A Novel Security Situation Awareness Method for Offshore Wind Power Networking System Based on Vague‐CNN‐LSTM Model

ABSTRACT Situation awareness is a critical foundation for the safe operation of offshore wind power. To address the uncertainty risk caused by offshore wind power penetration in the extremely complex marine environment, a novel security situation awareness method for the offshore wind power networking system is proposed on the basis of the vague‐CNN‐LSTM model. Firstly, a partition model of offshore wind system topology is built according to the location of offshore wind farm access nodes, which can quickly capture the elements of the system situation. Secondly, a vague set is introduced to propose an interlaced offshore wind power interval division method integrating the truth‐membership degree and pseudo‐membership degree functions. Thirdly, the vague set interval prediction model based on vague‐CNN‐LSTM for offshore wind power is established to forecast the future fluctuation range of offshore wind power from different criteria, including support, opposition and hesitation uncertainty. Fourthly, early warning indexes for the security situation of the offshore wind power networking system are proposed so that the real‐time and future security risks of the entire system can be perceived from the multiple layers of node‐branch‐area‐network. Finally, the effectiveness of the proposed method is validated using a case study of an actual offshore wind farm in China.