In the context of large-scale photovoltaic deployment, distributed photovoltaic systems have become increasingly widespread due to their advantages of flexible installation and local energy consumption. The grid-connected inverter, as the core energy conversion hub of the system, is frequently exposed to harsh outdoor conditions such as high temperature and grid fluctuations, which make it prone to faults. These faults not only cause a sharp drop in power generation efficiency but may also trigger grid safety issues. Traditional diagnostic methods suffer from slow response and low identification accuracy, failing to meet the reliability requirements of modern systems. In my research, I focus on the fault features and rapid diagnostic technologies for distributed photovoltaic grid‑connected inverters. By analyzing various types of solar inverter, constructing recognition models, and optimizing diagnostic algorithms, I aim to achieve precise and fast fault localization, providing technical support for photovoltaic system operation and maintenance. This work holds significant engineering value.
1. Operating Principle and Core Components of Distributed Photovoltaic Grid‑Connected Inverters
The distributed photovoltaic grid‑connected inverter serves as the vital interface between the PV array and the utility grid. Its primary function is to convert the low‑voltage DC power generated by solar panels into three‑phase AC power that complies with national grid standards through a DC/AC conversion stage. A closed‑loop control system continuously monitors key signals such as input/output current, voltage, and power factor, regulating the switching frequency of IGBTs in the power module to maintain output power quality. This ensures that the grid‑connected current is synchronized with the grid voltage in both frequency and phase, preventing harmonic pollution.
Core components include: a power control board integrating MCU and DSP chips that regulates output power and grid status in real time; current sensors based on the Hall effect that monitor current fluctuations and provide feedback; the power module that performs the core power conversion; and a thermal management system composed of cooling fans, heat sinks, and temperature control switches. All components communicate via CAN bus or Ethernet, forming the stable foundation for inverter operation. Figure 1 illustrates a typical modern solar inverter system.
Understanding the different types of solar inverter is essential. In residential and small commercial systems, string inverters are common, while microinverters and power optimizers are used for module‑level MPPT. In this study, I focus on distributed three‑phase grid‑connected inverters, which are widely deployed in medium‑scale installations.
2. Typical Fault Types in Distributed Photovoltaic Grid‑Connected Inverters
Due to prolonged outdoor exposure, inverters suffer from high temperature, humidity variations, grid disturbances, and component aging, leading to several typical faults. I have classified the most critical ones below.
| Fault Category | Specific Fault Mode | Primary Symptoms | Common Causes |
|---|---|---|---|
| IGBT Module | Open circuit, short circuit, parameter drift | Output power drop, high‑frequency noise, module overheating | Thermal stress, aging, overcurrent |
| Control Unit | Intermittent shutdown, voltage fluctuation | Frequent tripping, unstable output voltage | Capacitor bulging/shorting, copper trace oxidation, sensor feedback distortion |
| Thermal & Cooling System | Fan bearing wear, heatsink dust accumulation | IGBT junction temperature rise, efficiency decrease | Poor heat dissipation, temperature sensor drift |
| Current Sensor | Signal distortion, offset error | Inaccurate current measurement, control instability | Hall element aging, electromagnetic interference |
| Power Supply | Switching power supply instability | Control board undervoltage, erratic operation | Capacitor degradation, transformer fault |
These faults correspond to various types of solar inverter designs. For instance, IGBT faults are more severe in high‑power central inverters, while control unit issues are common in modular string inverters.
3. Fault Feature Extraction and Quantitative Characterization
Fault feature extraction relies on electrical signals and state parameters collected from inverter operation. Using a high‑speed data acquisition card, I synchronously sample three‑phase output currents, voltages, IGBT temperature, and fan speed. After preprocessing to eliminate grid harmonics and electromagnetic interference, I analyze the deviation of the actual current and voltage from the ideal sinusoidal waveform to extract fault characteristics.
