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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology JURNAL NASIONAL TEKNIK ELEKTRO KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization JURNAL INTEGRASI Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Rekayasa elektrika
Anang Tjahjono, Anang
Program Studi Teknik Elektro Industri, Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Other

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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
Studi Komparasi Fungsi Keanggotaan Fuzzy sebagai Kontroler Bidirectional DC-DC Converter pada Sistem Penyimpan Energi Prasetyono, Eka; Ashary, Wima; Tjahjono, Anang; Windarko, Novie Ayub
JURNAL NASIONAL TEKNIK ELEKTRO Vol 4, No 2: September 2015
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (539.57 KB) | DOI: 10.25077/jnte.v4n2.161.2015

Abstract

Bidirectional DC-DC converter is needed in the energy storage system. The converter topology used in this paper was a non-isolated bidirectional DC-DC buck-boost converter. This converter worked in two ways, which the charging mode stored energy into battery when load current was less than nominal main DC current (set point) and discharging mode transferred energy from battery to the load when its current exceeded set point value. Both of these modes worked automatically according to the load current. The charging and discharging currents were controlled by fuzzy logic controller which was implemented on microcontroller ARM Cortex-M4F STM32F407VG. This paper compares two types of fuzzy membership function (triangular and sigmoid) in controlling bidirectional DC-DC converter. The results showed that fuzzy logic controller with triangle membership function and sigmoid as control bidirectional DC-DC converter had no significant different response, both had an average error for charging and discharging process under 4% with ripple current on the main DC bus around 0.5%.  The computing time of program for fuzzy logic controller with triangular membership functions had 19.01% faster than sigmoid, and fuzzy logic computation time on a microcontroller with hardware floating point was 60% faster than software floating point.                                                                                                                                                        Keywords : Bidirectional DC-DC converter, Fuzzy logic controller and MikorkontrolerAbstrak—Bidirectional DC-DC converter merupakan converter yang diperlukan dalam sistem penyimpan energi. Topologi converter yang digunakan pada paper ini adalah non-isolated bidirectional DC-DC converter jenis buck–boost converter, converter ini dapat bekerja dua arah yaitu mode charging untuk menyimpan energi ke dalam baterai apabila arus beban kurang dari nilai nominal (set point) kemampuan main DC bus dan mode discharging untuk menyalurkan energi dari baterai ke beban bila arus beban melebihi nilai set point. Kedua mode tersebut bekerja secara otomatis sesuai dengan besarnya beban yang digunakan. Besarnya arus charging dan discharging dikontrol oleh kontrol logika fuzzy yang diimplemanetasikan pada mikrokontroler ARM Cortex-M4F STM32F407VG. Paper ini membandingkan dua jenis fungsi keanggotaan fuzzy (segitiga dan sigmoid) dalam mengontrol bidirectional DC-DC converter. Hasil yang diperoleh menunjukkan kontrol logika fuzzy dengan fungsi keanggotaan segi tiga dan sigmoid sebagai kontrol bidirectional DC-DC converter memiliki perbedaan respon yang tidak signifikan, keduanya memiliki rata-rata error untuk proses charging dan discharging dibawah 4% dengan ripple pada main DC bus 0.5%. Ditinjau dari waktu komputasi program, kontrol logika fuzzy dengan fungsi keanggotaan segitiga 19.01% lebih cepat komputasinya dibanding dengan sigmoid dan waktu komputasi logika fuzzy pada mikrokontroler dengan floating point hardware 60%  cepat dibanding dengan floating point secara software.Kata Kunci : Bidirectional DC-DC converter, Fuzzy logic controller dan Mikorkontroler.
Analysis of Load Flow and Short Circuit Against the Addition of Distributed Generation (DG) in Distribution Networks Ridwan, Ahmad; El Gazaly, Aejelina; Tjahjono, Anang
Journal of Renewable Energy, Electrical, and Computer Engineering Vol 2, No 1 (2022): March 2022
Publisher : Institute for Research and Community Service, Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v2i1.6807

