The proliferation of photovoltaic (PV) power generation as a cornerstone of renewable energy has brought the reliability of its core components into sharp focus. Among these, the solar inverter is a critical power electronic interface that converts the direct current (DC) produced by PV panels into grid-compatible alternating current (AC). Its operational stability directly dictates the efficiency, safety, and longevity of the entire PV system. Consequently, developing robust online monitoring techniques for the solar inverter is of paramount practical significance to facilitate predictive maintenance, minimize downtime, and reduce the probability of catastrophic failures.
Traditional monitoring architectures for inverters predominantly rely on wired communication systems. These systems, while functional, are often hampered by significant drawbacks: high installation complexity due to extensive cabling, limited deployment flexibility, and relatively high power consumption at sensing nodes. These limitations are particularly pronounced in large-scale or remotely located PV farms. To address these challenges, this research proposes a novel, low-power, long-range wireless monitoring system specifically designed for solar inverter status assessment. The core innovation lies in the integration of an improved Hall-effect current sensor with a communication module based on the Long Range (LoRa) spread spectrum technology. This system enables the real-time acquisition of critical current signatures from the inverter’s output, facilitating remote fault diagnosis and health assessment.
The architecture of the proposed monitoring system is conceptualized within a three-layer Internet of Things (IoT) framework: the Perception Layer, the Network Layer, and the Application Layer. The Perception Layer is responsible for data acquisition and includes the solar inverter under test and the custom-designed LoRa-enabled monitoring node. The Network Layer handles data transmission, comprising a LoRa gateway that aggregates data from multiple nodes and relays it to a central server via cellular (4G) or Ethernet backhaul. Finally, the Application Layer consists of a computer-based processing platform that analyzes the received data, performs state prediction, and presents the monitoring results. This layered approach ensures modularity and scalability for monitoring multiple inverters across a wide area.
Design of the LoRa-Enabled Wireless Sensing Terminal
The heart of the Perception Layer is the wireless sensing terminal. Its design focuses on autonomy, accuracy, and ultra-low power consumption to enable long-term deployment. The terminal integrates several key subsystems: an improved self-powered Hall-effect current sensor, a signal conditioning circuit, a power management unit, and the LoRa transmission module.
Improved Self-Powered Hall-Effect Current Sensor
Monitoring the output current of a solar inverter provides vital insights into its switching behavior and health. We employ a non-intrusive Hall-effect principle for current measurement. The fundamental physics is governed by the Hall effect: when a current-carrying conductor is placed in a magnetic field, a voltage (the Hall voltage, $U_H$) is generated perpendicular to both the current and the field. For a Hall element, this voltage is given by:
$$ U_H = \frac{R_H I B \cos\alpha}{d} $$
where:
- $R_H$ is the Hall coefficient, a material property,
- $I$ is the control current flowing through the Hall element,
- $B$ is the magnetic flux density generated by the conductor current being measured,
- $\alpha$ is the angle between the magnetic field and the Hall element’s plane,
- $d$ is the thickness of the Hall element.
By maintaining $I$, $\alpha$, and $d$ constant, $U_H$ becomes directly proportional to $B$, which in turn is proportional to the current in the nearby conductor. This linear relationship allows for accurate current measurement.
The innovative aspect of our sensor design is its self-powering capability, eliminating the need for battery replacement or hardwired power lines. An energy harvesting coil is placed around the same current-carrying busbar. The alternating magnetic field from the busbar induces an AC voltage in this coil. This harvested energy is then processed to power the entire sensor node. The sensor’s position is typically on the DC link or output AC lines of the solar inverter, providing a direct signature of its operational state.
