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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

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

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

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

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

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%.

Implementation of Fast Fourier Transform and Artificial Neural Network in Series Arc Fault Identification and Protection System on DC Bus Microgrid Dimas Okky Anggriawan; Epyk Sunarno; Eka Prasetyono; Suhariningsih Suhariningsih; Muhammad Fauzi
Jurnal Teknologi Terpadu Vol 11, No 2 (2023): JTT (Jurnal Teknologi Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v11i2.1869

A microgrid is a cluster of electrical sources and loads that are interconnected and synchronized. Microgrid operation is typically divided into two modes, isolated or connected to the grid with a single or standalone control system. In this context, it can enhance the reliability and quality of electricity supply for connected customers. When using a microgrid system, it is important to consider the risk of series arc faults. Series arc faults are sudden bursts of flames resulting from ionization of gas between two electrode gaps. These faults can occur due to manufacturing defects, installation Errors, aging, or corrosion on conductor rods, leading to imperfect connections. Detecting series arc faults in DC microgrid system operations can be challenging using standard protective devices. Failure in the protection system can pose risks of fire, electrical shock hazards, and power loss in the DC microgrid.Therefore, a device has been designed to detect series arc faults by utilizing the fast Fourier transform method and artificial neural network, which function to analyze DC signal and make decisions when faults occur by examining the average sum of current frequency values during normal and fault conditions. In this study, the average sum of current frequency values during normal conditions was found to range from 0.35437 to 0.36906 A, while during fault conditions, it ranged from 0.21450 to 0.22793 A, with an average protection identification time of 1087 ms and an ANN output accuracy of 99.98%.

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

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

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

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

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 %.

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