A comparative analysis of dual-axis solar tracking systems under diverse meteorological parameters conditions

Introduction 1.1 Overview and Problem Statement

The global shift toward sustainable energy has positioned solar photovoltaics (PV) as a top option to replace carbon-intensive fossil fuels [1] . However, the efficiency of standard PV installations is limited by their static design. The sun's position changes both daily and seasonally, which means fixed-tilt systems fail to capture maximum sunlight for most of the day [2] . Dual-axis solar tracking systems (DASTS) address this by adjusting the panel's orientation along both horizontal and vertical axes to stay perpendicular to the sun's rays [3] . Despite their theoretical benefits, the real-world performance of DASTS is affected by various weather conditions. Factors such as high humidity, temperature changes, and cloud density can introduce "tracking lag" or sensor errors in traditional closed-loop systems [4] . The main issue explored in this study is the lack of a solid framework to evaluate DASTS efficiency under changing weather conditions-like monsoon cycles or cloudy days-where diffuse radiation often exceeds direct sunlight, making simple light-seeking sensors ineffective [5] .

Literature Review

Previous research consistently shows that dual-axis tracking can boost energy capture by 30% to 45% compared to fixed-tilt systems, depending on geographic location [6][7] . Some experimental prototypes have shown peak efficiency gains as high as 58% during clear-sky summer months [8] . However, the literature also points out several critical challenges: source than the panel generates [9,10] .

Thermal Effects:

Research by Santos et al. indicates that while tracking increases sunlight, it can also raise cell temperatures, which negatively impacts PV efficiency. Adding cooling or thermal monitoring is important for maintaining net gains [11,12] .

Weather Variability:

Most tracking algorithms are designed for clear-sky conditions. Under various weather scenarios like high aerosol optical depth or heavy rain, tracking accuracy drops significantly without secondary inputs like GPS or astronomical clocks [13,14] .

Proposed Method and Solution

To address these issues, this study proposes a Hybrid-Controlled Dual-Axis Tracking System combined with a real-time Meteorological Monitoring Station (MMS). Unlike traditional systems that rely only on LDR sensors, this proposed solution uses a hybrid algorithm that switches between:

1.3.1 Active Mode: Using an LDR matrix for precise tracking during clear-sky conditions.

Chronological Mode:

Employing a pre-programmed solar position algorithm when sensors detect low visibility (e.g., fog, heavy cloud, or rain) [15] . By conducting a sideby-side comparison of this hybrid system against single-axis and fixed-tilt systems across various weather conditions, this study aims to create a reliable model for maximizing solar energy output in areas with high environmental variability.

Materials and Methods

Fill factor

A solar cell's maximum current and voltage are known as the short-circuit current and open-circuit voltage, respectively. However, at both of these operational locations, the solar cell's power is zero. The fill factor (FF) is an important parameter that influences the power conversion efficiency of an organic solar cell. The Fill Factor (FF) is a typical method for measuring the efficiency of a solar photovoltaic module. It measures the actual greatest power that may be achieved. The FF is defined as the ratio of the solar cell's maximum output to the total (multiplication) of Voc and Isc, which are specified as follows:

Short Circuit Current (ISC)

This is the greatest amount of current that Solar PV panels can produce while the voltage drop is zero. There is no power generated because the cables in the panels are directly linked or short-circuited.

Open Circuit Voltage (VOC)

This is the maximum voltage a solar photovoltaic panel may produce when no current flows through the circuit loop.

Because there is no load connected to the output circuit, no current is produced.

Highest Possible Power (Pmax)

When current and voltage are coupled to provide the most power, this is referred to as the highest possible power (Pmax).

It does not occur due to open-circuit voltage conditions (VOC) or short-circuit circumstances (ISC). The cell at

VOC is considered ideal zero, just as the voltage at ISC is. As a result, Pmax does not occur at maximum current but rather at a constant power (IMPP), which is less than ISC. In a similar vein, Pmax occurs at a certain voltage (VMPP), which is actually lower than VOC.

Therefore, Pmax = VMPP × IMP (2.2)

Efficiency

The ability of solar panel to produce maximum amount of power.

Efficiency = Isc × Voc× FF/Pin (2.3)

Results

This chapter discusses the results gained from the intended Dual axis system and their comparison with current systems. The graphs were used to evaluate the system's performance under both clear and cloudy skies. The system's output power and efficiency were computed. Furthermore, the efficiency and power gain calculations were performed to complete the performance study of the constructed system. This chapter discusses the results of the design and performance study of PV power pack Dual-axis solar tracking. The Fill Factor is one of the most important parameters for the maximum power that can be produced from a solar cell.

Observations recorded in tracking and non-tracking mechanism

 Tracking Condition (Fig 3):

In this case, the tracking system is designed to maintain a high Fill Factor at all times. The Fill Factor is always above 90%. It is important to note that there is a dip at 11:00 AM, indicating that the system is adjusting to the position of the sun. However, the Fill Factor is restored to maximum potential within a short time. This chart shows us clearly that the tracking panel is far superior to the non-tracking panel by a huge margin in the morning and early afternoon.

