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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology JTT (Jurnal Teknologi Terpadu) JURNAL INTEGRASI Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Energi & Kelistrikan INTEK: Jurnal Penelitian Jurnal INOVTEKSeri Elektro Jurnal Rekayasa elektrika Emitor: Jurnal Teknik Elektro Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Dimas Okky Anggriawan
Politeknik Elektronika Negeri Surabaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Other

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Journal : Jurnal Rekayasa elektrika

 1 
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 | Full PDF (869.076 KB) | 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 %.
Parallel Balancing Battery using Adaptive Power Sharing and ANN SOC Estimator Mokhamad Zuhal Muflih; Gilang Andaru Trinandana; Eka Prasetyono; Dimas Okky Anggriawan
Jurnal Rekayasa Elektrika Vol 17, No 3 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1585.874 KB) | DOI: 10.17529/jre.v17i3.20671

Abstract

The battery balancing method is commonly used in cell circuits and battery circuits to maintain the power continuity on the DC Bus. The power continuity on the DC Bus is guaranteed if the load continues to get a power source, even if either the battery or power supply malfunctions. Besides, the battery balancing method is also used to protect the battery from excessive charging current pliers flowing into the battery. Therefore, the State-of-Charge (SoC) should be concern in balancing the maintained battery condition on both systems and avoiding overcharging. Artificial Neural Network (ANN) is used in this paper to determine the value of battery SoC. Based on simulations using MATLAB 2018, SoC values with ANN showed accurate results with error values below 0.1%. Based on the simulation results, the two batteries, which are arranged to have a difference of SoC value of 0.3%, will achieve a balanced SoC value for 28.45 seconds from the simulation.
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 %
Implementasi Fuzzy Logic Untuk Identifikasi Jenis Gangguan Tegangan Secara Realtime Ahmad Alvi Syahrin; Dimas Okky Anggriawan; Eka Prasetyono
Jurnal Rekayasa Elektrika Vol 16, No 3 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1025.081 KB) | DOI: 10.17529/jre.v16i3.17692

Abstract

In the modern era, AC voltage variations are still often a problem. This variation causes power quality decrease even damage the equipment. Voltage variations that often occur are short and long duration. The variation consist of 6 types namely Interruption, Sag, Swell, Sustained-Interruption, Undervoltage, Overvoltage. To facilitate repairs when there is a voltage variation in the electric power system, it is necessary to have an identification that can detect and distinguish any interference that occurs. Therefore, this paper proposes a fuzzy logic method for identifying types of voltage variations. This type of voltage variation identifier requires a disturbance simulator as a voltage source with varying values. To distinguish between short duration and long duration disturbances, is the time duration of the disturbance appears. The design of the voltage variation identification algorithm uses the sugeno fuzzy inference system with 2 inputs namely magnitude vrms and timer, and 1 output is the type of voltage interference. Moreover, prototype design using AMC1200 voltage sensor, microcontroller, and display. To validate the proposed algorithm, compared with standard measuring tools and simulations. Results show that the proposed algorithm has a very good performance with an accuration compared to the standard measuring instrument of 99.8%.
Implementasi Fuzzy Logic Untuk Identifikasi Jenis Gangguan Tegangan Secara Realtime Ahmad Alvi Syahrin; Dimas Okky Anggriawan; Eka Prasetyono
Jurnal Rekayasa Elektrika Vol 16, No 3 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i3.17692

Abstract

In the modern era, AC voltage variations are still often a problem. This variation causes power quality decrease even damage the equipment. Voltage variations that often occur are short and long duration. The variation consist of 6 types namely Interruption, Sag, Swell, Sustained-Interruption, Undervoltage, Overvoltage. To facilitate repairs when there is a voltage variation in the electric power system, it is necessary to have an identification that can detect and distinguish any interference that occurs. Therefore, this paper proposes a fuzzy logic method for identifying types of voltage variations. This type of voltage variation identifier requires a disturbance simulator as a voltage source with varying values. To distinguish between short duration and long duration disturbances, is the time duration of the disturbance appears. The design of the voltage variation identification algorithm uses the sugeno fuzzy inference system with 2 inputs namely magnitude vrms and timer, and 1 output is the type of voltage interference. Moreover, prototype design using AMC1200 voltage sensor, microcontroller, and display. To validate the proposed algorithm, compared with standard measuring tools and simulations. Results show that the proposed algorithm has a very good performance with an accuration compared to the standard measuring instrument of 99.8%.
Parallel Balancing Battery using Adaptive Power Sharing and ANN SOC Estimator Mokhamad Zuhal Muflih; Gilang Andaru Trinandana; Eka Prasetyono; Dimas Okky Anggriawan
Jurnal Rekayasa Elektrika Vol 17, No 3 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v17i3.20671

