The effective prediction of bus load can provide an important basis for power system dispatching and planning and energy consumption to promote environmental sustainable development. A bus load forecasting method based on variational modal decomposition (VMD) and bidirectional long short-term memory (Bi-LSTM) network was proposed in this article. Firstly, the bus load series was decomposed into a group of relatively stable subsequence components by VMD to reduce the interaction between different trend information. Then, a time series prediction model based on Bi-LSTM was constructed for each sub sequence, and Bayesian theory was used to optimize the sub sequence-related hyperparameters and judge whether the sequence uses Bi-LSTM to improve the prediction accuracy of a single model. Finally, the bus load prediction value was obtained by superimposing the prediction results of each subsequence. The example results show that compared with the traditional prediction algorithm, the proposed method can better track the change trend of bus load, and has higher prediction accuracy and stability.
In the field of deep learning, for problems and tasks that are sensitive to time series, such as natural language processing or speech recognition, the recurrent neural network is usually more suitable. Long short-term memory (LSTM) is a representative network structure in recurrent neural network. It is time-dependent and enables a global representation of features. However, some problems such as the network parameters of LSTMs limit the applicability of their solutions. This paper proposes an improved hybrid structure of graph convolutional neural network and recurrent neural network. In the input layer, a two-dimensional convolutional neural network is used to build a text corpus map. Graphic embedding is used to preserve the global structure of the entire text graph structures. The LSTM layer and attention mechanism are used to fully implement text classification and improve the computational efficiency. The test results show that the hybrid network structure has better operation speed on the IMDb dataset.
Large-scale power outage events bring serious economic losses to national and social development and cause bad impacts, and the reasonable formulation of a unit black-start strategy is the basis of power outage recovery. Firstly, the Dijkstra shortest path algorithm is used to search for the optimal recovery path of the unit to be started after a major blackout occurs, and secondly, a comprehensive index of the unit start sequence considering the unit characteristics and unit distance is defined to guide the unit start sequence formulation by combining the unit capacity, climbing rate, start power, and other characteristics with the recovery time and capacitance value of the recovery path. Finally, based on the unit start sequence and optimal recovery path, and taking into account the unit start constraints, a complete unit black-start strategy is proposed, and the effectiveness of this strategy is verified by IEEE30 node system simulation. The results show that the proposed unit black-start strategy can reduce the unit recovery time and improve the recovery success rate.
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