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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

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optimization of MR damper model based on improved genetic neural network algorithm 24

Optimization of MR damper model based on improved genetic ne...

引用

3rd International Symposium on control Engineering and Robotics, ISCER 2024

作者： Zhang, Tieshan Ren, Zhong Department of Electrical Engineering Ningxia Institute of Science and Technology Shizuishan Ningxia753000 China

ISBN: (纸本)9798400709951

As a smart vibration control device, there is a broad application prospect for the magnetorheological (MR) damper. And establishing its precise mechanical model is one of the key factors for excellent control. In this paper, the dynamic performance of MR damper is tested. And the neural network structure of the forward and reverse models are determined by statistical methods. Then, aiming at the problem of early maturity and slow convergence speed of traditional genetic neural network (GA-BP), an improved genetic neural network (IGA-BP) algorithm with linear transformation of fitness, adaptive crossover and mutation probability is proposed. Finally, The BP neural network, GA-BP neural network and IGA-BP neural network are used to identify the forward and reverse models of the damper, respectively. The results show that the IGA-BP algorithm proposed in this paper has faster convergence speed and higher model accuracy, and the models can better reflect the dynamic characteristics of the MR damper. © 2024 ACM.

关键词： Neural network models

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Research on Coordinated and Optimized Operation of Distribution networks Containing High Permeability Distributed Renewable Energy 3

Research on Coordinated and Optimized Operation of Distribut...

引用

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

作者： Wang, Yanze Lin, Fengshan Liu, Han State Grid Dandong Electric Power Supply Company Dandong China

ISBN: (纸本)9798350328691

With the increasingly serious problems of global warming and environmental pollution, how to save energy and reduce emissions is receiving more and more attention from people around the world. Vigorously developing new energy has become an unstoppable trend. This paper focuses on the coordination and optimal operation of distribution network under the uncertainty environment. Based on the day ahead operation optimization, Model predictive control is adopted, and a multi period coordination and optimal operation strategy of distribution network taking into account the fluctuation of renewable energy output and load forecasting is proposed. The objective of real-time correction of the operation strategy results of the day ahead scheduling is to minimize the deviation of the operation strategy of the day ahead real-time correction. Through example analysis, it is verified that Model predictive contro l can deal with the errors in the day ahead based on the day ahead scheduling strategy and real-time rolling optimization scheduling. ©2023 IEEE.

关键词： Model predictive control

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Artificial Intelligence Method for V2G oriented Power Distribution network control Strategy and Capacity Analysis 3

Artificial Intelligence Method for V2G oriented Power Distri...

引用

3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Tao, Li Bo, Wang Yongbin, Su Cheng, Li State Grid Hubei Electric Power Co. Ltd. Jingzhou Power Supply Company Hubei Jingzhou434000 China State Grid Jingzhou Power Supply Company Economic and Technological Research Institute Hubei Jingzhou434000 China State Grid Hubei Electric Power Co. Ltd. Tianmen Power Supply Company Hubei Tianmen431700 China

ISBN: (纸本)9798331519032

With the popularization of electric vehicle (EV), Vehicle-to-Grid (V2G) technology has become an important means to balance the supply and demand of power grid and improve the flexibility of power grid. This article aims to explore a V2G-oriented power distribution network (PDN) regulation strategy and artificial intelligence (AI) method for carrying capacity analysis, so as to realize the efficient and stable operation of the power grid. In this article, a data fusion method based on modified mind evolutionary algorithm (MMEA) is proposed to deal with complex data in PDN. Then, based on the data fusion results, a V2G control strategy is designed. Finally, MMEA algorithm is used to analyze the V2G carrying capacity of PDN. The results show that this method has achieved remarkable results in data fusion, control strategy optimization and carrying capacity analysis, and improved the operation efficiency and stability of PDN. To sum up, the V2G-oriented PDN control strategy and AI method for carrying capacity analysis proposed in this article provide strong support for the practical application of V2G technology and the intelligent development of power grid. © 2024 IEEE.

关键词： Power distribution networks

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Harmonics and Reliability Analysis of Distribution network in Presence of Multi-DG using PSO and DigSILENT 3

Harmonics and Reliability Analysis of Distribution Network i...

