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Optimal Variable Speed Control of BLDC Diesel Generator to Enhance Fuel Efficiency Bakhtiar Sudibyo; Heri Suryoatmojo; Daniar Fahmi
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 9 No. 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.460

Abstract

The growing adoption of renewable energy technologies still faces challenges such as instability, intermittent, and limited energy storage capacity. Diesel engine generators, known for their stability and reliability, remain essential as primary or backup power sources, especially in remote areas. However, conventional diesel generators operating at constant speed are inefficient in fuel consumption and produce high emissions. This study investigates the implementation of a variable-speed diesel generator system using a BLDC (Brushless Direct Current) generator controlled by a fuzzy logic-based controller (FLC). The proposed system adjusts engine speed and the duty cycle of the converter to optimize fuel efficiency while maintaining voltage and frequency stability. Simulation results demonstrate that the system reduces fuel consumption by up to 7.6% (0.86 liters/hour) for a 100 kW generator. Additionally, the FLC effectively stabilizes voltage and frequency during load changes and finally enhancing overall system performance.
The Governor Predictive Controlled Based on LSTM for Optimizing Cofiring Power Generator Operation Addien Wahyu Wiranata; Dimas Anton Asfani; Daniar Fahmi
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.549

Abstract

The renewable energy with cofiring technology has a significant impact on the use of Biomass. The use of biomass with different qualities greatly affects the performance of a plant. Deep Learning Time Series Forecasting is designed for predicting two control parameters cofiring powerplant operation consist of governor control and output generator. Long Short-Term Memory (LSTM) combined with Multilayer Perceptron, Convolutional, and Adaptive Moment Estimation (ADAM) optimizer algorithms are utilized to optimize the process governor control and predict generating power output. Correlation analysis is used to determine the input variables and resulting input parameters of governor control prediction consist of Temperature Steam, Pressure Steam, Output Generator, Coal Flow, Flow Steam. Moreover, the input variable for prediction generation power output are steam flow, steam temperature, coal flow, and steam pressure. The combination of Deep Learning Forecasting is successfully to predict both operation parameter percentage errors of 5.33%.