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Fast insecurity early warning is the key technique to resist the dynamic insecurity risk, which becomes intractable due to the strong nonlinearity of hybrid AC/DC grids and the high uncertainty of wind generation. Considering dynamic security constraints and wind power uncertainty, this paper presents an insecurity early warning method based on decision tree (DT) and semi-supervised deep learning. First, semi-supervised deep learning is deployed to estimate the dynamic security limit of the critical interface of hybrid AC/DC grids. The system security is assessed by comparing the actual power transfer of the critical interface with the security limit. Then, operating conditions (OCs) are ranked into different insecure levels according to the type of preventive control actions that is needed to ensure the system security. Finally, oblique DT is utilized to identify insecurity classification boundaries in the wind power injection space. Insecure OC sets are constructed based on these classification boundaries. Simulation results of the real-life Jiangsu-Shanghai interconnected grid in east China demonstrate that the proposed method can fast construct the insecure OC sets corresponding to different insecure levels. [ABSTRACT FROM AUTHOR]

In view of the uncertainty and intermittency of wind power, this paper proposes an optimal economical dispatch (ED) model and develops a method to estimate risk and manage hybrid power systems (traditional + wind~power~systems) for the short-term (24 h) operations. The model and the method have taken into account the large wind power penetration and the wind variability. The particle swarm optimization (PSO) refid="ref1"/ algorithm with constraints is applied to solve the ED problem. Value at risk (VaR) refid="ref2"/ and integrated risk management (IRM) refid="ref3"/ are used separately to assess the risk, so that an optimal tradeoff between the profit and risk is made for the system operations. The model and the method are tested on the standard IEEE 30-bus power system and network in Shanghai. The validity of the model and the method has been approved. [ABSTRACT FROM PUBLISHER]