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Queen honey bee migration (QHBM) optimization for droop control on DC microgrid under load variation Aripriharta, Aripriharta; Al Rasyid, Mochammad Syarifudin; Bagaskoro, Muhammad Cahyo; Fadlika, Irham; Sujito, Sujito; Afandi, Arif Nur; Omar, Saodah; Rosmin, Norzanah
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.742

Transmission line impedance in DC microgrids can cause voltage dips and uneven current distribution, negatively impacting droop control and voltage stability. To address this, this study proposes an optimization approach using heuristic techniques to determine the optimal droop parameters. The optimizcv ation considers reference voltage constraints and virtual impedance at various load conditions, particularly resistive. The optimization problem is addressed using two techniques: queen honey bee migration (QHBM) and particle swarm optimization (PSO). Simulation results show that QHBM reaches an error of 0.8737 at the fourth iteration. The QHBM and PSO algorithms successfully optimized the performance of the DC microgrid under diverse loads, with QHBM converging in 5 iterations with an error of about 0.8737, and PSO in 40 iterations drawn error is 0.9 while keeping the current deviation less than 1.5 A and voltage error less than 0.5 V. The deviation of current control and virtual impedance values are verified through comprehensive simulations in MATLAB/Simulink.

Control energy management system for photovoltaic with bidirectional converter using deep neural network Widjonarko, Widjonarko; Utomo, Wahyu Mulyo; Omar, Saodah; Baskara, Fatah Ridha; Rosyadi, Marwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1437-1447

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.

Load forecasting analysis for estimating transformer capacity of Karangkates Substations using Holt-Winters method in Python Rahmawati, Yuni; Kaki, Gregorius Paulus Mario Laka; Aripriharta, Aripriharta; Sujito, Sujito; Afandi, Arif Nur; Wibawa, Aji Prasetya; Purwatiningsih, Ayu; Bagaskoro, Muhammad Cahyo; Omar, Saodah; Rosmin, Norzanah
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2222-2233

In the six years from 2010 to 2015, the peak load in the East Java region increased by an average of 284MW per year. Karangkates Substation is part of an interconnected electrical system that supplies Java Island. To ensure a high level of reliability in its service, it is necessary to prepare for load growth to make sure that it does not exceed its ideal conditions, therefore special analysis of transformer capacity is needed. Using the Holt-Winters (HW) method as a reference for processing the data can be used as a reference in planning and anticipating the growing electricity demand. The results of this study are with the accuracy of the HW method with mean absolute percentage error (MAPE) = 2.645%, while the accuracy of the fuzzy time series (FTS) method = 6.399%. A forecast result done with HW methods shows the transformer at the substation Karangkates reached its normal working capacity in March 2018 at 99.583% of installed capacity and exceeded the maximum capacity in April 2018 at 101.493% of installed capacity.

Test Hybrid PV System Performance Against Load Variations Aripriharta, Aripriharta; Amin, Muhammad Adib; Sujito, Sujito; Faiz, Mohamad Rodhi; Bagaskoro, Muhammad Cahyo; Susilo, Suhiro Wongso; Omar, Saodah
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol 9, No 1 (2025)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/crc.v9i1.23284

The depletion of fossil fuels has driven significant changes in the global electricity system. Solar Power Plants (PLTS) as a renewable energy source have shown the potential to contribute up to 25% of global electricity generation by 2050. The electricity system often experiences disturbances that can affect its performance and reliability. This study examines the performance of a 1.2 kW hybrid PV system under various load conditions. The system was tested with load variations of 2%, 7%, 22%, and 26% to assess its efficiency and performance. Despite a slight voltage drop of 4V Vac (-1.82%) at higher loads, the hybrid PV system consistently maintained a voltage range of 216-220V Vac, which meets the standard requirements. The system demonstrated high efficiency, averaging over 95%, with a peak efficiency reaching 98.5% at 2% load. These results confirm the effectiveness, safety, and reliability of the system under various load conditions. The findings are based on direct testing and measurements of a 1.2 kW hybrid PV system to evaluate the impact of load variations on its performance. The 1.2 kW hybrid PV system has been proven to be effective, safe and reliable in the face of load variations. These findings support the potential implementation of hybrid PV systems as a future renewable energy solution in the electricity sector

Test Hybrid PV System Performance Against Load Variations Aripriharta, Aripriharta; Amin, Muhammad Adib; Sujito, Sujito; Faiz, Mohamad Rodhi; Bagaskoro, Muhammad Cahyo; Susilo, Suhiro Wongso; Omar, Saodah
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol. 9 No. 1 (2025)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/crc.v9i1.23284

The depletion of fossil fuels has driven significant changes in the global electricity system. Solar Power Plants (PLTS) as a renewable energy source have shown the potential to contribute up to 25% of global electricity generation by 2050. The electricity system often experiences disturbances that can affect its performance and reliability. This study examines the performance of a 1.2 kW hybrid PV system under various load conditions. The system was tested with load variations of 2%, 7%, 22%, and 26% to assess its efficiency and performance. Despite a slight voltage drop of 4V Vac (-1.82%) at higher loads, the hybrid PV system consistently maintained a voltage range of 216-220V Vac, which meets the standard requirements. The system demonstrated high efficiency, averaging over 95%, with a peak efficiency reaching 98.5% at 2% load. These results confirm the effectiveness, safety, and reliability of the system under various load conditions. The findings are based on direct testing and measurements of a 1.2 kW hybrid PV system to evaluate the impact of load variations on its performance. The 1.2 kW hybrid PV system has been proven to be effective, safe and reliable in the face of load variations. These findings support the potential implementation of hybrid PV systems as a future renewable energy solution in the electricity sector

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