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 %.

scholarly journals Power quality disturbances classification using complex wavelet phasor space reconstruction and fully connected feed forward neural network

2021 ◽  
Vol 10 (6) ◽  
pp. 2980-2988
Author(s):  
R. Likhitha ◽  
A. Manjunatha
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Power Quality ◽  
Classification Accuracy ◽  
Feed Forward Neural Network ◽  
Time Activity ◽  
Feed Forward ◽  
Power Quality Disturbances ◽  
Complex Wavelet ◽  
Fully Connected

Power quality disturbances (PQD) degrades the quality of power. Detection of these PQDs in real time using smart systems connected to the power grid is a challenge due to the integration of energy generation units and electronic devices. Deep learning methods have shown advantages for PQD classification accurately. PQD events are non-stationary and occur at discrete events. Pre-processing of power signal using dual tree complex wavelet transform in localizing the disturbances according to time-frequency-phase information improves classification accuracy.Phase space reconstruction of complex wavelet sub bands to 2D data and use of fully connected feed forward neural network improves classification accuracy. In this work, a combination of DTCWT-PSR and FC-FFNN is used to classify different complex PSDs accurately.The proposed algorithm is evaluated for its performance considering different network configurations and the most optimum structure is developed. The classification accuracy is demonstrated to be 99.71% for complex PQDs and is suitable for real time activity with reduced complexity.

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