Abstract

The battery balancing method is commonly used in cell circuits and battery circuits to maintain the power continuity on the DC Bus. The power continuity on the DC Bus is guaranteed if the load continues to get a power source, even if either the battery or power supply malfunctions. Besides, the battery balancing method is also used to protect the battery from excessive charging current pliers flowing into the battery. Therefore, the State-of-Charge (SoC) should be concern in balancing the maintained battery condition on both systems and avoiding overcharging. Artificial Neural Network (ANN) is used in this paper to determine the value of battery SoC. Based on simulations using MATLAB 2018, SoC values with ANN showed accurate results with error values below 0.1%. Based on the simulation results, the two batteries, which are arranged to have a difference of SoC value of 0.3%, will achieve a balanced SoC value for 28.45 seconds from the simulation.
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 %.
Real-Time Detection of Power Quality Disturbance Using Fast Fourier Transform and Adaptive Neuro-Fuzzy Inference System Syahrin, Ahmad Alvi; Anggriawan, Dimas Okky; Prasetyono, Eka; Sunarno, Epyk; Wahjono, Endro; Sudiharto, Indhana; Suhariningsih, Suhariningsih
Jurnal Rekayasa Elektrika Vol 20, No 1 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i1.33695

Abstract

Power quality disturbances cause equipment damage or financial losses. Therefore, the electric power system needs to identify and distinguish any power quality disturbances to reduce problems. This paper proposes hybrid methods combining FFT and ANFIS algorithm for detection of power quality disturbances. There are 11 types of power quality disturbances that can be detected, such as sag, swell, undervoltage, overvoltage, voltage flicker, voltage harmonic, sag + harmonic, swell + harmonic, undervoltage + harmonic, overvoltage + harmonic, and flicker + harmonic. The parameters used to detect disturbances are Vrms, Duration, THDv (Total Harmonic Distortion voltage), and Fluctuation-Count. The detection process starts by sensing voltage and calculating all the parameters, where THDv was obtained by Fast Fourier Transform. All the parameters such as Vrms, Duration, THDv, and Fluctuation-Count are processed by Adaptive Neuro-Fuzzy Inference System, and the result is the type of disturbance. Matlab simulations show that the suggested method performs outstandingly to identify 11 type of Power Quality Disturbances with 99.3% accuracy.
Co-Authors Abel Aditya Aryaguna Achmad Luki Satriawan Agustin, Eva Inaiyah Ahmad Alvi Syahrin Aidin Amsyar Amsyar, Aidin Anang Budikarso Anang Budikarso, Anang Anang Tjahjono, Anang Anggara Trisna Nugraha Aryaguna, Abel Aditya Audya Elisa Rheinanda Bambang Sumantri Diana Alia Dirhamsyah Dirhamsyah Eka Prasetyono, Eka Endro Wahjono Endro Wahjono, Endro Epyk Sunarno Era Purwanto Firdaus, Aji Akbar Fuad, Muchamad Chaninul Gilang Andaru Trinandana H.S, Hendik Eko Hendik Eko H.S Indhana Sudiharto Jufriyadi, Mohammad Khalin Khalin Mentari Putri Jati Mike Yuliana MOCHAMAD ARI BAGUS NUGROHO Mochammad Zulfikar Trysnawan Mokhamad Zuhal Muflih Muchamad Chaninul Fuad Muhammad Fauzi Muhammad Khanif Khafidli Novie Ayub Windarko Rachma Prilian Eviningsih, Rachma Prilian Riza, Andrian SALSABILA, MUTIARA NADHIFAH Suhariningsih Suhariningsih Suhariningsih Suhariningsih Sunaryo, Epyk Suryono sutedjo Sutedjo Sutedjo Sutedjo Syahrin, Ahmad Alvi Tesa Eranti Putri Trysnawan, Mochammad Zulfikar Wahyudi, Sri Yunardi, Riky Tri Zamzami, Mochamad Ilham