DC Voltage Sensor Research Articles

Implementation of Data Parsing on Arduino Mega and NodeMCU ESP8266 for Web-Based Monitoring of E-Bike Charging Station

The rising trend of electric bicycles in Indonesia necessitates an efficient monitoring system for electric bicycle charging stations to ensure optimal performance and timely decision-making. This study focuses on developing and testing a web-based monitoring system utilizing Arduino Mega and NodeMCU ESP8266 for real-time data transmission and analysis. The system integrates multiple sensors, including ACS712 current sensors, DC voltage sensors, and PZEM004T sensors, to monitor key parameters such as current, voltage, power, and energy. Data parsing, a crucial process for efficient transmission, is performed on the Arduino Mega before being sent to the NodeMCU ESP8266, which then forwards the data to a web server. The research employs the Research and Development (R&D) method, encompassing stages of information gathering, system planning, implementation, and evaluation. Testing revealed that data parsing failures were due to data loss and format corruption caused by serial communication issues. Various transmission delays (0 ms, 100 ms, 200 ms, and 500 ms) were tested to evaluate their impact on data loss, communication latency, and system responsiveness. The results indicate that a 200 ms delay offers an optimal balance between data transmission frequency and system stability, with minimal data loss and stable latency. The study also explored the effects of data reception delays on the web server, highlighting that lower transmission frequencies result in more stable reception delays and better overall performance. Efficient network resource usage and system responsiveness were achieved with appropriate delay configurations.

Prototype of a Digital Measuring Device Based on Atmega 328P for Measuring Current, Voltage, Electrical Power, and RLC Components

Research on digital measuring device prototypes has developed over the past few years. However, there are still challenges in measuring current, voltage, electrical power, and RLC component parameters accurately. This research aims to design a digital system to measure electrical quantities such as current, voltage, resistance, inductance, and capacitance using Atmega 328P. Data retrieval is done by implementing the system as a whole, namely the AC and DC electrical measurement system and the measurement of RLC components. The results showed that the system can measure and display the result of measuring current, voltage, electric power, and RLC components correctly. The accuracy percentage for the components measuring instrument on the resistor test is 99.35%, the error is 0.65%, and the precision is 99.87%. In the inductor test measurement, the percentage value of accuracy is 94.11%, the error is 5.89%, and the precision is 96.77%. In the capacitor test measurement, the accuracy percentage is 98.03%, the error is 1.97%, and the precision is 98.65%. On the DC voltage sensor, the accuracy percentage is 97.31%, error 2.69%, and precision 99.77%.

Battery Management System Using IoT

Abstract: This study presents an Arduino-based system for advising users of nearby EV stations and tracking the status of their automobile batteries. The MQ-2 gas sensor, DHT11 temperature sensor, and DC voltage sensor are the three main sensors that the system interfaces with through the use of an Arduino Uno microcontroller as the central controller. When the battery temperature rises over safe limits, the DHT11 sensor monitors it and sends out alarms. By detecting gases being released into the atmosphere, the MQ-2 gas sensor warns customers of possible battery problems. Through the use of a Java web application, the DC voltage sensor suggests local EV stations and notifies users when levels are low. The sensor measures voltage and percentage. By delivering timely alerts and making charging facilities easier to use, this integrated solution raises user awareness of battery health and promotes safer driving. By fusing Arduino technology with sensor data and web application capability, it illustrates how IoT can be used practically in the automotive industry.

Performance analysis of photovoltaic panel using machine learning method

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.

RSSI Measurement Analysis of Zigbee-Based Wireless Sensor Networks in Various Topologies for Solar Panel Monitoring

