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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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A Full-Bridge Bidirectional DC-DC Converter with Fuzzy Logic Voltage Control for Battery Energy Storage System Prasetyono, Eka; Sunarno, Epyk; Fuad, Muchamad Chaninul; Anggriawan, Dimas Okky; Windarko, Novie Ayub
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.883 KB) | DOI: 10.24003/emitter.v7i1.333

Abstract

Renewable energy sources require an energy storage system because its are fluctuating and electricity producing at certain times, even sometimes not in accordance with the needs of the load. To maintain continuity of electricity, smart battery energy storage system is needed. Therefore, this paper of a full-bridge bidirectional DC-DC Converter (FB-BDC) with Fuzzy Logic Control (FLC) is designed and implemented for battery energy storage application. The FLC has error and delta error of voltage level as input and duty cycle of FB-BDC as output. The FB-BDC is controlled by a microcontroller ARM Cortex-M4F STM32F407VG for voltage mode control. The FB-BDC topology is selected becuase battery storage system needed isolated and need high voltage ratio both for step-up and step-down. The main purpose of FB-BDC to perform bidirectional energy transfer both of DC-Bus and battery. Moreover, FB-BDC controls the DC-Bus voltage according to referenced value. The power flow and voltage on DC-Bus is controlled by FLC with voltage mode control. The experiment result shows the ability of FLC  voltage mode control to control FB-BDC on regulate charging voltage with an error 1% and sharing voltage 1.5% form referenced value.
Load Identification Using Harmonic Based on Probabilistic Neural Network Anggriawan, Dimas Okky; Amsyar, Aidin; Prasetyono, Eka; Wahjono, Endro; Sudiharto, Indhana; Tjahjono, Anang
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.473 KB) | DOI: 10.24003/emitter.v7i1.330

Abstract

Due to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attention  in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic load
Hardware implementation of series DC arc fault protection using fast Fourier transform Dirhamsyah Dirhamsyah; Diana Alia; Dimas Okky Anggriawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20521

Abstract

This paper proposes method of series DC arc fault protection using low cost microcontroller. Series DC arc fault occurs when gap between conductor or wire flows a current. Series DC arc fault can cause fire hazard if do not detected and protected. However, Series DC arc fault is difficult to detected using conventional protection. To detect series DC arc fault accurately using fast Fourier transform (FFT). FFT is used to transform signal in time domain to frequency domain. Series DC arc fault has different characteristic compared by normal current in frequency domain. Therefore, the proposed algorithm for protection of series DC arc fault based on magnitudes of the current in frequency domain. Hardware system is implemented by 100 V DC power supply and DC arc fault generator. Test result is conducted experimentally under varying of load current such as 2 A, 2.5 A, 3 A, 3.5 A, 4 A and 5 A. Experimental testing results show that Series DC arc fault protection has time for trip of 0.48 s, 0.26 s, 1.04 s, 0.68 s, 0.44 s and 0.48, respectively. The fastest time for trip is 0.26 s with current of 2.5 A. Therefore, the proposed algorithm for series DC arc fault protection can operate to trip accurately and have the good performance.
Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya Aji Akbar Firdaus; Riky Tri Yunardi; Eva Inaiyah Agustin; Tesa Eranti Putri; Dimas Okky Anggriawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14816

Abstract

Photovoltaic (PV) is a renewable electric energy generator that utilizes solar energy. PV is very suitable to be developed in Surabaya, Indonesia. Because Indonesia is located around the equator which has 2 seasons, namely the rainy season and the dry season. The dry season in Indonesia occurs in April to September. The power generated by PV is highly dependent on temperature and solar radiation. Therefore, accurate forecasting of short-term PV power is important for system reliability and large-scale PV development to overcome the power generated by intermittent PV. This paper proposes the Jordan recurrent neural network (JRNN) to predict short-term PV power based on temperature and solar radiation. JRNN is the development of artificial neural networks (ANN) that have feedback at each output of each layer. The samples of temperature and solar radiation were obtained from April until September in Surabaya. From the results of the training simulation, the mean square error (MSE) and mean absolute percentage error (MAPE) values were obtained at 1.3311 and 34.8820, respectively. The results of testing simulation, MSE and MAPE values were obtained at 0.9858 and 1.3311, with a time of 4.591204. The forecasting has minimized significant errors and short processing times.
Identifikasi Jenis Gangguan Pada Jaringan Distribusi Menggunakan Metode Artificial Neural Network Abel Aditya Aryaguna; Dimas Okky Anggriawan; Suhariningsih Suhariningsih
INOVTEK - Seri Elektro Vol 3, No 1 (2021): INOVTEK Seri Elektro
Publisher : Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ise.v3i1.1954

