Optimizing Solar Panel Cleaning with Kalman Filter-Enhanced Mobile Robotics - Hendi Purnata; Riyani Prima Dewi; Afrizal Abdi Musyafiq; Novita Asma Ilahi; Erna Alimudin; Saepul Rahmat (International Journal of Integrated Engineering, 2025)
This study proposes the use of the Kalman filter method to accurately determine the position of the robot so that it can monitor the efficiency of the solar panels. This method is applied to the mobile solar panel cleaning robot, the Kalman filter is used to process data from the Inertial Measurement Unit (IMU) sensor on the robot specifically on the z-axis to accurately determine the position of the robot in the Cartesian coordinate system. The robot’s performance tests show that the accuracy of the displacement measurement of the encoder corresponds to the pulse value. The test results showed that the use of the Kalman filter could significantly reduce the total error in the sensor data, namely when before using the Kalman filter, the total error from the reference axis gradient was 47.17 degrees, while by using the Kalman filter, the total error was 0.23 degrees, which means that the effectiveness of dust cleaning by the robot showed that the robot was able to reach the target coordinates with a high level of accuracy. Then, the mobile solar panel cleaning robot is taken simultaneously to monitor and maintain the efficiency of the solar panel in terms of dust and temperature drop. The efficiency of solar panels with a temperature drop of 5-6 degrees Celsius. The result of this study is a solar panel cleaning robot equipped with the Kalman filter algorithm to lower the temperature and clean dust. The total movement error of the robot was 0.73 for the X coordinates and 0.79 for the Y coordinates. The decrease in temperature had a positive effect on the increase in power by 2% from 85% to 87%. The results of this study show that the performance of the system is maintained in optimal conditions even though temperature fluctuations are successfully treated to increase the efficiency of the system, the temperature reduction according to the standard conditions (STC) is still not optimal, so further research and improvement is needed in the temperature reduction to achieve higher efficiency.