Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. However, the existing framework still has the following two problems: (1) weather mode prediction and power forecasting are highly dependent on the accuracy of numerical weather prediction (NWP), but the existing classification forecasting framework ignores the impact from NWP errors; (2) the validity of the classification forecasting framework comes from the accurate prediction of weather modes, but the existing framework lacks the analysis and decision-making mechanism of the reliability of weather mode prediction results, which will lead to a significant decline in the overall accuracy when weather modes are wrongly predicted. Therefore, this paper proposes a day-ahead PV power forecasting method based on irradiance correction and weather mode reliability decision. Firstly, based on the measured irradiance, K-means clustering method is used to obtain the daily actual weather mode labels; secondly, considering the coupling relationship of meteorological elements, the graph convolutional network (GCN) model is used to correct the predicted irradiance by using multiple meteorological elements of NWP data; thirdly, the weather mode label is converted into one-heat code, and a weather mode reliability prediction model based on a convolutional neural network (CNN) is constructed, and then the prediction strategy of the day to be forecasted is decided; finally, based on the weather mode reliability prediction results, transformer model are established for unreliable weather and credible weather respectively. The simulation results of the ablation experiments show that classification prediction is an effective strategy to improve the forecasting accuracy of day-ahead PV output, which can be further improved by adding irradiance correction and weather mode reliability prediction modules.

Accurately identifying the fault type of an optical current transformer (optical CT) and evaluating the fault severity can provide strong support for the operation and maintenance of a direct current (DC) power system. In response to the problems that current research overlooks, the spatiotemporal features when making fault identification, which restrain the improvement of identification accuracy, and consider fault identification as an assessment of fault severity, which is unable to provide effective information for actual operation and maintenance work, this paper proposes an optical CT fault severity assessment model based on scene generation and spatiotemporal feature fusion. Firstly, a CNN-Transformer model is constructed to mine the fault characteristics in spatial and temporal dimensions by feature fusion, achieving accurate identification of fault types. Secondly, an improved synthetic minority oversampling method is adopted to generate virtual operating scenes, and the operating range under different operating states of the optical CT is statistically obtained. Finally, based on Shapley Additive Explanations (SHAP), the importance of the feature of optical CT is evaluated under different fault types. Reliant on the importance of features and operating range under different running states, the severity of the fault is assessed by quantifying the difference between the fault state and the normal state of the optical CT under the identified fault type. This study validated the effectiveness of the proposed method using actual operational data from an optical CT at a converter station in Hebei Province in China.

With the rapid increase in renewable energy penetration and the expansion of multi-regional interconnected power systems, there is a growing need to forecast the power output of renewable energy power plant clusters within a region. Existing methods primarily utilize spatio-temporal correlations between stations to directly predict cluster output, but they still have the following shortcomings: (1) lack of analysis and utilization of the similar output characteristics between wind and solar power stations; and (2) inadequate integration of individual plant characteristics and adaptability across different prediction spatial scales. Therefore, this study proposes a method for forecasting and correcting daily power generation zones of wind–solar clusters based on output similarity clustering. First, the output similarity characteristics of wind and solar plants within the cluster are evaluated, and a similarity matrix is constructed to cluster the plants into sub-clusters. Second, a single-site power prediction model based on the BiLSTM model and multi-task learning is constructed to aggregate preliminary power prediction results from individual sites within sub-clusters. Finally, a cluster power prediction correction model based on the GCN-Transformer model is developed to refine preliminary predictions using spatio-temporal correlations between sub-clusters. Simulation results demonstrate that the proposed method, through its integrated approach combining clustering partitioning, multi-task learning, and spatio-temporal correlation correction within a comprehensive forecasting workflow, achieves improvements of 15.2323%, 19.0581%, and 0.0283% over the baseline GCN model in terms of MAE, RMSE, and R-score, respectively. This effectively enhances the accuracy of power forecasting for wind-solar power plant clusters.

Accurate photovoltaic power probability forecasting assists dispatch departments in making rational decisions. Joint probability distributions constructed using Copula functions can flexibly characterize complex nonlinear correlations and tail dependencies among random variables. However, existing research has not thoroughly explored the multivariate dynamic coupling characteristics related to forecasting errors, nor has it sufficiently considered the complementary advantages among different Copula functions. To address this, we propose a method for forecasting photovoltaic power output probabilities days in advance, integrating multivariate dynamic Copula functions with reinforcement learning. First, to capture the time-varying structure of photovoltaic power-related variables, we introduce a sliding time window for segmented modeling of historical data, fitting marginal probability distributions for predicted irradiance, forecasting power, and forecasting error. Second, a joint probability distribution of dynamic Gaussian Copula and t-Copula is constructed based on historical samples within the time window, generating a probabilistic prediction interval for the target time. Finally, reinforcement learning is employed to adaptively combine the probability prediction intervals derived from both Copula types, yielding the final photovoltaic power probability forecast. Simulations using actual operational data from a photovoltaic power plant in Shanxi Province validate the effectiveness of the proposed method.

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