Introduction
The rapid growth of the solar energy industry has intensified the demand for reliable monitoring systems to ensure the stable operation of critical components such as solar inverter. As the core device in photovoltaic (PV) systems, inverter convert direct current (DC) from solar panels into alternating current (AC) for grid integration. However, inverter failures—particularly in switching devices—can disrupt power generation and compromise system efficiency. Traditional wired monitoring methods suffer from high power consumption and limited communication range. To address these challenges, this study proposes a wireless monitoring system leveraging LoRa (Long Range) technology, which combines low power consumption with extended communication capabilities.
System Architecture
The proposed monitoring system adopts a three-tier architecture: perception layer, network layer, and application layer (Table 1).
| Layer | Components | Function |
|---|---|---|
| Perception Layer | Solar inverter, LoRa sensor module | Collects DC-side current data via Hall-effect sensors and transmits wirelessly. |
| Network Layer | LoRa gateway, 4G/ethernet, cloud server | Relays data from sensors to the cloud for centralized processing. |
| Application Layer | Computer platform, predictive algorithms | Analyzes data to detect faults (e.g., open-circuit failures) and predict inverter health. |
Key Formula for Hall-Effect Sensing:
The Hall voltage UinUin, which correlates with the measured current, is derived as:Uin=RH⋅B⋅cosαΔUin=ΔRH⋅B⋅cosα
where RHRH is the Hall coefficient, BB is the magnetic flux density, αα is the angle between the magnetic field and sensor, and ΔΔ represents material thickness.
Hardware Design
1. LoRa Sensor Terminal
The sensor terminal integrates a Hall-effect current sensor, STM32 microcontroller, and SX1278 LoRa transceiver. Key specifications include:
- Frequency Range: 169–915 MHz
- Transmission Power: Up to 17 dBm
- Communication Distance: 5 km (line-of-sight)
- Power Consumption: < 8 dBm during active transmission
Circuit Design for Signal Conditioning:
To minimize noise, a low-pass filter with capacitance C=220 pFC=220pF and inductance L=1 nHL=1nH is implemented. The analog-to-digital converter (ADC) samples data at 100 μs intervals to ensure real-time monitoring.
2. Fault Detection Logic
Switch failures in solar inverter manifest as anomalies in DC-side current. The system triggers an alert when the current deviates beyond thresholds defined by:Ithreshold=±3σ+μIthreshold=±3σ+μ
where σσ is the standard deviation and μμ is the mean current under normal operation.
Software Platform
The cloud-based platform processes data through three stages:
- Data Preprocessing: Noise reduction using a moving average filter.
- Feature Extraction: Identifies patterns such as sudden drops (open-circuit faults) or harmonics (IGBT degradation).
- Predictive Analytics: Employs machine learning models to forecast inverter lifespan based on historical data.
Algorithm for Noise Reduction:Ifiltered[n]=1N∑k=0N−1Iraw[n−k]Ifiltered[n]=N1k=0∑N−1Iraw[n−k]
where NN is the window size (e.g., N=10N=10).
Experimental Validation
1. Test Setup
A 5 kW solar inverter was monitored under varying load conditions. Faults were artificially induced to evaluate detection accuracy.
2. Results
- Communication Reliability: Packet loss rate < 0.1% at 3 km.
- Fault Detection: 98.7% accuracy in identifying open-circuit faults within 0.02 s.
- Power Efficiency: Sensor terminal consumed 12 mW during operation, 80% lower than traditional systems.
Table 2: Performance Comparison
| Parameter | Proposed System | Traditional System |
|---|---|---|
| Communication Range | 5 km | 100 m |
| Power Consumption | 12 mW | 60 mW |
| Fault Detection Time | 0.02 s | 0.5 s |
Discussion
The integration of LoRa technology into solar inverter monitoring addresses critical limitations of wired systems. By enabling long-range, low-power communication, the system ensures real-time fault detection without compromising energy efficiency. Future work will focus on enhancing predictive algorithms for early detection of partial shading or aging-related degradation.
Conclusion
This study demonstrates a robust, wireless solution for monitoring solar inverter in real time. The LoRa-based system achieves high accuracy in fault detection while operating at ultralow power levels. As the solar industry evolves, such innovations will play a pivotal role in maximizing system uptime and reducing maintenance costs.