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automatic Generation control of a two-area power system using Genetic Algorithm and Particle Swarm Optimization 8

Automatic Generation Control of a two-area power system usin...

引用

8th international conference on computing in engineering and Technology, ICCET 2023

作者： Muchafangeyi, Kundayi Gill, Amandeep Singh, Manjeet Department of Electrical Engineering Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9781839539237

automatic Generation control (AGC) seeks to maintain a balance between demand and generation and highquality power for all consumers. In this paper, the setup resembles a typical regional electricity system. It has been established through generator-tripping experiments that the simulation grid's frequency characteristics match those of the power grid in real life. To compare and contrast various control techniques the well-established AGC control strategy model is used in this paper. The simulation-based modelling approach for AGC of a two-area thermally coupled power system is discussed. For improving the performance of PID controllers, Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are put forward. The goal is to quickly return the frequency to its normal desired value and have the least undershoots and overshoots in cases of load variations. The PSO and GA methods are applied and compared respectively in terms of settling time, undershoots and overshoots. It has been identified that the outcomes from the proposed approach are effective and reveal improved system performance. © The Institution of engineering & Technology 2023.

关键词： Genetic algorithms

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Potato plant leaf diseases detection and identification using convolutional neural networks

Potato plant leaf diseases detection and identification usin...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Gurusamy, Sriram Natarajan, B. Bhuvaneswari, R. Arvindhan, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Galgotias University India

ISBN: (纸本)9781032669663

Potato plant leaf diseases can cause significant crop losses and impact the quality of potato products. Early identification of these diseases can aid in timely intervention and treatment, minimizing the spread of diseases and reducing crop losses. In this work, we propose the incorporation of Convolutional Neural Networks (CNNs) for the automatic detection and identification of diseases present in potato plant leaves. We compiled a diverse dataset of images of potato plant leaves and used it to train and evaluate a CNN for the classification of potato leaf diseases. The results of the research work shows that the CNN was able to achieve high levels of precision score, F1 score, recall and accuracy over the training and testing splits of the dataset. Our findings demonstrate the potential of CNNs for the automatic identification of diseases present in potato plant leaves, and suggest that this approach could be applied to other types of plant diseases as well. The proposed method has the capability to improve crop management and disease control efforts by enabling the automatic and early detection of leaf diseases, which could have significant economic and environmental benefits. According to our result, the CNN offers a promising method for identifying and detecting leaf diseases on potato plants. This method may also be utilized to create an automated system for disease diagnostics in potato harvests. For the goal of identifying diseases in potato leaves, a Deep Learning (DL) base CNN model has been introduced in this research work. The proposed CNN technique has secured 98.1% accuracy rate for the Plant Village dataset, shows 3% improvement when compared to the existing methods. © 2024 The Author(s).

关键词： Convolutional neural networks

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Small Object Detection for UAVs Using Deep Learning Models on Edge computing: A Comparative Analysis 5

Small Object Detection for UAVs Using Deep Learning Models o...

引用

5th international conference on Circuits, control, Communication and computing, I4C 2024

作者： Vasishta, M S Saathvik Reddy Amireddy, Abhinava Teja Shrivastava, Pranjal Raghuram, S. Talasila, Viswanath Shastry, Prasad N Ramaiah Institute of Technology Dept. of Electronics and Telecommunication Engineering Bangalore India Ramaiah Institue of Technology Dept. of Electronics and Communication Engineering Bangalore India Ramaiah Institute of Technology Dept. of Computer Science Engineering Bangalore India Ramaiah Institute of Technology Dept. of Electronics and Communication Engineering Bangalore India Bradley University Dept. of Electrical and Computer Engineering PeoriaIL United States

ISBN: (纸本)9798331528539

This paper presents a comparative analysis of DNN-models, including TensorFlow Lite, SSD-MobileNet, Mask R-CNN and YOLOv8, for the high-precision detection and classification of small objects in aerial search and rescue scenarios. The study features a proprietary dataset collected through drone flights over an extended period. The model performance was evaluated on compact computing platforms like Jetson Nano and Raspberry Pi, emphasizing detection accuracy and computational efficiency. Results reveal trade-offs between model complexity and real-time processing capabilities. The findings guide the selection of on-board computer models, thereby enhancing drone capabilities in critical operations © 2024 IEEE.