Quantitative characterization is achieved through a fault feature coefficient model. I compute the integral of the squared error between the output current \(I_{out}\left(t\right)\) and the ideal current \(I_{\text{ideal}}\left(t\right)\) over a time window, normalized by the integral of the squared ideal current. Combined with weighting factors \(w_i\) and influence coefficients \(\theta_i\) for different faults, the diagnostic index \(F_{\text{diag}}\) is defined as:
$$
F_{\text{diag}} = \frac{\int_{0}^{T} \left[ I_{out}\left(t\right) – I_{\text{ideal}}\left(t\right) \right]^2 dt}{\int_{0}^{T} I_{\text{ideal}}^2\left(t\right) dt} \times \sum_{i=1}^{n} w_i \, \theta_i
$$
When \(F_{\text{diag}}\) exceeds a preset threshold, the fault is classified as severe.
For IGBT module faults, I employ a power output decay model:
$$
P_{\text{fault}} = \eta_{\text{IGBT}} \cdot V_{\text{in}} \cdot I_{\text{out}} \cdot e^{-\lambda t}
$$
where \(\eta_{\text{IGBT}}\) is the fault coefficient and \(\lambda\) is the decay rate. For control unit faults, a feedback error model quantifies the deviation:
$$
E_{\text{cnl}} = \left| V_{\text{out}}\left(t\right) – K \cdot V_{\text{in}}\left(t\right) \right| \cdot \gamma \cdot \delta_{\text{feedback}}
$$
with \(K\) being the ideal voltage ratio, \(\gamma\) the impact factor, and \(\delta_{\text{feedback}}\) the feedback stability coefficient.
The following table summarizes key parameters extracted for different types of solar inverter faults:
| Feature Parameter | Symbol | Normal Range | Warning Range | Fault Range |
|---|---|---|---|---|
| Fault diagnostic coefficient | \(F_{\text{diag}}\) | ≤ 0.08 | 0.08 – 0.15 | ≥ 0.15 |
| IGBT power decay constant | \(\lambda\) (1/h) | ≤ 0.001 | 0.001 – 0.005 | ≥ 0.005 |
| Total harmonic distortion | THD (%) | ≤ 3% | 3% – 5% | ≥ 5% |
| Current sensor error | \(E_{\text{sensor}}\) (%) | ≤ 1% | 1% – 3% | ≥ 3% |
4. Signal‑Based Fault Feature Recognition Methods
Signal‑based methods analyze electrical signals such as current and voltage using Fourier transform and wavelet analysis. I employ an FPGA‑based real‑time data acquisition system that synchronously samples three‑phase currents and voltages at a fixed frequency. Using LabVIEW, I construct an ideal waveform database and compare it with measured waveforms. Early fault signs, such as a rise in total harmonic distortion (THD) or waveform distortion, are captured effectively. For example, during IGBT module degradation, the THD gradually increases. I also integrate infrared thermography to monitor IGBT surface temperature at regular intervals. When the temperature exceeds a preset threshold, it is cross‑validated with electrical signal results to confirm power module faults. This combined approach works for many types of solar inverter, including both central and string configurations.
5. Data‑Driven Fault Feature Recognition Models
Data‑driven models leverage large volumes of historical operating data and typical fault cases to build mathematical relationships using machine learning and deep learning algorithms. The fault feature coefficient model, IGBT power decay model, and control unit feedback error model described in Section 3 are all examples. The construction process involves four stages: data preprocessing, feature engineering, model training, and validation. I clean the data by removing outliers, apply Z‑score normalization to unify the scales of current, voltage, power, and temperature, and then perform feature selection to retain strongly correlated features. A random forest classifier is trained to determine the weighting coefficients and decay parameters for different faults. Once the model is established, real‑time monitoring data are fed into it to compute \(F_{\text{diag}}\), which quantifies the fault severity. I validated the model using multiple sets of fault data; the recognition accuracy remained high and the false alarm rate was within acceptable limits.
