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CN-121980767-A - Large wind power base self-adaptive wake evolution rule simulation method

CN121980767ACN 121980767 ACN121980767 ACN 121980767ACN-121980767-A

Abstract

The application discloses a large wind power base self-adaptive wake evolution law simulation method, which relates to the technical field of wind power engineering and comprises the steps of constructing a comprehensive monitoring system, acquiring multi-space-time scale data, extracting key flow field characteristics, establishing an atmosphere-group-field-machine full-coupling simulation model based on the key flow field characteristic parameters, establishing a large wind power base field group self-adaptive wake model adapting to different wind power base environments based on simulation results by utilizing a flow similarity function, constructing a three-dimensional field group wake interference model based on the principle of conservation of momentum and conservation of mass, outputting a full-field three-dimensional wind speed loss field and a turbulence enhancement field, and carrying out multi-objective collaborative optimization on a wind power base based on the full-field three-dimensional wind speed loss field and the turbulence enhancement field. The application aims at solving the core technical problems of low accuracy and reliability of wind resource assessment of a large wind power base under complex terrain caused by insufficient modeling precision and wake effect calculation distortion in the prior art.

Inventors

  • WANG KAIRAN
  • WU YUANHAO
  • MA JUNPENG
  • KONG BIN
  • LU XIAOLI
  • HAN ZHIJIE

Assignees

  • 宁夏回族自治区电力设计院有限公司

Dates

Publication Date
20260505
Application Date
20251230

Claims (7)

  1. 1. A large wind power base self-adaptive wake evolution law simulation method is characterized by comprising the following steps: Integrating remote sensing satellite data, laser radar observation, wind tower actual measurement, SCADA operation data and meteorological aircraft observation, constructing a comprehensive monitoring system, acquiring multi-space-time scale data and extracting key flow field characteristics; based on key flow field characteristic parameters, a dynamic downscaling method is adopted to establish an atmosphere-group-field-machine full-coupling simulation model, and wake dynamic evolution under different environmental parameters is simulated to obtain simulation results; Establishing a large wind power base station group self-adaptive wake model adapting to different wind power base station environments by utilizing a flow similarity function based on simulation results; On the basis of a field group self-adaptive wake model, a three-dimensional field group wake interference model is constructed based on the principles of momentum conservation and mass conservation, interaction among wake flows and a combined attenuation rule are quantized, and a full-field three-dimensional wind speed loss field and a turbulence enhancement field are output; Based on the full-farm three-dimensional wind speed loss field and the turbulence enhancement field, the multi-objective collaborative optimization result is output for macroscopic site division, wake flow recovery zone setting and microscopic unit arrangement scheme of the wind power base.
  2. 2. The method for simulating the self-adaptive wake evolution law of a large wind power base according to claim 1, wherein integrating remote sensing satellite data, laser radar observation, wind tower actual measurement, SCADA operation data and meteorological aircraft observation, constructing a comprehensive monitoring system, acquiring multi-space-time scale data and extracting key flow field features comprises the following steps: integrating remote sensing satellite data, laser radar observation, wind tower actual measurement, SCADA operation data and meteorological aircraft observation, constructing an air-space-ground integrated comprehensive monitoring system, and collecting multi-space-time scale data; mapping the preprocessed multi-time space scale data to a multi-scale nested grid system, and extracting key flow field features on each grid node.
  3. 3. The method for simulating the self-adaptive wake evolution law of the large wind power base according to claim 1, wherein the method for simulating the dynamic wake evolution under different environmental parameters to obtain simulation results by establishing an atmosphere-group-field-machine full-coupling simulation model based on key flow field characteristic parameters by adopting a dynamic downscaling method comprises the following steps: Based on a key flow field characteristic parameter set, adopting a dynamic downscaling and multi-grid nesting technology to establish an atmosphere-group-field-machine full-coupling simulation model; And carrying out data assimilation cycle correction based on the full-coupling simulation model, and carrying out multi-scene simulation and verification by combining a virtual anemometer tower technology to obtain a high-precision simulation result.
  4. 4. The method for simulating the adaptive wake evolution law of a large wind power base according to claim 1, wherein the step of establishing the adaptive wake model of the large wind power base group adapted to different wind power base environments by using the flow similarity function based on the simulation result comprises the following steps: Setting a plurality of groups of control simulation working conditions through a full-coupling simulation model, extracting key response parameters, and constructing a flow similarity function of associating macroscopic environmental factors and microscopic wake parameters through multiple nonlinear regression; And constructing a space background field based on simulation results, constructing a large wind power base field group self-adaptive wake model by combining a flow similarity function, performing field group wake coupling calculation, and outputting a three-dimensional field group wake field.
  5. 5. The method for simulating the evolution law of the self-adaptive wake of the large wind power base according to claim 1, wherein the method for constructing the three-dimensional wake interference model based on the principle of conservation of momentum and conservation of mass on the basis of the wake model of the self-adaptive wake of the field group, quantifying the interaction and the combined attenuation law among the wake, and outputting the full-field three-dimensional wind speed loss field and the turbulence enhancement field comprises the following steps: Defining and dispersing a calculation domain according to a three-dimensional field group wake field, and calculating full-field momentum loss by adopting a nonlinear superposition algorithm based on a momentum conservation principle; and synchronously calculating turbulence intensity, carrying out coordinate transformation and non-uniform flow field coupling, and outputting a standardized core data field, wherein the core data field comprises a full-field three-dimensional wind speed loss field and a full-field three-dimensional turbulence enhancement field.
  6. 6. The method for simulating the self-adaptive wake evolution law of the large wind power base according to claim 1, wherein the method for simulating the macroscopic site division, wake restoration zone setting and microscopic unit arrangement of the wind power base based on the full-field three-dimensional wind speed loss field and the turbulence enhancement field outputs a multi-objective collaborative optimization result, and the method comprises the following steps: generating an initial scheme by taking the maximization of the overall capacity and the minimization of wake loss as core targets and combining multiple constraint conditions, and outputting a pareto optimal macroscopic planning scheme set with multiple target balances through evaluation and iterative evolution of a three-dimensional field group wake interference model; based on the pareto optimal macroscopic programming scheme set, the fan position and model combination are searched for through an optimization algorithm in an iterative mode, and an optimal microscopic site selection scheme is output.
  7. 7. A computer readable storage medium comprising one or more program instructions for execution by a processor of a large wind power base adaptive wake evolution law simulation method according to any one of claims 1-6.

