Publication Search

Sea Surface Temperature (SST) is an important parameter in oceanographic studies because it influences climate dynamics, ocean circulation, and marine ecosystems. Continuous monitoring of SST in open sea areas requires a reliable system capable of operating autonomously. This study develops a solar-powered ocean buoy designed to measure sea surface temperature while simultaneously evaluating the performance of a solar panel as the main energy source. The system uses a DS18B20 sensor to measure SST and an INA219 sensor to monitor the voltage, current, and power of the solar panel, while an ESP32 microcontroller functions as the central data processing unit. The results show that sea surface temperature tends to remain relatively stable with small daily variations, whereas the temperature and performance of the solar panel exhibit larger fluctuations due to direct exposure to solar radiation and changing weather conditions. Solar panel performance also shows significant variations in current and power depending on the intensity of sunlight. To analyze the influence of SST variations on solar panel performance, a statistical analysis using Analysis of Variance (ANOVA) was conducted. The ANOVA results, based on the calculated F-value and the significance value (p-value) at a confidence level of α = 0.05, indicate that SST variations have a significant effect on solar panel performance, demonstrating that the proposed solar-powered buoy system can operate autonomously and has potential for long-term SST monitoring in offshore areas.

The increasing demand for electrical energy and the limited availability of fossil fuels have driven the development of renewable energy sources, including marine current energy, which remains underutilized in coastal and remote maritime regions. This study presents the design and realization of a small-scale marine current power generation prototype using a horizontal axis propeller turbine with a NACA S814 blade profile and analyzes the effect of turbine rotational speed on electrical power output. The system converts marine current kinetic energy into mechanical energy through turbine rotation and subsequently into DC electrical energy using a generator, which is stabilized by a Buck–Boost Converter and Maximum Power Point Tracking (MPPT) for charging a 12 VDC battery. Real-time monitoring of electrical and mechanical parameters is implemented using an Internet of Things (IoT)–based system comprising an ESP32 microcontroller, a PZEM-017 sensor, and an RPM sensor. Experimental results demonstrate a positive correlation between water flow rate, turbine rotational speed, and generator output voltage. The system begins operating at a minimum flow rate of 35.2 L/s at 56 RPM, producing 0.2 V, while optimal performance is achieved at 45.3 L/s and 516 RPM, generating up to 13.3 V. These results indicate that the proposed prototype is a viable alternative renewable energy source for marine applications.

This research aims to design and develop a prototype wave power plant that utilizes the vertical motion of a buoy as a source of mechanical energy, which is then converted into electrical energy using a recoil starter mechanism. The system is designed to be installed at the stern of a prototype ship. The vertical movement of the buoy caused by ocean waves is transmitted to the recoil starter through a drive rope, producing a stable one-way rotational motion. This rotation is further transmitted to a gearbox to increase rotational speed before driving a DC generator. The electrical energy generated is stored in a 12 VDC battery, supported by a buck–booster converter to stabilize the output voltage. This study employs an experimental engineering approach to evaluate system performance based on empirical test data. The main components of the system include a buoy as a wave energy collector, a recoil starter as the initial rotating mechanism, a DC generator as the electrical energy producer, a buck–booster converter as a voltage regulator, a 12 VDC battery as an energy storage unit, and a monitoring system based on an ESP32 microcontroller integrated with a PZEM-017 sensor. Experimental results show that the recoil starter operates effectively in driving the generator under both no- load and buoy-loaded conditions. Increases in generator rotational speed are directly proportional to increases in output voltage and current. The PZEM-017 sensor demonstrates a high level of measurement accuracy, approaching 100% when compared with a multimeter. Overall, the proposed wave power generation system functions reliably and shows potential for further development as a small-scale alternative renewable energy source.