Abstract

Berkembangnya kebutuhan masyarakat terhadap tenaga listrik saat ini meningkat pesat, sehingga perlindungan terhadap jaringan distribusi sangatlah penting untuk menjamin pelayanan tenaga listrik. Paper ini menyajikan algoritma yang diusulkan untuk identifikasi variasi tegangan durasi pendek. Artificial Neural Network (ANN) digunakan untuk mengidentifikasi 7 jenis varasi tegangan durasi pendek seperti sinyal normal, sag instantaneous, sag momentary, sag temporary, swell instantaneous, swell momentary, dan juga swell temporary. Simulasi untuk membangkitkan gangguan menggunakan software MATLAB Simulink yang telah disimulasikan dan mendapat nilai untuk input data ke ANN. Hasil algoritma yang diusulkan sangatlah efektif untuk identifikasi, dimana ANN dengan 5 x 5 neuron pada lapisan tersembunyi memiliki tingkat akurasi 100%.
Penggunaan Fast Fourier Transform Pada Identifikasi Arc Fault Pada Berbagai Jenis Kabel Mochammad Zulfikar Trysnawan; Hendik Eko H.S; Dimas Okky Anggriawan
INOVTEK - Seri Elektro Vol 2, No 3 (2020): INOVTEK Seri Elektro
Publisher : Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ise.v2i3.1473

Abstract

Arc merupakan loncatan bunga api yang disebabkan karena adanya pelepasan energi dari kabel penghantar. Arc fault menghasilkan panas yang dapat merusak isolasi kawat sehingga dapat menyebabkan terjadinya bahaya kebakaran. Namun keterbatasan akan hal memonitoring seluruh jalur pengawatan menjadi kendala dalam pendeteksian secara dini adanya gangguan arcing. Dirancang sebuah alat identifikasi arc fault pada kabel berjenis serabut dan pejal, yang mana dapat mencegah kebakaran dikarenakan keterlambatan untuk mengamankan bahaya arcing. Pada alat ini memanfaatkan AMC1301 sebagai sensor tegangan dan sensor arus. Sistem ini bekerja mengamankan instalasi saat terjadi gangguan serta dapat mengirim kondisi secara real status dari jalur pengawatan (ada gangguan arc atau tidak). Kondisi dari sistem instalasi yang terbaca oleh sensor diolah oleh mikrokontroler dan metode yang digunakan adalah mendeteksi munculnya komponen frekuensi tinggi pada arus sistem menggunakan Fast Fourier Transform (FFT). Apabila mendeteksi adanya gangguan busur seri AC, maka mikrokontroler akan mengolah data dengan FFT dan diidentifikasi jenis kabel uji sesuai karakteristiknya ketika terjadi gangguan. Penelitian ini dibangun pada sistem tegangan rendah 220V/50Hz dengan arus gangguan sebesar 0,83A dengan beban resistif. Data pengujian menunjukkan bahwa AFCI dengan metode FFT mampu mendeteksi gangguan busur seri AC dan memberikan proteksi pada sistem dengan rata-rata waktu pemutusan 872 ms.
DESAIN DAN IMPLEMENTASI INTERLEAVED BOOST CONVERTER UNTUK POWER FACTOR CORRECTION MENGGUNAKAN PENGENDALI LOGIKA FUZZY Mentari Putri Jati; Era Purwanto; Bambang Sumantri; Sutedjo Sutedjo; Dimas Okky Anggriawan
JURNAL INTEGRASI Vol 12 No 1 (2020): Jurnal Integrasi - April 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.784 KB) | DOI: 10.30871/ji.v12i1.1430

