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Fault Detection and Classification in the ieee 9-Bus System Integrated with distributed Generation 3

Fault Detection and Classification in the IEEE 9-Bus System ...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Gani, Md. Manjurul Das, Partha Prathim Mitra, Joy Chittagong University of Engineering & Technology Department of EEE Chattogram Bangladesh

ISBN: (纸本)9798350388282

The importance of integrating distributed Generation (DG) into power system networks has increased in recent years due to rising electricity demand and limited production capacity. DG provides various solutions to various challenges, including providing alternative energy sources, reduces losses in transmission & distribution, and enhancing voltage stability etc. However, it also poses difficulties such as optimal placement, sizing, as well as fault detection in the system, etc. This paper proposes a comprehensive approach to address these difficulties through DG optimization and fault detection and classification in the ieee 9-bus System. The suggested approach optimizes the placement and sizes of DG by minimizing active power losses and improving the voltage profile. The optimal result in the ieee 9-bus system is achieved with a 40 MW DG unit located at bus location 5. The fault detection and classification aspect utilizes Discrete Wavelet Transform (DWT) techniques to identify abnormal conditions and provide accurate and quick responses to maintain the desired level of reliability in the power system. The proposed approach was tested on different case studies in the MATLAB/SIMULINK environment. The proposed method can quickly detect and respond to all types of power system faults without errors, ensuring the stability of the power system. The research findings have significant implications for the optimal integration of DG in power systems and the development of effective fault detection and classification methods. © 2024 ieee.

关键词： distributed power generation

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Addressing Collective Computations Efficiency: Towards a Platform-level Reinforcement Learning Approach 3

Addressing Collective Computations Efficiency: Towards a Pla...

引用

3rd ieee international conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Aguzzi, Gianluca Casadei, Roberto Viroli, Mirko Univ Bologna Cesena Italy

ISBN: (数字)9781665471374

ISBN: (纸本)9781665471374

Aggregate computing is a macro-level approach for programming collective intelligence and self-organisation in distributed systems. In this paradigm, system behaviour unfolds as a combination of a system-wide program, functionally manipulating distributed data structures called computational fields, and a distributed protocol where devices work at asynchronous rounds comprising sense-compute-interact steps. Interestingly, there exists a large amount of flexibility in how aggregate systems could actually execute while preserving the desired functionality. The ideal place for making choices about execution is the aggregate computing platform (or middleware), which can be engineered with the goal of promoting efficiency and other non-functional goals. In this work, we explore the possibility of applying Reinforcement Learning at the platform level in order to optimise aspects of a collective computation while achieving coherent functional goals. This idea is substantiated through synthetic experiments of data propagation and collection, where we show how Q-Learning could reduce the power consumption of aggregate computations.

关键词： Large-Scale distributed systems Aggregate computing Reinforcement learning

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A Clustering Method of Electric Power Customers Based on DBSCAN Algorithm 3

A Clustering Method of Electric Power Customers Based on DBS...

引用

3rd ieee international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Fan, Liping Peng, Linfang Li, Huangqiang Du, Fengyi Huang, Yu Liu, Yunfei Wang, Zhixin Zheng, Yuelin State Grid Yichang Electric Power Supply Company Hubei Yichang China College of Computer and Information China Three Gorges University Hubei Yichang China

ISBN: (纸本)9798350380989

Providing personalized services for electricity users with different needs has become an important part of deepening the reform of the electricity sales side market system, and achieving efficient and accurate electricity customer clustering is an important foundation for improving personalized services. In order to achieve efficient and accurate power customer clustering, a power customer clustering method based on DBSCAN algorithm is proposed in the paper. Firstly, the data in the power dataset is preprocessed and feature values are extracted from it. Secondly, a power customer clustering model based on the DBSCAN algorithm is constructed. Finally, the results obtained from the clustering model are visualized and analyzed. The experiment shows that when the parameter Eps of DBSCAN clustering algorithm is 0.006 and MinPts is 10, the overall clustering effect is good. At this time, the number of clusters is 5, and the profile of these 5 customer groups is obtained. This provides strong support for personalized power supply services for power enterprises and meeting the different needs of power customers. © 2024 ieee.

关键词： Clustering Analysis DBSCAN Clustering Algorithm distributed computing Power Customers Clustering

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Implementing Fuzzy Logic in Cognitive sensor Networks for Environmental Monitoring 3

Implementing Fuzzy Logic in Cognitive Sensor Networks for En...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Aarif, Mohd Anjum, Afsana Sharma, Tripti Arikrishnan, A. Rao, Vuda Sreenivasa Balakumar, A. Aligarh Muslim University Department of Commerce Uttar Pradesh Aligarh India Jazan University Dept. of Information Technology & Security Jazan Saudi Arabia Rungta College of Engineering & Technology Department of Cse Chhattisgarh India V.R.S College of Engineering and Technology Department of Mathematics Villupuram Arasur India Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh Vaddeswaram India K. Ramakrishnan College of Engineering Department of Ece Trichy Samayapuram India

ISBN: (纸本)9798350369083

Cognitive sensor Networks are sophisticated systems that use adaptive mechanisms, fuzzy logic, fuzzy clustering algorithms, and other cutting-edge technologies to improve environmental monitoring through flexibility, accurate decision-making, and efficient handling of sensor data uncertainties. The goal of this research is to better monitor the environment by integrating fuzzy logic into cognitive sensor networks. Fuzzy Inference systems (FIS), rule-based decision making, fuzzy logic controllers (FLC), adaptive fuzzy systems, and fuzzy clustering techniques are some of the components of the complete framework suggested to address the issues given by uncertainties and imprecisions inherent in environmental data. By creating language variables and membership functions, the FIS represents the complex interactions between input sensor data and ambient conditions. Fuzzy rules and input sensor data provide the foundation for the well-informed judgements made by FLCs. Fuzzy output is transformed into useful control signals through the use of defuzzification techniques. The system has adaptive mechanisms that allow fuzzy logic parameters to be dynamically adjusted in response to changing environmental conditions. With 89.8% accuracy, 91.2% precision, 88.6% recall, and an F1 score of 89.9%, the suggested system outperforms the current approaches, PN-WSNA and CogLEACH, after much testing and analysis. © 2024 ieee.

