Power quality improvement for grid interconnected solar PV system using neural network control algorithm

2018 ◽  
Vol 9 (2) ◽  
pp. 187
Author(s):  
Bellamkonda Pragathi ◽  
Rajagopal Veramalla ◽  
Fazal Noorbasha ◽  
Bangarraju Jampana
Keyword(s):  
Neural Network ◽  
Quality Improvement ◽  
Power Quality ◽  
Control Algorithm ◽  
Network Control ◽  
Neural Network Control ◽  
Solar Pv ◽  
Pv System ◽  
Power Quality Improvement ◽  
Solar Pv System

A Review on DSTATCOM Neural Network Control Algorithm for Power Quality Improvement

2015 ◽  
Vol 785 ◽  
pp. 363-367
Author(s):  
N.H. Baharudin ◽  
Syed Idris Syed Hassan ◽  
Puteh Saad ◽  
Tunku Muhammad Nizar Tunku Mansur ◽  
Rosnazri Ali
Keyword(s):  
Neural Network ◽  
Quality Improvement ◽  
Power Quality ◽  
Control Algorithm ◽  
Industrial Applications ◽  
Network Control ◽  
Phase System ◽  
Neural Network Control ◽  
Neural Network Controller ◽  
Power Quality Improvement

This paper reviews neural network control algorithm for power quality improvement. Further, this paper focuses on the neural network control algorithm for DSTATCOM and surveys its area of improvements. Various architectures of Neural Network such as Adaline/Widrow-Hoff, perceptron, Back-propagation (BP), Hopfield, and Radial Basis Function (RBF) that has been reviewed in this paper. It is found that many researches on theoretical works and single phase system are widely performed, whereas its application on distribution network for three phase system is hardly found. Even so much improvement that have been done by researchers theoretically to improve the drawbacks of Neural Network controller; there are still wide gaps for verification through experimental implementation and industrial applications.

scholarly journals Power enhancement in a grid connected solar PV System by using PLL-neural based converter

2019 ◽  
Vol 5 (11) ◽  
pp. 1-9
Author(s):  
Avinash Kumar ◽  
Vivek Kumar Kostha ◽  
Satyam Kumar Prasun
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Control Algorithm ◽  
Reactive Power ◽  
Network Control ◽  
Neural Network Control ◽  
Solar Pv ◽  
Pv System ◽  
The Neural Network ◽  
Solar Pv System

This work deals with neural network control algorithm-based grid connected to solar photo voltaic (PV) system consisting of DC-AC converter. The reference solar-grid current for three-leg VSC are estimated using neural network control algorithm. The neural network control algorithm based solar PV system is modeled in MATLAB R2018a along with SIMULINK.. This study presents an artificial neural network-based controller for regulating the level of active and reactive power output. First, the three phase currents from the VSI are measured and compared with the three reference currents. The neural network is trained to have minimum output error. It was concluded that the power output from the system was found to be190 KVA in case of system having no intelligent controller and 700 KVA in case of system with AAN based control. The voltage of output is maintained to be 20 kV in the grid system for analysis purpose. Thus the proposed control is expected to be implemented in the renewable energy resources for better output.

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