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ANN-BASED DIRECT TORQUE CONTROL SCHEME OF AN IM DRIVE FOR A WIDE RANGE OF SPEED OPERATION

NEURAL NETWORK WORLD 2021年第6期31卷 395-414页

作者： Jeyashanthi, J. BarsanaBanu, J. Sethu Inst Technol Dept Elect & Elect Engn Virudunagar Tamil Nadu India SBM Coll Engn & Technol Dept Elect & Elect Engn Dindugal Tamil Nadu India

Induction motor (IM) drives with direct torque control (DTC) enable fast torque response without the need for complex orientation conversions or inner loop current loop. In the speed estimation responses, however, there is a significant level of torque ripple. The voltage source inverter adds acoustic noise and needs a high sampling frequency since it operates at a high and variable switching frequency. This work describes an ANN-based DTC technique for controlling the speed of an IM drive over a large speed range. To achieve good dynamic performance of induction motor drive, the ANN-based speed controller will replace the speed controller, switching tables, and hysteresis comparators. The neural network was trained using the back-propagation algorithm. The goal of a neural speed controller is to improve the system ability to respond quickly to changes in process variables while also mitigating the impacts of external perturbations. The projected ANN based DTC considerably and simply tracks the reference speed thus improves the efficiency of speed-torque of induction motors with quicker responses for rapid varying of speed reference and torque as that of Electric Vehicles in any uneven roads circumstances. MATLAB/Simulink software is used to evaluate the drive performance for both transient and dynamic operations. The proposed control performance is simulated and compared to a DTC-based traditional PI speed controller. In comparison to PI, the results show that ANN has better and faster effects. The torque ripple gets reduced by 1.5% in ANN (artificial neural network) controller compared to PI controller. The THD (total harmonic distortion) is reduced by 6.38% from PI controller to ANN controller.

关键词： artificial neural networks back propagation algorithm induction motors pid controller torque control total harmonic distortion

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Using 2W-PE method based on machine learning to accurately predict field strength distribution in flat-top obstacle environment

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2022年 144卷

作者： Li Anqi Yin Chengyou Zhang Qianqian Natl Univ Def Technol Coll Elect Engn Hefei 230037 Anhui Peoples R China

A new two-way parabolic equation (2W-PE) method is proposed in this paper which uses machine learning to determine boundary condition equations, and accurately predicts the electromagnetic field strength distribution in the environment of media ground and obstacles. On the basis of previous researches, the obstacle area is decomposed according to the principle of domain decomposition, and the processes of solving 2W-PE in each subdomain are briefly explained. Because the direction and strength of the incident waves on the boundaries vary greatly with the propagation environment, it is difficult for us to determine boundary condition equations. The machine learning method is introduced here, and method of moments (MoM) is applied to generate sample data sets, thus, coefficients in boundary condition equations can be trained through the non-end-to-end neural network combined with backpropagation algorithm and gradient descent method. So that no matter how the environment changes, appropriate boundary conditions can be obtained by the well-trained neural network and help improving the accuracy of 2W-PE. Simulation results show that the accuracy of the new 2W-PE method based on machine learning is better than that of 2W-PE with traditional boundary conditions by comparing with the results of MoM, which also reflects the advantages of machine learning in radio wave propagation analysis. Therefore, this paper provides an innovative and reliable method for accurately predicting field distribution in flat-top obstacle environments.

关键词： Two-way parabolic equation Machine learning Artificial neural network back propagation algorithm Surface impedance boundary condition Field strength prediction

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A Stable Nonlinear in Parameter Neural Network Controller for a Class of Saturated Nonlinear Systems

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IFAC Proceedings Volumes 2014年第3期47卷 2533-2538页

作者： K. Esfandiari F. Abdollahi H.A. Talebi The Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

This paper deals with the tracking problem for a class of uncertain nonlinear systems subjected to actuator saturation constraint. Despite most of proposed control schemes for saturated systems which employed linear in parameter neural networks (LPNNs), in the present work nonlinear in parameter neural network (NLPNN) is introduced to support global approximation property. To compensate the effect of the input saturation constraint an auxiliary system is introduced and the error dynamics are modified based on the auxiliary states. Then, learning rules are achieved based on the back propagation (BP) algorithm and by adding two robustifying terms to the standard BP learning rules the stability of the overall system is ensured via Lyapunov direct method. Finally simulations performed on a “generalized pendulum” nonlinear system to illustrate the effectiveness of the proposed tracking control scheme.

