## 2 Methodology  
This paper collects some results of research work, part of which is carried out as part of a research project, \"PRFU: University Training Research Project\", with the participation of [18]. The simulation of our models was carried out using the software \"MATLAB-SIMULINK\", which allowed us to create a library of specialized simulation blocks. The annual meteorological data was collected through the meteorological station installed at the University of Khemis Miliana. The prototype allowed tracking the movement of the sun in real-time.  
To maximize actual PV production in the field, solar trackers must meet several dimensions during the different phases of the PV project life: design, installation, operation, and maintenance. The challenges are many and diverse, ranging from the lay of the land to the need for local content, local workforce skills, weather conditions, and budget constraints. The efficient performance of a solar tracker requires comprehensive realizations.  
Our work is based on a new idea by designing an automatic microcontroller-based solar tracker with a hybrid algorithm comprising both active and chronological algorithms for locating the sun's position. This hybrid algorithm combines both mathematical models and sensors to determine the precise sun's position and thereby harness optimal solar energy for all weather conditions.  
Experimental results show that the hybrid solar tracking algorithm can yield higher solar power than traditional active and chronological algorithms. The solar tracking process is fully automated, maximizing the collection and management of solar energy for the solar system.  
The proposed solar tracker has light-dependent resistors (LDRs), an Arduino microcontroller connected with Wi-Fi, a servo motor, a current sensor, and a solar panel with a supporting metallic servo bracket. This electromechanical system consists of one driver with a servo motor for rotating in east and west directions. The solar panel produces a voltage proportional to the sunlight intensity, while the LDRs determine the system misalignment and send signals to the microcontroller, which in turn automatically adjusts the motors to correct the solar panel position.  
Active and chronological algorithms are employed in solar tracking. The active algorithm is a closed-loop tracking mechanism based on the control principle with feedback. A light sensor, which detects sun brightness, acts as the input to the system controller. The acquired values are then analyzed by the microcontroller, which controls the motor motion to orientate the solar panel towards the sun. While the active solar tracker produces high tracking accuracy during clear and sunny days, its performance may degrade when the weather is cloudy or when the light sensor is sheltered. Conversely, the chronological algorithm controls the solar panel movement by identifying the sun's path using sun tracking mathematical models. The microcontroller calculates the sun's position and directs the motor to move the solar panel towards the sun at preset time intervals.  
In summary, with a large-scale implementation, the proposed hybrid solar tracker can harness optimal solar energy for all weather conditions, thereby considerably cutting electricity costs for public and private organizations.  
To evaluate the performance of active, chronological, and hybrid algorithms for solar tracking, experiments were conducted. The solar tracker prototype must be in an open field where it can receive sunlight throughout the day. Voltage measurements were taken from the morning at 4 a.m. to the evening at 8 p.m. at every 60-min interval. Readings from the traditional fixed-mount solar panel were also recorded to determine the baseline voltage. Subsequently, an analysis was performed to determine the energy generated, consumed, and gained by the hybrid solar tracker.