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All Journal JURNAL NASIONAL TEKNIK ELEKTRO JTT (Jurnal Teknologi Terpadu) Jurnal INOVTEKSeri Elektro JAREE (Journal on Advanced Research in Electrical Engineering)
Suhariningsih Suhariningsih
Politeknik Elektronika Negeri Surabaya
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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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%.
Battery Management System (BMS) Dengan State Of Charge (SOC) Metode Modified Coulomb Counting Puspita Ningrum; Novie Ayub Windarko; Suhariningsih Suhariningsih
INOVTEK - Seri Elektro Vol 1, No 1 (2019): INOVTEK Seri Elektro
Publisher : Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (850.862 KB) | DOI: 10.35314/ise.v1i1.1022

Abstract

Baterai merupakan media penyimpanan energi listrik dalam bentuk energi kimia yang dapat dikonversikan menjadi daya. Dalam kasus yang ditemukan baterai mudah mengalami kerusakan dan memiliki life time yang pendek. Kerusakan pada baterai disebabkan karena penggunaan yang tidak ideal dan baterai tidak dilengkapi sistem proteksi dan monitoring, sehingga baterai tetap beroperasi meskipun dalam kondisi over-voltage, over-current dan over-heat saat charging dan ditambah mengalami under-voltage pada saat discharging. Pada jurnal ini disampaikan perancangan sistem BMS (Battery Management System) untuk 2 jenis baterai yaitu Lead Acid 12V 7Ah dan Li-ion 12V 4Ah. BMS memiliki tiga fungsi, yaitu computation , monitoring ,dan protection. Sensor tegangan, sensor arus ACS 712 dan sensor suhu DHT22 digunakan untuk mengirimkan informasi mengenai kondisi baterai ke mikrokontroler Arduino Mega 2560 sebagai pusat kendali. Akurasi pengukuran State Of Charge (SOC) mempunyai aspek yang penting dalam perancangan Battery Management System. Pengukuran  SOC secara tepat dapat menghindarkan baterai dari kondisi  overcharge dan undercharge.  Dari  hasil  pengujian diharapkan bahwa BMS mampu membaca nilai tegangan, arus, suhu, SOC, AH, dan WH. Hasil pembacaan parameter dapat tersimpan pada SD Card. Sistem proteksi pada BMS akan aktif ketika baterai dalam kondisi tidak ideal sehingga baterai tidak mudah rusak dan dapat menekan penurunan life time.
Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS) Puspita Ningrum; Novie Ayub Windarko; Suhariningsih Suhariningsih
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 1 (2021): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i1.150

Abstract

Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords: battery management system, modified coulomb counting, state of charge
Perbandingan Performa Metode Maximum Power Point Tracking Human Psychology Optimization (HPO), Artificial Bee Colony (ABC) dan Fuzzy Logic Controller (FLC) pada Flyback Converter Kondisi Parsial Shading Moh. Zaenal Efendi; Mochammad Rody Dwirantono; Suhariningsih Suhariningsih; Lucky Raharja
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1022.2023

Abstract

Maximum Power Point Tracking (MPPT) is a method to track the power point of an energy source with the intention to generate maximum power. The surface of the Solar Panel has the possibility of being blocked when it receives sunlight. The barrier can be in the shape of shadows of objects that are nearby solar panels. The problem causes the power generated to be not optimal and makes more than one MPPT peak on the characteristics of P-V. This paper compares several methods of MPPT such as Human Psychology Optimization (HPO), Artificial Bee Colony (ABC), and Fuzzy logic Controller (FLC) under partial shading conditions, the comparison of three method by simulation. This algorithm hooks up to a flyback converter to provide MPP. From the results of MPPT accuracy in partial shading situations, the ABC and HPO approach methods can achieve GMPP with more than 82.22 % accuracy. For convergence, ABC needs extra time to discover GMPP. From the results, the Fuzzy approach can track however nevertheless trapped on LMPP.
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

Abstract

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%.
Co-Authors Abel Aditya Aryaguna Dimas Okky Anggriawan Eka Prasetyono, Eka Epyk Sunarno Lucky Raharja MOCHAMMAD RODY DWIRANTONO Mohammad Zaenal Efendi Muhammad Fauzi Novie Ayub Windarko Puspita Ningrum