2results about How to "Improve correction ability" patented technology

This invention discloses a motor control method based on Hall effect elliptic correction and sector linear models, comprising: installing orthogonal Hall sensor pairs and determining elliptic correction compensation parameters through offline correction; dividing mechanical sectors and establishing a linear model representing the correspondence between electrical angles and mechanical angles for each mechanical sector; performing elliptic correction on the original output signals of the orthogonal Hall sensor pairs according to the elliptic correction compensation parameters, and calculating the real-time mechanical angles based on the elliptic-corrected output signals; querying the mechanical sector where the real-time mechanical angles are located, and calculating the corresponding real-time electrical angles according to the linear model; and generating corresponding motor control signals based on the real-time electrical angles. This invention replaces hardware precision with algorithms, resulting in extremely low hardware costs, low computational load, low processor requirements, and the ability to correct traditionally defective motors to a usable or even high-performance level, significantly improving supply chain utilization and product cost-effectiveness.

The application discloses a kind of multi-mode wind energy resource month scale prediction revision method and system based on U-Net, belong to wind energy resource evaluation and climate numerical prediction cross technical field, for the month scale wind speed of climate mode output is revised.The method constructs climatic wind speed field using ERA5 reanalysis, calculates month scale 10m wind speed anomaly as revision benchmark;Obtain the month scale historical return data of multiple dynamic climate models, extract 10m wind speed and multiple layers meteorological elements, uniform interpolation and standardization, form sample set.Based on sample set, the U-Net revision model with encoding and decoding structure is constructed, and the feature combination and hyperparameter are optimized through cross-validation, to learn the nonlinear mapping from multi-mode prediction field to ERA5 anomaly field.The optimal model is used to revise the future month scale prediction, to generate wind speed products with more similar spatial structure and amplitude distribution to observations, to provide high credible wind energy climate information for wind power planning, power generation planning and power grid dispatching.