Power Management Unit (PMU)
The PMU is crucial for managing the harvested, intermittent energy. It consists of an impedance matching circuit to maximize power transfer from the coil, a full-bridge rectifier to convert AC to DC, a supercapacitor for energy storage, a voltage regulator to provide a stable supply for the electronics, and a control switch. The supercapacitor charges until its voltage reaches a predefined threshold. Once this threshold is crossed, the control switch closes, allowing the stored energy to power the microcontroller, sensor circuitry, and LoRa radio. After data transmission, the node enters a deep sleep state, minimizing draw and allowing the supercapacitor to recharge. The power flow can be summarized in the following states:
| System State | Supercapacitor Activity | Sensor & Radio Activity | Primary Function |
|---|---|---|---|
| Energy Harvesting / Charging | Charging from coil | OFF (Deep Sleep) | Accumulate energy |
| Active Measurement & Transmission | Discharging | ON (Sampling, Processing, TX) | Acquire data and transmit packet |
| Recovery | Charging from coil | OFF (Deep Sleep) | Replenish energy for next cycle |
LoRa Communication Module and RF Front-End
For long-range, low-power communication, we selected the Semtech SX1278 LoRa transceiver chip, integrated into an iWL881A module. LoRa technology employs Chirp Spread Spectrum (CSS) modulation, which offers exceptional receiver sensitivity and strong resistance to interference. The key link budget parameters for LoRa are related by:
$$ \text{Link Budget} = P_{tx} – \text{Sensitivity} = P_{tx} – \left( -174 + 10\log_{10}(BW) + NF + \text{SNR}_{req} \right) $$
where $P_{tx}$ is transmit power, $BW$ is bandwidth, $NF$ is receiver noise figure, and $\text{SNR}_{req}$ is the required signal-to-noise ratio. LoRa’s ability to operate with negative $\text{SNR}_{req}$ (below the noise floor) is what grants it exceptional range. The relationship between Spreading Factor (SF), data rate ($R_b$), and sensitivity is a critical trade-off:
$$ R_b = \frac{SF \cdot BW}{2^{SF}} \cdot \frac{4}{4+CR} $$
where $CR$ is the coding rate. Higher SF increases sensitivity and range but reduces data rate and increases airtime (and thus energy consumption).
A critical part of the RF design is the impedance matching network between the transceiver’s RF output pin (e.g., PA_BOOST) and the antenna. Proper matching ensures maximum power transfer and spectral efficiency. A typical matching circuit for the 470 MHz band used in our system involves series and shunt inductors (L) and capacitors (C). The component values (e.g., L2, C17, C18) are tuned based on the specific antenna impedance and PCB layout to achieve a voltage standing wave ratio (VSWR) close to 1:1 at the operating frequency. The matching network topology is generally a $\pi$-network or L-network configured to transform the antenna impedance (typically targeted at 50 $\Omega$) to the complex conjugate of the PA output impedance.
| Parameter | Value / Specification | Impact |
|---|---|---|
| Core Chip | Semtech SX1278 | Enables LoRa CSS modulation |
| Frequency Band | 476.5 MHz | License-free sub-GHz band, good propagation |
| Transmit Power | +17 dBm (configurable) | Determines range; higher power consumes more energy |
| Spreading Factor (SF) | SF7 to SF12 (configurable) | Higher SF = Longer range, lower data rate |
| Bandwidth (BW) | 125 kHz (typical) | |
| Module | iWL881A with STM8L MCU | Provides ultra-low-power processing and control |
Communication is orchestrated by a low-power microcontroller (e.g., STM8L). The node spends most of its time in a deep sleep mode. A hardware pin (P6) is used to wake the LoRa module. Upon waking, the sensor data is read via a UART interface. Another pin (P5) controls the timing of the data frame transmission. The microcontroller toggles these pins according to a strict timing sequence to ensure reliable data handoff and transmission, after which the entire node returns to deep sleep. In our field tests, with a +8 dBi antenna at the gateway and a transmit power of +17 dBm, we achieved stable communication links over distances exceeding 5 km, with received signal strength indicator (RSSI) values consistently better than -115 dBm, confirming the suitability of LoRa for wide-area monitoring of solar inverter installations.
Fault Diagnosis Principle for Solar Inverters
The primary target for monitoring in a voltage-source solar inverter is its switching devices, typically Insulated-Gate Bipolar Transistors (IGBTs). These devices are the most prone to failure due to thermal and electrical stress. An open-circuit or short-circuit fault in an IGBT directly distorts the output current waveforms. By continuously monitoring these waveforms, characteristic patterns associated with specific faults can be identified.
Consider a standard three-phase, two-level voltage source inverter for a solar inverter application. In a 180-degree conduction mode, each switch conducts for half of the fundamental period. Under normal balanced conditions with a resistive load, the three-phase output currents ($i_a$, $i_b$, $i_c$) are sinusoidal and displaced by 120 degrees. The DC link current $i_{dc}$ exhibits a characteristic six-pulse ripple pattern. When a fault occurs, such as an open-circuit in one of the upper switches (e.g., VT1), the corresponding phase current loses its positive half-cycle. This distortion manifests as a significant deviation in the shape, amplitude, and symmetry of the phase currents and the DC link current.
