Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

1.665

P-Index

This Author published in this journals

All Journal Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics Journal of Engineering and Technological Sciences Journal of Science and Applicative Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)

Edi Leksono

Institut Teknologi Bandung

Author-ID : 2460567

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 19 Documents Claim Missing Document

Claim Missing Document

Articles

1 2

Compensation of INS/LBL Navigation Errors in a Polynomial Sound-Speed-Profile Yohannes Sampang Martua Simamora; Harijono A. Tjokronegoro; Edi Leksono; Irsan S. Brodjonegoro
Journal of Engineering and Technological Sciences Vol. 54 No. 2 (2022)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2022.54.2.11

This paper presents an autonomous underwater vehicle (AUV) navigation scheme that pairs an inertial navigation system (INS) and a long baseline (LBL) acoustic positioning system. The INS is assigned to be the main navigation aid because of its faster rate. Meanwhile, the LBL provides position reference for compensation of the INS’ main inherent drawback, i.e., accumulating errors. However, the LBL has to deal with time-of-flight (ToF) measurements that may not be carried out under line-of-sight (LoS) circumstances. This is because the propagation speed of underwater acoustic waves is subject to the sound-speed-profile (SSP) of the area in question. This paper’s contribution is to consider the SSP in ToFs while addressing the above scheme. Specifically, the discrete approach to raytracing was implemented. For a given ToF, the Snell’s parameter of the wave is estimated and subsequently used to compute the horizontal range. The ToF results are then used to estimate the position of the AUV, while the position is obtained from a depth sensor. It was shown by simulation that the estimators can provide navigation with accuracy <0.5 m2, as it manages to compensate for errors. Since the estimation of Snell’s parameter is prone to exhibit imaginary numbers, future work should consider a more robust method to tackle this problem.

Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory Azam Zamhuri Fuadi; Irsyad Nashirul Haq; Edi Leksono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

Pemodelan Manajemen Energi Microgrid pada Sistem Bangunan Cerdas FX Nugroho Soelami; Edi Leksono; Irsyad Nashirul Haq; Justin Pradipta; Putu Handre Kertha Utama; Aretha Fieradiella Pahrevi; Faizatuzzahrah Rahmaniah; Meditya Wasesa
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

From the electricity system point of view, smart buildings can be seen as an integration of a microgrid electricity network that connects solar PV, storage system, and building load distribution. The operation condition of the microgrid needs to be evaluated and optimized to obtain efficient and reliable performance. This contribution presents an energy management modeling for the microgrid optimization process in a smart building system. The energy sources connected to the microgrid are solar PV, battery storage system, and the PLN (utility) grid. Combinations of load scenarios are evaluated, which consists of building a lighting system, water pump, dan HVAC system. The optimization goal is to find the optimal estimation of Self Consumption (SC) and Self Sufficiency (SS) values. A simulation result before the optimization shows that the system is operating with SC of 63.2% and SS of 96.32%. After the optimization, the values become SC = 84.68% and SS = 83.27%. Therefore, the amount of energy sourced from the Solar PV system is increased and the microgrid is working more optimally.

Estimasi Kondisi Muatan dan Kondisi Kesehatan Baterai VRLA dengan Metode RVP Danang Widjajanto; Beny Maulana Achsan; Fajar Muhammad Noor Rozaqi; Augie Widyotriatmo; Edi Leksono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 2: Mei 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Optimization of battery usage, including VRLA battery which is often used for large amounts of energy storage at low prices, is usually pursued by implementing Battery Management System (BMS). To carry out BMS, information about the condition of charge and health is needed. The State of Charge (SoC) is defined as the ratio of the current remaining capacity of the battery to the capacity of the battery before discharge, while the State of Health (SoH) is the ratio between the measured full capacity of a battery to its nominal capacity when it is still in a new condition. SoC and SoH estimation can be held indirectly by using the voltage and current at the battery terminals. This study uses the Coulomb Counting (CC) method and Support Vector Regression (SVR) to estimate SoC and SoH of VRLA batteries which are used as backup energy for the nanogrid system in the laboratory. This study uses a Python machine learning module which enables the implementation of SVR with various types of kernels including linear kernels, polynomial kernel, and RBF kernel. The tests carried out in this research using the grid search module show that the best performance is obtained when using the RBF kernel.

