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Detection and Classification of Faults Using Intelligent Approach in Microgrid network 3

Detection and Classification of Faults Using Intelligent App...

引用

3rd IEEE International conference on Intelligent Techniques in control, optimization and Signal Processing, INCOS 2024

作者： Balasubramanian, Sujatha Preetha Roselyn, J. Srm Institute of Science and Technology Department of Eee Chennai India

ISBN: (纸本)9798350361186

Microgrids serve as vital for incorporating renewable energy sources into the electricity grid. Classifying faults is difficult because of the reverse power flow, blinding of protection, and sympathetic tripping. Conventional overcurrent protection relays have challenges in microgrids due to the restricted fault current generated by grid-connected renewable energy inverters. The paper proposes an approach for classifying faults in a microgrid to address these problems. To classify the fault types based on the Multilayer Feedforward Neural network (MLFNN). The implementation of MLFNN uses four feature selections like dphi/dt (differential ROCPAD), dv/dt (differential ROCOV(pu/s)), dp/dt (differential active power change with time (pu/s)) and dq/dt (differential reactive power change with time(pu/s). Using these four features in the proposed MLFNN gives better accuracy. The proposed method is assessed using a 6-bus Radial AC microgrid system. Different test cases were undertaken to locate the fault classification by varying fault resistances in the range of 1 to 100 ohms. Moreover, this proposed work has shown favourable results using other models, such as support vector machines, for fault categorization. © 2024 IEEE.

关键词： Reactive power

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Simulation study on vibration suppression of middle and low-pressure cylinder communicating pipe of steam turbine 3

Simulation study on vibration suppression of middle and low-...

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3rd International conference on Energy Internet and Power Systems

作者： Wang, Ting Li, Yigang Yu, Longwen Zhang, Jinbao Mu, Qiwei Shao, Jianlin Wang, Yaowen Liu, Guanwei Xian Xire Saving & Emiss Control Technol Co Ltd Xian 710054 Peoples R China Xian Thermal Power Res Inst Co Ltd Xian 710054 Peoples R China Guoneng Sanhe Power Generat Co LTD Langfang 065201 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

Zero output technology is extensively used to expand heating capacity and electric peak shaving for low-pressure cylinders in thermal power plants. Problems of communicating pipe vibration under a small volume flow rate are common. In this paper, the optimal structure of the butterfly valve plate with diversion is proposed, and the numerical simulation under the condition of maximum vibration is carried out by CFD. The results show that after the structure optimization, the steam flowing from the butterfly valve plate along the flow direction is no longer attached to the valve disc, and most of it flows along the middle of the steam inlet. The unbalanced force in three directions at three monitoring positions is reduced after optimization. The vibration displacement and velocity at the three monitoring points are all reduced, and the maximum vibration at the expansion joint is reduced by 13.6% and 42.5%, respectively. The results indicate that the optimized structure can suppress the communicating pipe vibration effectively.

关键词： Steam power plants

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optimization of Convolution Operators' Backpropagation for Domestic Accelerators: Convolution Operators: Convolution kernel operation Backpropagation optimization to Enhance Performance and Efficiency in Convolutional Neural network 24

Optimization of Convolution Operators' Backpropagation for D...

引用

3rd International conference on Computer, Artificial Intelligence and control Engineering, CAICE 2024

作者： Li, Wencong Chen, Yongqing Li, Bin School of Computing and Artificial Intelligence Zhengzhou University Zhengzhou China Co. Ltd Zhengzhou China

ISBN: (纸本)9798400716942

Since the emergence of Convolutional Neural networks (CNNs), their special structure of weight sharing and high efficiency have found widespread applications in various fields such as speech recognition and image processing. To enhance computational efficiency and performance, convolutional operations are computed in parallel on domestic GPU-like accelerators, while CPUs are utilized for intricate logical decision-making or task scheduling. The paper focuses on an optimization method for backpropagation operators based on domestic GPU-like accelerators. By employing domestic GPU-like accelerators combined with the Implicit GEMM algorithm, it achieves universal operators, significantly reducing compilation time while maintaining performance without a substantial gap, thereby enhancing the efficiency and performance of CNN model training. The generic version of operators can substantially simplify the development and implementation process of CNN models. It possesses scalability and flexibility, catering to various application scenarios and requirements, enabling simultaneous processing of multiple data samples and computational tasks. Experimental results indicate that the efficiency of the generic version of operators meets expectations and can substitute for most operators in network training. © 2024 ACM.