Abstract— Solar panels require performance monitoring when energy shortages occur in the area. Monitoring solar panels requires a detection device to detect solar panels, including the ACS712 current sensor for monitoring the output current of the solar panel, a DC voltage sensor for monitoring battery performance, and a DHT11 temperature sensor for monitoring the temperature of the solar panel storage battery. The WSN technology that can work is ZigBee communication. This ZigBee communication research uses various topology methods, including star, tree, and mesh topologies. Solar panel monitoring tests are carried out indoors and outdoors. The ZigBee communication device used is the XBee S2C device, which operates at a frequency of 2.4 GHz. From a comparison of various topologies, the mesh topology is a topology with good performance because it has the characteristic of choosing a router with the fastest path to reach the coordinator. It is proven by monitoring solar panels with an optimal distance of 80 m, which is carried out from outdoors with an RSSI value of -84 dBm, a throughput of 0.55 kbps, and a delay of 2.77 ms, producing power for 150 minutes with an average current of 0.14 A, voltage of 9.86 V, and temperature of 31.48°C. The number of disturbances and weather conditions greatly affect signal propagation, which can hamper the data transmission process. Throughput fluctuates due to uncertain weather conditions, and delay fluctuates due to a lot of interference from other radio signals.
 Keyworrds— Wireless Sensor Nirbael (WSN), RSSI, ZigBee, Panel Surya, Thinger.IO

Development of an Arduino-Based Monitoring System for Pico-Hydro Power Plants

One of the problems faced by people in remote areas is the unavailability of infrastructure for power distribution. The available rivers can be used to generate electrical energy. The developed systems must be inexpensive, easy to operate and reliable. It should be equipped with a monitoring system to display the output. This study aims to develop a pico-hydro power plant and its monitoring system to meet the power needs in remote areas. The power plant consists of a small turbine, a generator, a battery charger, and a battery while the monitoring system consists of a current sensor, voltage sensor, Arduino Nano, and a display. The system was tested in the laboratory and the river. The test of the DC voltage sensor, AC voltage sensor, and DC current sensor produces errors of 1.88%, 5.24%, and 1.25% respectively. The no-load test shows that the system generates a voltage of 12.77 - 16.6 VDC and 224 - 245 VAC when rotated at 696 - 1363 RPM. When loaded, the system generates voltages of 12 - 12.8 VDC and 225 - 247 VAC when rotated at 1332 - 1564 RPM. The developed system can be used by people in remote areas to meet their power needs.

Sensorless Capacitor Voltage Detection Method of Cascaded H-Bridge STATCOM Based on Special Switching Mode Identification

The cascaded H-bridge (CHB) static synchronous compensator (STATCOM) has been widely used in wind farm and conflux station. As the grid-connected voltage increases, the number of cascaded CHB proportionately increases. In order to achieve the capacitor voltage balance, it is necessary to independently detect the capacitor voltage of all H-bridge cells, which raises the cost of capacitor voltage detection. To solve this problem, the mapping relationship is studied between the converter output voltage and capacitor voltages by analyzing carrier phase-shifted sinusoidal pulse width modulation technique (CPS-SPWM), and special switching mode is firstly found. Based on the above finding, without changing the conventional CHB topology, an improved sensorless detection method is proposed to solve the problems of detection error, large computation burden and the application limitation in existing sensorless detection methods. Compared with conventional sensor detection, this method not only saves DC voltage sensors and data transmission channels but also suppress the third harmonics in the converter current. Finally, the effectiveness of the detection method applied on CHB STATCOM is proved by simulation and experiment.

Perencanaan dan Pembuatan Real Time Monitoring System Dari Pada Modul Active Solar Photo Voltaic Tracker Berbasis Internet Of Things

There are still monitoring system in Solar Power Plant that do it manually using measuring and recording tools directly. This makesthe data obtained is also limited. From here the idea emerged to develop a monitoring system using the IoT concept so that monitoring onSolar Power Plant that can be monitored just by looking at the website page, so current and voltage data can be easily monitored. In addition,the resulting display is easy to analyze because the display can be digital and can be monitored with a PC or Smart Phone. This monitoringsystem is used to monitor the current and voltage of a solar power plant with solar cell capacities of 50WP, 80WP, and 100WP. The SolarPower Plant that used is an active type of solar tracker. In the application of using Solar Power Plant that, there is a current and voltagestarting from the solar cell output, accumulator output, AC load input, and DC load input. The monitoring results that have been carried out willthen be automatically sent to the Thingspeak application using the SIM800L Module. The thingspeak application will send data in the form ofnumbers and graphics in real time. By comparing the value of the measurement results on the Arduino and the Thingspeak application, thevalue of the percentage of reading errors on the measuring instrument is obtained. 3. In accordance with the tests that have been carried out,the percentage of read errors / errors can occur because of the reading accuracy of the current sensor measurements and AC-DC voltagesensors with different thingspeak applications. Each sensor has a percentage value of 0.16% AC & DC voltage sensor measurement error,1.2% AC & DC current sensor, and 2.8% data transmission observation.