Abstract

This study tries to determine the level of change in short-circuit fault currents on certain buses in the Andalas University distribution network due to the installation of a new generator. Simulation of load flow and short circuit faults uses a 20 kV Andalas University distribution network system model to which a renewable generator with a capacity of 200 kW will be added. The simulation results of the load flow on a 20 kV distribution system paralleled with DG show that the voltage drop is still in accordance with the provisions of PT. PLN, this is due to the voltage drop in the distribution system is not up to 10% of the nominal 20 kV. While the short circuit simulation results, the largest single-phase and three-phase short-circuit current values occur at the Nursing_P location of 9.362 kA. However, the short circuit capacity has not yet reached a maximum voltage of 20 kV 500 MVA or 14.4 kA. So that the amount of short circuit current contributed by Nursing_P is within normal limits and does not require additional equipment to protect the fault current.
Load Identification Using Harmonic Based on Probabilistic Neural Network Dimas Okky Anggriawan; Aidin Amsyar; Eka Prasetyono; Endro Wahjono; Indhana Sudiharto; Anang Tjahjono
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
Studi Komparasi Fungsi Keanggotaan Fuzzy sebagai Kontroler Bidirectional DC-DC Converter pada Sistem Penyimpan Energi Eka Prasetyono; Wima Ashary; Anang Tjahjono; Novie Ayub Windarko
JURNAL NASIONAL TEKNIK ELEKTRO Vol 4 No 2: September 2015
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (539.57 KB) | DOI: 10.25077/jnte.v4n2.161.2015

Abstract

Bidirectional DC-DC converter is needed in the energy storage system. The converter topology used in this paper was a non-isolated bidirectional DC-DC buck-boost converter. This converter worked in two ways, which the charging mode stored energy into battery when load current was less than nominal main DC current (set point) and discharging mode transferred energy from battery to the load when its current exceeded set point value. Both of these modes worked automatically according to the load current. The charging and discharging currents were controlled by fuzzy logic controller which was implemented on microcontroller ARM Cortex-M4F STM32F407VG. This paper compares two types of fuzzy membership function (triangular and sigmoid) in controlling bidirectional DC-DC converter. The results showed that fuzzy logic controller with triangle membership function and sigmoid as control bidirectional DC-DC converter had no significant different response, both had an average error for charging and discharging process under 4% with ripple current on the main DC bus around 0.5%.  The computing time of program for fuzzy logic controller with triangular membership functions had 19.01% faster than sigmoid, and fuzzy logic computation time on a microcontroller with hardware floating point was 60% faster than software floating point.                                                                                                                                                        Keywords : Bidirectional DC-DC converter, Fuzzy logic controller and MikorkontrolerAbstrak—Bidirectional DC-DC converter merupakan converter yang diperlukan dalam sistem penyimpan energi. Topologi converter yang digunakan pada paper ini adalah non-isolated bidirectional DC-DC converter jenis buck–boost converter, converter ini dapat bekerja dua arah yaitu mode charging untuk menyimpan energi ke dalam baterai apabila arus beban kurang dari nilai nominal (set point) kemampuan main DC bus dan mode discharging untuk menyalurkan energi dari baterai ke beban bila arus beban melebihi nilai set point. Kedua mode tersebut bekerja secara otomatis sesuai dengan besarnya beban yang digunakan. Besarnya arus charging dan discharging dikontrol oleh kontrol logika fuzzy yang diimplemanetasikan pada mikrokontroler ARM Cortex-M4F STM32F407VG. Paper ini membandingkan dua jenis fungsi keanggotaan fuzzy (segitiga dan sigmoid) dalam mengontrol bidirectional DC-DC converter. Hasil yang diperoleh menunjukkan kontrol logika fuzzy dengan fungsi keanggotaan segi tiga dan sigmoid sebagai kontrol bidirectional DC-DC converter memiliki perbedaan respon yang tidak signifikan, keduanya memiliki rata-rata error untuk proses charging dan discharging dibawah 4% dengan ripple pada main DC bus 0.5%. Ditinjau dari waktu komputasi program, kontrol logika fuzzy dengan fungsi keanggotaan segitiga 19.01% lebih cepat komputasinya dibanding dengan sigmoid dan waktu komputasi logika fuzzy pada mikrokontroler dengan floating point hardware 60%  cepat dibanding dengan floating point secara software.Kata Kunci : Bidirectional DC-DC converter, Fuzzy logic controller dan Mikorkontroler.
FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER Mohammad Imron Dwi Prasetyo; Anang Tjahjono; Novie Ayub Windarko
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 1 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i1.290

