Wind power plant layout planning method and system based on optimization algorithm

Abstract

The invention belongs to the technical field of wind power plant layout planning, and discloses a wind power plant layout planning method and system based on an optimization algorithm. The method comprises the following steps: constructing a wind power plant space analysis model and a wind power plant layout planning model by using GeoAI and an optimization algorithm based on an ArcGIS system; the method comprises the following steps: acquiring real-time GIS (Geographic Information System) data, preprocessing, and importing into an According to the preprocessed real-time GIS data, performing wind power plant space analysis by using a wind power plant space analysis model, and visualizing an obtained real-time wind power plant space analysis result; and according to a real-time wind power plant space analysis result, performing wind power plant layout planning by using the wind power plant layout planning model, and visualizing an obtained real-time wind power plant layout planning scheme. According to the invention, the problems of poor layout planning effect, lack of comprehensive analysis and low intelligent degree in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm layout planning, and in particular to a wind farm layout planning method and system based on an optimization algorithm. Background Art

[0002] With the rapid development of renewable energy, wind power, as an important clean energy source, has become crucial for environmental protection, energy security, economic development, and sustainable development. The rationality of wind farm layout planning directly impacts its power generation efficiency and economic benefits. Therefore, scientific, rational, and efficient wind farm layout planning has become a key development direction in this field.

[0003] In the field of wind farm layout planning, although existing technologies have promoted the development of the industry to a certain extent, they still have many defects, which limit the optimization of wind farm layout planning and the maximization of economic benefits. Specifically, the defects of existing technologies mainly include:

[0004] 1) Poor layout planning: Existing technologies often plan wind farm layouts based on fixed rules, without considering the actual needs of wind farms and their impact on the environment. This results in layout planning that lacks scientificity and rationality, affecting its effectiveness.

[0005] 2) Lack of comprehensive analysis: Existing technologies often focus on analyzing single factors, such as wind speed and terrain, while ignoring the combined impact of multiple factors. This results in incomplete layout planning results and makes it difficult to adapt to complex and changing natural environments;

[0006] 3) Low degree of intelligence: Existing technologies are mainly based on manual experience, with high labor costs and workload, lack of spatial analysis mechanism for wind farms, and cannot realize automated wind farm layout planning. The degree of intelligence cannot meet the requirements. Summary of the Invention

[0007] In order to solve the problems of poor layout planning effect, lack of comprehensive analysis and low intelligence in the prior art, the present invention aims to provide a wind farm layout planning method and system based on an optimization algorithm.

[0008] The technical solution adopted in the present invention is:

[0009] A wind farm layout planning method based on an optimization algorithm comprises the following steps:

[0010] Based on the ArcGIS system, using GeoAI and optimization algorithms, we built a wind farm spatial analysis model and a wind farm layout planning model;

[0011] Collect real-time GIS data of the wind farm, pre-process the real-time GIS data to obtain pre-processed real-time GIS data, and import the pre-processed real-time GIS data into the ArcGIS system;

[0012] Based on the pre-processed real-time GIS data, the wind farm spatial analysis model is used to perform wind farm spatial analysis and visualize the real-time wind farm spatial analysis results.

[0013] Based on the real-time wind farm spatial analysis results, the wind farm layout planning model is used to carry out wind farm layout planning, and the real-time wind farm layout planning scheme obtained is visualized.

[0014] Furthermore, based on the ArcGIS system, using GeoAI and optimization algorithms, a wind farm spatial analysis model and a wind farm layout planning model were constructed, including the following steps:

[0015] Collect and pre-process several historical GIS data of different wind farms to obtain several pre-processed historical GIS data, and then import the pre-processed historical GIS data into the ArcGIS system;

[0016] Based on the ArcGIS system, a corresponding historical wind farm spatial 3D simulation model is constructed according to the pre-processed historical GIS data of each wind farm;

[0017] Use the deep learning and spatial data analysis fusion algorithm in the GeoAI tool to build an initial wind farm spatial analysis model;

[0018] Based on several historical wind farm spatial three-dimensional simulation models, the initial wind farm spatial analysis model is optimized and trained to obtain the final wind farm spatial analysis model, and several historical wind farm spatial characteristics and several historical wind farm spatial analysis results are generated;

[0019] Use the swarm intelligence optimization algorithm in the GeoAI tool to build an initial wind farm layout planning model;

[0020] According to several historical wind farm spatial characteristics and several historical wind farm spatial analysis results, the initial wind farm layout planning model is optimized and trained to obtain the final wind farm layout planning model.

