Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artificial neural network angle generation

2012 ◽  
Author(s):  
Faete J. T. Filho ◽  
Leon M. Tolbert ◽  
Burak Ozpineci
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Real Time ◽  
Multilevel Inverters ◽  
Artificial Neural

Fault Diagnosis and Reconfiguration of Multilevel Inverter Switch Failure-A Performance Perspective

2016 ◽  
Vol 6 (6) ◽  
pp. 2610
Author(s):  
T.G. Manjunath ◽  
Ashok Kusagur
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Comparative Analysis ◽  
Fault Diagnosis ◽  
Distribution Systems ◽  
Multilevel Inverters ◽  
Modified Genetic Algorithm ◽  
Artificial Neural ◽  
A Performance

<p>Multilevel Inverters (MLI) gains importance in Distribution systems, Electrical Drive systems, HVDC systems and many more applications. As Multilevel Inverters comprises of number of power switches the fault diagnosis of MLI becomes tedious. This paper is an attempt to develop and analyze the fault diagnosis method that utilizes Artificial Neural Network to get it trained with the fault situations. A performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA), which optimizes the Artificial Neural Network (ANN) that trains itself on the fault detection, and reconfiguration of the Cascaded Multilevel Inverters (CMLI) is attempted. The Total Harmonic Distortion (THD) occurring due to switch failures or driver failures occurring in the CMLI is considered for this comparative analysis. Elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN are the performance parameters considered in this comparative analysis. Optimization is involved in the process of updating the weight and the bias values in the ANN network.  Matlab based simulation is carried out and the results are obtained and tabulated for the performance evaluation. It was observed that Modified Genetic Algorithm performed better than the Genetic Algorithm while optimizing the ANN training.</p>

scholarly journals Fault Diagnosis and Reconfiguration of Multilevel Inverter Switch Failure-A Performance Perspective

2016 ◽  
Vol 6 (6) ◽  
pp. 2610
Author(s):  
T.G. Manjunath ◽  
Ashok Kusagur
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Comparative Analysis ◽  
Fault Diagnosis ◽  
Distribution Systems ◽  
Multilevel Inverters ◽  
Modified Genetic Algorithm ◽  
Artificial Neural ◽  
A Performance

<p>Multilevel Inverters (MLI) gains importance in Distribution systems, Electrical Drive systems, HVDC systems and many more applications. As Multilevel Inverters comprises of number of power switches the fault diagnosis of MLI becomes tedious. This paper is an attempt to develop and analyze the fault diagnosis method that utilizes Artificial Neural Network to get it trained with the fault situations. A performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA), which optimizes the Artificial Neural Network (ANN) that trains itself on the fault detection, and reconfiguration of the Cascaded Multilevel Inverters (CMLI) is attempted. The Total Harmonic Distortion (THD) occurring due to switch failures or driver failures occurring in the CMLI is considered for this comparative analysis. Elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN are the performance parameters considered in this comparative analysis. Optimization is involved in the process of updating the weight and the bias values in the ANN network.  Matlab based simulation is carried out and the results are obtained and tabulated for the performance evaluation. It was observed that Modified Genetic Algorithm performed better than the Genetic Algorithm while optimizing the ANN training.</p>

scholarly journals Enhance the accuracy of control algorithm for multilevel inverter based on artificial neural network

2020 ◽  
Vol 20 (3) ◽  
pp. 1148
Author(s):  
Mohammed Rasheed ◽  
Moataz M. A. Alakkad ◽  
Rosli Omar ◽  
Marizan Sulaiman ◽  
Wahidah Abd Halim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Optimization Technique ◽  
Harmonic Distortion ◽  
Multilevel Inverter ◽  
Multilevel Inverters ◽  
Harmonic Elimination ◽  
Selective Harmonic Elimination ◽  
Artificial Neural

<p>In converters or multilevel inverters it is very important to ensure that the output of the<br />multilevel inverters waveforms in term of the voltage or current of the waveforms is<br />smooth and without distortion. The artificial neural network (ANN) technique to<br />obtaining proper switching angles sequences for a uniform step asymmetrical modified<br />multilevel inverter by eliminating specified higher-order harmonics while maintaining<br />the required fundamental voltage and current waveform. However, through this paper a<br />modified CHB-MLI are proposed using artificial intelligence optimization technique<br />based on modulation Selective Harmonic Elimination (SHE-PWM). A most powerful<br />modulation technique that used to minimize a harmonic contants during the outout<br />waveform of multilevel inverter is a SHE-PWM method. The proposed a five-level<br />Modified Cascaded H-Bridge Multilevel Inverter (M-CHBMI) with ANN controller to<br />improve the output voltage and current performance and achieve a lower Total<br />Harmonic Distortion (THD). The main aims of this paper cover design, modeling,<br />prediction for real-time generation of optimal switching angles in a single-phase<br />topology of modified five level CHB-MLI. due to the heavy cost of computation to<br />solving transcendental nonlinear equations with specified number, a real-time<br />application of Selective Harmonic Elimination-Pulse Width Modulation (SHE-PWM)<br />technique is limited. SHE equations known as a transcendental nonlinear equation that<br />contain trigonometric functions. The prototype of a 5-level inverter in Digital Signal<br />Processing (DSP) TMS320F2812 reveals that the proposed method is highly efficient<br />for harmonic reduction in modified multilevel inverter.</p>

scholarly journals Real-time online fingerprint image classification using adaptive hybrid techniques

2019 ◽  
Vol 9 (5) ◽  
pp. 4372
Author(s):  
Annapurna Mishra ◽  
Satchidananda Dehuri
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Real Time ◽  
Particle Swarm ◽  
Superior Performance ◽  
Functional Link ◽  
Fingerprint Classification ◽  
Artificial Neural ◽  
Hybrid Classifiers

<p class="Abstract">This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique  is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification.</p>

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