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Optimising short-term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load sequence, resulting in accuracy reduction. To achieve accurate and efficient short-term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the complicated dynamic features and learn the time compliance relationship of load sequence. Based on CNN-GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important features. Then, the overall framework of short-term load forecasting based on CNN-GRU-Attention network is proposed, and the coupling relationship between each designed stage is revealed. Finally, the developed framework is implemented on one realistic load dataset of distribution networks, and the experimental results verify the proposed method outperforms the state-of-the-art models in common metrics.

The commutation failure is the most prevalent fault in line-commutated converter based HVDC systems, which may result in transient overvoltage on the sending-side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large-scale wind turbines and implement effective stability control measures. In this paper, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. To compensate for potential inaccuracies in the data-driven method, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented, along with an analytical expression for the peak value of transient overvoltage. On this basis, an overvoltage analysis method integrating the model-driven and data-driven techniques is proposed, and the improved DT algorithm is ap-plied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the actual Northwest China local region hybrid AC/DC power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method.

The occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method.