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An efficient model for capturing gas transients in the energy flow of integrated electricity-gas systems

ENERGY REPORTS 2023年 9卷 127-136页

作者： Du, Hui Lin, Tao Li, Qingyan Fu, Xiyue Xu, Xialing Wuhan Univ Sch Elect Engn & Automat Hubei Engn & Technol Res Ctr AC DC Intelligent Distribut Network 8 Donghu South Rd Wuhan 430072 Peoples R China Cent China Branch State Grid Cooperat China 225 Xudong Rd Wuhan 430077 Peoples R China

Gas-fired units provide great flexibility to accommodate the intermittent nature of renewable energy sources and are also considered to be an ideal substitute for retiring coal-fired generations. However, the increasing deployment of gas-fired units deepens interdependency between electric power and natural gas, calling for efficient modeling of the heterogeneous energy flow in the integrated electricity-gas systems (IEGS). Plenty of research works show the necessity of considering gas flow transients in the IEGS, which can be described by a set of partial differential equations (PDEs). To ensure a tractable optimization, the PDEs are often discretized using the finite/infinite difference method, which may result in a large computational scale for achieving good precision. To this end, this paper proposes an efficient model for capturing gas flow transients in the IEGS, which can be taken as the operational constraints in the optimal energy flow model for IEGS coordinated planning, scheduling, and control. For improving the computational efficiency, the PDE-based transient gas flow model is discretized using a space-time orthogonal collocation (OC) method, which provides high-quality results using fewer spatial-temporal discrete points than differential methods. Further, aiming at the difficulty in the determination of the number of space-time collocation points, an adaptive strategy is proposed and embedded in the proposed model utilizing the local fitting errors of state variables related to pipelines. Case studies show the superiority in computational accuracy and efficiency of the proposed model over the existing models based on Wendorff difference and global OC. And an illustrative case based on the modified IEGS benchmark system composed of the IEEE 118-bus power system and the Belgian 20-node gas network exhibits the application value of the proposed model in the optimization and control of the IEGS. (c) 2023 The Author(s). Published by Elsevier Ltd.

关键词： Energy internet Integrated electricity-gas system (IEGS) Transient gas pipe flow Space-time orthogonal collocation (OC) Partial differential equation (PDE)

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Evaluation of Financial Risk Efficiency Based on Big Data and Quantum Neural network 6

Evaluation of Financial Risk Efficiency Based on Big Data an...

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6th International conference on Intelligent Computing and control systems, ICICCS 2022

作者： Zhou, Mo Student Affairs Office of Rizhao Polytechnic Shandong Rizhao 276800 China

ISBN: (纸本)9781665410359

In the information age, big data has become the main medium for enterprise growth. Through big data, it can enhance the anti-risk ability of financial service companies, improve the management efficiency of financial companies, optimize service quality, and expand diversified service channels. Although internet finance has greatly increased the flexibility of finance and promoted the growth of the financial industry, internet economy has also brought certain risks. In order to make the growth of internet economy active and healthy, this article is based on big data. And quantum neural network to evaluate financial risk efficiency. © 2022 IEEE.

关键词： Big data

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Distributed internet of Things Load Balancing using Deep Reinforcement Learning 23

Distributed Internet of Things Load Balancing using Deep Rei...

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6th International conference on Electronics, Communications and control Engineering, ICECC 2023

作者： Ong, Yi Jun Durga Kumar, Burra Venkata Jalan Sunsuria Bandar Sunsuria Selangor Darul Ehsan Sepang43900 Malaysia

ISBN: (纸本)9798400700002

The current emerging fifth-generation (5G) system has a significant impact on the usage of internet of Things (IoT) devices. Load balancing plays an important role in the distributed system, as it is directly associated with the performance of the whole system. Therefore, in this paper, the author recommends a load-balancing architecture for distributed IoT systems based on deep reinforcement learning (DRL), which is capable of dealing with dynamic and large-scale network situations. Specifically, the author recommends implementing a two-layer load balancing architecture. The top layer uses the long short term memory (LSTM) based Dueling Double Deep Q-Learning network (D3QN) model for clustering the IoT devices. The bottom layer uses the plain DRL with more than one behavior policy on joint exploration. The key objective of this paper is to improve load balancing by using the technique mentioned above, as the data source and the infrastructure of the distributed IoT system can be dynamic. Experiments are conducted by using real-world datasets for evaluating the implementation. The outcome shows that the implementation of DRL on load balancing has indeed achieved a significant improvement in the performance of the distributed IoT system compared to other simplified DRL models and static clustering methods. © 2023 ACM.