DC-Link Voltages Digital Sampling for Cascaded H-Bridge Rectifiers With Single Voltage Sensor Under Loads Unbalanced Condition

Direct current (dc) voltage sensor in each cell of the cascaded H-bridge rectifier (CHBR) is traditionally indispensable for capacitor voltage balancing control. The cost of dc sensors will increase with the number of cells. This letter proposes a strategy for dc-voltage digital sampling based on sliding window recursive least square (RLS), which employs only one voltage sensor. Sliding window RLS is used to decouple voltage information within one sampling interval. For the actual situation where the modulation waves of cells are different, this method owns faster updating rate and higher detection precision compared to the existing multisampling method. A downscale test platform of the single-phase CHBR is built to verify the correctness and effectiveness of the proposed method. The result shows that better current quality and steadier dc voltage are achieved.

Realtime Monitoring Design of Solar Cell Power Plant Based on IoT

Solar panels are electricity generators by utilize sunlight. Monitoring of electrical energy produced by solar cells is needed by consumers to find out how much power is produced. Previously, monitoring was done by looking at the voltmeter and ampere meter indications. This study was designed a prototype to monitor current, voltage, and light intensity through the Arduino Uno-based internet network. For the solar system panels, AC and DC current and voltage sensors are used, as well as calibrated light sensors, then a data transmission system integrated into the Blynk application, designing an Arduino Uno microcontroller-based system with the Blynk application on a Smartphone via a wi-fi communication module (esp8266). Parameters DC current and connection, AC current and connection. The highest light intensity is obtained at PLTS when exposed to sunlight and load at 02:30 and the lowest is at 09:30 due to weather. The results obtained are measurements from each sensor, used directly and displayed on the Blynk Application, monitored remotely from the internet network.

TS Model-LMI Based Observer for Improving Active Power Filter Performance

To avoid the negative effects of using a control signal with a ripple, which is generated by the feedback of measured active power filter (APF) variables, a nonlinear observer is employed in this paper. The observer design, through the use of exact TS models and Lyapunov-based LMI conditions, is achieved. Both the APF output current and the DC voltage are estimated by the observer, and they are used in the cascade control feedback. In this way, high gains in the inner control loop are employed, giving place to a control signal without undesired harmonic components or overmodulation. This allows an APF performance improvement for compensation tasks and for reducing the undesired components injection to the mains. A simulation and experimental comparison between APF results using observer and APF results without using observer is presented. Better results are achieved for the observer version case, reducing the THD from 47.6% to 4.8% in experimental conditions, satisfying the IEEE Standard 519TM-2014. Also, load change tests are carried out, where the stability of the system is kept. Moreover, by using the observer, a DC voltage sensor was not required, reducing the number of system sensors.

Analysis of Peak Power Capacity on Rooftop Solar PV 1.25 kWp at Sun Conditions 90 Degrees

This paper describes the optimization of energy conversion when solar radiation occurs at peak power conditions, namely at 11.00 am to 2.00 pm where the position of the sun is parallel to the layout of the PV Rooftop installation. The panels used are 5 units with the type of polycrystalline with a capacity of 1 panel unit consisting of 250 Wp. The position of the panels installed on the roof of the Andalas University building is based on an angle of 90o degrees with a position of ±255m above sea level. The advantages obtained when placed on the roof of the building are due to the minimal impact of shadow effects and environmental disturbances. Data retrieval using DC current and voltage sensors is then connected to the Arduino Uno microcontroller which is then interfaced in graphic form. Considerations in the installation of PV by reviewing the weather conditions at that time where the conditions were sunny and the air was clean with the aim that the performance when solar radiation entered the solar cells could be produced more optimally. Based on the data obtained at peak power, PV can convert DC power to 972.56 Wp. In the final stage of collecting data recorded on this PC, it can later be used as a reference for installing solar panels for household electricity scales in the West Sumatra region.