Gas turbine generators play an important role in providing electrical energy, especially in the maritime sector, but they are vulnerable to disturbances such as overcurrent and undervoltage, which can cause equipment damage. This study aims to design and test an automatic protection system based on the ESP32 microcontroller with the INA219 sensor to detect current and voltage, as well as a relay as a circuit breaker. The method used is an experimental approach including static and dynamic testing, both with and without a 5W AC lamp load as a simulation of real loading conditions. Test results show that the average sensor reading error is 2.65% for current and 1.76% for voltage, which is still within the ±3% tolerance limit. The system is able to disconnect the load when any parameter exceeds the protection threshold, although there are slight inconsistencies in the relay response due to sensor reading fluctuations. In conclusion, this automatic protection system is proven to be 85% accurate and responsive in maintaining the operational reliability of the gas turbine generator, making it applicable as a preventive solution against electrical disturbances in marine environments.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

This research focuses on optimizing the control system to improve voltage stability in a 10 kW Solar Power Plant (PLTS) located in a tropical region. The main issue addressed is voltage fluctuation caused by the intermittent nature of solar radiation (200–1200 W/m²) and temperature variations (20–50°C), which result in up to 12% overshoot in the inverter. The proposed method implements a Proportional-Integral-Derivative (PID) controller optimized using the Particle Swarm Optimization (PSO) algorithm with real-time irradiation input data. The research integrates a 100 Hz digital low-pass filter to mitigate sensor noise under low irradiation conditions. Simulation results show that the PID-PSO system successfully reduces overshoot from 12.1% to 4.2% under high irradiation, and decreases settling time from 0.62 seconds to 0.31 seconds. The digital filter effectively reduces measurement deviation from 7.2% to 2.8% at 200 W/m² irradiation. The PSO optimization achieved optimal convergence within 37 iterations with an Integral of Time-weighted Absolute Error (ITAE) value of 0.18. This study concludes that the implementation of PID-PSO with a digital filter significantly enhances the voltage stability of the PLTS by 20.3% compared to conventional PID control and is ready to be applied in tropical-region smart grid systems.

This study aims to design and test a Dual Axis Solar Tracker to improve the energy absorption efficiency of solar panels on ships. The system is designed with a two-axis movement mechanism (horizontal and vertical) using a linear actuator motor controlled by Arduino Nano and ESP32. Testing was conducted on a 20 WP solar panel in Surabaya for 30 days, divided into three methods: 10 days using an LDR sensor, 10 days using an RTC, and 10 days in static conditions without a sensor. Voltage, current, and power data were measured every 30 minutes at 07.00–17.00 WIB. The test results show that the RTC method provides the highest and most stable output power, according to the sun's movement patterns in tropical areas, while the LDR method responds quickly to changes in light intensity but is less stable in changing weather. Static installation produces the lowest power. This system is able to maintain the panel orientation perpendicular to the sun's rays, thus increasing energy efficiency compared to static systems. These findings prove that dual-axis solar tracker technology, especially with an RTC sensor, is effective in dynamic maritime environments and can be a practical solution for optimizing renewable energy on ships. The most effective results using RTC sensors demonstrated the most stable and high power output, especially since the sun in tropical areas like Surabaya moves fairly consistently following a cyclical pattern. The success of this system not only increases the energy output of solar panels but also provides a practical solution for renewable energy applications in tropical climates.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

This research aims to design and build an Internet of Things (IoT)-based electrical energy monitoring system on a Darrieus turbine prototype to optimize energy yield. This research using the Research and Development or RnD method, this method was chosen by researchers because it can provide solutions, enable the development and application of more effective learning methods. The system uses NodeMCU ESP32 sensors to measure important parameters such as voltage, current, power, and energy produced by the turbine. The collected data is then analyzed and visualized through IoT platforms such as ThingsBoard, which allows real- time monitoring of the turbine's performance. The integration of IoT in this system is expected to improve energy use efficiency by providing useful information for management and decision-making in turbine operations. This research also fills the gap in Darrieus turbine development with an approach that combines IoT-based monitoring and energy yield optimization, and can be applied to improve the efficiency of renewable energy in Indonesia