关键词： Environmental monitoring

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 ieee.

关键词： Convolutional neural networks

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 ieee.

关键词： Convolutional neural networks

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Circumcenter Based Mobile Beacon Aided Localization in Wireless sensor Networks 3

Circumcenter Based Mobile Beacon Aided Localization in Wirel...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Thilagavathi, P. Manickam, J. Martin Leo St. Joseph's College of Engineering Department of Information Technology Chennai119 India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai119 India

ISBN: (纸本)9798350322842

Locating the unknown sensors with a high degree of precision in wireless sensor networks is a vital issue. The localization method suggested by various researchers focused only on the mobility of an anchor (or) beacon node. To determine the unknown node's position, mobile anchor allowed to assist the unknown sensors and focused only on minimum coverage at the border region. So, the sensors which are remotely from the mobile beacon will have inaccurate localization. The proposed circumcenter localization algorithm provides the complete coverage of all nodes at the border region and also computes its position using adjustable sensing radii. With this approach, the location error will be reduced and the location accuracy of the complete sensor node will be guaranteed Via a series of simulation steps using the NS2 simulator, the effectivenes s of the s ugges ted technique is assessed © 2023 ieee.

关键词： sensor nodes

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Fully distributed Event-Triggered Consensus of Linear Multi-Agents systems With Input Delay and Unknown Disturbance 3

Fully Distributed Event-Triggered Consensus of Linear Multi-...

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3rd international conference on Communication Technology and Information Technology, ICCTIT 2023

作者： Dong, Yixiang Ni, Hongquan Li, Zeshuai College of Information Science and Engineering Northeastern University Shenyang110819 China Department of Electrical Engineering ShanXi Electric Power Technical College Shanxi 710100 China

ISBN: (纸本)9798350359664

This paper focuses on the fully distributed event-triggered linear multi-agents issue with network delay and external disturbance. Based on the designed observer, slide mode control theory is introduced to deal with the unknown disturbance. Compared with the previous related works, the deterministic sinusoidal disturbance is no longer needed, and a known network delay is introduced for practical reasons. Without requirement of global information and Zeno behavior, the event-triggered consensus protocol is exported, which is fully distributed. By utilizing a 4 nodes multi-agents system simulation, we attested the validity of our control laws. © 2023 ieee.

关键词： Multi agent systems

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The Fuzzy Power Flow Based Network Planning of Distribution Networks with distributed Generation 3

The Fuzzy Power Flow Based Network Planning of Distribution ...

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3rd international conference on Energy Engineering and Power systems, EEPS 2023

作者： Li, Jin Sun, Xuewen Chen, Long Liu, Chang Zhang, Mingbo Wang, Zilin State Grid Ezhou Electric Power Supply Co. Ltd. Ezhou China Ezhou Electric Power Survey & Design Institute Co. Ltd. Ezhou China

ISBN: (纸本)9798350313857

This article establishes a chance constrained programming model with the overall efficiency of compensating capacitors and distributed generations as the goal, and optimizes the configuration of distributed generations and compensating capacitors as a whole. By utilizing the proposed improved harmony algorithm, the total investment in distributed generations and compensation capacitors has been reduced, achieving the optimal balance between the economic benefits, environmental benefits, and voltage quality of the system. Through simulation calculations based on a 54 node distribution network example, it can be proven that the proposed fuzzy programming method in power flow calculation only requires three power flow calculations for each solution, which has high efficiency and is easy to apply to planning research on larger areas. © 2023 ieee.

关键词： distributed power generation

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distributed Fault Diagnosis System Based on Machine Learning 3

Distributed Fault Diagnosis System Based on Machine Learning

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3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Guan, Mengru Fu, Yanyou Wang, Zhaoyue Zhang, Jiaqian Sias University Henan China

ISBN: (纸本)9798350343922

This research focuses on the development of a machine learning-based distributed fault diagnosis system that aims to improve the accuracy and real-time detection of faults in large-scale distributed systems. By deeply analysing key information such as log data, performance metrics, and network traffic of distributed systems, we design adaptable machine learning algorithms to handle distributed environments of complexity and large scale. In our research, we pay special attention to real-time fault diagnosis to detect and diagnose faults in a timely manner by monitoring and analysing the data flow of distributed systems in real time, thus minimising the potential impact on the system. The proposed machine learning algorithms are not only able to handle large-scale datasets efficiently, but also have the flexibility to adapt to different system architectures and network topologies, while exhibiting good scalability and robustness. In addition, we attempt to introduce an augmented learning approach to enable the fault diagnosis system to actively learn and optimise in order to maintain an efficient fault diagnosis capability in a dynamic and distributed environment. This innovative approach is expected to further improve the system's adaptability to emerging fault modes, thereby enhancing the system's intelligence and self-optimisation capabilities. Overall, this study introduces advanced techniques in machine learning for fault diagnosis in the field of distributed systems and lays the foundation for a more accurate, real-time and intelligent fault diagnosis system. © 2023 ieee.

关键词： Adversarial machine learning

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