This study aims to develop a mini SCADA system as a simpler and more affordable alternative solution for educational and research purposes, especially in the control of relays in distribution system modules. Design and implement of a mini SCADA prototype are able to control the relay in real-time while evaluating the performance of the system in terms of response speed, control accuracy, and operational reliability. The research adopts both quantitative and qualitative approach with laboratory experiment methods at Malikussaleh University, utilizing Arduino Uno, PZEM-004T sensors, relay modules, and LabVIEW software integration for monitoring and controlling. The test results showed that the system successfully monitored electrical parameters (voltage, current, power, and frequency) with a good level of sensor accuracy (error ≤0.03%), and was able to activate automatic protection against under/over voltage and over current conditions with a fast response according to the set limit. These findings confirm that mini SCADA is not only feasible as a means of practical learning, but also has the potential to be an early model for the implementation of small-scale distribution control such as microgrids. In addition, this study provides empirical evidence that simple hardware integration with open-source software can result in efficient and applicable control systems. In conclusion, this study makes a significant contribution to expanding the understanding of SCADA integration with distribution relays, while opening up further research opportunities related to system communication optimization to reduce response delays and improve operational resilience in the future.. This study aims to develop a mini SCADA system as a simpler and more affordable alternative solution for educational and research purposes, especially in the control of relays in distribution system modules. Design and implement of a mini SCADA prototype are able to control the relay in real-time while evaluating the performance of the system in terms of response speed, control accuracy, and operational reliability. The research adopts both quantitative and qualitative approach with laboratory experiment methods at Malikussaleh University, utilizing Arduino Uno, PZEM-004T sensors, relay modules, and LabVIEW software integration for monitoring and controlling. The test results showed that the system successfully monitored electrical parameters (voltage, current, power, and frequency) with a good level of sensor accuracy (error ≤0.03%), and was able to activate automatic protection against under/over voltage and over current conditions with a fast response according to the set limit. These findings confirm that mini SCADA is not only feasible as a means of practical learning, but also has the potential to be an early model for the implementation of small-scale distribution control such as microgrids. In addition, this study provides empirical evidence that simple hardware integration with open-source software can result in efficient and applicable control systems. In conclusion, this study makes a significant contribution to expanding the understanding of SCADA integration with distribution relays, while opening up further research opportunities related to system communication optimization to reduce response delays and improve operational resilience in the future.
This study implements a low-cost Software Defined Radio (SDR)-based receiver system for the Automatic Identification System (AIS) operating at frequencies of 161.975 MHz and 162.025 MHz using a 44 cm V-dipole antenna with a 90° angle. Spectrum analysis is performed using Airspy to identify AIS signal characteristics in real time within the frequency domain, with measured Signal-to-Noise Ratio (SNR) values of 21.3 dB for AIS 1 and 21.4 dB for AIS 2. The signal decoding process is carried out by AIS-catcher, which handles GMSK demodulation and efficiently extracts AIS message payloads. The decoded data is then visualized using OpenCPN in the form of a digital map to monitor vessel positions and movements in real time. SDRangel is utilized as a supporting platform for signal observation, SDR device configuration, and additional analysis of reception quality. System performance evaluation demonstrates stable capability in receiving and decoding AIS signals with a satisfactory success rate, although it is still affected by interference and propagation conditions. GNU Radio is used in a limited capacity as a signal processing environment for filtering and basic parameter adjustment. The results confirm that low-cost SDR is an effective, flexible, and economical solution for implementing AIS monitoring systems at the research and basic application levels.
Sleep is an important physiological process that plays a role in maintaining the balance of biological and psychological functions. Lifestyle changes, such as high stress levels and a lack of physical activity, can affect a person’s sleep quality. This study aims to analyze the influence of health and lifestyle factors on sleep quality and to develop a predictive model for sleep quality using the Random Forest algorithm. This study uses the Sleep Health and Lifestyle dataset with a classification approach into two categories, namely Ideal Sleep and Non-Ideal Sleep, determined based on sleep duration parameters referring to the concept of a U-shaped relationship and the sleep duration recommendations from the National Sleep Foundation. The data were processed through preprocessing and class imbalance handling using the SMOTE method, then split into training and testing data. The Random Forest model was built through hyperparameter tuning and evaluated using accuracy and Area Under the Curve (AUC) metrics. The results show that the Random Forest model achieved good classification performance with an Accuracy of 91.26%, Precision of 91.78%, Recall of 91.26%, and F1-Score of 91.30%. In addition, the model obtained an Area Under the Curve (AUC) value of 0.962, indicating very good classification capability. Based on the Feature Importance analysis results, the features with the greatest influence on sleep quality are Heart Rate, Stress Level, Physical Activity, and Daily Steps. The findings indicate that the combination of the SMOTE method and Random Forest is effective for predicting sleep quality based on health and lifestyle factors.
Distributed generation (DG) ha s emerged as an effective solution for improving power quality, enhancing voltage profiles, and reducing power losses in electrical distribution systems. This study investigates the impact of renewable-energy-based DG integration on the voltage profile and power losses of the Unsyiah distribution feeder in Banda Aceh, Indonesia. The distribution network was modeled and analyzed using ETAP 16.0.2, while MATLAB R2021a with the backpropagation artificial neural network (ANN) method was employed to estimate solar radiation intensity and wind speed data for photovoltaic (PV) and wind power plant (WPP) generation. The ANN model was trained using environmental and climate data, producing regression values of 0.84901 for solar radiation prediction and 0.85083 for wind speed prediction, indicating satisfactory predictive performance. Two DG placement scenarios with a penetration level of 20% of the total feeder load (388.4 kW) were evaluated at Bus USK 01 and Bus USK 24. Simulation results demonstrate that DG integration significantly improves the voltage profile, particularly at buses located near the end of the feeder where voltage drops are more severe. The optimal scenario was achieved by placing wind-power-based DG at Bus USK 24, which reduced active power losses from 11.8 kW to 8.1 kW and reactive power losses from 13.1 kVAR to 8.6 kVAR. Overall, the integration reduced active power losses by 31.35% and reactive power losses by 34.35%. The findings confirm that both DG placement location and DG type strongly influence the effectiveness of voltage profile enhancement and power loss reduction in radial distribution systems.
Linear Low-Density Polyethylene (LLDPE) is a widely used polymer in the plastics and packaging industries because of its flexibility and good mechanical properties. The increasing global demand for LLDPE encourages the development of more energy- and cost-efficient production technologies. This study aims to analyze the technical and economic feasibility of building an LLDPE plant using Advanced Sclairtech Technology (AST). The plant is designed with a production capacity of 287,000 tons/year and optimized using a Heat Exchanger Network (HEN). The design and evaluation of the HEN are carried out using Aspen Energy Analyzer software based on the Temperature Interval and Composite Curve methods. The analysis results show that implementing heat integration can reduce the high-pressure steam requirement from 204,541 kg/hour to 30,770 kg/hour and the cooling water requirement from 6,491,808 kg/hour to 1,527,774 kg/hour. The utility savings achieved exceed 80%, with a reduction in utility costs of USD 6,750 per hour. Economic analysis yields a Payback Period (PBP) of 3.03 years, a Net Present Value (NPV) of USD 84.15 million, an Internal Rate of Return (IRR) of 24.53%, and a Break-Even Point (BEP) of 24.46% of production capacity. Based on the technical and economic analysis results, the LLDPE plant using AST technology is considered feasible. It has the potential to be developed as a more efficient and sustainable LLDPE production technology.