关键词： Drones

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Development of Healthcare Monitoring System with Pollution control in Industrial Sectors Using the Internet of Things 5

Development of Healthcare Monitoring System with Pollution C...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Rajeswari, N Raja Arunkumar, G. Vadivu, M Senthil Gupta, Manish Sethuraman, R. Rajkumar, M. Saveetha Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Lovely Professional University Division of Research and Development Punjab Phagwara India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Information Technology & Engineering Vellore Institute of Technology Vit University Department of Smart Computing Tamil Nadu Vellore India

ISBN: (纸本)9798350321425

Rapid improvement in industrialization and urbanization leads to various impacts on environmental conditions. This leads to a rise in pollution that causes various constraints on human health. Earlier prediction and maintenance of pollution is the solution to obtain a greener ecosystem. This is done through machine learning techniques adopting the Internet of Things. The Internet of Things helps to obtain a smarter environment through real-time monitoring with the control system. Thus machine learning helps to analyze the amount of toxic substances that are emitted from the industries and recorded periodically. They are represented as online controlling and monitoring systems. The sensors and actuators with control devices are used to record the external environmental conditions. The prior intimation of the healthcare is monitored appropriately and intimated to solve earlier. The classification and detection of infections and diseases are done through image processing techniques. Thus the proposed system helps to obtain automatic detection of healthcare and pollution detection systems through machine learning with computational systems. © 2023 IEEE.

关键词： Internet of things

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Broccoli Classification: A Fusion of CNN and AdaBoost 1

Broccoli Classification: A Fusion of CNN and AdaBoost

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1st IEEE international conference on Electronics, computing, Communication and control Technology, ICECCC 2024

作者： Kaur, Arshleen Sharma, Rishabh Thapliyal, Nitin Manwal, Manika Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttrakhand Dehradun248002 India Graphic Era Deemed to be University Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350371802

Although precision farming has been introduced, differentiation of crop species still calls for the development of quick and reliable classification tools used in the process of optimizing cultivation practices, which ultimately boost crop yields. Here we propose a new classification method to identify broccoli varieties particularly of 'Pusa KTS-1', 'Green Magic', and 'Calabrese' with the use of an integrated approach that fuses the strength of Convolutional Neural Networks (CNN) and AdaBoost. This research makes a striking improvement in the accuracy of crop variety classification using the fusion of CNN for powerful feature extraction and AdaBoost which leverages the hard instances to increase the accuracy of the classifier. A database of broccoli images was prepared, auxiliary operations were performed and its diversity was improved to make the model not weak and generalized enough. On this dataset, the CNN-AdaBoost ensemble was trained, validated, and tested and the model achieved an overall accuracy of 93.7% which outperformed standalone models. The model's performance at boundaries validates its potential for automatic classification, which is important for precision agriculture's targets of higher productivity and efficiency. The results of this research consequently transcend beyond the scope of broccoli variety classification, lending ways for improvements in agricultural technologies through the adoption of machine learning. In the future, the model will be applied to other crops and will be used to solve many agricultural problems. Its parameters will be improved to make it more convenient for daily use. © 2024 IEEE.

关键词： Machine learning

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Cloud Resource Load Prediction Based on EEMD-LSTM 4

Cloud Resource Load Prediction Based on EEMD-LSTM

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4th international conference on Computer science, Electronic Information engineering and Intelligent control Technology, CEI 2024

作者： Cheng, Xuemei Ye, Xiaojiang Hou, Zhijian Zhang, Yunhui School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan430200 China School of Mechanical and Electrical Engineering Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9798350378542

With the rapid advancement of cloud computing, accurate load prediction has become essential for optimizing system performance. The nonlinear and non-stationary characteristics of cloud resource load data have increased the complexity of prediction tasks. To address this challenge, this paper proposes a multi-input multi-output load prediction model based on Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM) networks. The model reduces data complexity through EEMD decomposition while effectively capturing the temporal dependencies in the sequence data using LSTM. Experiments conducted on publicly available cluster datasets from Alibaba demonstrate that the EEMD-LSTM model significantly outperforms traditional models, such as LSTM, Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), in predicting CPU and memory usage, as evaluated by metrics including Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). © 2024 IEEE.

关键词： Empirical mode decomposition

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Cloud Based Load Balancer for Energy Conservation 3

Cloud Based Load Balancer for Energy Conservation

引用

3rd IEEE international conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Agith, Kaavya Sivakumar, P. Gideon Samuel Babu, L. Kuber Visnu, S. Divya, S. Dhiwakar, J. Rajalakshmi Engineering College Department of Electrical and Electronics Chennai India

ISBN: (纸本)9798350304329

Imbalanced electrical loads result in reduced efficiency of connected equipment and wiring, leading to substantial energy waste and financial losses. Maintaining equilibrium in the electrical system can be accomplished by evenly distributing single-phase loads across the three phases, either through manual or automatic methods. Real-time monitoring of voltage and current levels across all transformer phases and individual loads enables automated load distribution or management. This data can be instantly uploaded to cloud storage for remote accessibility, facilitating efficient load balancing and scheduling. A processing device executes an algorithm that analyzes the incoming data and determines how to redistribute loads across different phases based on the received current measurements. This process aims to bring the system closer to a balanced state. TRIAC Switches enable the transfer of loads from the current phase to any other phase. This system enhances efficiency and stability while minimizing time, energy, and financial losses. © 2024 IEEE.