Abstract

In recent years there is an increasing demand for closely regulated dc power supply. Most of the power electronic converters in these power supplies used full wave rectifier. Bulky filter capacitor in rectifier can effect non-sinusoidal input current waveform (distorted). The difference in voltage and current waveform affected the power factor system. Interleaved Boost Converter (IBC) as Power Factor Correction (PFC) with a fuzzy logic controller to be added in the system to achieved near to unity power factor. IBC operated in Discontinuous Conduction Mode (DCM). Power factor near to unity can be achieved while rectifier supplied resistive load, the waveform of load current through back to input source have the same form with the input voltage. Simulation and hardware implementation is used by varying loads. The experimental results with a variable load value of IBC as PFC can improve the power factor system from 0.67 to 0.93.
Identifikasi Gangguan Open Circuit Dan Short Circuit Pada Instalasi Photovoltaic Array Dengan MPPT Berbasis Artificial Neural Network Khalin Khalin; Sutedjo Sutedjo; Dimas Okky Anggriawan
Energi & Kelistrikan Vol 14 No 1 (2022): Energi dan Kelistrikan: Jurnal Ilmiah
Publisher : Sekolah Tinggi Teknik PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/energi.v14i1.1554

Abstract

In the field of photovoltaic, the last few years have been very hotly discussed and researched as a new renewable source to produce electricity that cannot be exhausted. In the development effort there must be some problems arising from the existence of a new system. As with open circuit and short circuit interference. Therefore, The Identification of Open Circuit and Short Circuit Interference in Photovoltaic Array Installation with MPPT Based Artificial Neural Network is present to solve the problem. For identification of the location of the disruption is carried out on each photovoltaic string by knowing the voltage and current when there is an open circuit or short circuit interference, as well as the output power of the MPPT is used to determine the type of interference that occurs. Identification of interference using the Artificial Neural Network method with the purpose of this system can find out the location of interference and the type of open circuit or short circuit interference in photovoltaic array installations with MPPT. So that it is easy to know the location of the disturbance that is useful to maximize handling quickly and precisely. Keywords: Photovoltaic array, open circuit, short circuit, MPPT, Artificial Neural Network ABSTRAK Dalam bidang photovoltaic, beberapa tahun terakhir sangat hangat menjadi perbincangan dan penelitian sebagai sumber baru terbarukan untuk menghasilkan energi listrik yang tidak bisa habis. Dalam upaya pengembangnnya pasti ada beberapa permasalahan yang timbul dari adanya sistem baru. Seperti halnya adanya gangguan open circuit dan short circuit. Maka dari itu, Identifikasi Gangguan Open Circuit dan Short Circuit pada Instalasi Photovoltaic Array dengan MPPT Berbasis Artificial Neural Network hadir untuk menyelesaikan masalah tersebut. Untuk identifikasi lokasi gangguan dilakukan pada setiap string photovoltaic dengan mengetahui tegangan dan arus ketika terjadi gangguan open circuit maupun short circuit, serta daya keluaran dari MPPT digunakan untuk mengetahui jenis gangguan yang terjadi. Identifikasi gangguan menggunakan metode Artificial Neural Network dengan tujuan sistem ini dapat mengetahui lokasi gangguan dan jenis gangguan open circuit atau short circuit pada instalasi photovoltaic array dengan MPPT. Sehingga memudahkan untuk mengetahui lokasi gangguan yang berguna untuk memaksimalkan penanganan secara cepat dan tepat. Kata kunci: Photovoltaic array, open circuit, short circuit, MPPT, Artificial Neural Network
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.
Co-Authors Abel Aditya Aryaguna Achmad Luki Satriawan Agustin, Eva Inaiyah Ahmad Alvi Syahrin Alia, Diana Amsyar, Aidin Amsyar, Aidin Anang Budikarso Anang Budikarso, Anang Anang Tjahjono, Anang Anggara Trisna Nugraha Aryaguna, Abel Aditya Bambang Sumantri Dirhamsyah Dirhamsyah Eka Prasetyono, Eka Endro Wahjono Endro Wahjono, Endro Epyk Sunarno Era Purwanto Firdaus, Aji Akbar Fuad, Muchamad Chaninul Fuad, Muchamad Chaninul Gilang Andaru Trinandana H.S, Hendik Eko Hendik Eko H.S Indhana Sudiharto Jufriyadi, Mohammad Khafidli, Muhammad Khanif Khalin Khalin Mentari Putri Jati Mike Yuliana MOCHAMAD ARI BAGUS NUGROHO Mochammad Zulfikar Trysnawan Mokhamad Zuhal Muflih Muhammad Fauzi Novie Ayub Windarko Rachma Prilian Eviningsih, Rachma Prilian Rheinanda, Audya Elisa 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