The fast growth of photovoltaic (PV) power generation raises the concern of grid instability due to its intermittent nature. Solar irradiance forecasting is becoming an effective way to support the power ramp rate control, mitigate power intermittency, and improve grid resilience. However, most previous research focuses on the predictor applied to the central PV farm but fails to fit the distributed PV system well. This article proposes a novel deep learning architecture, namely, group solar irradiance neural network, used for solar irradiance forecasting. The strategy adopts a convolutional graph neural network to mine distributed PVs’ graphs and identify the critical features. Meanwhile, the scheme includes a long-short-term memory recurrent neural network to capture the temporal correlations. The proposed solution shows an improved universality for model training and results in accurate and reliable predictions compared to other solutions. Furthermore, the learning architecture can be applied to the master–slave deployment scheme to reduce overall system cost. The proposed algorithm is tested with the data sourced from the U.S. National Renewable Energy Laboratory. The evaluation demonstrates the highest accuracy and best fitting for solar irradiance forecasting in comparison with other prediction methods.

A large-scale photovoltaic (PV) power plant is composed of hundreds or thousands of solar panels, which makes fault diagnosis a challenging problem due to its complexity. It is known that the PV power output characteristics dramatically change with environmental conditions. Thus, it is difficult to distinguish a short-circuit line fault from mismatching conditions induced by partial shading or unbalanced generation. The article proposes a novel fault diagnosis scheme that is based on real-time system identification to determine normal or abnormal operations. It does not require any additional hardware or measurement support. The identification and analysis include both short- and long-term characteristics of the generation system to mitigate disturbances and improve the robustness. The effectiveness of the proposed scheme is validated by simulations and experiments under a wide range of test conditions.

The fast-increasing penetration of photovoltaic (PV) power raises the issue of grid stability due to its intermittency and lack of inertia in power systems. Solar irradiance forecasting effectively supports advanced control, mitigates power intermittency, and improves grid resilience. Irradiance forecasting based on data-driven methods aims to predict the direction and level of power variation and indicate quick action. This article presents a comprehensive review and comparative analysis of data-driven approaches for time-series solar irradiance forecasting. It systematically evaluates nineteen representative models spanning from traditional statistical methods to state-of-the-art deep learning architectures across multiple performance dimensions that are critical for practical deployment. The analysis aims to provide actionable insights for researchers and practitioners when selecting and implementing suitable forecasting solutions for diverse solar energy applications.

The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model.