Single Phase Inverter with Power Monitoring using Arduino

The inverter is an electronic device which able to convert DC electricity into AC electricity. In the development of renewable energy resources, for example, solar power plants, inverters are needed. In this research, an inverter is made by real-time power monitoring. The inverter is made using unipolar PWM modulation and a full H bridge topology. PWM signals are generated from Arduino microcontrollers that will control MOSFET switching. Power monitoring is done by installing a DC voltage sensor and INA219 DC current sensor while on the output side using the PZEM 004t (v3) sensor and results will be displayed on the OLED. The experiment was successfully carried out by producing an approaching sine wave signal with a frequency of 50 Hz and 220 V AC voltage. The efficiency of the inverter is low because of loss from the transformer which has a no-load efficiency of 68.7%. When the 15W load has installed the efficiency of the inverter is 31.83% and when the 20W load has installed the efficiency of the inverter is 34.72%. Finally, when the smartphone’s charger is installed the total efficiency was 49.96%.

Modified Control Strategy for Shunt Active Power Filter With MRAS-Based DC Voltage Estimation and Load Current Sensor Reduction

Failure of any sensor involved in the operation of shunt active power filter (SAPF) not only degrades its performance but also deteriorates power quality. The purpose of the proposed technique is to eliminate load current, dc voltage, and filter current sensors, thus reducing the hardware cost of SAPF. In addition to the load and filter current sensor reduction, this article proposes a new dc voltage estimation technique, which will be used to eliminate the dc voltage sensor present in conventional SAPF. The proposed dc voltage estimation technique is based on model reference adaptive system (MRAS). A comparative analysis of the conventional SAPF (i.e., including all sensors) is performed with the proposed sensor reduction method to verify the effectiveness of the proposed method. Stability and sensitivity analysis of the estimation technique is performed in detail. The proposed work is simulated on the MATLAB/SIMULINK platform under various load conditions and disturbances. Further, it is validated on OPAL-RT 5700 platform.

Optimal IP Current Controller Design Based on Small Signal Stability for THD Reduction of a High-Power-Density PFC Boost Converter

This paper presents an optimal design for the inner current-control loop of the continuous current conduction mode (CCM) power factor correction (PFC) stage, which can be used as the front stage of the two-stage AC/DC telecom power supply. The conventional single-phase CCM-PFC boost converter is implemented with proportional–integral (PI) controllers in both the voltage and current-control loops to regulate the output DC voltage to the specified value and to ensure the input current follows the input voltage, which offers a converter with a high-power factor (PF) and low current total harmonic distortion (THD). However, due to the slow dynamic response of the PI controller at the zero-crossing point of the input supply current, the input current cannot fully follow the input voltage, which leads to high THD. In this paper, we investigate a digitally controlled PFC converter with an optimally designed inner current-control loop using a doubly-fed control loops integral-proportional (IP) controller to reduce the THD and to offer an input current with a unity PF. For the economic design of a digitally controlled PFC converter, two isolated AC and DC voltage sensors are designed for interfacing with the microcontroller unit (MCU). PSIM software as well as experimental prototype was used to test the converter performance using the proposed designed current controllers and isolated voltage sensors. We achieved a high-power-density, digitally controlled, telecom PFC stage with a power factor more than 99% and THD of about 5.50%.

