In the context of the escalating global energy crisis, renewable energy sources have garnered widespread attention. Solar inverters, as critical components for energy conversion and key devices connecting photovoltaic arrays to the grid, play a pivotal role in maintaining system stability. Enhancing the resilience of solar inverters through proactive maintenance, condition monitoring, and fault detection is essential to address increasingly complex operational and environmental challenges. Traditional fault detection methods for solar inverters primarily rely on threshold-based judgments and statistical analysis techniques, which often perform poorly when handling complex or ambiguous data, leading to high rates of false positives and negatives. Consequently, machine learning techniques have been extensively applied in the fault diagnosis domain in recent years.
For instance, random forests have been utilized to enhance Concordia transforms for improved fault classification robustness. Convolutional neural networks have been modified by replacing fully connected layers with global average pooling layers. Additionally, whale optimization algorithms have been employed to optimize the input weights and hidden layer neuron thresholds of extreme learning machines, enhancing model approximation capabilities and significantly improving recognition accuracy. In data-driven approaches, existing sensor-based rapid fault diagnosis techniques enable swift switch fault localization without additional sensors by computing fault characteristic values. Data mining techniques have been developed for precise fault diagnosis using sparse Bayesian extreme learning machines, effectively addressing data imbalance issues. Improved variational mode decomposition combined with convolutional neural networks has been applied for soft fault diagnosis, resolving challenges in fault feature extraction and boosting diagnostic accuracy. For insulated gate bipolar transistor fault detection, strategies based on voltage mean value feature extraction combined with kernel extreme learning machines optimized by improved lion swarm algorithms have achieved rapid and accurate fault diagnosis. Support vector machines have been enhanced through radial basis kernel functions and grid search optimization, significantly improving classification accuracy; however, their performance heavily depends on kernel function selection and parameters.
With the rapid advancement of artificial intelligence, autoencoders have emerged as powerful unsupervised learning models, gaining considerable attention. By learning compressed representations of input data, autoencoders can identify anomalous patterns during data reconstruction, thereby effectively diagnosing potential faults. Autoencoders learn low-dimensional data representations to capture primary features, enabling dimensionality reduction and anomaly detection in high-dimensional data. They have been successfully applied to fault detection in industrial equipment, demonstrating efficiency in detecting various abnormal patterns. Moreover, autoencoder-based models have been used for early discharge data inflection point prediction and fault detection in lithium batteries. Despite their value in feature extraction, autoencoders struggle to analyze coupling correlations between data when handling complex dependencies, making it imperative to enhance their capability in processing intricate fault data. Furthermore, most studies rely on simulated data without validation on real datasets to test model effectiveness and generalization.
Attention mechanism, as an emerging machine learning technique, has attracted significant interest from researchers and is widely applied in fields such as visual recognition and sequence prediction. Neural network models based on attention mechanisms have been used for steel plate defect detection, combining attention mechanisms with neural networks to confirm feasibility. Through strategies like dynamic weight allocation and capturing long-range dependencies, attention mechanisms empower models to dynamically focus on critical parts of data, markedly improving efficiency and accuracy in handling complex data, thus creating conditions for further enhancing autoencoder capabilities in processing complex fault data.
Therefore, I propose a fault detection method for solar inverters based on an autoencoder integrated with an attention mechanism. By incorporating the attention mechanism into the autoencoder model, the accuracy and response speed of fault detection in solar inverters are improved, validated through testing on a real photovoltaic dataset. Results indicate that this method combines the advantages of autoencoders in feature extraction with the attention mechanism’s ability to handle complex data, significantly outperforming traditional methods in key performance indicators such as detection accuracy, recognition rate, and F1 score. Additionally, the model’s real-time processing capability makes it suitable for rapid fault detection and identification in actual solar inverters, supporting operational stability and grid safety.
Solar inverters are essential in converting direct current from photovoltaic panels to alternating current for grid integration. A typical three-phase grid-connected solar inverter structure includes photovoltaic panels on the left, outputting current into the solar inverter. The inverter circuit consists of bridge arms, each comprising two MOSFETs. Direct current is processed by the inverter to produce alternating current, filtered through LR circuits before supplying the grid. Key parameters include DC voltage output from photovoltaic panels, DC filtering capacitor, DC filtering current, and three-phase AC output voltages and currents.
Faults in solar inverters can arise from conditions such as load short circuits, uneven heat dissipation, and bridge arm straight-through, affecting power device operation and potentially causing damage. Common faults in solar inverters are categorized into DC-side and AC-side faults. DC-side faults include input overvoltage/undervoltage, DC current abnormalities, and DC circuit short circuits. AC-side faults encompass output overvoltage/undervoltage, excessive output current, and AC circuit short circuits. Classifying these faults allows for targeted detection and troubleshooting, enhancing the stability and reliability of solar systems.