关键词： cloud based control cloud based energy management data logging load balancing

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Feasibility of Wide Area Network Traffic Distribution in Cloud computing Enabled Wireless Networks

Feasibility of Wide Area Network Traffic Distribution in Clo...

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2024 international conference on E-Mobility, Power control and Smart Systems, ICEMPS 2024

作者： Mimani, Sushant Pillai, Sanjaikanth E. Vadakkethil Somanathan Jindal, Mayank Jiwani, Nasmin Kiruthiga, T. GoDaddy SnohomishWA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States San Jose CA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Cloud computing enabled Wi-Fi networks provide an outstanding opportunity for corporations to distribute extensive area community (WAN) site visitors and advantage from reduced latency because of the proliferation of hyper connectivity. The feasibility of implementing a Wide Area Network (WAN) traffic distribution system in cloud computing enabled wireless networks was studied. The study aimed to determine the practicality and effectiveness of this approach in improving network performance. For the experiment, a sample network topology was designed to simulate real-world scenarios and subjected to diverse traffic loads. The results showed that the WAN traffic distribution system was indeed feasible and could effectively distribute and balance network traffic in cloud computing enabled wireless networks. Furthermore, key metrics such as network latency, packet loss, and throughput were significantly improved compared to traditional networks without the WAN distribution system. Further research and development in this area could potentially lead to improved network efficiency and enhanced user experience in the rapidly growing field of cloud computing. Those consequences reveal the feasibility of deploying WAN site visitors distribution in cloud computing-enabled wireless networks, and serves as a starting point for further study aiming to improve the performance of such networks. © 2024 IEEE.

关键词： Wide area networks

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Application analysis of three-phase four-wire system combining adjustable reactance and zero-sequence filter 9

Application analysis of three-phase four-wire system combini...

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9th international conference on Energy Materials and electrical engineering, ICEMEE 2023

作者： Zhou, Xiaoxiong Zhang, Guangming Shi, Zhihan College of Electrical Engineering and Control Science Nanjing Tech University Nanjing211899 China

ISBN: (纸本)9781510672765

This paper presents the design of a power quality governor combining adjustable reactance and zero sequence filter, including a zero-sequence filter module, an adjustable reactance module, a zero-blocker module, a current sampling module and an MCU intelligent control system. The zigzag transformer module is designed to reduce the current in the neutral line through its own electromagnetic effect in order to achieve power savings. The adjustable reactor is introduced for non-linear loads and works by sampling the current of each phase of the three-phase circuit, calculating its unbalance through the MCU module to output the corresponding three-phase PWM waveform to adjust the adjustable reactor winding, thus adjusting the three-phase reactor winding connected in series in the three-phase circuit to achieve the purpose of balancing the three-phase load. To address the problem of unbalanced three-phase voltages, a zero resistor is connected in series with the three-phase circuit to produce a large zero-sequence impedance, which reduces the three-phase unbalance current caused by the three-phase unbalance voltage. This paper achieves the goal of reducing the zero sequence harmonic components in the three-phase circuit and reducing the current on the neutral line to achieve power saving. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tunable filters Transformers Nonlinear filtering Power supplies Design and modelling computing systems Device simulation Harmonic generation Power grids Pulse signals

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Fault Diagnosis of INFO-SVM Transformer Based on ADASYN 3

Fault Diagnosis of INFO-SVM Transformer Based on ADASYN

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3rd international conference on electrical engineering and control science, IC2ECS 2023

作者： Feng, Xin Wu, Weili School of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350382426

The imbalance between different types of DGA data in transformers makes power transformer fault diagnosis models tend to identify the tested samples as categories with a higher proportion in the training samples, which can lead to a decrease in consistency for imbalanced datasets. At the same time, common methods have the problem of low accuracy in fault diagnosis. In this regard, an ADASYN-INFO-SVM transformer fault diagnosis model is proposed, which can synthesize samples of the original few class data while retaining the characteristics of the few class data, so that the number of different types of samples in the training set reaches equilibrium. At the same time, the vector weighted average algorithm (INFO) is used to optimize the support vector machine (SVM) while achieving automatic optimization of SVM parameters, it can also improve the overall diagnostic accuracy. The results show that ADASYN can effectively reduce the imbalance phenomenon, fully extract fault information, and the fault diagnosis accuracy of the INFO optimized SVM model after imbalance processing reaches 96.58%, which is better than the optimization results of other commonly used algorithms. © 2023 IEEE.

关键词： Fault diagnosis INFO-SVM Transformer Unbalanced data processing

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