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Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Ravi Samikannu, K. Vinoth, Narasimha Rao Dasari, Senthil Kumar Subburaj
2023 Intelligent Automation and Soft Computing  
Gaussian activation is employed for determining the output voltage with help of the controller. At last, the output layer offers the last value in GPIC-MDCNNC Model.  ...  For considering the power and voltage, Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier (GPIC-MDCNNC) Model is introduced for the grid-connected renewable energy system.  ...  Deep learning in Gaussian PI Controller Deep Convolutional Neural Learned Controller (GPIC-DCNLC) Model depends on the working of the human brain.  ... 
doi:10.32604/iasc.2023.026069 fatcat:sr7rp2da7ndgtkedc4ygc6fi4e

Sensors Integrated Control of PEMFC Gas Supply System Based on Large-Scale Deep Reinforcement Learning

Jiawen Li, Tao Yu
2021 Sensors  
Thus, the integrated controller of the PEMFC gas supply system based on distributed deep reinforcement learning (DDRL) is proposed to solve this problem, it combines the original airflow controller and  ...  hydrogen flow controller into one.  ...  Nevertheless, the steady-state accuracy of their PEMFC system proved to be weak since the robust controller generally does not work in the optimal state, and the underlying calculations are complicated  ... 
doi:10.3390/s21020349 pmid:33419164 fatcat:hxowbd22nzdnddow2z2kvpp4am

Electrical Power System Harmonic Analysis Technology Based on Fast ICA BSS Algorithm

Chen Yu, Meng Jintao, Guo Rongxing
2013 Advances in Information Sciences and Service Sciences  
This paper mainly analyzed the algorithm performance of convergence speed and steady state error under the selection of different step size.  ...  This paper used the gradient blind signal separation algorithm to carry out the electrical power system harmonic signal analysis through the simulation experiment.  ...  process is still a Gaussian process, so it can't be separated  ... 
doi:10.4156/aiss.vol5.issue7.6 fatcat:kewkmkslkjbmpnqstd3q5fd6gu

Vector control of a grid-connected rectifier/inverter using an artificial neural network

Shuhui Li, Donald C. Wunsch, Michael Fairbank, Eduardo Alonso
2012 The 2012 International Joint Conference on Neural Networks (IJCNN)  
Conventionally, this type of converters is controlled using the standard decoupled d-q vector control approach [5] [6] [7] [8] .  ...  Future research issues regarding the control of grid-connected converters using DP-based neural networks are analyzed.  ...  In the steady-state condition, Eq.  ... 
doi:10.1109/ijcnn.2012.6252614 dblp:conf/ijcnn/LiWFA12 fatcat:jlvmy2xfu5ga7ckbsaco2jlkje

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

Mukul Chankaya, Ikhlaq Hussain, Aijaz Ahmad, Hasmat Malik, Fausto Pedro García Márquez
2021 Electronics  
This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm.  ...  control is accomplished using the variable step-size incremental conductance (VSS-InC) technique.  ...  Meta-heuristic optimization techniques (MOT) have been frequently used for PI controller gain optimization, which reduces the V dc transients during a steady and dynamic state and increase the DC link  ... 
doi:10.3390/electronics10222770 fatcat:sedwwcl55fh5vfxbom552ec2ke

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Kah Yung Yap, Charles R. Sarimuthu, Joanne Mun-Yee Lim
2020 Journal of Modern Power Systems and Clean Energy  
In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques.  ...  Index Terms--Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning  ...  - state oscillation (%) ±0.050 ±0.046 ±0.050 ±1.000 MPPT efficiency (%) ±97.00 99.91 ±99.00 Finding A pigeon-inspired optimization is used to optimize MPPT under PSC.  ... 
doi:10.35833/mpce.2020.000159 fatcat:rkjlikuyl5bgjclbtvdxnjj4oy

Intelligent Controller Based on Distributed Deep Reinforcement Learning for PEMFC Air Supply System

Jiawen Li, Tao Yu
2021 IEEE Access  
Finally, the application of CIED-MD3 (with its better global search ability and optimization speed) is demonstrated to the model-free PEMFC air flux intelligent controller.  ...  In this paper an intelligent controller based on distributed deep reinforcement learning which exerts better control over the air flux of a proton exchange membrane fuel cell (PEMFC) air supply system  ...  ONLINE TEST 1) LOAD ADDING/SHEDDING CONDITION During the simulation process, it can be found out that the time it takes to shift from Unstable state to steady operation is far less than 10s.  ... 
doi:10.1109/access.2021.3049162 fatcat:rzys2tzozzg6rp55t4ndhmeh3i

Performance and Resilience of Cyber-Physical Control Systems with Reactive Attack Mitigation [article]