Abstract

Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus,  dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.Keywords: SOC, BMS, Coloumb Counting, OCV, FFNNEstimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus,  dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.Kata kunci: SOC, BMS, Coloumb Counting, OCV, FFNN
Identifikasi Gangguan Qualitas Daya Pada Transformer Distribusi Untuk Menjaga Kualitas Jaringan Mokhamad Firdaus Karyapraja; Anang Tjahjono; Eka Prasetyono
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 6 No 2 (2019): JETT Desember 2019
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jett.v6i2.2956

Abstract

Hampir dari seluruh transformator yang terpasang di indonesia belum adanya pemantauan secara langsung guna untuk menjaga kualitas energi listrik yang akan disalurkan menuju pelanggan. Dengan adanya pemantauan secara langsung pada transformator dapat dilakukannya pendeteksian gangguan secara langsung hal ini akan membuat pihak penyalur tenaga listrik membantu mempercepat menangani masalah yang terjadi. Makalah ini menyajikan pengidentifikasi gangguan dengan cara melakukan pemodelan dari gangguan yang akan di identifikasi. Dengan bantuan perangkat elektronik yang dapat melakukan pemodelan gangguan menggunakan Artficial Neural Network sehingga perangkat elektronik dapat mengenali gangguan yang terjadi pada transformator.
EFFICIENT MAXIMUM POWER POINT ESTIMATION MONITORING OF PHOTOVOLTAIC USING FEED FORWARD NEURAL NETWORK Hasnira Hasnira; Novie Ayub Windarko; Anang Tjahjono; Mochammad Ari Bagus Nugroho; Mentari Putri Jati
JURNAL INTEGRASI Vol 12 No 2 (2020): Jurnal Integrasi - Oktober 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v12i2.2161

Abstract

The development of the utilization of solar panels in the future will continue to increase. One characteristic form of solar panels is the I-V curve which can be used to analyze the amount of solar panel output power. By knowing the I-V curve, we can get Maximum Power Point Estimation (MPPE) value that can be supported by solar panels. Information about the estimated value of the maximum solar panel power is an important part in determining the loading capacity, while maintaining the life of the equipment used. Feed Forward Neural Network with Back Propagation Algorithm (FFBP) has proven to be able to provide MPPE value information on solar panel output. The input values ​​in ANN are the voltage and current of the solar panel, while the output of ANN is in the form of an estimated power value. MPPE simulation results obtained an average error of 0.04 points between actual power (MPP) and estimated power (MPPE).
ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER MENGGUNAKAN BACK PROPAGATION NEURAL NETWORK Mohammad Imron Dwi Prasetyo; Hasnira Hasnira; Novie Ayub Windarko; Anang Tjahjono
JURNAL INTEGRASI Vol 12 No 2 (2020): Jurnal Integrasi - Oktober 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v12i2.2163

Abstract

The battery is an important component in the context of implementing renewable energy. The type of battery that has a density in energy storage is lithium polymer. The parameter in the battery that must be considered is the State of Charge (SOC) estimation. In general, the SOC battery estimation uses the coloumb counting method because the difficulty level is low. However, there are weaknesses in the dependence on the utility of the current sensor which is used as an accumulation of the integral of the incoming and outgoing currents over time. In this study presents Back Propagation Neural Network (BPNN) as an algorithm for estimating SOC based on OCV-SOC characteristic curves. The OCV-SOC characteristic curve of the battery is obtained from the battery pulse test. Battery voltage, current and discharging time are used as the first BPNN input layer for the estimation of Open Circuit Voltage (OCV). OCV will be learned as the second BPNN input layer for estimating battery SOC. The results of SOC estimation simulations obtained an average error of 0.479% against the real SOC based on the characteristic curve of OCV - SOC.
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 %.
Co-Authors Achmad Luki Satriawan Ahmad Ridwan Aidin Amsyar Amsyar, Aidin Anang Budikarso Ashary, Wima Budiarti, Rizqi Putri Nourma Dimas Nur Prakoso Dimas Okky Anggriawan Eka Prasetyono, Eka El Gazaly, Aejelina Endro Wahjono Endro Wahjono, Endro Firdaus, Aji Akbar Habibi, Muhammad Nizar Hasnira Hasnira Indhana Sudiharto Mas Sulung Wisnu Jati Mentari Putri Jati Mochammad Ari Bagus Nugroho Mohammad Imron Dwi Prasetyo Mohammad Imron Dwi Prasetyo Mokhamad Firdaus Karyapraja Novie Ayub Windarko Panduman, Yohanes Yohanie Fridelin Wima Ashary