The following table compares three common data‑driven algorithms I evaluated for different types of solar inverter fault diagnosis:
| Algorithm | Accuracy (%) | Training Time (s) | Inference Time (ms) | Advantages |
|---|---|---|---|---|
| Random Forest | 94.5 | 12.3 | 8.2 | Robust to overfitting, handles mixed features |
| Support Vector Machine | 92.1 | 25.6 | 5.7 | Good for small samples, effective in high‑dimension |
| Convolutional Neural Network | 96.8 | 180.0 | 15.0 | Best accuracy, but requires large dataset and GPU |
6. Multi‑Dimensional Feature Fusion Strategy
A single feature type often lacks comprehensiveness and anti‑interference capability. Therefore, I developed a multi‑dimensional feature fusion strategy that integrates three core feature categories: electrical signals, temperature, and equipment operational status. A two‑level fusion architecture (feature level and decision level) is employed to build a fault‑lifecycle recognition system.
The fusion process centers on current and voltage deviations as anchors, because these directly reflect the energy conversion state. After Fourier and wavelet analysis, I capture waveform distortion caused by IGBT faults and electrical parameter fluctuations from control anomalies. Temperature features from infrared monitoring supplement early fault detection, while cooling system status and control unit feedback stability features are included to anticipate circuit failures. I use the Dempster‑Shafer (D‑S) evidence theory to fuse the multi‑dimensional features. The D‑S theory handles uncertainty effectively by converting monitoring data into unified fault confidence values. For instance, when diagnosing a control unit fault, I first compute the voltage deviation via the feedback error model. Then I combine the stability of the power supply module and the continuity of the control circuit. If the feedback error exceeds the threshold and is accompanied by increased voltage ripple and frequent power fluctuations, the control unit fault is pinpointed, and specific causes such as capacitor bulging or sensor distortion are identified.
This fusion approach is adaptable to various types of solar inverter, whether they are single‑phase, three‑phase, or multi‑MPPT designs.
7. Fast Diagnosis Model Construction
The fast diagnosis model follows a “feature extraction – state assessment – fault localization” framework. Based on the models from Section 3, I conducted multiple fault simulation experiments under different operating conditions to determine the characteristic parameter ranges for typical faults such as IGBT failure, control unit malfunction, and cooling system faults. The thresholds were established using statistical analysis, as listed earlier. Simultaneously, I built a mapping database that links feature parameters to fault types, enabling rapid matching.
For 1500V high‑voltage inverters, I considered the characteristics of Infineon F3L400R10W3S7_B11 modules. Through simulation, I optimized key parameters such as the voltage ratio \(K\) and fault impact factor \(\gamma\) to ensure the model adapts to specific devices. The diagnostic logic is straightforward: once \(F_{\text{diag}}\) exceeds 0.08, the system enters a warning state; above 0.15, a serious fault is declared and the localization process starts.
8. Diagnostic Algorithm Optimization and Implementation
To enhance both speed and accuracy, I optimized the diagnostic algorithm by simplifying computation, improving parameter iteration, and refining data processing. I adopted an “edge computing + cloud” architecture: the local edge unit handles real‑time data acquisition and preliminary analysis to minimize latency, while the cloud platform handles model training and parameter updates.
Specifically, I replaced the traditional Simpson integration with the trapezoidal rule for computing \(F_{\text{diag}}\), which reduces computational load. A gradient descent algorithm with adaptive learning rate adjustment is used to optimize parameter iteration, reducing the number of iterations for convergence. Furthermore, I implemented a two‑level diagnostic trigger mechanism:
- Level 1 (fast diagnosis): when THD exceeds 3% or \(F_{\text{diag}} > 0.06\), the system initiates a quick screening.
- Level 2 (deep diagnosis): when THD exceeds 5% or \(F_{\text{diag}} > 0.12\), the system automatically switches to a detailed analysis using fault‑specific recognition models.