Subhash Lakshminarayana, Jabir Shabbir Karachiwala, Teo Zhan Teng, Rui Tan, David K.Y. Yau
2019 arXiv   pre-print
We apply the proposed framework to the voltage control system of power grids and run extensive simulations using PowerWorld.  ...  The design of the optimal attack is based on a Markov decision process (MDP) formulation, which is solved efficiently using the value iteration method.  ...  Matrices A and B denote the propagation and control matrices, respectively. The initial system state x[0] and process noise w[t] are independent Gaussian random variables.  ... 
arXiv:1904.09445v1 fatcat:rqdpbeiprjbs5ai3nj7jjozmva

Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems [article]

Oindrila Chatterjee, Shantanu Chakrabartty
2020 arXiv   pre-print
towards electrical resonance under steady-state operation; and (c) An annealing procedure that controls the trade-off between active-power dissipation and the speed of convergence.  ...  Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process.  ...  Thus, during the post-learning phase or in steady-state, the network will not dissipate any power, and the reactive energy is used to maintain its current network state or memory.  ... 
arXiv:1908.05377v3 fatcat:qt4qhnrjyjgtrgw2rv5tkbncnm

Novel Intelligent Control Technology for Enhanced Stability Performance of an Ocean Wave Energy Conversion System

Kai-Hung Lu, Chih-Ming Hong, Xiaojing Tan, Fu-Sheng Cheng
2021 Energies  
To improve the online learning capability of FLWBFN, differential evolution with particle swarm optimization (DEPSO) is used to tune the learning rates of FLWRBFN.  ...  Therefore, the proposed intelligent controller can maintain the above power balance and voltage constant and reduce fluctuation.  ...  of rotor speed, AC bus, DC link voltage and real power caused by fault occurrence, and can quickly return to the steady state, so as to achieve a better control effect.  ... 
doi:10.3390/en14072027 fatcat:wa5z7tv7nrclpekogpffhh65ly

Applying Robust Intelligent Algorithm and Internet of Things to Global Maximum Power Point Tracking of Solar Photovoltaic Systems

En-Chih Chang, Chao-Yang Lee
2020 Wireless Communications and Mobile Computing  
Experimental results show the mathematical analysis and performance enhancement of a prototype algorithm-controlled solar PV system based on digital signal processing under transient and steady-state loading  ...  and neural network learning algorithm.  ...  Gaussian kernels and possess fast learning speed, leading to better transient and steady state for MPPT in PV systems.  ... 
doi:10.1155/2020/8882482 fatcat:syeq44w7ijbn5gy22w5i3ibev4

Current Distribution Method of Induction Motor for Electric Vehicle in Whole Speed Range based on Gaussian Process

Fang Xie, Wenjie Hong, Wenming Wu, Kangkang Liang, Chenming Qiu
2019 IEEE Access  
A current distribution method based on Gaussian process is proposed in this paper.  ...  INDEX TERMS Control system, current distribution, flux-weakening region, Gaussian process, induction motor, output torque. 165974 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  As a machine learning algorithm, Gaussian process has a good regression prediction ability.  ... 
doi:10.1109/access.2019.2953293 fatcat:fckfg3ps7bbyta27vd53buoy34

Table of Contents

2020 IEEE Transactions on Power Systems  
Egerstedt 3192 A Data-Based Learning and Control Method for Long-Term Voltage Stability . . . . . . . . . . . . H. Cai, H. Ma, and D. J.  ...  3274 Probabilistic Power Flow Based on a Gaussian Process Emulator . . . . . . . . Y. Xu, Z. Hu, L. Mili, M. Korkali, and X.  ... 
doi:10.1109/tpwrs.2020.2998987 fatcat:xcbvoq254vebjjbl3ybxrobwge

An Ant Colony Optimized MPPT for Standalone Hybrid PV-Wind Power System with Single Cuk Converter

Neeraj Priyadarshi, Vigna Ramachandaramurthy, Sanjeevikumar Padmanaban, Farooque Azam
2019 Energies  
Fuzzy Logic Control (FLC) inverter controlling strategy is adopted in this presented work compared to classical proportional-integral (PI) control.  ...  Satisfactory practical results have been realized using the dSPACE (DS1104) platform that justify the superiority of proposed algorithms designed under various operating situations.  ...  The ability of proposed hybrid control system is decided on the basis of optimal use of battery voltage by sensing PV and wind DC bus voltage.  ... 
doi:10.3390/en12010167 fatcat:kh4roawqvfc7lojtnfidynxop4

Field-oriented Controlled Permanent Magnet Synchronous Motor Drive with Dynamic-parameter Speed Controller Based on Generalized Regression Neural Network

Yung-Chang Luo, Hsu-Hung Zheng, Chia-Hung Lin, Ying-Piao Kuo
2021 Sensors and materials  
The decoupled FOC PMSM drive was established using the current and voltage of the stator.  ...  The MATLAB/Simulink © toolbox was used to establish the simulation scheme, and all the control algorithms were realized using a TI DSP 6713-and-F2812 control card.  ...  Machine learning with optimization algorithms uses searched datasets to establish an adaptive training model to perform the prediction function.  ... 
doi:10.18494/sam.2021.3271 fatcat:wrrzvahpmfhvdac5xh2j36kl2q
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