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All Journal Teknika: Jurnal Sains dan Teknologi Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer ELINVO (Electronics, Informatics, and Vocational Education) CESS (Journal of Computer Engineering, System and Science) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknologi Elekterika Technologic Jurnal Nasional Teknik Elektro dan Teknologi Informasi J-Innovation Jurnal Rekayasa elektrika
Endro Wahjono
Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network Dimas Okky Anggriawan; Endro Wahjono; Indhana Sudiharto; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (911.405 KB) | DOI: 10.17529/jre.v19i1.27120

Abstract

This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %
Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network Dimas Okky Anggriawan; Endro Wahjono; Indhana Sudiharto; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i1.27120

Abstract

This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %
Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time Endro Wahjono; Dimas Okky Anggriawan; Achmad Luki Satriawan; Aji Akbar Firdaus; Eka Prasetyono; Indhana Sudiharto; Anang Tjahjono; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.15093

Abstract

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 %.
Desain dan Impelementasi Multilevel Inverter Tiga Tingkat Untuk Mereduksi Harmonisa Bagus Afif Nasrudin; Sutedjo Sutedjo; Endro Wahjono
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Vol 12, No 2 (2023): Edisi Desember 2023
Publisher : Fakultas Teknik Elektro - Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/setrum.v12i2.16638

Abstract

Suplai kualitas daya listrik yang baik merupakan elemen yang sangat dibutuhkan bagi peralatan listrik. Umumnya inverter konvensional menghasilkan THD tegangan dan arus yang cukup tinggi yang mana berpengaruh pada meningkatnya rugi-rugi akibat harmonisa sehingga efisiensi yang dihasilkan semakin turun. Oleh karena itu diusulkan sebuah sistem terdiri dari baterai sebagai sumber dc yang digunakan untuk mensuplai multilevel inverter yang merupakan salah satu jenis inverter yang memiliki bentuk gelombang tegangan maupun arus yang bertingkat. Multilevel inverter dimodulasi dengan metode Alternate Phase Opposition Disposition (APOD-PWM) yang mana teknik modulasi ini membutuhkan masing-masing dari empat bentuk sinyal pembawa untuk bentuk gelombang keluaran inverter tiga tingkat dengan masing-masing sinyal pembawa digeser fasanya sebesar 120˚ dari sinyal yang berdekatan secara bergantian. Dari data hasil pengujian, multilevel inverter 3 tingkat dapat mengubah 36 VDC  menjadi 15.6 Vrms AC yang kemudian dinaikkan tegangannya oleh trafo step-up menjadi 215 Vrms AC dengan persentase error 2.2%. Keluaran dari multilevel inverter tiga tingkat setelah melalui filter dengan beban lampu pijar 400 Watt menghasilkan THD tegangan sebesar 7,4% dan THD arus sebesar 7.6%.
Co-Authors Abdul Rahman Wachid Achmad Luki Satriawan Ade Rochmanu Afifuddin Rizqi Afifuddin Rizqi Anang Budikarso Anang Tjahjono, Anang Bagus Afif Nasrudin Dimas Okky Anggriawan Eka Prasetyono, Eka Era Purwanto Farid Dwi Murdianto Firdaus, Aji Akbar Indhana Sudiharto Irianto Irianto Lugiana Nur Fitriah Rhamadani Lugiana Mentari Putri Jati MOCHAMAD ARI BAGUS NUGROHO Moh Rifqi Faqih Muhammad Nur Sururi Novie Ayub Windarko Ony Asrarul Qudsi Qudsi, Ony Asrarul Rifadil, Mochammad Machmud Rusli, Muhammad Rizani Sutedjo sutedjo Sutedjo Syechu Dwitya Nugraha Teguh Tri Arvianto Vania Kurnia Alvi