[0021] Furthermore, the wind farm spatial analysis model is constructed based on the 3D-DBN-GAT algorithm, and the wind farm spatial analysis model includes a wind farm spatial feature extraction module constructed based on the 3D-DBN algorithm and a wind farm spatial analysis module constructed based on the GAT algorithm.

[0022] Furthermore, based on several historical wind farm spatial three-dimensional simulation models, the initial wind farm spatial analysis model is optimized and trained to obtain a final wind farm spatial analysis model, and several historical wind farm spatial features and several historical wind farm spatial analysis results are generated, including the following steps:

[0023] Setting a corresponding real wind farm spatial analysis result for each historical wind farm spatial three-dimensional simulation model, and dividing several historical wind farm spatial three-dimensional simulation models into a model test set and a model training set;

[0024] Input the model training set, optimize the initial wind farm spatial analysis model and obtain the optimized wind farm spatial analysis model;

[0025] Input the model test set to test the optimized wind farm spatial analysis model and obtain several historical wind farm spatial analysis results;

[0026] Compare and statistically analyze several historical wind farm spatial analysis results obtained from the model test with several corresponding real wind farm spatial analysis results to obtain the model test accuracy;

[0027] If the test accuracy of several models is greater than the accuracy threshold, the final wind farm spatial analysis model is output, and several historical wind farm spatial features and several historical wind farm spatial analysis results are generated.

[0028] Furthermore, the wind farm layout planning model is constructed based on the ISGA algorithm, and the wind farm layout planning model includes a format setting module, an impact factor generation module, a fitness function update module, an initial solution generation module, an iterative update module and a solution vector analysis module which are connected in sequence.

[0029] Furthermore, based on several historical wind farm spatial characteristics and several historical wind farm spatial analysis results, the initial wind farm layout planning model is optimized and trained to obtain the final wind farm layout planning model, including the following steps:

[0030] Setting preset layout planning decision indicators for each historical wind farm spatial analysis result to obtain several historical layout planning scheme formats;

[0031] Analyze the spatial characteristics of each historical wind farm to obtain several historical influencing factors. Based on these historical influencing factors, set several historical optimization goals and obtain several historical fitness functions.

[0032] The current historical layout planning scheme format is encoded into the individual vectors of the initial wind farm layout planning model, and based on the current historical fitness function and the current individual vectors, the initial wind farm layout planning model is optimized and trained to obtain the optimized wind farm layout planning model;

[0033] Traverse all historical fitness functions of each historical layout planning scheme format, repeat the previous step, continue to train the optimized wind farm layout planning model, and obtain the final wind farm layout planning model.

[0034] Furthermore, based on the pre-processed real-time GIS data, a wind farm spatial analysis model is used to perform a wind farm spatial analysis, and the obtained real-time wind farm spatial analysis results are visualized, including the following steps:

[0035] Based on the ArcGIS system, a corresponding real-time wind farm spatial 3D simulation model is constructed according to the pre-processed real-time GIS data of the wind farm;

[0036] Use the wind farm spatial feature extraction module of the wind farm spatial analysis model to extract wind farm spatial features from the real-time wind farm spatial three-dimensional simulation model to obtain real-time wind farm spatial features;

[0037] According to the real-time wind farm spatial characteristics, the wind farm spatial analysis module of the wind farm spatial analysis model is used to perform wind farm spatial analysis to obtain real-time wind farm spatial analysis results;

[0038] Use a real-time wind farm spatial 3D simulation model to visualize the real-time wind farm spatial analysis results.

[0039] Furthermore, based on the real-time wind farm spatial analysis results, a wind farm layout planning model is used to perform wind farm layout planning. The visualized real-time wind farm layout planning scheme includes the following steps:

[0040] Inputting the real-time wind farm spatial analysis results and the real-time wind farm spatial characteristics into the wind farm layout planning model, using the format setting module of the wind farm layout planning model to match the real-time wind farm spatial analysis results with real-time layout planning decision indicators, and setting the real-time layout planning scheme format according to the real-time layout planning decision indicators;

[0041] Using the impact factor generation module of the wind farm layout planning model, the real-time wind farm spatial characteristics are analyzed to obtain real-time impact factors. Based on the real-time impact factors, the real-time optimization goals of the wind farm layout planning model are set;

[0042] Using the fitness function update module of the wind farm layout planning model, the preset fitness function of the wind farm layout planning model is updated according to the real-time optimization target to obtain a real-time fitness function;

[0043] Using an initial solution generation module of a wind farm layout planning model, encoding a real-time layout planning scheme format into individual vectors of the wind farm layout planning model, and performing initial solution generation based on the individual vectors to obtain a plurality of initial solutions; the initial solutions correspond to initial real-time layout planning schemes;

[0044] Using the iterative update module of the wind farm layout planning model, several initial solutions are iteratively updated according to the real-time fitness function, and the optimal solution with the best real-time fitness value is output;

[0045] Use the solution vector analysis module of the wind farm layout planning model to analyze the individual vectors of the optimal solution and obtain the optimal real-time layout planning solution;

[0046] Use a real-time 3D wind farm spatial simulation model to visualize the optimal real-time layout planning solution.

