The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat of cyberattacks, which can result in power outages and disruptions. This paper presents a semi-supervised hybrid deep learning model that combines a Gated Recurrent Unit (GRU)-based Stacked Autoencoder (AE-GRU) with anomaly detection algorithms, including Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptical Envelope. Using GRU units in both the encoder and decoder sides of the stacked autoencoder enables the effective capture of temporal patterns and dependencies, facilitating dimensionality reduction, feature extraction, and accurate reconstruction for enhanced anomaly detection in smart grids. The proposed approach utilizes unlabeled data to monitor network traffic and identify suspicious data flow. Specifically, the AE-GRU is performed for data reduction and extracting relevant features, and then the anomaly algorithms are applied to reveal potential cyberattacks. The proposed framework is evaluated using the widely adopted IEC 60870-5-104 traffic dataset. The experimental results demonstrate that the proposed approach outperforms standalone algorithms, with the AE-GRU-based LOF method achieving the highest detection rate. Thus, the proposed approach can potentially enhance the cybersecurity in smart grids by accurately detecting and preventing cyberattacks.

Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, and productivity with the desired performance. In this study, an innovative technique is introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component Regression (PCR) and Partial Least Square (PLS), and the Triple Exponentially Weighted Moving Average (TEWMA) statistical monitoring scheme. The TEWMA scheme is known for its sensitivity to uncovering changes of small magnitude. Nevertheless, TEWMA can only be utilized for monitoring single variables and ignoring the correlation among monitored variables. To alleviate this difficulty, the LVR methods (i.e., PCR and PLS) are used as residual generators. Then, the TEWMA is applied to the obtained residuals for fault detection purposes, where the detection threshold is computed via kernel density estimation to improve its performance and widen its applicability in practice. Real data with different fault scenarios from a 9.54 kW photovoltaic plant has been used to verify the efficiency of the proposed schemes. Results revealed the superior performance of the PLS-TEWMA chart compared to the PLS-TEWMA chart, particularly in detecting anomalies with small changes. Moreover, they have almost comparable performance for large anomalies.

The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.

Photovoltaic (PV) power prediction is essential for integrating solar energy into the power grid and optimizing energy supply and demand management. However, accurate prediction is challenging due to the variability and intermittency of solar radiation, influenced by diverse weather conditions. This study enhances PV power prediction accuracy by evaluating various ensemble machine-learning models: CatBoost, Random Forest (RF), Gradient Boosting (GB), and XGBoost. These models are chosen for their capacity to model complex nonlinear patterns and enhance predictive accuracy through ensemble aggregation of weak learners. SHapley Additive exPlanations (SHAP) are employed to identify critical variables affecting PV power prediction, confirming solar radiation as the most significant factor. To account for time dependence and focus on essential variables, reduced dynamic models are developed. These models omit non-essential variables and incorporate lagged solar irradiation and PV power data, effectively capturing temporal dependencies and improving accuracy. Evaluation using real-world data from five PV systems in Brisbane, Australia, demonstrates that reduced dynamic models consistently outperform their static counterparts. Among the dynamic models, Gradient Boosting achieves the highest average R$^{2}$ of 0.979, followed closely by CatBoost and Random Forest at 0.977, while XGBoost achieves 0.975. In contrast, static models such as XGBoost, Random Forest, and Gradient Boosting yield lower average R$^{2}$ values of 0.9668, 0.9658, and 0.9452, respectively. These results underscore the critical influence of solar radiation on PV system performance, highlighting the effectiveness of dynamic modeling approaches in enhancing prediction precision and supporting informed decision-making in energy management strategies.