In this work, I present a comprehensive study on anomaly detection for photovoltaic (PV) inverter operational data using machine learning techniques. As the penetration of solar generation grows rapidly, ensuring the reliability and efficiency of PV inverters becomes critical. Traditional threshold-based methods often fail to capture complex, non-linear, and temporal dependencies inherent in inverter measurements. To address these challenges, I propose a multi‑stage detection framework that combines reconstruction, prediction, and fusion strategies. The entire methodology is validated on a real 500 MW PV plant, demonstrating significant improvements in detection sensitivity, latency, and generalization.
Introduction and Background
Photovoltaic inverters are the core of any solar power system, responsible for converting DC power from solar panels into AC power suitable for grid integration. Various types of solar inverter exist, including string inverters, central inverters, microinverters, and hybrid inverters. Each type of solar inverter operates under different electrical and environmental conditions, making anomaly detection a challenging task. For instance, central inverters typically handle large power blocks and are exposed to high thermal stress, while microinverters operate at lower power but require precise monitoring of many distributed units. In my study, I focus on central inverters commonly used in utility‑scale PV plants, but the proposed method is adaptable to other types of solar inverter as well.
The operational data from PV inverters usually include voltage, current, frequency, temperature, active power, and fault codes, all sampled at high frequency. These time series exhibit strong periodicity, non‑linear coupling, and gradual degradation patterns. Traditional anomaly detection relies on fixed thresholds or rule‑based alarms, which suffer from high false‑positive rates, missed detections, and delayed responses. Machine learning offers a data‑driven alternative that can model the underlying dynamics and detect subtle anomalies before they escalate into failures.
The image above illustrates a modern hybrid inverter, one of the popular types of solar inverter for residential and commercial applications. While my work primarily addresses central inverters, the machine learning framework can be generalized to any type of solar inverter with minimal modifications. In the following sections, I describe the core methodological components: an autoencoder with weighted reconstruction error, a Long Short‑Term Memory (LSTM) network for temporal trend and cyclical deviation, and a multi‑model fusion classifier.
Methodology: Reconstruction‑Prediction‑Fusion Framework
1. Autoencoder with Weighted Reconstruction Error
The autoencoder learns a compressed representation of normal operating data and reconstructs input vectors. Anomalies are detected when the reconstruction error exceeds a threshold. However, simple Euclidean distance is insensitive to features with low variance or varying importance. I define a multi‑modal weighted reconstruction error that accounts for per‑feature variance and importance:
$$
\varepsilon_{mm} = \sum_{i=1}^{n} \lambda_i \cdot \frac{(x_i – \hat{x}_i)^2}{\sigma_i^2 + \epsilon}
$$
where \(x_i\) is the \(i\)-th original feature, \(\hat{x}_i\) is the reconstructed value, \(\sigma_i^2\) is the historical variance of that feature, \(\lambda_i\) is an importance coefficient, and \(\epsilon\) is a small constant to avoid division by zero. This formulation amplifies the contribution of low‑variance but critical features (e.g., frequency stability) while suppressing noise on highly volatile channels.
To further prevent the autoencoder from overfitting to anomalous patterns, I add a sparsity penalty on the hidden layer activations:
$$
\varepsilon_{sparse} = \| \mathbf{x} – \hat{\mathbf{x}} \|_2^2 + \beta \sum_{j=1}^{h} |z_j|^{\alpha}
$$
Here \(\mathbf{z} = [z_1, \dots, z_h]\) are the encoder outputs, \(\beta\) controls regularization strength, and \(\alpha\) (typically \(0 < \alpha < 1\)) imposes a non‑linear shrinkage. The sparsity constraint forces the encoder to use only a few active neurons, improving generalization and robustness.
2. LSTM for Temporal Prediction and Residuals
PV inverter data exhibit strong temporal dependencies: diurnal cycles, weather‑induced ramps, and gradual efficiency decay. An LSTM network is trained to predict future values based on a sliding window of past observations. I design two complementary anomaly indicators based on the prediction residuals.
Trend‑Deviation Integral Residual: This metric accumulates the signed residuals over a window \(W\), capturing persistent deviations:
$$
R^{(t)}_{int} = \sum_{\tau=0}^{W-1} \frac{y_{t-\tau} – \hat{y}_{t-\tau}}{\Delta t} \cdot \Delta t
$$
where \(y\) is the actual value, \(\hat{y}\) is the LSTM prediction, and \(\Delta t\) is the sampling interval. A large positive integral indicates a sustained upward drift (e.g., overheating), while a negative one suggests degradation (e.g., power drop).
Cyclic Similarity Offset: To detect anomalies that disrupt the daily periodic pattern, I compute the cosine similarity between the current day’s segment and the corresponding segment from the previous day:
$$
S_{cycle} = 1 – \frac{\langle \mathbf{Y}_t, \mathbf{Y}_{t-T_d} \rangle}{\|\mathbf{Y}_t\| \cdot \|\mathbf{Y}_{t-T_d}\|}
$$
Here \(T_d\) is the period (24 hours). \(S_{cycle}\) ranges from 0 (identical pattern) to 2 (completely opposite). Values exceeding a threshold indicate abnormal shading, partial blockage, or inverter efficiency loss.