Peningkatan Kinerja Microgrid Bangunan Kampus dengan Simulasi Multi Skenario dan Analisis Sensitivitas Justin Pradipta; Koko Friansa; Irsyad Nashirul Haq; Edi Leksono; Hanafi Kusumayudha; Salsabila Regita; Mediya Wasesa
Journal of Science and Applicative Technology Vol 5 No 2 (2021): Journal of Science and Applicative Technology December Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v5i2.458

Penelitian ini mengevaluasi kinerja microgrid cerdas dengan tujuan untuk meningkatkan ketersediaan pasokan listrik dan renewable fraction (RF). Evaluasi dilakukan dengan simulasi multi skenario yang mencakup produksi dan konsumsi energi. Simulasi dibagi tiga, yaitu skenario dasar, skenario uji, dan skenario rekomendasi. Skenario uji terdiri dari uji kapasitas sistem, penggantian komponen, dan analisis sensitivitas. Didapatkan dari skenario dasar bahwa ketersediaan pasokan listrik selama setahun telah terpenuhi, dengan RF 30,5%; cost of energy (CoE) Rp2.019/kWh; dan waktu otonomi baterai (WOB) 11,1 jam. Dari hasil analisis didapatkan beberapa rekomendasi berupa penggantian komponen baterai dan modul surya, penambahan kapasitas baterai, dan pengaturan batas state of charge (SoC) pada baterai untuk meningkatkan RF. Skenario rekomendasi tersebut berhasil meningkatkan ketersediaan pasokan listrik dan mencapai target dengan nilai WOB sebesar 37 jam dan RF sebesar 46,4% pada awal siklus hidup proyek; serta WOB sebesar 25,5 jam dan RF sebesar 29,1% pada akhir tahun ke 25, dengan CoE sebesar Rp6.448/kWh. Analisis sensitivitas operasi baterai lead-acid menunjukkan bahwa untuk mendapatkan RF maksimal rentang pengaturan SoC berada pada 0-20%. Sedangkan untuk baterai Li-Ion, rentang SoC adalah 0-25%.

Pengembangan Pengontrol Tegangan Sistem Mikrogrid Cerdas Menggunakan Sistem Baterai Penyimpan Energi Putu Handre Kertha Utama; Irsyad Nashirul Haq; Edi Leksono; Justin Pradipta; M Daya Imannata; Timothius Pratama Tjahja
Journal of Science and Applicative Technology Vol 6 No 2 (2022): Journal of Science and Applicative Technology December Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v6i2.594

A power outage on a conventional grid can cut the electricity supply to the entire load. In contrast, Microgrid (MG) can still supply at least the most critical local loads even though blackout occurs in the main grid. MG can also utilize renewable energy sources such as solar and wind energy to generate electricity. That is possible by the advancement of the battery energy storage system (BESS). The BESS able to maintains electricity supply to the load even in outages. The inverter on the SBPE also plays a role in stabilizing the MG output voltage by supplying or absorbing reactive power in the MG system. This paper focuses on the control development of the battery inverter primary controller. The droop control design utilizes the deadband around the nominal voltage. That becomes the improvement of the droop control method used in this study compared to the initial formulation of the droop method. The proposed method was then tested through simulation with four different scenarios. The BESS will operate in the voltage range 194.9V to 234.6V with a droop control deadband in the voltage range 198.0V to 231.0V. Based on the simulation results, the addition of SBPE with the MG scheme on the existing system can improve the quality of the voltage received by the load from 0.994p.u. to 0.997p.u. The simulation also shows that the load still gets a power supply even though there is a blackout on the main grid.

Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine Edi Leksono; Auditio Mandhany; Irsyad Nashirul Haq; Justin Pradipta; Putu Handre Kertha Utama; Reza Fauzi Iskandar; Rezky Mahesa Nanda
Journal of Engineering and Technological Sciences Vol. 55 No. 2 (2023)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2023.55.2.1

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system.

