4results about How to "Guaranteed dynamic performance" patented technology

This invention discloses a method for estimating the speed and position of a permanent magnet synchronous motor (PMSM) based on a third-order switched extended state observer. Specifically, based on the mathematical model of the PMSM, a back-EMF observer based on a sliding mode observer is constructed to obtain the estimated back-EMF. Further, the estimated back-EMF signal is processed using a low-pass filter and amplitude normalization. The processed back-EMF is then used as the input signal to a position estimation scheme based on the third-order switched extended state observer, achieving accurate estimation of the PMSM's speed and position under different operating conditions. This invention is simple in principle and easy to implement. It combines the advantages of both third-order linear and third-order nonlinear extended state observers, improving estimation accuracy and dynamic performance while reducing the adverse effects of noise on the estimation scheme. This solves the technical problems of low estimation accuracy, high noise sensitivity, and difficulty in parameter tuning in existing estimation schemes.

This invention relates to a method and system for access control of power grid control data interfaces based on graph neural networks, belonging to the field of anomaly detection technology, and solving the problem of overly simplistic and mechanical anomaly detection in existing technologies. The method includes: constructing a graph model of the power grid control data system using user nodes, data interface nodes, and data resource nodes from historical data; obtaining the edge weight access strength from the user node to be analyzed to the target resource node based on the constructed graph model; performing three-stage corrections on the attention coefficients based on causal constraints, adversarial robustness, and interpretability based on a graph attention neural network, obtaining the embedding vector representations of the user node to be analyzed and the target resource node; predicting the access control probability score of the user node to be analyzed to the target resource node based on the embedding vector representation and the edge weight access strength; and assigning corresponding operation permissions to the user node to be analyzed based on the probability score. This invention can effectively resist potential attacks and significantly improve the efficiency of power grid access control.

This application discloses a control method, device, terminal, medium, and product for a grid-type inverter in a power transmission network. The solution provided in this application, in this embodiment, constructs a nonlinear state-space model of the grid-type inverter and reconstructs it into a q-LPV model. Combined with polyhedral representation and distributed passive LMI constraints, it can effectively handle the strong nonlinear dynamic characteristics exhibited by the grid-type inverter under complex operating conditions in the power transmission network. This ensures the stability and dynamic performance of the system when facing large-signal disturbances such as high-power disturbances and load abrupt changes, and avoids instability problems caused by the limitations of linearized models.

The application discloses an immersive AI process three-dimensional dynamic visualization method based on a particle system, and relates to the technical field of computer graphics and three-dimensional visualization. The immersive AI process three-dimensional dynamic visualization method based on the particle system comprises AI process association operation, AI process particle processing and UI interaction control. The AI process particle processing is executed to obtain an AI particle dynamic structure, the AI process key year switching is executed to obtain camera track-particle disturbance parameter linkage, and the UI interaction control is executed to obtain AI process dynamic visualization time axis interaction, so that the accuracy of immersive AI process three-dimensional dynamic visualization data is improved, and the technical problem of low accuracy of immersive AI process three-dimensional dynamic visualization data in the prior art is solved.