Application of Neural Network in Power System Inter-harmonic Detection

2010 ◽  
Author(s):  
Li Wang ◽  
Xiao-hua Wang
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Detection

The Active Power Filter Research Based on ANN Harmonic Detection and PR Control

2011 ◽  
Vol 403-408 ◽  
pp. 1668-1671
Author(s):  
Dong Fang Wang ◽  
Qian Jin Liu ◽  
Bao You Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Active Power Filter ◽  
Active Power ◽  
Harmonic Detection ◽  
Static Error ◽  
Power Filter ◽  
Resonant Control ◽  
Strong Ability

This article proposed the adaptive harmonic detection based on artificial neural network，and which can test the amplitude and phase of all the harmonics. This paper also proposed the quasi-resonant control ratio strategy of active power filter. This strategy can compensate a specific harmonic of power system in real time, can achieve Static error-free control and with a strong ability of anti-grid frequency fluctuations. The results show that the method is feasible and effective.

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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