Enhancing the efficiency of renewable energy on ships is crucial for reducing dependency on fossil fuels. This research employs the Research and Development (R&D) method, aiming to design and implement a solar panel optimization system for battery charging, with a focus on increasing power efficiency and providing real-time performance monitoring. The system is designed using Maximum Power Point Tracking (MPPT) technology to maximize the solar panel's power output. A 200Wp solar panel with dimensions of 1290 x 760 x 30 mm was utilized. Static testing results show that the deployed sensors possess a high degree of accuracy, with an average error of 0.71% for the temperature sensor and only 1.81% for the light sensor used to monitor environmental conditions. Dynamic and system integration tests prove that the MPPT implementation significantly increases power output efficiency by 30.83% compared to a system without MPPT. Furthermore, the system with MPPT charges the battery approximately 27% faster. Additionally, the developed Modbus protocol-based monitoring system enables comprehensive and remote monitoring of key parameters such as voltage, current, temperature, and light intensity via a cloud database. Data communication reliability tests confirmed the system's capability to transmit entire data packets to a Google Sheets database at a periodic interval of 15 seconds without failure. Based on these results, the developed solar panel optimization system is feasible for implementation in maritime environments to enhance the utilization efficiency of renewable energy and the operational reliability of onboard systems.

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.

Renewable energy is non-fossil energy that can be renewed and managed sustainably, such as solar, wind, water, and biomass. To overcome dependence on weather conditions, this energy can be combined with alternative energy sources such as piezoelectric sensors, which can convert pressure into electrical energy. This study aims to design a prototype monitoring system for hybrid electric energy using solar panels, wind turbines, and piezoelectric elements as alternative energy sources on board ships. The use of Internet of Things (IoT) technology is key to monitoring and managing these energy sources in real-time and automatically through integrated sensors and software. This research uses the Research and Development (R&D) method by creating a prototype that combines solar panels, wind turbines, and piezoelectric components as energy sources and designing a monitoring system based on the Internet of Things (IoT) using the Kodular application. Several system components were tested through two types of testing: static and dynamic testing. The test results showed that the solar panel had the most stable performance with a voltage of approximately 16.50V and a current of 2.41A occurring between 13:00 and 16:00 WIB. The wind turbine was able to operate at low wind speeds and reached its highest voltage of 3.63V and current of 1.14A on the fifth day at 20:00 WIB with a wind speed of 1.1 knots. The piezoelectric system generated power according to the pressure and frequency of footstep impacts. The highest voltage recorded for a 70kg subject was 4.11V with a current of 0.19A. This hybrid system has the potential to be an environmentally friendly energy support solution on board ships.

This research aims to develop and evaluate the performance of a steam plant prototype designed as an alternative source of electrical energy to support the Vessel to Grid (V2G) concept. Utilization of backup energy on ships is becoming important as electricity demand increases and demands for a more sustainable electrical system. This system relies on ESP32 microcontroller technology as a control center that functions to monitor and control several key parameters, including steam pressure, combustion temperature, boiler water level, and the generated electrical voltage. The research method used is an experiment with a static and dynamic testing approach. Static testing is carried out to measure the performance of main components such as the boiler, turbine, and generator separately, while dynamic testing focuses on evaluating the overall system by involving the integration of sensors and supporting actuators. The test data is then analyzed quantitatively to determine the system's response to variations in steam pressure, temperature, and other operational conditions. The results show that the steam produced by the boiler is able to rotate the turbine, thereby driving the generator to produce electricity. The maximum voltage achieved is 25.7 volts at a steam pressure of 50 psi. The highest energy conversion efficiency was recorded at 4%, while the lowest efficiency was 0.9%. These findings demonstrate that, despite its relatively low efficiency, the prototype can function as an alternative energy source and emergency backup solution. Thus, this research provides an initial contribution to supporting the implementation of the V2G concept through the development of a small-scale steam plant-based energy conversion system.