引用

3rd IEEE International conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Patel, Hemant Saw, Bikash Kumar Bohre, Aashish Kumar Yadav, Omkar Bera, Tushar Kanti Nit Durgapur Dept. of Electrical Engineering Durgapur India

ISBN: (纸本)9798350304732

This paper presents an approach to the PSO for the optimal allocation of DGs. To improve the voltage profile, reduce harmonics, and upgrade the network's overall system reliability, a multi-objective function ( f F_ m-obj) has been implemented. This f F_ m-objalso decreases reactive and active power losses. The optimization process involves load flow calculations for the DN and computer simulations performed using MATLAB-DigSILENT to analyze the impact of DG. The IEEE 33 bus DN is executed in this work to incorporate DG. DG effects are investigated for reliability and harmonics under with-DG and without-DG conditions. The system performance outcomes have been improved, such as reactive power, active power, voltage profile, THD, and reliability. Thus, the suggested approach produces superior effects using the presented method. © 2023 IEEE.

关键词： Distributed Generation (DG) Distribution network (DN) Particle swarm optimization (PSO) Reliability Total Harmonics Distortion (THD)

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Research on Wireless Communication Self-matching optimization network Algorithm for New Power System Response Regulation Requirements 3

Research on Wireless Communication Self-matching Optimizatio...

引用

3rd International conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Zhang, Tongwen Ocean University of China Haide College Qingdao China

ISBN: (纸本)9798350316674

Wireless AD hoc network has the advantages of point-to-point flexible connection and communication, and reasonable networking can effectively enhance data fusion and transmission performance. In this paper, based on the reliable data transmission technology of wireless AD hoc network based on graph theory, the optimal network partitioning algorithm of distributed AD hoc network is proposed to meet the bidirectional interaction requirements of a large number of distributed power sources and new-type source-load terminals such as electric vehicle charging piles in power system. The algorithm takes the point connectivity of nodes and the remaining working energy as the composite measure and adopts stochastic distributed maximum dominant set theory to optimize the monitoring grid. Through the simulation analysis, the grid partitioning according to the proposed algorithm can effectively improve the data fusion efficiency and data stability in the response regulation process of the new power system. © 2023 IEEE.

关键词： Maximum control Set Monitoring Grid New Power System Wireless AD Hoc network

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A Multi-level Voltage/Var Stochastic optimization Method for Distribution networks Considering Communication Conditions 3

A Multi-level Voltage/Var Stochastic Optimization Method for...

引用

3rd International conference on Power Systems and Electrical Technology, PSET 2024

作者： Wu, Yufeng Xie, Songqing Chai, Jinyu Liao, Wang Wei, Xu Chen, Fei Weng, Jiaming Liu, Dong Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Shanghai China Marine Design & Research Institute of China Merchant Ship R&D Department Shanghai China

ISBN: (纸本)9798350353204

Active distribution networks are typical cyber-physical systems, where faults in the cyber system may trigger voltage violations, and uncertainties of renewable energy in the physical system may lead to voltage fluctuations. In light of this, a multi-level voltage/var stochastic optimization method for distribution networks considering communication conditions is proposed. The proposed method achieves optimal coordination of inverters with different communication conditions under the renewable energy uncertainty. In the central level, a stochastic optimization problem is formulated by optimizing the output of inverters with normal communication, which achieves optimal coordination in mathematical form with inverters suffering communication interruption. It is noted that, at the central level, decisions are made for inverters with normal communication, which consider the local adjustment model for inverter suffering communication interruption. At the local level, local terminal decides whether to follow the decisions made at the central level or locally adjust output of inverters based on communication states and voltage violation states. The proposed method is validated in modified IEEE 33-node distribution network. © 2024 IEEE.

关键词： Power distribution networks

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Predictive adaptive protection scheme for relay co-ordination of directional overcurrent relay using Student Psychology based optimization 3

Predictive adaptive protection scheme for relay co-ordinatio...

引用

3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its control, PARC 2024

作者： Kaur, Gurpreet Moulik, Bedatri Rao, K. Uma Amity University Deptt. of Electrical and Electronics Engineering UP Noida India Amity University Amity Institute of Technology UP Noida India R.V College of Engg Deptt. of Electrical and Electronics Engineering Banglore India

ISBN: (纸本)9798350309409

The novel Predictive Adaptive Protection Scheme (PAPS) presented in this paper addresses the coordination issues related to Directional Overcurrent Relays (DOCRs). Coordination becomes more difficult when renewable energy sources (RES) are integrated into the distribution grid because the on/off states of distribution generators (DG) alter the topology of the grid. These modifications affect short-circuit currents, which impairs dependability and causes protection relays miscoordination. In order to mitigate this, Directional Overcurrent Relay (DOCR) settings are optimised by Student Psychology Based optimisation (SPBO), which is essential for managing the propagation of bi-directional fault currents, the overall relay response time should be minimised. Considering various operational scenarios of distributed generators (DGs) in the grid, the successful performance of the suggested Protection Scheme (PAPS) in coordinating DOCRs is verified through the incorporation of the IEEE 14-bus distribution network. © 2024 IEEE.