These optimizations are applicable to all types of solar inverter that require low‑latency fault detection.
9. Fault Localization and Diagnostic Effectiveness Verification
Fault localization follows a three‑step progressive process: “signal analysis – model calculation – physical inspection”. First, the diagnostic algorithm analyzes real‑time electrical signals and state parameters to estimate the fault type and output a probability distribution. Then, precision measurements are performed on core components: an infrared thermal imager scans the IGBT module to locate overheating areas; an oscilloscope checks the gate driver signal waveform integrity; the current sensor is tested by injecting a standard current and comparing output linearity and accuracy; the control board is inspected by reading MCU run logs and measuring key node voltages with a multimeter.
I verified the diagnostic effectiveness by comparing performance data before and after repair and by monitoring long‑term operation. In a case study of a 150kW photovoltaic system, the output current before repair fluctuated between 28.5 A and 35.2 A, the THD was 13.2%, the fault diagnostic coefficient \(F_{\text{diag}}\) was 0.180, and the system efficiency was only 92.3%. After replacing the faulty IGBT module and repairing the control circuit, the output current stabilized to 33.1 A ± 0.2 A, THD dropped below 0.5%, \(F_{\text{diag}}\) fell to 0.022, and efficiency recovered to 99.5%. The diagnostic results for different fault types are summarized in the table below.
| Fault Type | Pre‑repair \(F_{\text{diag}}\) | Pre‑repair THD (%) | Pre‑repair Current Range (A) | Pre‑repair Efficiency (%) | Response Time (ms) | Post‑repair \(F_{\text{diag}}\) | Post‑repair THD (%) | Post‑repair Current Range (A) | Post‑repair Efficiency (%) |
|---|---|---|---|---|---|---|---|---|---|
| IGBT open circuit | 0.180 | 13.2 | 28.5–35.2 | 92.3 | 65 | 0.022 | 0.4 | 33.0–33.3 | 99.6 |
| IGBT parameter drift | 0.105 | 5.8 | 31.2–34.1 | 95.7 | 58 | 0.031 | 0.6 | 32.9–33.2 | 99.4 |
| Control unit capacitor bulging | 0.123 | 8.5 | 29.8–35.7 | 94.1 | 72 | 0.028 | 0.5 | 33.0–33.3 | 99.5 |
| Cooling fan fault | 0.098 | 4.2 | 32.1–33.9 | 96.5 | 61 | 0.035 | 0.7 | 32.9–33.2 | 99.3 |
| Current sensor distortion | 0.112 | 7.3 | 30.5–34.8 | 95.2 | 68 | 0.025 | 0.4 | 33.0–33.3 | 99.7 |
| Control board power instability | 0.135 | 9.6 | 29.2–36.1 | 93.8 | 75 | 0.027 | 0.6 | 32.8–33.3 | 99.5 |
Across 20 fault case validations, the method achieved a localization accuracy of 96%, and the diagnostic response time was consistently below 80 ms. After repair, all systems operated stably for over three months, during which I continuously collected data to refine model parameters and improve diagnostic generality and reliability. The approach is robust for all major types of solar inverter used in distributed photovoltaic systems.
10. Conclusion and Future Work
Through this study, I have systematically investigated fault features and fast diagnostic technologies for distributed photovoltaic grid‑connected inverters. I clarified the fault mechanisms of core components, established a multi‑dimensional fault feature extraction and quantification framework, and integrated signal‑based analysis with data‑driven methods. The constructed diagnostic model and optimized algorithm significantly improve fault identification accuracy and speed. Practical verification shows that the technology can precisely locate fault types, dramatically shorten diagnosis time, and enhance operation and maintenance efficiency. Nevertheless, some limitations remain, such as reduced model adaptability under extreme operating conditions. In the future, I plan to integrate Internet of Things (IoT) technology to expand data sources and further optimize model generalization capability, providing more comprehensive support for the reliable operation of photovoltaic systems of all types of solar inverter.