关键词： Adaptive tracking control back propagation algorithm Input saturation Neural networks Nonlinear systems

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Security assessment accounting uncertainties in line parameters and control variables with the considerations of transmission line unavailability

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Journal of Electrical Systems and Information Technology 2018年第3期5卷 576-593页

作者： Pushpendra Singh L.S. Titare S.C. Choube L.D. Arya Department of Electrical Engg. Rajkiya Engineering College Banda UP 210201 India Department of Electrical Engg. Govt. Engineering College Jabalpur MP 482 011 India Department of Electrical & Elex. UIT-RGPV Bhopal MP 462036 India Department of Electrical Engg. Medi-Caps University Indore MP 453331 India

This paper presents a new algorithm for voltage stability security assessment accounting uncertainties in the line parameters and control variables. Security index has been evaluated using Monte-Carlo simulation with & without consideration of unavailability of transmission lines, which is used to perform contingency selection. Further, probabilistic insecurity index at various loading conditions considering voltage stability limit has been obtained using cut-set method for single & double line outages. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power flow methodology. Few cases have been used to train back propagation algorithm (BPA). Obtained results for contingency selection based on security index have been compared with well-established methods.

关键词： Contingency ranking Probabilistic insecurity index Artificial neural network back propagation algorithm

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Method of Short-Circuit Fault Diagnosis in Transmission Line Based on Deep Learning

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第5期36卷

作者： Li, Tong Zhao, Hai Zhou, Xiaoming Zhu, Shidong Yang, Zheng Yang, Hongping Liu, Wei Zhou, Zhenliu Northeastern Univ Shenyang 110819 Liaoning Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Elect Power Res Inst Shenyang 110004 Liaoning Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang 110004 Liaoning Peoples R China Shenyang Inst Engn Shenyang 110136 Liaoning Peoples R China Shenyang LIGONG Univ Shenyang 110159 Liaoning Peoples R China

It is important to locate the fault distance and identify the fault types quickly, take effective measures to maintain line stability, and minimize the losses timely when there are short-circuit faults in transmission lines. For this purpose, a method based on deep learning is proposed for short-circuit faults identification in the transmission line. According to the similarity of samples in the reconstruction phase, a minimum neighborhood sample set is selected from the massive samples firstly, and then, the samples are trained using the back propagation algorithm along time in a recurrent neural network (RNN) with long-short term memory (LSTM) units. Compared with existing algorithms, the experimental results show that this algorithm meets the requirements of rapid fault diagnosis in the case of variable parameters, and higher fault type recognition accuracy and lower fault distance error can be obtained.

关键词： Short-circuit fault diagnosis deep learning recurrent neural network back propagation algorithm

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Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

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ENERGIES 2019年第13期12卷 2463-2463页

作者： Qu, Zhengwei Xu, Chenglin Ma, Kai Jiao, Zongxu Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

关键词： automatic generation control fuzzy neural network control thermostatically controlled loads back propagation algorithm particle swarm optimization

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A type-2 diabetes early warning system using particle swarm optimized artificial neural networks: a case of trans Nzoia county of Kenya

A type-2 diabetes early warning system using particle swarm ...

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作者： Mbirika, Douglas H University of Nairobi

This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Trans Nzoia County of Kenya using Particle Swarm Optimized Artificial Neural Networks. With the skyrocketing prevalence of type 2 diabetes in Kenya the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the effectiveness of the system as an early warning system for the disease. Data was collected from diabetes clinics in hospitals within Trans Nzoia County of Kenya. The collected data was first preprocessed by R software to select the best generalizing attributes which were thereafter used to model an Artificial Neural Network. Particle Swarm Optimization algorithm was used to explore the global minima of the solution curve which was later exploited using the back propagation algorithm. The network attained a 70% and 66.23% accuracy on training and test data respectively. The network also attained a sensitivity of 70.23% and a specificity of 64.24%. This clearly shows that the system can be used as an early warning system for type 2 diabetes mellitus.