Pemodelan dan Simulasi MPPT pada Sistem PLTS Menggunakan Metode DNN Edi Leksono; Robi Sobirin; Reza Fauzi Iskandar; Putu Handre Kertha Utama; Mochammad Iqbal Bayeqi; Muhammad Fatih Hasan; Irsyad Nashirul Haq; Justin Pradipta
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 4: November 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i4.7931

The maximum power point tracking (MPPT) feature in solar power plants is an essential function in increasing the efficiency of electricity production. The incremental conductance (InC) algorithm controls MPPT, aiming to maximize the output power of photovoltaic (PV) panels and increase the efficiency of the solar power plant system. Even though the InC algorithm is simple and practical, this algorithm tends to lack support in precise switching speeds, is sensitive to the measurement precision level, and is inadequate to eliminate power oscillations due to tight switching cycles. The deep neural network (DNN) algorithm has the potential to answer the challenges of MPPT dynamics. DNN’s learning capabilities enable the controller to better recognize the dynamics of shifts in maximum power values, thereby providing more appropriate contact actuation. The input for the DNN is the duty ratio produced by the InC algorithm. The DNN algorithm was implemented on three DC-to-DC power converter topologies, namely buck, boost, and buck-boost, to determine MPPT performance under standard tests and actual environmental conditions. DNN has demonstrated the ability to reduce oscillation effects, speed up steady-state time, and increase efficiency. In actual environmental conditions, the results showed that the buck converter consistently produced the highest power, followed by the boost and the buck-boost converters. Regarding performance efficiency, the buck converter achieved the highest efficiency at 94.58%, followed by the boost converter at 90.79%. Conversely, the buck-boost converter had the lowest performance efficiency, with an efficiency of 79.34%.

Design of Fuzzy Logic Controller for Inductor Based Cell Balancing in Battery Management System Hartono Hartono; Edi Leksono
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 10 No 2 (2023): List of the Accepted Article for Future Issues
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v10i2.4359

Lithium-ion batteries are a popular energy storage used in various applications including electronic devices, electric vehicles, renewable energy systems, and microgrids. The limitations in capacity and voltage of individual battery cells necessitate arranging them in series and parallel configurations to meet the energy demands of the system. However, connecting cells in series can potentially lead to charge imbalance among cells, resulting in reduced battery pack capacity and triggering safety concerns. Numerous topologies and control methods for battery cell balancing systems have been explored in previous research. In this study, two fuzzy logic controllers with different membership function shapes are designed to drive the duty cycle of PWM signals for inductor-based cell to cell balancing. Based on simulation results, the designed controllers demonstrate good performance compared to conventional methods.

Co-Authors Achmad Noerkhaerin Putra Aretha Fieradiella Pahrevi Auditio Mandhany Augie Widyotriatmo Azam Zamhuri Fuadi Beny Maulana Achsan Brian Yuliarto Danang Widjajanto Deddy Kurniadi Deddy Kurniadi, Deddy F.X. Nugroho Soelami Faizatuzzahrah Rahmaniah Fajar Muhammad Noor Rozaqi FX Nugroho Soelami Hanafi Kusumayudha Haq, Irsyad Nashirul Harijono A. Tjokronegoro Harijono A. Tjokronegoro Harijono A. Tjokronegoro Harijono A. Tjokronegoro Hartono Hartono Irsan S. Brodjonegoro Irsan S. Brodjonegoro Irsyad Nashirul Haq Irsyad Nashirul Haq Irsyad Nashirul Haq Irsyad Nashirul Haq Irsyad Nashirul Haq Justin Pradipta Justin Pradipta Justin Pradipta Justin Pradipta Justin Pradipta Justin Pradipta Koko Friansa M Daya Imannata Meditya Wasesa Mediya Wasesa Mochammad Iqbal Bayeqi Muhammad Fatih Hasan Pratikto Pratikto Putu Handre Kertha Utama Putu Handre Kertha Utama Putu Handre Kertha Utama Reza Fauzi Iskandar Rezky Mahesa Nanda Robi Sobirin Salsabila Regita Soelami, F.X. Nugroho Timothius Pratama Tjahja Tua Agustinus Tamba Tua Agustinus Tamba Yohannes S.M. Simamora Yohannes Sampang Martua Simamora Yul Yunazid Nazaruddin Yul Yunazid Nazaruddin Zainal Abidin Zainal Abidin

Title

Found 19 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 19 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 19 Documents
Search