[0047] A wind farm layout planning system based on an optimization algorithm is used to implement a wind farm layout planning method. The system includes a model building unit, a real-time data acquisition unit, a wind farm space analysis unit, and a wind farm layout planning unit that are connected in sequence.

[0048] The beneficial effects of the present invention are:

[0049] The present invention discloses a wind farm layout planning method and system based on an optimization algorithm, which fully considers the actual needs of the wind farm and its impact on the environment, realizes scientific and reasonable layout planning, and significantly improves the power generation efficiency and economic benefits of the wind farm; GIS data includes multi-faceted and multi-dimensional data such as geographic space, wind resources, terrain, land and ecological environment, and wind farm layout planning is carried out based on GIS data, comprehensively considering multiple influencing factors, thereby improving the adaptability of wind farm layout planning to complex and changeable natural environments; using the ArcGIS system and advanced artificial intelligence and swarm intelligence optimization algorithms, the dependence on manual experience is greatly reduced, the labor cost and workload are reduced, and through the automated spatial analysis mechanism and automatic layout planning mechanism, the intelligent automated layout planning of the wind farm is realized, meeting the needs of efficient and accurate planning of modern wind farms.

[0050] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of the wind farm layout planning method based on optimization algorithm.

[0052] Figure 2 It is a structural block diagram of the wind farm layout planning system based on optimization algorithm. DETAILED DESCRIPTION

[0053] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1:

[0055] like Figure 1 As shown, this embodiment provides a wind farm layout planning method based on an optimization algorithm, comprising the following steps:

[0056] S1: Based on the Arc Geographic Information System (ArcGIS), using the integrated algorithm of Geospatial Artificial Intelligence (GeoAI) and swarm intelligence optimization, a wind farm spatial analysis model and a wind farm layout planning model were constructed. The steps included:

[0057] S1-1: Collect and pre-process several historical Geographic Information System (GIS) data of different wind farms to obtain several pre-processed historical GIS data, and import the pre-processed historical GIS data into the ArcGIS system;

[0058] Historical GIS data includes historical geographic spatial data, historical initial wind farm layout planning schemes, historical wind resource data, historical terrain data, historical land use data, and historical ecological environment data;

[0059] Historical geospatial data: The most basic GIS data type, including spatial features such as points, lines, and surfaces, as well as their geographic location information; in wind farm planning, this may include spatial features such as roads, buildings, rivers, and administrative boundaries.

[0060] Historical wind resource data: This data specifically describes wind speed, direction, frequency, and other characteristics. It is typically obtained through meteorological stations, remote sensing technology, or numerical simulations and is a key input for wind farm planning.

[0061] Historical terrain data: Describes terrain features such as elevation, slope, and aspect. This data is crucial for assessing the feasibility of wind farms and optimizing turbine layout, as terrain affects the distribution and intensity of wind flow.

[0062] Historical land use data: including land use type, land cover, land ownership, and land planning data; this has a certain impact on wind farm layout planning;

[0063] Historical ecological and environmental data: including data on natural ecology, environmental quality, and resource conditions; used to assess the impact of wind farms on the ecological environment, improving the comprehensiveness of GIS data;

[0064] S1-2: Based on the ArcGIS system, construct the corresponding historical wind farm spatial 3D simulation model according to the pre-processed historical GIS data of each wind farm;

[0065] S1-3: Use the deep learning and spatial data analysis fusion algorithm in the GeoAI tool to build an initial wind farm spatial analysis model;

[0066] The wind farm spatial analysis model is built based on the 3D-DBN (3D-DBN)-Graph Attention Network (GAT) algorithm, and includes a wind farm spatial feature extraction module based on the 3D-DBN algorithm and a wind farm spatial analysis module based on the GAT algorithm.