Pengukuran Parameter Sistem PV Power Plant Tersambung Pada Jaringan Tenaga Listrik Berdasarkan Real Time Clock

The research describes the performance and accuracy of various interconnected sensors from the measurement results of photovoltaic energ y conversion from power plants connected to the electricity grid from PLN. In data retrieval, PLX-DAQ software is used as a software for data loggers. The hardware used is a Pollycristallin type solar panel with a capacity of 2.08 kW, Arduino, and several sensor components that are used as hardware. In this developed tool, the component used to measure the temperature and surface of the panel is the DHT11 sensor, the component for measuring DC current is the ACS1712 sensor, the component for measuring DC voltage is the ZMPT101B sensor, and the component for measuring currents and voltages sourced from PLN electricity is sensor PZEM-004. Arduino will record data from these components, which is then interfaced to Excel Spreadsheet processing in the form of presenting data tables and graphics in real time. The DC voltage sensor calibration test has an average error of 0.63% and an AC voltage of 0.42%, while the DC current sensor has an average error of 2.21%, and the AC sensor current has an average error of 2.07% with all sensors in the normal reading limit category. The measurement results with the maximum electrical energ y consumption capacity in the DC power measurement reached 1777.8 W, while for the maximum AC power capacity measurement it reached a value of 1834 W and occurred during peak loads at 12.00 PM .

Control System for Smart Home Energy Management

The Increasing energy demand for residential load is often associated with a lot of energy waste due to lack of a good energy management system. In this work, we propose an intelligent energy management system (EMS) which is designed to control the electrical energy source from solar energy and the power plant from PLN resources both automatically and manually based on the Internet of Things. This control system can be controlled remotely during the process of connecting and disconnecting backup power sources that can be accessed through smartphone devices. Based on the test results of the control system of displacement of energy sources, the DC voltage sensor accuracy value is 99.44% and the PLN voltage measurement has an accuracy of 98.31%. In the control system transfer to a backup power source with manual control has an average duration of time of 0.74 seconds while the automatic control has an average duration of time of 0.98 seconds to 1.33 seconds and for automatic control with priority reading the battery capacity of 12.75 Volts can do transfer from the PLN power source to the backup power source and transfer to the PLN source if the voltage is less than 10.5 Volts with a 100% success rate.

Current Progress towards the Integration of Thermocouple and Chipless RFID Technologies and the Sensing of a Dynamic Stimulus.

To date, no printable chipless Radio Frequency Identification (RFID) sensor-related publications in the current literature discuss the possibility of thermocouple integration, particularly for the use in extreme environments. Furthermore, the effects of a time-dependent stimulus on the scattering parameters of a chipless RFID have never been discussed in the known literature. This work includes a review of possible methods to achieve this goal and the design and characterization of a Barium Strontium Titanate (BST) based VHF/UHF voltage sensing circuit. Proof-of-concept thermocouple integration was attempted, and subsequent testing was performed using a signal generator. These subsequent tests involved applying ramp and sinusoid voltage waveforms to the circuit and the characteristics of these signals are largely extracted from the scattering response. Overall conclusions of this paper are that thermocouple integration into chipless RFID technology is still a significant challenge and further work is needed to identify methods of thermocouple integration. With that being said, the developed circuit shows promise as being capable of being configured into a conventional chipless RFID DC voltage sensor.

Sliding mode observer‐based AC voltage sensorless model predictive control for grid‐connected inverters

Recently, model predictive control has been widely used to control grid-connected inverters due to its advantages. However, the conventional model predictive control methods usually require two AC voltage and current sensors to sample the grid voltages and currents. Particularly, the DC voltage sensor is also required to calculate the values of the voltage vectors. The inverter will lose its stability once these sensors fail. Thus, in this study, to improve the operational reliability of the grid-connected inverters, a sliding mode observer-based AC voltage sensorless model predictive control is proposed. First, a sliding mode observer is designed to estimate the grid voltage. Next, a new adaptive compensation strategy is proposed to remove the phase and amplitude errors caused by the low-pass filter. The main novelty of this compensation strategy is that it is immune to the actual grid frequency. Therefore, the grid voltage can be identified accurately, even under frequency deviation conditions. Besides, the stability and parameter designing method of the sliding mode observer is also analysed to provide a theoretical basis for its implementation. Finally, based on the observed grid voltage, the AC voltage sensorless model predictive control is achieved. Detailed comparative experimental results show the validity of the proposed method.

Offline Self-Check Function for DC Voltage Sensor of Three-Level AC Drive Systems Used in Steel Rolling Mills

Offline Self-Check Function for DC Voltage Sensor of Three-Level AC Drive Systems Used in Steel Rolling Mills