scholarly journals vector control of Induction machine using distributed delay neural flux observers

2019 ◽  
Author(s):  
Daniel Igbokwe
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Vector Control ◽  
Induction Motor ◽  
Electric Vehicle ◽  
Induction Machine ◽  
Distributed Delay ◽  
Artificial Neural

Vector control of induction motor, has gaineddominance due to the improvement of computing hardware and the extended use of induction motor beyond industrial uses such asthe electric vehicle, and automated medicalmachinery. The removal of the flux sensorsnecessitates the use of estimators to estimatethe flux of induction machine. This paper usethe artificial neural network to estimate theflux of the induction motor along the directand quadrature axis, and make it available forfeedback. Distributed delay neural networkswhich are more suited for time series problems is used in place of plain feed forwardneural networks.

scholarly journals ANFIS controller for vector control of three phase induction motor

2020 ◽  
Vol 19 (3) ◽  
pp. 1177
Author(s):  
Girisha Joshi ◽  
Pinto Pius A J
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Vector Control ◽  
Induction Motor ◽  
Control Technique ◽  
Fuzzy Logic Controllers ◽  
Inference System ◽  
Artificial Neural ◽  
Logic Controllers

For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

scholarly journals Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

2014 ◽  
Vol 41 (2) ◽  
pp. 249-258 ◽  
Author(s):  
Erol Egrioglu ◽  
Ufuk Yolcu ◽  
Cagdas Hakan Aladag ◽  
Eren Bas
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Linear Time ◽  
Time Series Forecasting ◽  
Non Linear ◽  
Artificial Neural

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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