关键词： internet of things

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Hydroacoustic Target Recognition Based on Multidiscriminative Generative Adversarial network

Hydroacoustic Target Recognition Based on Multidiscriminativ...

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2024 IEEE International conference on Unmanned systems, ICUS 2024

作者： Du, Xiaoli Wen, Yintang Chen, Fubo Liu, Yangjun Zhang, Yuyan Luo, Xiaoyuan School of Electrical Engineering Yanshan University Qinhuangdao China School of Electrical Engineering Xian Technological University Xian China

ISBN: (纸本)9798350384185

To address the issue of sample scarcity in hydroacoustic target recognition, a multi-discriminative generative adversarial network (MD-GAN) method is proposed. Firstly, the fixed mapping interval method is adopted to preprocess the ship signal and extract the LOFAR spectrogram of the target. Then the designed MD-GAN is used to perform data enhancement on these spectrograms. By integrating different numbers and configurations of discriminator networks, we generate sample data and evaluate their quality and diversity. Furthermore, we simplify the hidden layers of the VGG16 network to obtain a version with fewer parameters but still excellent classification performance. The effectiveness of the method under small samples is verified by analyzing the impact of different generated samples on the target recognition accuracy. Experimental results show that the recognition accuracy reaches 95.87%, indicating that the method has great potential in the field of hydroacoustic target recognition, especially in the case of limited samples. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Power quality through Harmonic Reduction: A Triple-Memristor Hopfield Neural network Approach 8

Enhancing Power Quality through Harmonic Reduction: A Triple...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Varaprasad, K.S.B. Sukhi, Y. Subramaniyan, Shanmugapriya Dhandapani, Ramya Rajendiran, M. Kaliappan, S. Visakha Institute of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam530027 India R.M.K. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Tiruvallur601206 India Srm Institute of Science & Technology Department of Electrical and Electronics Engineering Tamil Nadu Kattankulathur603203 India Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Harmonic distortion in power systems is a significant concern that impacts the efficiency, reliability, and safety of electrical networks. Implementing effective harmonic distortion mitigation solutions, such as filters or advanced control systems, can be expensive and may necessitate substantial changes to existing infrastructure. This paper proposes a Triple-Memristor Hopfield Neural network (TMHNN) as a novel method for mitigating harmonic distortion in power systems. The main objective of the proposed approach is to improve the quality of power by mitigating the harmonics. The TMHNN is used to predict the actual harmonic current injected by specific harmonic sources within a power system. The results demonstrate the significant advantages of the proposed TMHNN method over existing techniques such as Artificial Neural network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural network (RNN). TMHNN achieves the highest efficiency at 96.3%, lowest error rate at 4.16% and minimizing total harmonic distortion (THD), with the lowest value of 3.74%. These results highlight TMHNN's superior performance in terms of efficiency, harmonic distortion reduction, and accuracy. © 2024 IEEE.

关键词： Harmonic distortion

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Wireless Sensor network Access control Technology with Joint Optimization of Delay and Power Consumption 3

Wireless Sensor Network Access Control Technology with Joint...

