Performance Evaluation Of Hybrid System Monitoring Solar Panels Based On WSN Case In Smart Regional Drinking Water Company (PDAM)

Abstract

This research is to design and build a wireless sensor network system to monitor the performance of the Regional Drinking Water Company (PDAM) smart solar module on the network server using the DS18B20 temperature sensor and the INA219 sensor. Of course, the use of solar panels also greatly reduces costs for consumers, so the design of this tool is a combination of switching methods between solar panels, batteries, and the State Electricity Company (PLN). There are still many obstacles to using solar panels such as: how effectively they absorb solar energy, and this can contribute to or reduce damage to digital meters not working. The working principle of this tool uses the Wireless Sensor Network (WSN) method so that the designed tool is placed on several solar panel modules on the Smart PDAM so that the sensor sends data to the network and technicians can control it in real-time. Data collection techniques use observation techniques and documentation to obtain test data. This research uses quantitative descriptive analysis techniques that are inductive, objective, and scientific, with data obtained in the form of numbers or statements that are evaluated and analyzed. The results of this study with the PDAM PCB board testing scenario with an average of 320.2 measurements with a multimeter showed an average result of 322.8 with a relative error of 2.6%. The test results with Smart PDAM components showed an average power consumption of 1789.2 mW. The observations of the two panels gave an average result of 887.1 on panel 1 and an average result of 908.7 on panel 2. The results of the DS18B20 temperature sensor comparison test showed an average error of 0.5%. The result of a switching scenario with 20 experiments, resulted in 9 switching operations to the battery and 11 switching operations to the solar panel. Monitoring the solar module with the INA219 sensor, 277 voltage, current, and power data were determined with an average of 17.1 V, 513.9 mA, and 481.5 mW. The calculation results use the exponential moving average formula to predict the 278th data with an exponential moving average of 20 based on sensor data showing a value of 294.7 mA while using a simple moving average is 590.1 mA.