In the global energy transition, the stable operation of photovoltaic power generation systems is crucial for ensuring the supply of clean energy. As the core equipment of photovoltaic power stations, types of solar inverters directly determine power generation efficiency and power supply stability. Traditional periodic maintenance models often waste resources, and passive repair after faults interrupts power generation and causes economic losses. With the development of the Internet of Things, big data, and artificial intelligence, data-driven fault diagnosis and self-healing technologies have emerged. By collecting real-time operational data through sensors and combining intelligent algorithms to accurately identify faults and actively repair them, these technologies open a new path for improving station reliability and reducing operation and maintenance costs. In this paper, I present my research on fault diagnosis and self-healing technology for intelligent string-type inverters, which represent one of the most advanced types of solar inverters currently deployed. I focus on deep learning-based diagnosis and adaptive self-recovery strategies, supported by extensive experimental data and mathematical models.
1. Overview of Types of Solar Inverters and the Intelligent String-Type Inverter
Before delving into the diagnostic methods, it is essential to understand the various types of solar inverters used in modern photovoltaic systems. The major types of solar inverters include central inverters, string inverters, microinverters, and intelligent string-type inverters. Central inverters are suitable for large-scale utility plants but suffer from single-point failure risks. Traditional string inverters offer modularity but lack advanced monitoring capabilities. Microinverters provide per-panel optimization but increase cost. The intelligent string-type inverter, which is the subject of my study, combines the benefits of modular design with embedded communication modules, multi-level protection, and adaptive MPPT algorithms. These types of solar inverters achieve 5–10% higher energy yield compared to conventional string inverters by mitigating mismatch losses. The circuit topology of a typical intelligent string-type inverter is shown below.
In photovoltaic power stations, intelligent string-type inverters achieve efficient AC/DC conversion and support remote monitoring via communication modules. Their modular design reduces mismatch losses and operation & maintenance costs. Common faults include open circuits and short circuits in power devices caused by electrical stress and temperature variations. To address these issues, I have developed a comprehensive fault diagnosis and self-healing system, which I will describe in the following sections.
2. Fault Diagnosis Methodology Based on Deep Learning
2.1 Selection of Deep Learning Models
To select the optimal deep learning model for fault diagnosis of types of solar inverters, I built a simulation platform using 300 inverter operation data sets from an actual photovoltaic power station (200 normal samples and 100 fault samples). I compared four models: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results are summarized in Table 1.
| Model Type | Data Processing Format | Feature Extraction Capability | Diagnosis Accuracy (%) | Training Time (min) | Generalization Error |
|---|---|---|---|---|---|
| CNN | Convert current/voltage waveforms into 2D images | Local features and spatial structure | 92.3 | 18.7 | 0.072 |
| RNN | Raw time-series data | Temporal dependency capture | 85.6 | 22.4 | 0.115 |
| LSTM | Raw time-series data | Long-term dependency memory | 95.1 | 28.3 | 0.058 |
| GRU | Raw time-series data | Simplified long-term dependency modeling | 94.2 | 24.6 | 0.063 |
From Table 1, CNN suffers information loss during data conversion, resulting in relatively low diagnosis accuracy. RNN faces gradient vanishing problems that limit performance. In contrast, LSTM and GRU effectively handle long-term dependencies through special gating structures. Among them, LSTM achieves the highest diagnosis accuracy of 95.1% with the lowest generalization error. Therefore, considering diagnosis precision and generalization performance, LSTM is the most suitable deep learning model for fault diagnosis of intelligent string-type inverters among all types of solar inverters.
2.2 LSTM Model Structure and Mathematical Formulation
LSTM improves upon RNN by introducing a cell state and three gate units: forget gate, input gate, and output gate. The structure is illustrated conceptually in the following equations. Let \( x_t \) be the input at time step \( t \), \( h_{t-1} \) the previous hidden state, and \( C_{t-1} \) the previous cell state. The forget gate decides which information to discard from the cell state:
$$
f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f)
$$
The input gate controls how much new information is added:
$$
i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i)
$$
$$
\tilde{C}_t = \tanh(W_C \cdot [h_{t-1}, x_t] + b_C)
$$
The cell state is updated as:
$$
C_t = f_t \odot C_{t-1} + i_t \odot \tilde{C}_t
$$
The output gate determines the hidden state:
$$
o_t = \sigma(W_o \cdot [h_{t-1}, x_t] + b_o)
$$
$$
h_t = o_t \odot \tanh(C_t)
$$
where \( \sigma \) is the sigmoid activation function, \( \odot \) denotes element-wise multiplication, and \( W_f, W_i, W_C, W_o \) and \( b_f, b_i, b_C, b_o \) are learnable parameters. This gating mechanism allows LSTM to capture long-term dependencies in the operational data of types of solar inverters, making it highly effective for fault prediction.
2.3 Model Training and Optimization
To train the LSTM model for fault diagnosis of types of solar inverters, I implemented a series of steps including data preprocessing, hyperparameter tuning, and performance evaluation. Table 2 summarizes the key techniques and their implementation details.
| Implementation Stage | Technical Method | Core Operations and Objectives |
|---|---|---|
| Data Preprocessing 1 | Time-series augmentation | Expand 300 original data sets to improve generalization |
| Data Preprocessing 2 | Dataset split | Split into 60% training, 20% validation, 20% testing for full pipeline |
| Hyperparameter Tuning | Grid search | Optimize hidden layers, learning rate, etc., based on validation performance |
| Model Training 1 | Cross-entropy loss | Backpropagation update weights to improve diagnosis accuracy |
| Model Training 2 | Early stopping | Monitor validation loss to prevent overfitting |
| Performance Evaluation | Accuracy, F1-score | Quantify diagnosis performance on test set |
| Iterative Optimization | Structure parameter adjustment | Iteratively improve model based on evaluation results |
During training, I used the Adam optimizer with an initial learning rate of 0.001. The final LSTM model comprised two hidden layers with 64 neurons each. After 200 epochs of training with early stopping patience of 20 epochs, the model achieved 95.1% accuracy on the test set, confirming its robustness for fault diagnosis across various types of solar inverters.