As an archipelagic country, Indonesia has significant potential for the utilization of renewable energy, particularly wind energy in maritime areas with low wind speeds (3–6 m/s). This study aims to design and test a vertical Savonius wind turbine system equipped with a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm as a power source for shipboard water heating systems. The research method applied is Research and Development (R&D), integrating several components such as a DC generator, full-wave bridge rectifier, INA219 current sensor, anemometer cup sensor, ESP32 microcontroller, and a monitoring interface utilizing Google Spreadsheet and a 20x4 LCD. The system was tested under two operating conditions: without MPPT and with MPPT. The experimental results show that the application of the MPPT algorithm successfully increased power output by up to 272.64% while maintaining voltage stability despite varying wind speeds. Nevertheless, the average output power of 2.605 W remained insufficient to meet water heating requirements within a short time. For example, charging a 12V 50Ah battery would require approximately 9.6 consecutive days of operation, highlighting the system’s limitations in high-demand scenarios. Despite these constraints, the findings demonstrate that the vertical Savonius wind turbine integrated with MPPT has strong potential as a clean and environmentally friendly alternative energy solution for maritime applications, particularly for small-scale onboard electrical loads. This study contributes to renewable energy utilization in the shipping sector and provides a foundation for further technological development and optimization.

A 300 kVA generator set is an important tool as a backup power source in many sectors. Manual monitoring of generator set performance is often inefficient and can cause delays in detecting problems. Various advances in Internet of Things (IoT) technology have enabled the design of remote monitoring systems to overcome these limitations. This study aims to develop an IoT-based monitoring tool capable of monitoring important generator set parameters such as voltage, current, power, and engine temperature in real-time. This will enhance generator set management efficiency and reduce the risk of damage. The method used is an experimental approach involving the creation of an IoT-based monitoring system prototype. Operational data from the generator set is collected using sensors and then transmitted to an IoT platform for analysis. This research uses PZEM-004T sensors to monitor parameters such as power, current, and voltage. RTD PT100 sensors monitor parameters such as engine temperature, oil temperature, and turbo temperature. Honeywell PX3 sensors monitor oil pressure. Honeywell 1GT101DC sensors monitor engine speed. The results of the study show that the device works well with an average error of 0.76% from the sensors used. The greater the distance between the Wi-Fi modem and the device, the slower the data response speed.

Electric Vehicles Require A Reliable Monitoring System To Maintain Their Efficiency And Safety. This Study Designs A Monitoring System For Battery Condition And Vehicle Location Based On The Esp32 Microcontroller And The Mit App Inventor Application. The System Uses The Pzem 017 Sensor To Monitor Voltage, Current, Power, Energy, As Well As Battery Capacity And Health, And The Neo-7m Gps Module For Location Tracking. Data Is Displayed In Real Time Through An Android Application. Test Results Show That The System Provides Accurate Information, With Minimal And Statistically Insignificant Measurement Errors According To The Paired T-Test. The System Also Remains Stable At Various Vehicle Speeds (Anova), And The Location Tracking Demonstrates An Average Accuracy Of 4.77 Meters In Open Areas. In Enclosed Areas, The Error Increases To An Average Of 10.26 Meters. The System Is Also Capable Of Automatically Calculating Battery Capacity And Health. Overall, The Developed System Proves To Be Effective, Efficient, And Flexible In Supporting The Reliability And Safety Of Electric Vehicles.

This research aims to monitor the performance of a 2x100Wp Solar Power Plant (PLTS) using the parameters of current, power, and solar intensity. The monitoring system is designed to record and analyze the electrical output of the solar panel in real-time. The experimental method is used by collecting data directly through current and voltage sensors, as well as light sensors connected to a microcontroller. Data was collected to see the relationship between solar intensity and electrical power generated. The test results show that the intensity of the sun greatly affects the amount of current and power generated by the solar panel. This monitoring system can be a tool for learning and analyzing the performance of small-scale solar power systems in a laboratory environment.

