Abstract
The design of a novel reflective double-sided solar panel automatic solar tracker, focusing on enhancing photoelectric conversion efficiency through dual-precision dual-axis tracking and improved particle swarm optimization (PSO) algorithms. By leveraging low-frequency wakeup, wireless communication, and intelligent charging technologies, the tracker maximizes solar energy absorption and power output. Experimental results demonstrate significant improvements in tracking accuracy and power generation.
1. Introduction
Since the implementation of the “531 New Deal for Photovoltaics” in 2018, the domestic photovoltaic (PV) industry has transitioned from government-subsidized electricity to market-driven grid parity, fostering qualitative changes in competition within the industry. Double-sided solar panels, which absorb additional radiation on their rear side, have significantly increased electricity generation and efficiency, becoming a pivotal technology supporting grid parity for PV components. According to statistics from the China Photovoltaic Industry Association and the International Technology Roadmap for Photovoltaic (ITRPV), the demand for downstream applications of bifacial PV modules is expected to grow, with a domestic market share of 14% in 2019, rising above 50% by 2025 and exceeding 70% by 2030.
2. Overview of the Tracking Device Design
2.1 Device Structure Design
The PV tracking device is structured in two layers: the upper layer accommodates two double-sided solar panels, while the lower layer connects to tracking motors. To ensure effective reflection, the four sides of the support frame are open, and reflectors are installed accordingly. The reflectors are attached to the bottom edge of the frame and extend outward at a predetermined angle. Sensors are positioned between the two solar panels on both the front and rear sides.
| Components | Description |
|---|---|
| 1. Frame | Supports the entire structure |
| 2. Azimuth tracking motor | Controls azimuthal movement |
| 3. Reflectors | Enhance rear side irradiance |
| 4. Front sensor cartridge | Collects front side light signals |
| 5. Rear sensor cartridge | Collects rear side light signals |
| 6. Elevation tracking motor | Controls elevation movement |
| 7. Support stand | Provides stability to the frame |
2.2 Reflector Configuration
The reflectors consist of four symmetrical reflective mirrors on the front, back, left, and right sides. Their functions include reflecting sunlight from the front to the rear of the solar panels to increase irradiance and creating angle differences between reflected beams for detection by the rear sensor cartridge.
Table 1: Reflector Dimensions and Light Angles
| Parameters | Description | Value |
|---|---|---|
| h | Vertical height from the bottom of the reflector to the solar panel | Varies |
| l | Length of the PV support frame | Varies |
| L1 | Length of the left and right reflectors (equals frame width w) | Varies |
| L2 | Length of the front and rear reflectors | Varies |
| θ | Angle of incidence | Calculated |
| β | Angle of reflection | Calculated |
3. Sensor Cartridge Design
Light signals are collected through a combination of coarse adjustment using the rear sensor cartridge and fine adjustment using the front sensor cartridge. The front cartridge is a square box with a square window, housing five photoresistors. The rear cartridge is a rectangular box with strip windows on all four sides, each equipped with five photoresistors.
Table 2: Motor Coarse Adjustment Logic
| PA4~PA0 Input | Left_Num | B1 Rotation Angle β |
|---|---|---|
| 00000 | 0 | No rotation |
| 00001 > 1 & ≤ 3 | 1 | Turn left 90°-41θ |
| 00010 > 3 & ≤ 7 | 2 | Turn left 90°-31θ |
| 00011 > 7 & ≤ 15 | 3 | Turn left 90°-21θ |
| 00100 > 15 & ≤ 31 | 4 | Turn left 90°-θ1 |
4. Circuit Design
4.1 Overall Circuit System
The circuit system comprises a reader and PV tracking device nodes (tags). The STM32F103VBT6 microcontroller serves as the main controller. The reader initiates low-frequency wakeup signals and receives data feedback.
4.2 Low-Frequency Wakeup Circuit
The STM32 in the reader generates PWM waves to modulate the MAX9930 power monitor using OOK, which is then amplified by the MIC4424 and IRF7839 to produce a 125 kHz low-frequency signal. This signal wakes up the STM32 in the tracking device via the AS3933 serial interface.
4.3 Wireless Communication Circuit
The tracking device uses the CC1101 wireless communication chip to transmit data on the PV panel’s elevation, azimuth, output current, and voltage to the reader for display.
4.4 Adaptive Charging Circuit
The UC3906 adaptive charging control chip and its peripheral circuits manage the charging process, extending battery life by adjusting charging states based on real-time temperature and battery voltage.
5. Software Design
5.1 Low-Frequency Wakeup Program
The low-frequency wakeup program consists of initialization, sleep settings, low-frequency wakeup, and wireless transmission. The STM32 enters sleep mode after initialization and wakes up upon receiving a Manchester-encoded wakeup signal from AS3933.
5.2 Motor Tracking Program
Motor control logic is based on photoresistor signals from the front and rear sensor cartridges, with coarse and fine adjustments controlled through the STM32’s I/O ports.
5.3 MPPT Tracking Program
The MPPT program employs an improved PSO algorithm, with parameters adjusted dynamically to avoid local optimum traps in multi-peak scenarios.
6. Experimental Results
6.1 Tracking Accuracy
The tracking angles of the device were compared to theoretical values based on Cooper’s algorithm for 50 days at various times in Changzhou. The average tracking errors for elevation and azimuth angles were within 1.47° and 1.59°, respectively.
6.2 Power Generation Comparison
Over 50 days, the new dual-axis tracker was compared to a traditional single-axis tracker. The new device improved power generation by 67.6% due to enhanced rear side irradiance and tracking accuracy.
6.3 Simulation of MPPT Tracking
A 2×4 PV array model was simulated using Matlab/Simulink, demonstrating the improved PSO algorithm’s ability to escape local optimums and achieve precise MPPT tracking with a relative power tracking error below 1%.
7. Conclusion
The novel reflective double-sided solar panel tracker that combines low-frequency wakeup, wireless communication, and intelligent charging technologies to maximize solar energy absorption and power output. Experimental results show that the tracker achieves a tracking error of within 1.59° and significantly improves power generation compared to traditional methods. The improved PSO algorithm demonstrates excellent anti-interference and tracking efficiency, providing a solution for enhancing photoelectric conversion efficiency in double-sided solar panels.