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3rd International conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Lv, Jun Zhou, Hongyu Zhang, Hejun Fan, Pengzhan Dai, Feng Chen, Xiao State Grid Corporation of China Beijing100031 China State Grid Jiangsu Electric Power Co. Ltd Nanjing210018 China

ISBN: (纸本)9798350343922

Nowadays is the era of interconnected everything, and the entire network system is becoming increasingly large. At this time, the carrying capacity of the network will gradually become a constraint. Therefore, research on access control for large-scale wireless sensor networks (WSNs) is urgent. With the rapid increase in the number of network service terminal sites, the continuous improvement of network service latency requirements and security performance, and the significant increase in network energy consumption, the new generation of internet of Things (IOT) systems are facing a series of new technological challenges. In WSN, latency and power performance are often a pair of indicators that cannot be achieved simultaneously. How to optimize and balance these two indicators is a challenge in routing and MAC algorithms. We choose sensor nodes with different specifications and performance, and build a practical WSN test platform. On this platform, we measure the delay and power consumption data of the algorithm in the actual transmission process. The experimental results show that compared with the simulation research, the proposed algorithm also shows lower delay and power consumption on real hardware, which verifies the practicability of the algorithm. © 2023 IEEE.

关键词： Delay tolerant networks

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Fuzzy Logic-based Adaptive Multimedia Streaming for internet of Vehicles 97

Fuzzy Logic-based Adaptive Multimedia Streaming for Internet...

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97th IEEE Vehicular Technology conference (VTC-Spring)

作者： Yaqoob, Abid Muntean, Gabriel-Miro Dublin City Univ Performance Engn Lab Dublin Ireland Dublin City Univ Sch Elect Engn SFI Ctr Data Analyt Dublin Ireland Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin Ireland

ISBN: (纸本)9798350311143

Multimedia streaming for the internet of Vehicles has the potential to enhance road safety and transport efficiency for autonomous vehicles, and the in-car experience for passengers. MPEG-Dynamic Adaptive Streaming over HTTP (MPEG-DASH) framework has been widely deployed to optimize video streaming with respect to end-user quality of Experience (QoE). However, existing heuristic-based, reinforcement learning-based, or fuzzy-based adaptive algorithms, which use complex control laws and decision-making processes, are not well-suited to handle the non-stationary nature of road traffic environments. Consequently, these solutions often struggle to deliver optimal performance across multiple QoE objectives and under diverse network conditions. In this paper, we introduce FLAME, a novel adaptive multimedia streaming solution based on advanced fuzzy logic. FLAME incorporates interactive membership functions and fuzzy rules in its two variants, FLAME7 and FLAME5, resulting in reduced model complexities and training overheads. FLAME is adaptable to diverse video client settings and QoE goals. Our trace-driven experimental results demonstrate that FLAME solutions offer an improved uninterrupted streaming experience for connected vehicles. On average, FLAME outperforms other state-of-the-art solutions such as PENSIEVE, BOLA, FESTIVE, BBA, and ELASTIC by achieving 11.7% higher QoE.

关键词： Fuzzy logic Adaptive bitrate streaming MPEG-DASH QoE internet of vehicles

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Adapting UAV Communication for Extended Reach and Bandwidth

Adapting UAV Communication for Extended Reach and Bandwidth

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2024 IEEE International conference on Unmanned systems, ICUS 2024

作者： Wang, Zhiguo Yue, Ping Zhu, Chan Zhang, Taixian Ji, Lijian Zhong, Bin School of Internet of Things Engineering Guangdong Polytechnic of Science and Technology Guangzhou China Research Centers Guangdong Vocational Institute of Sport Guangzhou China School of Urban Construction Guangzhou City of Polytechnic Guangzhou China School of Modern Information Industry Guangzhou College of Commerce Guangzhou China

ISBN: (纸本)9798350384185

The theoretical investigation has delved into the capability of achieving wireless communication during the flight of Unmanned Aerial Vehicles (UAVs) and the establishment of Flying Ad-Hoc networks (FANETs). This paper examines and discusses the use of airborne Wi-Fi networks in UAV communications. This paper offers an in-depth analysis of an experimental system that deploys a wireless communication network, enhancing capacity and coverage using an intelligent Ad-Hoc network approach. Simulated test platform loading protocols enable UAVs to operate in real-world environments. The results show that, under current conditions, the infrastructure mode outperforms the specially appointed mode in throughput capacity. Significantly, the Ad-hoc model demonstrates greater energy efficiency and broader coverage, indicating its potential to improve the sustainability of UAV communication systems. The study also highlights that optimizing intelligent Ad-hoc networks through experimental setups or predictive simulations with network simulators can greatly enhance communication system capacity and coverage, providing valuable insights for future advancements in this field. © 2024 IEEE.