3. Multi‑Model Fusion and Classifier
Each detection module produces a distinct anomaly score. I fuse them into a unified decision via a weighted standardized‑distance mapping:
$$
S_{fuse} = \sum_{k=1}^{K} \omega_k \cdot \sigma\!\left( \frac{f_k – \mu_k}{\sigma_k + \epsilon} \right)
$$
where \(f_k\) is the raw score from the \(k\)-th model (e.g., reconstruction error, integral residual, cyclic offset), \(\mu_k\) and \(\sigma_k\) are its historical mean and standard deviation, \(\sigma(\cdot)\) is the sigmoid function, and \(\omega_k\) are learnable confidence weights. The fused score \(S_{fuse}\) is then fed into a lightweight classifier (e.g., logistic regression or random forest) for final anomaly/normal classification. This ensemble approach enhances robustness against individual model failures and adapts to different types of solar inverter and operating conditions.
Experimental Setup and Data
I conducted experiments on a 500 MW utility‑scale PV plant equipped with 160 central inverters. Each inverter connects 14 DC input strings and feeds a 110 kV substation. The SCADA system records 30+ parameters every minute. I selected 10 inverters with stable performance and low shading for validation. The test period spanned April 1 – 15, 2025. During this period, field maintenance logs confirmed several anomalies including sudden voltage dips, current spikes, frequency fluctuations, and gradual efficiency degradation due to loose terminal connections.
To evaluate the method, I created two sets of controlled anomaly injections:
- Abrupt‑change anomalies: Three synthetic events: a 20% output voltage drop, a 35% input current surge, and a 1.2 Hz frequency oscillation.
- Trend/cyclic anomalies: Real degradation data from inverter #037, where a loose terminal caused efficiency to decline from −0.64% to −11.89% over five days.
Results and Analysis
Abrupt‑Change Detection
Table 1 compares the proposed method against a conventional threshold‑based alarm system. The threshold method often detects events with a delay of several tens of seconds or misses them entirely (e.g., frequency fluctuations that do not cross a hard boundary). In contrast, the autoencoder’s weighted reconstruction error responds within 5–7 seconds for all three types, with zero false positives and zero missed detections.
| Event Type | Actual Time | Threshold Detection Time | Threshold Delay (s) | Proposed Detection Time | Proposed Delay (s) | False Alarm | Missed |
|---|---|---|---|---|---|---|---|
| Voltage drop (20%) | 09:41:13 | 09:42:00 | 47 | 09:41:18 | 5 | No | No |
| Current surge (35%) | 11:26:54 | 11:28:12 | 78 | 11:26:58 | 4 | No | No |
| Frequency fluctuation (1.2 Hz) | 13:17:02 | Not detected | — | 13:17:09 | 7 | No | No |
Trend and Cyclic Anomaly Detection
The LSTM‑based indicators proved especially effective for detecting gradual performance deterioration. Table 2 lists the daily efficiency data for inverter #037. On April 11, the efficiency deviation was only −2.48%, but the LSTM trend‑deviation integral already showed an abnormal accumulation. The cyclic similarity offset also began rising, indicating a disrupted diurnal pattern. The system issued an early warning on April 12 at 08:53, more than 5 hours before the anomaly became severe. By April 14, the warning came 9.6 hours in advance, giving ample time for preventive maintenance.
| Date | Daily Irradiation (kWh/m²) | Avg. Temperature (°C) | Theoretical Power (kW) | Actual Power (kW) | Efficiency Deviation |
|---|---|---|---|---|---|
| 2025-04-10 | 5.72 | 38.1 | 1556.2 | 1546.3 | −0.64% |
| 2025-04-11 | 5.61 | 39.3 | 1526.5 | 1488.6 | −2.48% |
| 2025-04-12 | 5.68 | 39.7 | 1546.1 | 1441.3 | −6.78% |
| 2025-04-13 | 5.65 | 40.5 | 1538.0 | 1394.7 | −9.30% |
| 2025-04-14 | 5.69 | 40.2 | 1549.3 | 1365.2 | −11.89% |
| Date | Actual Trend | Proposed Detection Time | Lead Time (h) | Missed? | Cyclic Offset |
|---|---|---|---|---|---|
| 2025-04-11 | Latent | No alarm | — | No | 0.07 |
| 2025-04-12 | Clear decline | 08:53 | 5.2 | No | 0.25 |
| 2025-04-13 | Continued deterioration | 07:42 | 8.1 | No | 0.32 |
| 2025-04-14 | Severe offset | 06:49 | 9.6 | No | 0.40 |
The cyclic similarity offset metric played a key role: even when the absolute power level was only mildly reduced (e.g., April 11), the shape of the daily curve changed, producing a non‑zero offset. By fusing this with the trend integral, the model achieved early detection that would be impossible with static thresholds.
Generalization to Different Types of Solar Inverter
To demonstrate applicability beyond central inverters, I performed a brief transferability test on a dataset from a 50 kW hybrid inverter (one of the popular types of solar inverter for rooftop systems). The hybrid inverter data included battery state‑of‑charge and bidirectional power flows. After retraining only the normal‑data statistics and the fusion weights (about 5 minutes of fine‑tuning), the same framework achieved 92% precision and 95% recall on a held‑out anomaly set containing voltage sags and battery overcharge events. This suggests that the reconstruction‑prediction‑fusion paradigm is indeed adaptable to various types of solar inverter with minimal re‑engineering.
Conclusion
In this paper, I have presented a machine learning framework for anomaly detection in photovoltaic inverter data. The approach integrates a weighted‑reconstruction autoencoder, an LSTM‑based temporal deviation and cyclic similarity indicator, and a multi‑model fusion classifier. Experiments on a real 500 MW PV plant demonstrate that the method reduces detection delay for abrupt anomalies to 5–7 seconds and provides up to 9.6 hours of early warning for gradual degradation. The framework is designed to be modular and can be easily retrained for different types of solar inverter, including string inverters, central inverters, microinverters, and hybrid inverters. Future work will focus on unsupervised adaptation to evolving operating conditions and integration with edge computing platforms for real‑time monitoring.