Power Grid Stability Prediction Model Based on BiLSTM with Attention

Abstract

The security and stability of the power grid can ensure the stable balance of the power under the normal actual operation condition. It is an important requirement to guarantee the rapid development of national economy. With the increase of the complexity of the power grid structure, the higher requirements for the stability of the grid are put forward. This paper presents a power grid stability prediction model based on Bi-directional long short-term memory network (BiLSTM) with attention mechanism, which can learn the function of different stability features and the relationship between features. Firstly, the pre-processing power grid stability features are transformed into three-dimensional vector matrix input into the BiLSTM network. The multi-layer neural network layer is used to extract the deep-seated stability information.Then, the attention layer is used to allocate the corresponding weight to the extracted stable features. Finally, through the full connection layer, it can be transformed into a one-dimensional vector, which can be used to extract the stability features represents whether the grid is stable or not. Through the analysis of the results of the public 2018 uci data set, our experimental results are better than other methods, and the effect is more significant after the attention mechanism is added.