Fault feature extraction is grounded in identifying and quantifying differences in physical and electrical parameters between normal and abnormal states of solar inverters, reflected through sensor data. As sensor data may contain noise and incomplete information, data cleaning is first performed, including filtering, denoising, and filling missing values. Data is then standardized or normalized to eliminate effects of different units and scales. Based on the working principles of solar inverters, an initial set of candidate features is selected, which may include steady-state and transient values of current and voltage, frequency components, and temperature. New features are constructed, such as ratios and rates of change of current and voltage, and energy content, which may have more direct correlations with fault types. The attention mechanism is leveraged to identify key features in data, training an attention model for fault feature extraction that assigns weights to each feature, reflecting their importance in fault detection. Dimensionality reduction techniques like principal component analysis are used to reduce feature count while preserving critical information.
The autoencoder is a neural network that learns compressed data representations through nonlinear transformations, consisting of symmetric encoder and decoder components. The encoder maps high-dimensional input data to a low-dimensional latent space, while the decoder reconstructs the latent representation back to high-dimensional space. This process optimizes network weights by minimizing the difference between input and reconstruction. To further enhance performance, the attention mechanism is introduced, enabling the model to focus on key information during data processing. In the autoencoder, the attention mechanism allocates different weights to input features, strengthening the model’s ability to identify crucial parts of data, thereby improving the quality of feature representation.
The training process for fault information extraction using the integrated attention mechanism is as follows. First, linear transformation is applied where input data undergoes initial transformation through a linear layer: $$z_t = W_e x_t + b_e$$ where \(W_e\) and \(b_e\) are the weight matrix and bias of the encoder, \(x_t\) is the input data at time \(t\) representing the feature vector of fault information, and \(z_t\) is the output after linear transformation.
Second, a long short-term memory layer is established where data is encoded through the LSTM layer: $$(h_t, c_t) = \text{LSTM}(z_t, h_{t-1}, c_{t-1})$$ where \(h_t\), \(c_t\), \(h_{t-1}\), and \(c_{t-1}\) are the hidden states and memory cell states at times \(t\) and \(t-1\), respectively, and \(\text{LSTM}(\cdot)\) denotes the computation process of the long short-term memory network.
Detailed computations of the LSTM unit include: $$i_t = \sigma(W_i z_t + U_i h_{t-1} + b_i)$$ $$f_t = \sigma(W_f z_t + U_f h_{t-1} + b_f)$$ $$o_t = \sigma(W_o z_t + U_o h_{t-1} + b_o)$$ $$g_t = \tanh(W_g z_t + U_g h_{t-1} + b_g)$$ $$c_t = f_t \odot c_{t-1} + i_t \odot g_t$$ $$h_t = o_t \odot \tanh(c_t)$$ where \(i_t\), \(f_t\), and \(o_t\) are the outputs of the input gate, forget gate, and output gate, respectively; \(\sigma\) is the sigmoid activation function regulating input influence on the memory cell; \(W_i\), \(W_f\), \(W_o\), \(W_g\) and \(U_i\), \(U_f\), \(U_o\), \(U_g\) are weight matrices for \(z_t\) and \(h_{t-1}\); \(b_i\), \(b_f\), \(b_o\), \(b_g\) are corresponding bias terms; \(g_t\) and \(c_t\) are the new candidate memory cell and updated memory cell, respectively; and \(\odot\) denotes element-wise multiplication.
Third, feature extraction is performed by capturing key features from the LSTM output: $$f_t = \tanh(W_f h_t + b_f)$$ Fourth, weight calculation based on multi-head attention mechanism: $$\alpha_t = \frac{\exp(e_t)}{\sum_{k=1}^T \exp(e_k)}$$ where \(\alpha_t\) is the weight of the attention mechanism; \(e_t\) is the attention score at time \(t\), \(e_t = \alpha’ \tanh(W_\alpha h_t + U_\alpha h_{t-1} + b_\alpha)\), with \(W_\alpha\), \(U_\alpha\), \(b_\alpha\) as weight matrices and bias, and \(\alpha’\) as the parameter of the attention layer, where \(\alpha’\) represents the transpose of \(\alpha\); \(e_k\) is the attention score at the \(k\)-th time; and \(T\) is the time series length.