关键词： Convolution

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A Search Method for controlled Islanding Surface Considering the Critical Nodes in Isolated network 3

A Search Method for Controlled Islanding Surface Considering...

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3rd International conference on Intelligent Power and Systems, ICIPS 2023

作者： Pu, Qingxin He, Xiangqiang Wang, Guosong Chang, Dongxu Zhu, Yihua Li, Chengxiang Power dispatching control center of Guizhou Power Grid Co. Ltd Guizhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350328691

controlled islanding is an important measure to ensure the stability of power system and avoid widespread power outages. However, the traditional research methods based on single-objective optimization neglect the isolated network. To address this issue, a controlled islanding strategy considering the critical nodes in proposed in this paper. Firstly, based on the electrical connections and power flow distribution between nodes, an initial islanding surface is determined with the objective of minimizing power flow impact. Then, considering the electrical coupling connectivity of critical nodes, the final islanding scheme is selected within the neighborhood search space of the initial surface to enhance the stability of isolated subsystems after islanding. The proposed method is simulated and validated based on the 39-noded system, and the simulation results validate the effectiveness and superiority of the method. ©2023 IEEE.

关键词： Electric load flow

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Particle Swarm optimization Algorithm Based RBF Neural network Prediction Model for the Load of Green Data center 3

Particle Swarm Optimization Algorithm Based RBF Neural Netwo...

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3rd International conference on control Theory and Applications, ICoCTA 2023

作者： Mei, Feng Jiang, Jinxia Liu, Xiu State Grid Zhejiang Electric Hangzhou China

ISBN: (纸本)9781839539824

With the development of big data, data centers (DCs) are springing up like mushrooms, and their energy consumption problems are increasingly prominent. The load forecasting of DC is the prerequisite for its workload scheduling and energy saving optimization. Particle swarm optimization algorithm based radial basis function (PSORBF) neural network prediction model is proposed to forecast data center load more accurately. The experimentation of PSORBF is implemented on MATLAB R2021a through a comparative study against the RBF neural network, and the expeirmental results verified the significance of the proposed solution in improving prediction accuracy of data center load. © The Institution of Engineering & Technology 2023.

关键词： Energy conservation

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AVC Planning Strategy of Power Grid Based on Genetic Algorithm Improved Neural network Quantile Regression 3

AVC Planning Strategy of Power Grid Based on Genetic Algorit...

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3rd International conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Lv, Chenxu State Grid Shanxi Electric Power Company Limited Xinzhou Branch Xinzhou034000 China

Aiming at the security risk of power system instability, this paper proposes a parameter optimization AVC planning strategy based on neural network. Based on a variety of power grid operation modes, the strategy innovatively uses genetic algorithm to improve the neural network quantile regression control variables to obtain state variables. This method can reduce the frequency of invalid operations, make the AVC control strategy more stable, and improve system security. In the actual case, the improved neural network quantile regression of genetic algorithm improves the accuracy by at least 29% compared with multiple linear regression. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Reactive Power optimization for Distribution network with Distributed Generators by Improved Driving Training‑Based optimization 3

Reactive Power Optimization for Distribution Network with Di...

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3rd International conference on Advanced Algorithms and Neural networks, AANN 2023

作者： Du, Songlin Hai, Tao Lu, Jianfeng Wang, Jun College of Electrical Engineering Guangxi University Nanning530004 China Nanning Cigarette Factory China Tobacco Guangxi Industrial Co.Ltd Nanning530004 China Guangxi Urban Building Thermal Environment Control Engineering Technology Research Center Hualan Design Group Ltd. Nanning530004 China

ISBN: (纸本)9781510668355

In order to solve the reactive power optimization problem of distribution network with distributed generators, this paper proposes an improved driving training-based optimization algorithm. Firstly, the reactive power optimization model is established with the reactive power output of distributed generators and static reactive power compensator as the control variables, and the minimum active power network loss, reactive power compensation cost and voltage overlimit deviation as the optimization objectives. Then the basic driving training-based optimization algorithm is improved by introducing good point set, golden sine algorithm, Gaussian variation and chaos disturbance. Finally, the enhanced IEEE33 bus system is taken as an example to verify and analyze the proposed reactive power optimization model and improved algorithm. The experimental results show that the proposed algorithm is effective in solving the reactive power optimization problem of distribution network with distributed generators. © 2023 SPIE.

