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Contact Name
Machrus Ali
Contact Email
machrus7@gmail.com
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jeerifortei@fortei.org
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Electrical and Information Technology Engineering Departement. Komplek Fakultas Teknik UGM, Jl. Grafika No.2, Yogyakarta, Senolowo, Sinduadi, Kec. Mlati, Kota Yogyakarta, Daerah Istimewa Yogyakarta 55281
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INDONESIA
Journal FORTEI-JEERI
ISSN : 27226956     EISSN : 27226948     DOI : 10.46962
Power System; Electric Power Generation, Transmission and Distribution, Power Electronics, Power system analysis, Protection system, Power Quality, Electrical machine and drives, Power Economic, Renewable Energy, Condition Monitoring and Diagnostics, and Energy Systems. Automation and Control; Instrumentation system, transmitter, industrial process control, PLC, SCADA, DSC, IoT in Industrial Automation, Optimal, Robust and Adaptive Controls, Non Linear and Stochastic Controls, Modeling and Identification, Robotics, Image Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligent and Expert System, Fuzzy Logic and Neural Network, Complex Adaptive Systems. Electronic and Microelectronics; Electromagnetic compatibility, devices and systems, microelectronics, micro- and nanofabrication of electronic devices, circuits and systems for electronics, electro mechanics and robotic, bioelectronics. Computer Engineering; Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, VLSI Design-Network Traffic Modeling, Performance Modeling, Dependable Computing, High Performance Computing, Computer Security. Informatic and Software Engineering; Information Search Engine, Multimedia Security, Computer Vision, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Next Network Generation, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems. Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods), Programming (Programming Methodology and Paradigm), Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data). Telecommunication Engineering; Antenna and Wave Propagation, Modulation and Signal Processing for Telecommunication, Wireless and Mobile Communications, Information Theory and Coding, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services, Security Network, and Radio Communication. Signal Processing; Digitization (sampling in time and quantizing in amplitude), Band-pass and base-band filtering, Beamforming (spatial filtering), Matched filtering and/or incoherent integration, Detection, classification, localization, and tracking. Biomedical Engineering; Biomedical Physics, Biomedical Transducers and instrumentation, Biomedical System Design and Projects, Medical Imaging Equipment and Techniques, Telemedicine System, Biomedical Imaging and Image Processing, Biomedical Informatics and Telemedicine, Biomechanics and Rehabilitation Engineering, Biomaterials and Drug Delivery Systems.
Articles 3 Documents
Search results for , issue "Vol. 2 No. 1 (2021): FORTEI-JEERI" : 3 Documents clear
Best Estimation Of Double Seasonal Pattern Electric Load Parameters Using Least Squares Method Ismit Mado
Journal FORTEI-JEERI Vol. 2 No. 1 (2021): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/forteijeeri.v2i1.11

Abstract

Forecasting is an important tool in planning an effective and efficient use of electrical loads. This paper presents an improvement in parameter estimation from previous studies. The results of previous studies indicate that the DSARIMA model is with MAPE about 2.06 percent. This model produces white noise residuals, but not normally distributed, which is thought to be due to outliers. Data smoothing is done to get the best data pattern. The analysis results show that the AR parameter iteration of the best DSARIMA model that is appropriate for short-term forecasting is with MAPE about 1.56 percent.
ORCA Algorithm for Unit Commitment Considering Electric Vehicle Inclusion A.N. Afandi
Journal FORTEI-JEERI Vol. 2 No. 1 (2021): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/forteijeeri.v2i1.18

Abstract

The most cost-effective operation of a power system is achieved in practice by combining different types of producing units to create a committed power plan to satisfy load demand changes at all periods of operation. To reduce overall costs while upholding set limitations, power outputs from generating units are distributed based on load demand at a certain moment. Economic Dispatch models are used to account for changes in load demand to compute the overall cost variations of operation to fulfill a unit commitment (UC). Orca Algorithm is used in this work to solve the UC problem with the IEEE-62 bus system as the model, where loads are linked with flexible loads where the flexible load in this study is determined by the driving habits of an electric vehicle (EV). The simulation results show that the Orca Algorithm solves the problem in the fewest iterations possible. Computations used to compute load demand changes over all periods are fast and smooth, with high convergence. UC problem is carried out in various power outputs, and total operating expenses. Furthermore, the EV has different driving characteristics as well as power users to cover the entire route for driving patterns with one-way and two trips.
The MAPWE-Boosted AEKF with Recursive-Against-Iteration Noise Statistic for Feature-Based SLAM Algorithm Heru Suwoyo
Journal FORTEI-JEERI Vol. 2 No. 1 (2021): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/forteijeeri.v2i1.22

Abstract

The unknown noise statistic might degrade the Filter performance or even lead to filter divergence. Accordingly, to enhance the classical EKF to approximate the recursive process and measurement noise statistic, based on Maximum A Posteriori creation and Weighted Exponent (WE) as the divergence suppression method, abbreviated as MAPWE, an adaptive EKF is proposed through this paper. Moreover, the existence of simplification during estimating noise statistics under MAP creation might also degrade its quality. Thus, the suboptimal MAP solution was also estimated based on Weighted Exponent. Indeed, the time-varying noise statistic under this process seems strongly accurate. But the complexity of the measurement covariance might also diverge from its positive definite characteristic. Thus, aiming to prevent this condition, the additional divergence suppression method was also involved in correcting the error state covariance in the smoothing step. This improvement is then used as SLAM algorithm for a mobile robot. Comparing to the conventional methods, it is better in term of RMSE for the estimated path and estimated map.

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