Fifth, weighted feature combination \(c_t\) is: $$c_t = \sum_{k=1}^T \alpha_k h_k$$ where \(\alpha_k\) is the attention weight at the \(k\)-th time, and \(h_k\) is the hidden state at the \(k\)-th time. Sixth, the decoding stage \(s_t\) is: $$s_t = \tanh(W_s c_t + U_s h_t + b_s)$$ where \(W_s\) and \(U_s\) are weight matrices of the decoder, and \(b_s\) is the bias. Seventh, output generation reconstructs data \(\hat{x}_t\) from the decoder: $$\hat{x}_t = W_d s_t + b_d$$ where \(W_d\) and \(b_d\) are the weight matrix and bias of the output layer, respectively. The mean squared error is used to represent prediction error \(E_{MS}\): $$E_{MS} = \sum_{t=1}^T \| x_t – \hat{x}_t \|^2$$
Based on the proposed autoencoder model, fault diagnosis for solar inverters is conducted. Collected data undergoes data cleaning to remove outliers and non-physical readings, such as negative voltages or currents, ensuring data quality. Missing values in the dataset are filled to maintain data completeness. Feature engineering techniques extract key features from the data to identify pre-fault variations. The model comprises encoding and decoding layers. In the encoding layer, the attention mechanism is introduced, allowing the model to automatically focus on important features in the data, thereby improving fault diagnosis accuracy. The attention weight calculation layer computes attention weights for each feature, further emphasizing the importance of key features in fault diagnosis. An LSTM network processes time-series data to extract temporal features. The encoding layer’s LSTM layers are stacked, gradually compressing input data dimensions and extracting key features from time-series data. In the decoding layer, LSTM layers progressively restore the original data dimensions. The weighted feature layer processes weighted features output from the encoding layer, preparing for the decoding stage. The decoding feature layer attempts to reconstruct input data, comparing it with original data. The error calculation layer computes the error between reconstructed and original data to evaluate model performance, and optimization algorithms adjust model parameters to reduce reconstruction error and improve accuracy. The classification calculation layer performs fault classification based on the optimized model, determining the operational state of solar inverters. Finally, the system outputs fault diagnosis results for solar inverters.
For experimental validation, the dataset is sourced from actual grid data, comprising 5,000 samples covering six fault types in solar inverters. Randomly, 4,000 samples are selected as the training set, and the remaining 1,000 as the test set. The fault types for distribution network solar inverters are categorized as shown in the table below.
| Fault Category | Specific Fault Type | Range Description | Label |
|---|---|---|---|
| DC-side fault | Input overvoltage/undervoltage | High voltage >1.1 times rated voltage, low voltage <0.9 times rated voltage | 1 |
| DC-side fault | DC current abnormality | ±20% of normal operating current | 2 |
| DC-side fault | DC circuit short circuit | Impedance <50% of normal impedance | 3 |
| AC-side fault | Output overvoltage/undervoltage | High voltage >1.1 times rated voltage, low voltage <0.9 times rated voltage | 4 |
| AC-side fault | Excessive output current | >10% to 20% of maximum rated current | 5 |
| AC-side fault | AC circuit short circuit | Impedance <50% of normal impedance | 6 |
Fault classification for distribution network solar inverters is analyzed in depth. DC-side faults include input overvoltage/undervoltage, DC current abnormality, and DC circuit short circuits. Input overvoltage may damage internal components or shorten lifespan, while input undervoltage may cause malfunction or reduced efficiency. Real-time monitoring of input voltage with alarm thresholds effectively detects input overvoltage/undervoltage faults. DC current abnormality may lead to cable overheating or fuse blowing, while low current indicates insufficient input power, affecting output performance. Real-time monitoring of input current and analyzing fluctuations detect DC current abnormality faults. DC circuit short circuits cause current surges, potentially damaging inverters or connected equipment; detection methods include measuring circuit impedance or monitoring current changes.
AC-side faults include output overvoltage/undervoltage, excessive output current, and AC circuit short circuits. Output overvoltage may damage downstream equipment, while output undervoltage may cause insufficient power supply, affecting device operation. Real-time monitoring of output voltage with alarm thresholds effectively detects such faults. Excessive output current may lead to cable overheating or inverter overload protection activation; real-time monitoring of output current detects excessive output current faults. AC circuit short circuits cause current surges, potentially damaging inverters or connected equipment; detection methods include measuring circuit impedance or monitoring current changes.
