Randomized Integral Gain of Pi Current Controller for A Single Pv Inverter System

Salimin, Suriana; Armstrong, Matthew; Zahawi, Bashar

doi:10.4236/eng.2013.51b040

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method of randomized technique has been proposed earlier to reduce THD in inverter system connected to the grid [10]. However, the development of power quality problem in off-grid network by improving the power electronic converter such as inverter topology, PWM switching schemes, filter arrangements, and others can be possible to make.…”

Section: B Power Quality Improvement In Off-grid Systemmentioning

confidence: 99%

Effect of Randomized PI Controller in Off Grid Network

Salimin

2019

IJIE

View full text Add to dashboard Cite

Off-grid connection of photovoltaic (PV) system use power electronic components such as inverters and supply a non-linear load as users. This produce harmonics which adversely affect the power quality of the distribution network. A sinusoidal pulse width modulation (SPWM) of the voltage source inverter (VSI) is proposed to act as a switching pulse. The scheme to mitigate the harmonic problem and phase error in singlephase full bridge inverter is considered using low pass filter and current controller. The current control parameter using a conventional Proportional Integral (PI) is simulated. This is extended with the modification of control method where randomization control parameter technique is added to produce a more efficient current controller system by using a random integral gain to the system. Result shows the latter technique is better for reduction of harmonics rather than conventional PI controller technique.

show abstract

Section: B Power Quality Improvement In Off-grid Systemmentioning

confidence: 99%

Effect of Randomized PI Controller in Off Grid Network

Salimin

2019

IJIE

View full text Add to dashboard Cite

show abstract

Application of PI Current Controller in Single Phase Inverter System Connected to Non Linear Load

Ajot

Salimin

Aziz

2017

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

METAPHOR: a machine-learning-based method for the probability density estimation of photometric redshifts

Cavuoti

Amaro

Brescia

et al. 2016

Mon. Not. R. Astron. Soc.

View full text Add to dashboard Cite

A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine learning based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z Probability Density Function (PDF), due to the fact that the analytical relation mapping the photometric parameters onto the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDF's obtained by the Le Phare SED template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.

show abstract

Randomized Integral Gain of Pi Current Controller for A Single Pv Inverter System

Cited by 3 publications

References 13 publications

Effect of Randomized PI Controller in Off Grid Network

Effect of Randomized PI Controller in Off Grid Network

Application of PI Current Controller in Single Phase Inverter System Connected to Non Linear Load

METAPHOR: a machine-learning-based method for the probability density estimation of photometric redshifts

Contact Info

Product

Resources

About