3. Fault Self-Healing Control Strategy and System Implementation
3.1 Self-Healing Architecture
The fault diagnosis and self-healing technology for intelligent string-type inverters implements self-repair through a systematic strategy. The core components include fault detection and isolation, redundancy switching, and adaptive control. High-precision sensors collect more than 20 operational and environmental parameters in real time. The LSTM model performs millisecond-level fault diagnosis. Once a fault is detected, the system immediately locates and isolates the faulty module, then automatically switches to a redundant backup component. In simulation experiments, the system completes fault handling within 200 ms, limiting power fluctuation to ±3%, thereby ensuring continuous stable power supply. When facing grid voltage fluctuations or environmental changes, the adaptive control strategy dynamically adjusts operating parameters to optimize power output. The architecture is shown in the following block diagram (not referenced by number, but described).
3.2 Mathematical Model of Self-Healing Control
The adaptive control algorithm uses a model predictive control (MPC) framework. Let \( P_{\text{ref}} \) be the desired power output, \( P_{\text{actual}} \) the actual power, and \( \Delta P = P_{\text{ref}} – P_{\text{actual}} \). The control objective is to minimize the cost function:
$$
J = \sum_{k=0}^{N-1} \left( \Delta P_{t+k}^T Q \Delta P_{t+k} + u_{t+k}^T R u_{t+k} \right)
$$
where \( u \) represents the control input (e.g., duty cycle modulation), \( Q \) and \( R \) are weighting matrices, and \( N \) is the prediction horizon. The optimization is solved online to generate the optimal switching sequence, ensuring minimal power deviation during fault recovery.
3.3 System Implementation
At the hardware level, I employed a field-programmable gate array (FPGA) controller to build a closed-loop system encompassing data acquisition, fault diagnosis, and command execution. The software integrates the LSTM diagnosis model and MPC algorithm, achieving deep hardware-software fusion. The key parameters of the implemented system are listed in Table 3.
| Parameter | Value |
|---|---|
| Sampling frequency | 10 kHz |
| Fault diagnosis latency | < 5 ms |
| Isolation and switching time | 200 ms |
| Power fluctuation during recovery | ±3% |
| Redundant modules per inverter | 2 (IGBT bridges) |
| Communication protocol | Modbus TCP |
This soft-hardware cooperative architecture dramatically shortens fault repair time and enhances the reliability and economy of types of solar inverters, providing solid technical support for efficient energy utilization.
4. Case Study and Performance Validation
4.1 Real Fault Diagnosis Case
To validate the effectiveness of the proposed technology, I present a case study on inverter INV-007 in an actual photovoltaic power station. At 3:15 AM on the 287th day of operation, an anomaly alarm was triggered. Sensors collected operational data 24 hours before the fault. After min-max normalization, the data was fed into the trained LSTM model with two hidden layers (64 neurons each). The model output provided prediction probabilities: 0.89 for power device aging, 0.23 for sensor failure, 0.17 for communication fault, and all other fault probabilities below 0.2. The actual inspection confirmed that the power device had indeed aged, demonstrating the model’s high accuracy in diagnosing faults across different types of solar inverters.
4.2 Simulation Platform Results
I built a simulation platform containing 10 intelligent string-type inverters and tested 12 typical fault scenarios. Table 4 compares the performance before and after applying the self-healing technology.
| Evaluation Metric | Traditional Scheme | Self-Healing Scheme | Improvement (%) |
|---|---|---|---|
| Mean Time Between Failures (MTBF) | 25,000 h | 38,000 h | +52% |
| Annual fault downtime | 120 h | 66 h | −45% |
| Power efficiency loss during grid voltage sag | 18% | 6.5% | −63.9% |
| Annual operation and maintenance cost | 85,000 CNY | 57,800 CNY | −32% |
These data clearly show that the combination of LSTM-based fault diagnosis and self-healing technology significantly improves the reliability and economy of types of solar inverters, providing strong support for efficient photovoltaic station operation.
5. Conclusion
In this paper, I have presented my research on fault diagnosis and self-healing technology for intelligent string-type inverters, which represent a critical class among the various types of solar inverters. By comparing CNN, RNN, LSTM, and GRU, I selected LSTM as the optimal diagnosis model. Through data preprocessing, model training, and case validation, the diagnosis accuracy exceeded 95%. The self-healing technology employs fault detection and isolation, redundancy switching, and adaptive control strategies, combined with hardware-software co-optimization. The results demonstrate a 52% increase in mean time between failures, a 45% reduction in annual fault downtime, a 63.9% reduction in power efficiency loss during grid disturbances, and a 32% reduction in annual O&M costs. These findings provide an effective technical path for promoting the intelligent upgrade of the photovoltaic industry. Future work will extend the method to other types of solar inverters, such as multi-level inverters and hybrid inverters, and explore edge-computing-based real-time implementation.