Description

Large wind power base self-adaptive wake evolution rule simulation method Technical Field The invention relates to the technical field of wind power engineering, in particular to a large-scale wind power base self-adaptive wake evolution law simulation method. Background As wind power development advances to complex terrain areas on a large scale, planning and design of wind power bases face serious challenges. The traditional method mainly relies on sparse anemometer tower data and a simplified wake flow model for evaluation, and is difficult to accurately describe mountain detours, atmospheric stability changes and complex wake flow superposition effects among large-scale fan groups. Under complex terrains, systematic deviation exists in wind resource evaluation, wake loss prediction distortion is caused, and further the problems of improper site selection of a unit, serious lower than expected overall power generation amount, damaged project economy and the like in industry commonality are caused. In the prior art, computational fluid dynamics simulation is introduced, but a systematic solution is still lacking in terms of how to efficiently fuse multi-source observation data, realize trans-scale coupling simulation from weather scale to unit scale, and establish a high-precision engineering wake model which can be self-adapted to different environments. Disclosure of Invention The invention provides a large wind power base self-adaptive wake evolution law simulation method, which comprises the following steps: Integrating remote sensing satellite data, laser radar observation, wind tower actual measurement, SCADA operation data and meteorological aircraft observation, constructing a comprehensive monitoring system, acquiring multi-space-time scale data and extracting key flow field characteristics; based on key flow field characteristic parameters, a dynamic downscaling method is adopted to establish an atmosphere-group-field-machine full-coupling simulation model, and wake dynamic evolution under different environmental parameters is simulated to obtain simulation results; Establishing a large wind power base station group self-adaptive wake model adapting to different wind power base station environments by utilizing a flow similarity function based on simulation results; On the basis of a field group self-adaptive wake model, a three-dimensional field group wake interference model is constructed based on the principles of momentum conservation and mass conservation, interaction among wake flows and a combined attenuation rule are quantized, and a full-field three-dimensional wind speed loss field and a turbulence enhancement field are output; Based on the full-farm three-dimensional wind speed loss field and the turbulence enhancement field, the multi-objective collaborative optimization result is output for macroscopic site division, wake flow recovery zone setting and microscopic unit arrangement scheme of the wind power base. The method for simulating the self-adaptive wake evolution law of the large wind power base comprises the steps of integrating remote sensing satellite data, laser radar observation, wind tower actual measurement, SCADA operation data and meteorological aircraft observation, constructing a comprehensive monitoring system, acquiring multi-space-time scale data and extracting key flow field characteristics, and comprises the following steps: integrating remote sensing satellite data, laser radar observation, wind tower actual measurement, SCADA operation data and meteorological aircraft observation, constructing an air-space-ground integrated comprehensive monitoring system, and collecting multi-space-time scale data; mapping the preprocessed multi-time space scale data to a multi-scale nested grid system, and extracting key flow field features on each grid node. The method for simulating the self-adaptive wake evolution law of the large wind power base comprises the steps of establishing an atmosphere-group-field-machine full-coupling simulation model based on key flow field characteristic parameters by adopting a dynamic downscaling method, simulating wake dynamic evolution under different environmental parameters to obtain simulation results, and comprises the following steps: Based on a key flow field characteristic parameter set, adopting a dynamic downscaling and multi-grid nesting technology to establish an atmosphere-group-field-machine full-coupling simulation model; And carrying out data assimilation cycle correction based on the full-coupling simulation model, and carrying out multi-scene simulation and verification by combining a virtual anemometer tower technology to obtain a high-precision simulation result. The method for simulating the self-adaptive wake evolution rule of the large wind power base comprises the steps of establishing a self-adaptive wake model of a large wind power base group adapting to different wind power base environments by utilizing a flow