Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.

The photovoltaic (PV) cell temperature strongly affects the performance and efficiency of the entire PV module. Thus, the accurate estimation of the cell temperature plays an important role in the health monitoring and energy assessment of PV systems. This article proposes a multi-state dynamic thermal model for PV modules, considering the heat-transfer mechanisms between the module and its environments, as well as between layers. The proposed model is benchmarked against field measurements at Aalborg University, Denmark. The results demonstrate the effectiveness of the model to characterize the internal behavior of the PV module under varying weather conditions. The performance of the proposed thermal model is also compared with prior-art models, i.e., two benchmark models, a one-state thermal model and two typical empirical equation-based models. The comparison further confirms that the estimation of cell temperature using the developed model is more accurate, presenting a reliable prediction of power production for further monitoring and diagnosis.

The motivation of this study is to solve the challenges posed by the nonstrict feedback structure and multiple timedelays factors in stochastic systems, which significantly complicate the structure of system structure and make the procedure of controller design more difficult. Firstly, a new LyapunovKrasovskii function is constructed in this study. A method is devised based on this function, which incorporates the characteristic of kernel functions in fuzzy logic systems and the concept of variable separation to effectively tackle challenges posed by complex system structures and time-delay factors, as well as reducing the burden of updating dynamic gains. Additionally, the application of the Nussbaum function technique efficiently resolves the issue of unknown control direction while ingeniously leveraging the distinctive properties of the Nussbaum function and the stochastic Barbalat's lemma. This approach provides a complete theoretical proof of the boundedness of the system signals and guarantees the asymptotically stable in probability. Ultimately, the proposed approach is validated by the exceptional performance of the simulation results for an electromechanical control system.

Ultra-short-term photovoltaic (PV) power forecasting holds significant importance in enhancing grid stability. Most PV power forecasting methods based on satellite images rely on pixel-level predictions, which are inefficient and redundant. Meanwhile, current deep-learning approaches struggle to establish correlations between large-scale cloud features and PV generation patterns. In this paper, an end-to-end model based on multimodal learning is proposed for directly obtaining multi-step PV power forecasts from satellite images and time series. To capture cloud dynamics and features within the region of interest (RoI), ConvLSTM-RICNN is utilized to encode satellite images. To mitigate the impact of noise and missing data in PV power, a robust fusion approach named DCCA-LF is introduced. This approach integrates deep canonical correlation analysis (DCCA) into late fusion (LF) to strengthen cross-modal feature alignment. The proposed model is verified using publicly available data from BP Solar in Alice Springs and Himawari-8, from January 1, 2020, to October 8, 2022. Comparison with current state-of-the-art research shows that the suggested model achieves the best RMSE and MAE with minimal complexity across all cloud conditions. Moreover, the proposed approach is robust to noise and missing data.