We can analyze this by looking at the relationship between switching states and currents. For a given switching state vector $S = [S_a, S_b, S_c]$, where $S_x = 1$ if the upper switch in leg x is ON and $S_x = 0$ if the lower switch is ON, the line-to-neutral voltages can be expressed in terms of the DC bus voltage $V_{dc}$:
$$
\begin{bmatrix} v_{aN} \\ v_{bN} \\ v_{cN} \end{bmatrix} = \frac{V_{dc}}{3} \begin{bmatrix} 2 & -1 & -1 \\ -1 & 2 & -1 \\ -1 & -1 & 2 \end{bmatrix} \begin{bmatrix} S_a \\ S_b \\ S_c \end{bmatrix}
$$
The phase currents are then determined by these voltages and the load impedance. A switching device fault alters the valid set of switching states, thereby distorting the voltage vectors applied to the load and, consequently, the measured currents. The monitoring system captures the DC link current $i_{dc}$ (or individual phase currents), which is a function of the switch states and the output currents:
$$ i_{dc}(t) = S_a(t) i_a(t) + S_b(t) i_b(t) + S_c(t) i_c(t) $$
A fault in a switch will cause a discrepancy between the expected $i_{dc}$ pattern for a commanded state and the measured pattern.
Software Platform and Experimental Validation
The Application Layer software platform is designed for both local and remote monitoring. The local software, running on a station computer connected to the LoRa gateway, is responsible for driving the data acquisition cycle, performing initial data storage, and executing real-time basic state monitoring algorithms. It forwards the aggregated data from all monitored solar inverter nodes to a remote server. The remote monitoring software focuses on advanced data analysis, historical trend visualization, and fault prediction. It applies signal processing techniques such as digital filtering (e.g., moving average, band-pass filters) to remove noise from the received current signals. Advanced algorithms, including spectral analysis or pattern recognition, can then be applied to the clean signal to extract features indicative of specific fault modes.
To validate the proposed monitoring system, we constructed a testbed using a three-phase bridge inverter topology. The system parameters for the experiment were as follows:
| Parameter | Value |
|---|---|
| Topology | Three-phase, two-level Voltage Source Inverter |
| Conduction Mode | 180-degree |
| DC Link Voltage ($V_{dc}$) | 100 V |
| Load | Balanced Resistive, 8 $\Omega$ per phase |
| Switching Devices | IGBT/Diodes (Simulation/Prototype defaults) |
| Fundamental Frequency | 50 Hz (Period = 0.02 s) |
| Pulse Width | 50% duty cycle for each 180-degree block |
The LoRa sensing node was installed to monitor the DC link current. Experiments were conducted for two distinct operational states of the solar inverter: 1) Normal healthy operation, and 2) A simulated fault condition with open-circuit failures in two switches (e.g., VT1 and VT2). The software platform successfully received the wireless current data in real-time. The captured waveforms for the two states showed markedly different characteristics. Under normal operation, the DC link current exhibited the expected periodic six-pulse waveform with consistent amplitude. Under the double open-circuit fault condition, the waveform was severely distorted, losing its symmetry and showing significant reduction in amplitude during certain intervals, corresponding to the missing switch actions. The software’s analysis module successfully flagged the anomalous pattern, demonstrating the system’s capability for remote fault detection. The key results are summarized below:
| Inverter State | DC Link Current Waveform Characteristic | Monitoring System Output |
|---|---|---|
| Normal Operation | Regular six-pulse ripple, periodic, balanced amplitude. | “Status: Normal” |
| Fault (VT1 & VT2 Open) | Distorted, asymmetric waveform with missing pulses and reduced amplitude segments. | “Alert: Anomalous current pattern detected. Possible switch fault.” |
This experimental validation confirms the feasibility of the proposed architecture. The integration of the self-powered sensor and LoRa communication created a viable, maintenance-friendly solution for the wireless monitoring of solar inverter health. The system’s low power consumption, derived from its energy harvesting design and LoRa’s efficient communication protocol, ensures long-term operational autonomy. The long communication range provided by LoRa technology makes it suitable for monitoring inverters distributed across large solar farms, overcoming the primary limitation of traditional wired systems.
Conclusion
In this work, we have presented a comprehensive design and implementation of a wireless online monitoring system for photovoltaic inverters. The system addresses the key limitations of conventional wired monitoring by leveraging two core technologies: an energy-harvesting, self-powered Hall-effect current sensor and the long-range, low-power LoRa communication protocol. The sensing terminal autonomously acquires the DC link current signal, which carries the fingerprint of the solar inverter‘s switching behavior. The data is then transmitted wirelessly over several kilometers to a central software platform for analysis.
The experimental results on a three-phase inverter prototype clearly demonstrate the system’s effectiveness. By analyzing the distinct current waveforms under normal and faulty (open-circuit switch) conditions, the monitoring system can successfully identify anomalous operation and trigger alerts. This capability enables early fault detection, which is crucial for preventing secondary damage and ensuring the reliability of solar power generation. The proposed system offers a scalable, cost-effective, and practical solution for enhancing the operational intelligence and maintenance strategies of modern PV power plants, contributing to the stability and efficiency of renewable energy infrastructure.