60% under noise levels of 40 dB, 30 dB, and 20 dB, respectively. The proposed methodology not only demonstrates robust PQD classification but also offers advantages in reduced computation time and memory demand, making it a promising tool for real-time power system monitoring and analysis. © 2024, The Authors. All rights reserved.

关键词： Electric loads

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Stabilization of Inverted Pendulum using Neural Network controller

Stabilization of Inverted Pendulum using Neural Network Cont...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： Swetha R Kumar R Gunavarshini S Nithiyashri G S Priyanka R Rithiga Department of Instrumentation and Control Systems Engineering PSG College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The paper aims to develop an innovative and robust control system for stabilizing an inverted pendulum using a neural network controller. This combines principles of control theory with the power of artificial intelligence to address the complex and dynamic nature of the inverted pendulum problem. The controller used here is neural network model predictive controller. The data obtained from the plant model is used to train the neural network. Various numbers of hidden layers were given and their behaviour was analyzed and the one with the minimum error was chosen. The controller is trained to produce a result with the least amount of error by providing the reference signal and the error value as inputs. After the successful training, the neural network controller stabilizes the inverted pendulum model.

关键词： Training Robust control Communication systems Computational modeling Neural networks Predictive models Real-time systems Data models control theory Artificial intelligence

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Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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Computer Modeling in engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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