4.3Key Findings and Significance

The "Overhead" Window At 01:00 PM, the gap between the two systems is minimal. This is the point where the sun's "natural" position is aligned with the non-tracking panel's fixed position. But even at this point, the tracking panel is superior.

Late Afternoon Degradation

An interesting feature that stands out on Fig 7 is that at 04:00 PM, there is a dramatic fall for both systems, with the non-tracking system registering a tiny "bump" at 05:00 PM at 5.5% efficiency as opposed to 0.5% for the tracking system. This could be because:

1. Mechanical Limitations: The tracker could be reaching its mechanical limit to rotate. 2. Obstruction: Shading could be affecting the tracker's position more than the stationary panel.

Conclusion

The dual-axis solar tracking system significantly enhances the efficiency of solar energy capture by continuously aligning photovoltaic panels with the sun's position. This dynamic adjustment allows for optimal sunlight exposure throughout the day and year, leading to increased energy output. The system's use of sensors, actuators, and microcontrollers ensures precise tracking, resulting in improvements in open-circuit voltage (Voc) and shortcircuit current (Isc). While the initial investment and maintenance costs are higher compared to fixed solar panels, the substantial boost in energy yield justifies the expenditure, particularly in regions with high solar insolation. and participation in virtual power plants. By focusing on these areas, the dual-axis solar tracking system can become a more sustainable, efficient, and widely adopted solution for maximizing solar energy utilization.

Acknowledgments

We would like to thank the Department of Farm Machinery, SHUATS, for the provision of research facilities and institutional support. We also acknowledge the significant contributions of previous researchers in this field, whose work provided the framework for the current study. We are grateful for the technical guidance and resources that were vital to the successful completion of this research.

References

1. GPCL Research Group. Design and performance evaluation of dual-axis solar tracking for enhanced photovoltaic efficiency. Genesis Publ Consortium Ltd. 2025;2(1):12-18.
2. Demirdelen T, et al. Performance evaluation of dualaxis solar tracking systems in varying climates. J Renew Energy Res. 2023;14(2):45-52.
3. MDPI Energies. A review and comparative analysis of solar tracking systems. Energies. 2025;18(10):2553.
4. IJRASET. Dual-axis solar panel tracking system: environmental impacts. Int J Res Appl Sci Eng Technol. 2025;13(4):2360-2365.
5. A novel UV sensor-based dual-axis solar tracking system: implementation and performance analysis [Internet]. 2021 [cited 2026 Mar 29]. Available from: https://www.researchgate.net/publication/352818779
6. Santos J, et al. Performance analysis of dual-axis solar tracking systems in equatorial regions. Sol Energy. 2024;198:112-120.
7. Following the sun: the impact of tracking systems on solar efficiency [Internet]. 2025 [cited 2026 Mar 29]. Available from: https://www.azocleantech.com/article.aspx?ArticleID = 1985
8. Novel high precision low-cost dual axis sun tracker based on three light sensors [Internet]. 2023 [cited 2026 Mar 29]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC98037 91/
9. Sumathi S, Gayathri G, et al. Design and implementation of solar tracking system using LDR sensor. Int J Adv Sci Eng. 2020;6(3):1456-1461.
10. Comparative performance evaluation of dual-axis solar trackers: enhancing solar harvesting efficiency [Internet]. 2025 [cited 2026 Mar 29]. Available from: https://www.researchgate.net/publication/382935552
11. Thermal management in dual-axis tracking systems. J Solar Eng. 2024;42(1):88-94.
12. Development of hybrid dual-axis solar tracking system for improved photovoltaic performance [Internet]. 2026 [cited 2026 Mar 29]. Available from: https://www.researchgate.net/publication/399251216
13. Implementation of dual axis solar system with weather monitoring for efficient power generation. JETIR. 2023;10(5):b221-b228.
14. Robustness of solar tracking algorithms under diverse meteorological parameters. IEEE Xplore. 2024. doi:10.1109/ISEC.2024.0012.
15. Andhra University. Performance enhancement of photovoltaic panels via hybrid-integrated tracking algorithms. Technical report. 2025.
16. Abu-Khader MM, Badran OO, Abdallah S. Evaluating multi-axes sun-tracking system at different modes of operation in Jordan. Renew Sustain Energy Rev. 2008;12(3):864-873.
17. Syafii, Nazir R, Kamshory, Hadi M. Improve dual axis solar tracker algorithm based on sunrise and sunset position. J Electr Syst. 2015;11(4):397-406.
18. Needelman DD, inventor. Determining the attitude of a spacecraft having at least one star sensor. United States patent US 2009/0009284 A1. 2009 Jan 8.
19. Khan MTA, et al. Design and construction of a microcontroller-based solar tracker. In: 6th International Conference on Electrical and Computer Engineering (ICECE 2010); 2010 Dec 18-20;
20. Mousazadeh H, Keyhani A, Javadi A, Mobli H, Abrinia K, Sharifi A. A review of principles and sun-tracking methods for maximizing solar energy harvesting. J Sol Energy Eng. 2011;133(1):011018.
21. Awasthi A, Shukla AK, Manohar MSR, Dondariya C, Shukla KN, Porwal D, et al. Review on sun tracking technology in solar PV system. Energy Rep. 2020;6:392-405.