scholarly journals SOLAR ENERGY CONTROL AND POWER QUALITY IMPROVEMENT USING MULTILAYER FEED FORWARD NEURAL NETWORK

2018 ◽  
pp. 1953-1963 ◽  
Author(s):  
R. Dehini
Keyword(s):  
Neural Network ◽  
Quality Improvement ◽  
Solar Energy ◽  
Power Quality ◽  
Feed Forward Neural Network ◽  
Energy Control ◽  
Feed Forward ◽  
Power Quality Improvement

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 Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Endro Wahjono ◽  
Dimas Okky Anggriawan ◽  
Achmad Luki Satriawan ◽  
Aji Akbar Firdaus ◽  
Eka Prasetyono ◽  
...  
Keyword(s):  
Neural Network ◽  
Power System ◽  
Power Quality ◽  
Electric Power System ◽  
Measurement Tool ◽  
Harmonic Detection ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Standard Measurement ◽  
Testing Stage

The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.

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