关键词： Artificial Neural Networks Particle Swarm Optimization back propagation algorithm Type 2 Diabetes Thesis

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Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

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Wuhan University Journal of Natural Sciences 2005年第6期10卷 1009-1012页

作者： ZHOU Hui-lin TIAN Mao CHEN Xiao-li School of Electronic Information Wuhan University Wuhan 430072 Hubei China

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

关键词： ground penetrating radar nonstationary signal dyadic wavelet transform feed-forward multi-layer perceptron back propagation algorithm

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Bioprospecting of novel peroxidase from Streptomyces coelicolor strain SPR7 for carcinogenic azo dyes decolorization

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CHEMOSPHERE 2023年 310卷 136836页

作者： Preethi, P. Sai Vickram, Sundaram Das, Raja Hariharan, N. M. Rameshpathy, M. Subbaiya, R. Karmegam, N. Kim, Woong Govarthanan, M. Sree Sastha Inst Engn & Technol Dept Biotechnol Chembarambakkam 600123 Tamil Nadu India Saveetha Inst Med & Tech Sci SIMATS Saveetha Sch Engn Dept Biotechnol Chennai 602105 Tamil Nadu India Vellore Inst Technol VIT Sch Adv Sci Vellore 632014 Tamil Nadu India Vellore Inst Technol VIT Sch Bio Sci & Technol Vellore 632014 Tamil Nadu India Copperbelt Univ Sch Math & Nat Sci Dept Biol Sci Jambo DrPOBox Kitwe 21692 Zambia Govt Arts Coll Autonomous PG & Res Dept Bot Salem 636007 Tamil Nadu India Kyungpook Natl Univ Dept Environm Engn Daegu 41566 South Korea

Peroxidase (POX) is a heme-containing oxidoreductase, its voluminous immuno-diagnostic and bioremediatory intuitions have incited optimization and large scale-generation from novel microbial repertoires. Azo dyes are the most detrimental classes of synthetic dyes and they are the common ecotoxic industrial pollutants in wastewater. In addition, azo dyes are refractory to degradation owing to their chemical nature, comprising of azoic linkages, amino moieties with recalcitrant traits. Moreover, they are major carcinogenic and mutagenic on humans and animals, whereby emphasizing the need for decolorization. In the present study, a novel POX from Streptomyces coelicolor strain SPR7 was investigated for the deterioration of ecotoxic dyestuffs. The initial me-dium component screening for POX production was achieved using, One Factor at a Time and Placket-Burman methodologies with starch, casein and temperature as essential parameters. In auxiliary, Response Surface Methodology (RSM) was recruited and followed by model validation using back propagation algorithm (BPA). RSM-BPA composite approach prophesied that combination of starch, casein, and temperature at optimal values 2.5%, 0.035% and 35 degrees C respectively, has resulted in 7 folds enhancement of POX outturn (2.52 U/mL) compared to the unoptimized media (0.36 U/mL). The concentrated enzyme decolorized 75.4% and 90% of the two azo dyes with lignin (10 mM), respectively. Hence, this investigation confirms the potentiality of mangrove actinomycete derived POX for elimination of noxious azo dyes to overcome their carcinogenic, mutagenic and teratogenic effects on humans and aquatic organisms.

关键词： Azo dyes back propagation algorithm Computational docking Peroxidase Statistical modeling Streptomyces coelicolor

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An application of Artificial Neural Network solution in the apparel industry for Job distribution to subcontractors

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ADVANCES IN ENGINEERING SOFTWARE 2022年 174卷

作者： Mumcu, Yelda Karatepe Kayar, Mahmut Bulur, Oyku Ceren Marmara Univ Vocat Sch Tech Sci Dept Elect Educ Istanbul Turkey Marmara Univ Tech Educ Fac Dept Text Educ Istanbul Turkey Marmara Univ Inst Pure & Appl Sci Departent Text Engn Istanbul Turkey

In this study, the results of the work distribution made with the TOPSIS method, which is frequently used in the distribution of work to the subcontractor workshops, were estimated using the Artificial Neural Networks Method (ANN). Here, the C* values used in the work distribution with the TOPSIS method were estimated by ANN. The correlation coefficient of the data obtained from the created model was found to be 99.99895% for learning. According to the results, it has been concluded that work distribution can be made by using the ANN method without making complex mathematical calculations in the distribution of work to the contract workshops.

关键词： Apparel industry Artificial Neural Network back propagation algorithm TOPSIS Job distribution Multi Criteria Decision Making (MCDM)

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