关键词： Distributed power generation

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Insulation life prediction of stacking multi-model fusion motors based on Conv1d-LSTM 3

Insulation life prediction of stacking multi-model fusion mo...

引用

2024 3rd International conference on Electrical, control and Information Technology, ECITech 2024

作者： Duan, Chengxin Xu, Yaosong Faculty of Electrical and Control Engineering Liaoning Technical University Liao Ning Huludao125105 China

The insulation system of the mine motor is key to ensuring its stable operation, which affects the efficiency of mine production and transportation and the benefit of enterprises. The prediction of motor insulation life can effectively prevent the deterioration of motor performance. Due to the complex underground environment, many factors affect the insulation of coal mine motors, which are not conducive to the selection of feature parameters. The insulation life prediction efficiency and accuracy of motors need to be improved. To solve this problem, this paper proposes a prediction method based on Conv1d (one-dimensional convolve) and LSTM (long short-term memory neural network), which integrates multiple models and the Stacking integrated algorithms. The operating data and environmental parameters of a coal mine motor under different conditions are analyzed. Three Conv1d-LSTM hybrid models with batch normalization, residual network, and dense number are used as the base learner, and the results are weighted by K-fold to verify the accuracy of the three models. The linear regression model is used as a meta-learner to prevent overfitting of the model and effectively improve the speed and accuracy of single network optimization. Compared with the traditional method, the results show that the optimization of the LSTM neural network by the multi-feature model has a higher accuracy in predicting the remaining life of motor insulation, which provides a new way for the prediction of motor insulation life. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Long short-term memory

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Active Distribution Grid Risk Prevention and control Method Based on Biological Immune Mechanism 3rd

Active Distribution Grid Risk Prevention and Control Method ...

引用

3rd International Joint conference on Energy, Electrical and Power Engineering, CoEEPE 2023

作者： Zhao, Hui Ma, Jun Yue, Youjun Wang, Hongjun Tianjin Key Laboratory of Complex System Control Theory and Tianjin University of Technology Tianjin China

ISBN: (纸本)9789819739394

Aiming at the insufficient optimization ability of traditional active distribution network (ADN) fault recovery, a combination of immune mechanism and immune genetic algorithm is proposed for risk prevention and control. First, a risk assessment model is constructed from the perspective of reliability of equipment operation and consequences of faults;second, for the first N scenarios with higher risk, a fault pre-recovery scheme is established with the objectives of restoring the most power supply, minimizing the number of switching times, and minimizing the network loss, and the immune genetic algorithm is used to solve the problem. The N scenarios are recorded in the set of expected incidents to expand the immune memory cell library;finally, the simulation is carried out using the IEEE 33-node distribution network model as an example, and the simulation results show the feasibility of the proposed risk prevention and control strategies and the algorithms used, which lays a foundation for the subsequent fast and accurate recovery of power supply. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electric power distribution

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optimization of Service Migration Decisions in Mobile Edge Computing Based on Markov Decision Processes 3

Optimization of Service Migration Decisions in Mobile Edge C...

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3rd International Joint conference on Information and Communication Engineering (JCICE)

作者： Zhang, Xiaoming Wen, Zhan Tang, Ran Wang, Xiaoke Liu, Hantao He, Miao Ren, Dehao Li, Wenzao Chengdu Univ & Informat Technol Sch Commun Engn Chengdu 610225 Peoples R China Educ Informatizat & Big Data Ctr Educ Dept Sichuan Prov Chengdu 610225 Peoples R China Educ Dept Sichuan Prov Chengdu 610225 Peoples R China Informat Consultat & Employment Guidance Ctr Chengdu 610225 Peoples R China

ISBN: (纸本)9798350388732;9798350388725

In order to make optimal migration decisions promptly and accurately in the presence of uncertainty in service migration in mobile edge computing, we propose a service migration optimization model based on Markov Decision Processes (MDP) that abstracts the underlying spatial relationships between users and servers using the distance between them. Utilizing a value iteration algorithm based on differential equations, we further reduce the decision latency in service migration by treating the costs incurred in the decision-making process as value functions, calculating the minimum cost generated during service migration. Simulation results demonstrate that the decision time of this method is 0.1% of that of other traditional Markov decision models using standard value iteration and policy iteration. Moreover, compared with random migration and constant migration, the strategy obtained by this method is closer to the standard strategy obtained by using value iteration. This proves that the model has high efficiency and accuracy in the task of service migration in mobile edge computing.

关键词： service migration mobile edge computing Markov decision process

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