关键词： Students

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An Optimized Inverse Neural network control Augmented with Feedback PI controller for Time-Varying Systems 3

An Optimized Inverse Neural Network Control Augmented with F...

引用

3rd International conference on Communication, control, and Intelligent Systems, CCIS 2024

作者： Daksh, ManMahendra Singh Mishra, Puneet Birla Institute of Technology and Science Department of Electrical and Electronics Engineering Rajasthan Pilani333031 India

ISBN: (纸本)9798331528201

Linear proportional-integral-derivative (PID) controllers often fail to provide adequate control performance for nonlinear and time-varying systems due to their constant parametric nature. To address the challenges posed by nonlinear and time-varying systems in their regulation, intelligent controllers using neural networks (NN) and fuzzy logic have been vastly investigated. Out of these techniques, NN-based controllers are more popular for the design of indirect adaptive control schemes owing to their immense learning capability. Yet, these NN controllers may fail to perform well if their hyperparameters, typically learning rate and initial weights, are not designed properly. The control performance may worsen if these NNs fail to mimic or learn the system's dynamics. To address this issue, the current work explores the possibility of employing bio-inspired optimization to tune the essential hyperparameters of NN, along with the inclusion of a PI controller in the loop to compensate for the unidentified dynamics. Through the extensive simulation studies carried out in this work, it was found that the learning capability of the INN assisted by a feedback PI controller, i.e. (INN+PI) controller, could achieve superior performance compared to INN only. This paper also compares the ability of the designed INN+PI controller with only the PI controller in terms of set point tracking, the controller's output on the basis of significant decrement in MSE and IAE, and elucidates the simulation results. © 2024 IEEE.

关键词： Time varying control systems

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Fault Diagnosis and Fault Tolerant control Algorithm based on Neural network in Modular Multi-level Converter (MMC) 3

Fault Diagnosis and Fault Tolerant Control Algorithm based o...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Xie, Jun Qian, Xiaodong Huang, Qiong Anhui Vocational and Technical College Anhui Hefei230011 China

ISBN: (纸本)9798331509675

The state-of-the-art modulation strategy of MMC is closely related to the quality of its output voltage waveform and system loss, and is related to whether the entire MMC system can maintain efficient and stable operation. In fact, all multi-level modulation technologies can be applied in MMC systems. To deal with the challenges, this study proposes a novel neural network-based MMC fault diagnosis (FD) and fault-tolerant efficient control (FTEC) algorithm. First, initial step for the modeling is to conduct the comprehensive analysis of the MMC architecture characteristics and prone faults. In particular, considering the schemes of open circuit (OC) and short circuit (SC) related faults, a model based on recurrent neural network (RNN) is established to extract and classify fault features, and the network structure of deep learning is improved. At the same time, an improved CNN model is applied to process the voltage signal of MMC sub-module. In improved part, efficient Softmax function is used to realize the probability distribution of multi-fault classification. The entire fault detection and analysis framework includes the following points: data collection, data pre-processing, network parameter initialization, fault feature training and classification decision. The experiment uses a self-built MMC simulation model to test the performance. At same time, in specific experiment, high voltage and low voltage are displayed independently to achieve a comprehensive analysis. © 2025 IEEE.

关键词： Recurrent neural networks

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PSO Optimized Observer Based Load Frequency controller with Reduced Particle Dimension 3

PSO Optimized Observer Based Load Frequency Controller with ...

引用

3rd IEEE International conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Srivastava, Geeta Ray, Anjan Kumar National Institute of Technology Sikkim Department Electrical and Electronics Engineering Ravangla India

ISBN: (纸本)9798350387193

In order to maintain the stability and reliability of a power system, it is essential to keep the frequency of the system within prescribed bounds. In this work, a PSO optimized observer based load frequency controller is proposed with reduced particle dimension. Output of each subsystem having dynamics is considered as the prospective state of the system. Unavailability of measured output leads to the requiremnt of design of an observer. Unlike the controller gains as the dimensional elements of the particles, the prospective poles of the system are considered as the components of each particle leading to the design of the controller. Such choice immediately removes the burden of ensuring system stability at each iteration for all particles. Furthermore, without using explicit observer gains, a set of offsets are used for complete design of an observer based controller while keeping both the controller and the observer poles of the system within a prescribed boundary. Simulation results and comparative study with existing methods demonstrate the better performance of the proposed approach. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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