The design and implementation of a dual-axis solar panel tracking system that integrates reflective mirrors, light-sensitive sensors, and an improved particle swarm optimization (PSO) algorithm. The system aims to maximize solar energy absorption and power output efficiency for bifacial solar panel. By combining hardware innovations with advanced control algorithms, the proposed solution achieves high-precision tracking, low-power operation, and rapid maximum power point tracking (MPPT) under dynamic shading conditions.
1. System Architecture and Design
1.1 Structural Overview
The tracking system comprises two layers:
- Upper Layer: Houses bifacial solar panel with integrated light-sensitive sensor modules.
- Lower Layer: Contains dual-axis tracking motors and a reflective mirror array.
The reflective array consists of four symmetrically arranged mirrors positioned below the solar panel. These mirrors serve two purposes:
- Redirect sunlight to the rear side of the bifacial solar panel, enhancing total irradiance.
- Generate angular differences in reflected light for coarse tracking via rear-side sensors.
The geometry of the mirrors is optimized using the following equations:⎩⎨⎧θ1=0.5arctan(2hw),θ2=0.5arctan(2hl),K1=2hsinθ1,K2=2hsinθ2,L1=l,L2=w,
where h is the vertical height from the mirror base to the solar panel, l and w represent the length and width of the solar panel frame, and K1, K2, L1, L2 define mirror dimensions.
1.2 Sensor Modules
Two types of light-sensitive sensor modules are employed:
- Front Sensor Module: A square-aperture dark box with five photoresistors for fine-tuning.
- Rear Sensor Module: A rectangular dark box with strip apertures and 20 photoresistors (5 per side) for coarse adjustment.
| Sensor Type | Function | Key Parameters |
|---|---|---|
| Front Module | Fine-tunes elevation/azimuth angles using differential signals from edge photoresistors. | Angular resolution: 1° |
| Rear Module | Coarse-tracks sunlight direction via reflected light angles. | Angular range: 0°–90° |
2. Circuit Design and Power Management
2.1 Core Components
The control system features:
- Main Controller: STM32F103VBT6 microcontroller.
- Wireless Communication: CC1101 transceiver for real-time data transmission.
- Low-Frequency Wake-Up: AS3933 receiver with 125 kHz OOK modulation.
- Adaptive Charging: UC3906-based circuit for battery management.
2.2 Key Circuit Specifications
| Circuit Module | Function | Performance Metrics |
|---|---|---|
| Low-Frequency Wake-Up | Reduces standby power to <10 μW | Wake-up latency: <50 ms |
| Wireless Communication | Transmits tracking angles and power data | Range: 100 m, Data rate: 250 kbps |
| Adaptive Charging | Manages 12V/24V battery systems | Efficiency: 92%, Temp. compensation: ±0.3%/°C |
3. Control Algorithms and Software
3.1 Dual-Axis Tracking Logic
The tracking algorithm combines coarse and fine adjustments:
- Coarse Adjustment: Uses rear sensor data to estimate the sun’s position.
- Fine Adjustment: Optimizes angles using front sensor differential signals.
The motor control equation for stepwise rotation is:CT=360β×4×9×322=32.2β,
where CT is the encoder pulse count, and β is the rotation angle.
3.2 Improved PSO Algorithm for MPPT
The modified PSO algorithm addresses multi-peak MPPT challenges under partial shading:
- Particle Velocity Update:
vidk+1=ωvidk+c1r1(Pbest,ik−xidk)+c2r2(Gbestk−xidk),
where ω, c1, c2 adaptively adjust to avoid local optima.
- Adaptive Parameters:
ω=ωmax−Mk(ωmax−ωmin),c1=c1,start+(c1,end−c1,start)[1−αcos(−M2k+1)/π],c2=c2,start+(c2,end−c2,start)[1−αcos(−M2k+1)/π].
| Parameter | Value | Description |
|---|---|---|
| ωmax | 0.9 | Initial inertia weight |
| ωmin | 0.4 | Final inertia weight |
| c1,start | 2.7 | Initial cognitive coefficient |
| c1,end | 1.2 | Final cognitive coefficient |
| c2,start | 0.5 | Initial social coefficient |
| c2,end | 2.2 | Final social coefficient |
4. Experimental Results
4.1 Tracking Accuracy
Field tests in Changzhou, China (31.43°N, 119.54°E) demonstrated:
| Metric | Average Error | Maximum Error |
|---|---|---|
| Elevation Angle | 1.47° | 1.59° |
| Azimuth Angle | 1.59° | 1.83° |
4.2 Power Output Comparison
The reflective dual-axis system outperformed traditional single-axis tracking:
| System Type | Avg. Daily Output (kWh) | Improvement |
|---|---|---|
| Single-Axis Tracking | 4.2 | Baseline |
| Proposed System | 7.1 | +67.6% |
4.3 MPPT Performance
Simulations of a 2×4 solar panel array under shading showed:
| Algorithm | Convergence Time (s) | Tracking Error |
|---|---|---|
| Standard PSO | 1.2 | 3.8% |
| Improved PSO | 0.75 | <1% |
5. Conclusion
The proposed dual-axis solar panel tracking system demonstrates significant advancements in energy harvesting efficiency through:
- Reflective Mirror Array: Enhances rear-side irradiance by up to 80%.
- Dual-Precision Tracking: Achieves angular errors below 1.6°.
- Adaptive MPPT: Improves power output stability under shading.
This work provides a comprehensive solution for optimizing bifacial solar panel performance, combining mechanical design, low-power electronics, and intelligent algorithms. Future extensions will focus on scalability for large-scale solar farms and integration with energy storage systems.