Adaptive identification method of new energy grid operation risk based on linear decision function

Abstract

Abstract The current conventional risk identification method of new energy grid operation mainly achieves risk identification by mining equipment status information, which leads to poor identification effect due to the lack of effective extraction of abnormal data features. In this regard, a linear decision function-based adaptive risk identification method for new energy grid operation is proposed. The data flow model is constructed by combining the linear decision function, and the features of interval abnormal data and fluctuating abnormal data are extracted. A sliding window model is constructed, and the unsupervised model is used to realize the effective update of new energy grid operation data. In the experiments, the proposed method is verified for recognition accuracy. The experimental results show that when the proposed method is used to identify the risk of grid operation, the effective data mis-deletion rate of the algorithm is low and has a more desirable identification effect.