Comparative experiments evaluate the detection effectiveness of the proposed method against other traditional methods. Traditional fault detection methods rely on simple threshold judgments and statistical analysis, performing poorly under complex or varying operational conditions. For example, when solar inverters operate in high-temperature environments, traditional thresholds may become inapplicable, leading to false alarms or missed detections. Standard autoencoder models, by learning high-dimensional features of data, have advanced compared to traditional methods. However, due to lack of focus on key features, their detection performance remains limited. Particularly for minor or early-stage faults, standard autoencoders may struggle to accurately identify. In contrast, the autoencoder model integrated with attention mechanism emphasizes critical fault features, significantly improving fault detection accuracy and response speed. The attention mechanism enables the model to distinguish the importance of different features, highlighting fault signals in complex data, thus achieving earlier and more accurate fault detection. The average accuracy of this model is 14.6% higher than traditional fault detection methods and 7.4% higher than standard autoencoder models. In terms of response speed, it is 65 seconds faster than traditional methods and 39 seconds faster than standard autoencoder models.
| Method | Average Accuracy (%) | Response Speed (s) |
|---|---|---|
| Traditional fault detection method | 83.5 | 86 |
| Standard autoencoder model | 90.7 | 60 |
| Autoencoder model with integrated attention mechanism | 98.1 | 21 |
| Neural network-based fault detection method | 85.3 | 60 |
To validate the effectiveness of the proposed method, parameters such as voltage, current, and temperature are extracted from actual solar inverter operational datasets for a series of comparative experiments. The detection effectiveness for each fault type over a one-month period shows random points distributed near the range descriptions, with colors distinguishing successful detections and failures. The loss trend of the proposed method during training demonstrates a decreasing loss rate, and the loss trend during actual testing confirms superior performance. Experimental results indicate that compared to traditional methods and autoencoders without attention mechanism, the autoencoder integrated with attention mechanism significantly improves fault detection accuracy, with classification accuracy ultimately reaching 98%. Performance on independent test sets also confirms its superiority.
The classification results for different fault types are presented in the table below. Accuracy refers to the proportion of correctly predicted samples by the classification model out of the total samples. Precision is the proportion of actual positive samples among those predicted as positive by the model. Recall is the proportion of actual positive samples correctly predicted as positive by the model. F1 score is the harmonic mean of precision and recall, balancing both metrics. Additionally, the model’s real-time processing capability is effectively verified under monitoring with TensorBoard tools. The table shows that the proposed autoencoder model with integrated attention mechanism has good application potential and practical benefits in fault detection for distribution network solar inverters.
| Fault Category | Specific Fault Type | Accuracy (%) | Precision (%) | Recall (%) | F1 Score |
|---|---|---|---|---|---|
| DC-side fault | Input overvoltage/undervoltage | 98.5 | 97.8 | 98.2 | 98.0% |
| DC-side fault | DC current abnormality | 97.0 | 96.5 | 96.8 | 96.6% |
| DC-side fault | DC circuit short circuit | 98.2 | 97.6 | 97.9 | 97.7% |
| AC-side fault | Output overvoltage/undervoltage | 98.7 | 98.0 | 98.4 | 98.2% |
| AC-side fault | Excessive output current | 97.5 | 97.0 | 97.2 | 97.1% |
| AC-side fault | AC circuit short circuit | 98.4 | 97.8 | 98.1 | 97.9% |
In conclusion, the proposed autoencoder model with attention mechanism demonstrates excellent experimental results in fault detection for solar inverters. Experimental outcomes show that the model has significant advantages over traditional methods in key performance indicators such as fault recognition accuracy, recall rate, and F1 score, enabling rapid localization of fault features, which is crucial for ensuring stable operation and maintenance of solar inverters. Moreover, the model’s real-time processing capability greatly enhances rapid diagnosis of faults in solar inverters, particularly important for grid safety and system efficiency. The research findings not only advance fault detection technology for solar inverters but also provide new insights for fault diagnosis technologies in other complex systems.
The integration of attention mechanisms into autoencoders represents a significant step forward in handling the intricate data patterns associated with solar inverter faults. By dynamically weighting input features, the model can adapt to varying operational conditions, reducing false alarms and improving reliability. Future work could explore the application of this approach to other renewable energy components, such as wind turbine inverters or battery management systems, further broadening its impact. Additionally, real-time implementation on edge devices could enhance deployment in remote or resource-constrained environments, making solar energy systems more resilient and efficient. The continuous evolution of machine learning techniques, combined with domain-specific adaptations, holds promise for even more robust fault detection solutions in the future.
Furthermore, the mathematical formulation of the proposed model allows for scalability to larger datasets and more complex fault scenarios. The use of LSTM layers ensures that temporal dependencies are captured, which is critical for solar inverters where faults may develop over time. The attention mechanism’s ability to focus on relevant time steps enhances this temporal modeling, making the model particularly suited for predictive maintenance applications. As solar energy adoption grows, such advanced diagnostic tools will become indispensable for maximizing uptime and reducing operational costs.
In summary, the fusion of autoencoders and attention mechanisms offers a powerful framework for addressing the challenges of fault detection in solar inverters. The empirical validation on real-world data underscores its practical utility, paving the way for wider adoption in industrial settings. By leveraging the strengths of both unsupervised learning and attention-based focus, this approach sets a new benchmark for accuracy and speed in renewable energy system monitoring.