The aim of this study is to design a battery charging system using solar panels, integrated with an IoT-based monitoring system to improve efficiency and ease of supervision. The system utilizes the photovoltaic effect to convert solar energy into electrical energy, which is then stored in a battery through the regulation of a solar charge controller. The main components in this design include a solar panel, lead-acid battery, ESP32 microcontroller, current and voltage sensors, and the Blynk application for remote monitoring. The research method involves several stages, including the design of hardware such as solar panels, current and voltage sensors, and a step-down regulator, as well as software development using the Blynk platform and ESP32 programming. Test results show that the system is capable of achieving charging efficiencies between 89.45% and 99.57%, with optimal performance under clear weather and maximum sunlight conditions. The IoT system performs well up to a distance of 10–15 meters, with an average data transmission delay of less than 1.5 seconds. Therefore, this device proves to be effective as an independent battery charging and energy monitoring solution in locations without permanent electricity access.

Oka Jamur Bali is one of the oyster mushroom cultivation farmers. Lack of efficiency in regulating temperature and humidity in mushroom barns is an important factor in the growth of mushroom fruiting bodies. This condition requires an innovative solution to regulate and monitor temperature and humidity in the barn area. This research aims to build a prototype using Arduino UNO, ESP32, DHT11, pump and Internet of Things (IoT) technology.  The monitoring system design uses Arduino UNO, NodeMCU ESP32, DHT11 and DC pump with 12V voltage. Arduino UNO functions as a control system to manage and control the temperature and humidity parameters of the mushroom barn environment with the DHT11 sensor used to accurately measure temperature and humidity. The combination of NodeMCU ESP32 and Arduino UNO aims for monitoring through the ThingSpeak platform which can be accessed with a smartphone connected to the internet network. Based on the results of the prototype of the automatic temperature and humidity control system, the system is able to measure the environmental conditions of the mushroom barn with humidity measurement results in the range of 79% - 82% and the average temperature during the test reached 29.35°C. The application of the system was able to reduce the temperature by 2.28% with an average temperature of 28.19°C and increase the relative humidity by 6.27% with an average humidity of 81.1% in the mushroom barn area. The results of income between the income generated by using the automatic temperature and humidity control system tool and the income of farmers without using the tool increased by Rp. 16,416.

Along with the development of the industrial sector, companies are competing to create quality and affordable products. To support production, automation tools have emerged that help the production process so that it is faster. One of the machines that is widely used by large factories is a 3-phase induction motor. 3-phase induction motors are often used in driving equipment in industry because they have a simple construction, are relatively cheap, lightweight, have high efficiency, and are easy to maintain. This study utilizes the A3114 hall effect sensor device, PZEM-PP4T Sensor, 20x4 LCD, ESP32 module, Relay, Mitsubishi D700, MCP 4725 Sensor. The tests carried out showed that the performance of the sensor for measuring speed, voltage, and current on 3-phase motors has a high level of accuracy. Testing of motor speed shows a difference in speed value (Rpm) between the hall effect sensor and the tachometer, with an average accuracy percentage of 4,12%, indicating that the hall effect sensor has a fairly good level of accuracy. For voltage measurement, the Pzem sensor compared to the Multitester produces an average accuracy percentage of 0,90%, indicating that the pzem sensor is very reliable. Meanwhile, current testing shows a difference in value between the pzem sensor and the multitester with an average accuracy percentage of 0,98%, indicating a fairly high level of accuracy. Overall, this system can be relied on for measuring operational parameters of 3-phase motors because it has consistent and good accuracy. With this research, the 3-phase induction motor monitoring system which was originally done manually has changed to automatic and has a fairly high level of work efficiency.