关键词： Wi-Fi

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Power quality Improvement using Hopfield Neural network in Grid Distribution System

Power Quality Improvement using Hopfield Neural Network in G...

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2023 International conference on Power, Instrumentation, Energy and control, PIECON 2023

作者： Bansal, Pranshu Bharati, Prashant Jeswani, Sambhav Arora, Ankita Dept. of Electrical Engineering Delhi India

ISBN: (纸本)9798350399769

The objective of this study revolves around the power quality enhancement in grid connected distribution systems through Hopfield Neural network (HNN) algorithm. The distribution system is linked to a non-linear load resulting in the generation of harmonics in the grid current. Distribution Static Synchronous Compensator (DSTATCOM) is implemented to mitigate harmonics. DSTATCOM contains IGBTs, that require firing signals to operate. The control algorithm provides the gate pulse by comparing grid and reference grid current;and aims at removing grid current harmonics for the improvement of system's performance. A comparative analysis of the HNN algorithm is executed with Self Tuning Filter and Synchronous Reference Frame Theory where metrics such as harmonic content, settling time and oscillations are taken under consideration. The distribution system and different control algorithms incorporated by the DSTATCOM have been simulated using MATLAB 2018b Simulink software. © 2023 IEEE.

关键词： Hopfield neural networks

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Mitigating Voltage Sag in Power systems with Proportional Integral control using Tangent Search Algorithm and Multi-Fidelity Bayesian Neural network 8

Mitigating Voltage Sag in Power Systems with Proportional In...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, Radhey Shyam Singh, Harsimran Vasudevan, K. Ekambaram, Saranya Katiyar, Atul Kaliappan, S. Indian Institute of Technology Industry Coordinator Odl Uttar Pradesh Kanpur208016 India Guru Kashi University Department of Electrical Engineering Punjab Bathinda151302 India Parul University Department of Electrical Engineering Gujarat Vadodara391760 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Mjp Rohilkhand University Bareilly Faculty of Engineering and Technology Department of Electrical Engineering Uttar Pradesh 243006 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Mitigation strategies for voltage sag in power systems using dynamic voltage restorers (DVRs) involve compensating for voltage dips by injecting the required voltage to maintain stability. However, DVRs present problems such as high initial costs in installation and subsequent costs in maintenance and replacement;it only corrects voltage sag within its capability to handle severe or long periods of voltage sags. Moreover, DVRs may need complex control algorithms and most accurate tuning for optimal performance under various grid conditions. To overcome these drawbacks, this manuscript proposes a hybrid approach for voltage sag mitigation in power systems with Proportional Integral (PI) controller. The proposed method is the combined execution of both Tangent Search Algorithm (TSA) and Multi-fidelity Bayesian Neural network (MFBNN). Hence, it is named as hybrid TSA-MFBNN approach. The major aim is to minimize the impact of voltage sags on sensitive equipment and maintain consistent power quality across the network and reduce the integral squared error (ISE) and Total Harmonic Distortion (THD) of the system. The PI controller's gain parameters are optimized by the proposed TSA, and its optimum values are predicted by the MFBNN. By then, the proposed technique is executed in MATLAB and compared with different existing approaches. The proposed method outperforms all existing approaches such as Artificial Neural network (ANN), Salp Swarm Algorithm (SSA), and Adaptive-network Fuzzy Inference System (ANFIS). The proposed technique demonstrates a THD of 2.38% and an ISE of 0.00410, indicating superior performance in reducing harmonic distortions and minimizing error. © 2024 IEEE.

关键词： Harmonic distortion

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