[0067] The wind farm spatial feature extraction module can process the three-dimensional spatial data of the historical wind farm spatial three-dimensional simulation model, extract the wind farm spatial features, and effectively reduce the dimensionality of high-dimensional data through the structure of the deep belief network, retaining the most important information while reducing the computational complexity; the wind farm spatial analysis module regards each point in the graph (such as the wind turbine position) as a node in the graph, converts the wind farm spatial features into a graph structure, and uses the GAT algorithm to establish a spatial relationship graph between nodes. Through the attention mechanism, the importance of each node in the graph is dynamically allocated, focusing on the areas or wind turbines that have a greater impact on the performance of the wind farm, analyzing the spatial relationship between the nodes in the graph, and mining the spatial patterns that have an important impact on the wind farm layout, power prediction, etc., locating the key areas that have a greater impact on the performance of the wind farm, and providing a basis for the layout planning of the wind farm. The graph structure conversion provides a flexible spatial analysis framework that can adapt to the analysis needs of wind farms of different scales and complexities. By analyzing the spatial relationship between nodes, the GAT module can provide more intuitive and interpretable wind farm spatial analysis results;

[0068] S1-4: Based on several historical wind farm spatial three-dimensional simulation models, the initial wind farm spatial analysis model is optimized and trained to obtain a final wind farm spatial analysis model, and several historical wind farm spatial features and several historical wind farm spatial analysis results are generated, including the following steps:

[0069] S1-4-1: Set the corresponding real wind farm spatial analysis results for each historical wind farm spatial 3D simulation model, and divide several historical wind farm spatial 3D simulation models into a model test set and a model training set;

[0070] S1-4-2: Input the model training set and optimize the initial wind farm spatial analysis model to obtain the optimized wind farm spatial analysis model;

[0071] S1-4-3: Input the model test set, perform model testing on the optimized wind farm spatial analysis model, and obtain several historical wind farm spatial analysis results;

[0072] S1-4-4: Compare and statistically analyze several historical wind farm spatial analysis results obtained from the model test with several corresponding real wind farm spatial analysis results to obtain the model test accuracy;

[0073] S1-4-5: If the test accuracy of several models is greater than the accuracy threshold, the final wind farm spatial analysis model is output, and several historical wind farm spatial features and several historical wind farm spatial analysis results are generated;

[0074] S1-5: Use the swarm intelligence optimization algorithm in the GeoAI tool to build an initial wind farm layout planning model;

[0075] The wind farm layout planning model is constructed based on the Improved Snow Geese Algorithm (FSGA) algorithm, and the wind farm layout planning model includes a format setting module, an influence factor generation module, a fitness function update module, an initial solution generation module, an iterative update module, and a solution vector analysis module connected in sequence;

[0076] A format setting module is used to match the real-time wind farm spatial analysis results with real-time layout planning decision indicators, and to set the real-time layout planning scheme format according to the real-time layout planning decision indicators; an impact factor generation module is used to analyze the real-time wind farm spatial characteristics to obtain real-time impact factors, and to set the real-time optimization target of the wind farm layout planning model according to the real-time impact factors; a fitness function update module is used to update the preset fitness function of the wind farm layout planning model according to the real-time optimization target to obtain the real-time fitness function; an initial solution generation module is used to encode the real-time layout planning scheme format into individual vectors of the wind farm layout planning model, and to generate initial solutions according to the individual vectors to obtain several initial solutions; an iterative update module is used to iteratively update several initial solutions according to the real-time fitness function, and output the optimal solution with the optimal real-time fitness value; a solution vector analysis module is used to perform solution vector analysis on the individual vectors of the optimal solution to obtain the optimal real-time layout planning scheme;

[0077] S1-6: Based on several historical wind farm spatial characteristics and several historical wind farm spatial analysis results, the initial wind farm layout planning model is optimized and trained to obtain the final wind farm layout planning model, including the following steps:

[0078] S1-6-1: Set preset layout planning decision indicators for each historical wind farm spatial analysis result to obtain several historical layout planning scheme formats;

[0079] S1-6-2: Analyze the spatial characteristics of each historical wind farm to obtain several historical impact factors. Based on these historical impact factors, set several historical optimization goals and obtain several historical fitness functions.

[0080] S1-6-3: Encode the current historical layout planning scheme format into individual vectors of the initial wind farm layout planning model, and optimize and train the initial wind farm layout planning model based on the current historical fitness function and the current individual vectors to obtain an optimized wind farm layout planning model;

[0081] S1-6-4: Traverse all historical fitness functions of each historical layout planning scheme format, repeat the previous step, continue training the optimized wind farm layout planning model, and obtain the final wind farm layout planning model;

[0082] S2: Collecting real-time GIS data of the wind farm, preprocessing the real-time GIS data to obtain preprocessed real-time GIS data, and importing the preprocessed real-time GIS data into the ArcGIS system;

[0083] Real-time GIS data includes real-time geospatial data, real-time initial wind farm layout planning scheme, real-time wind resource data, real-time terrain data, real-time land use data, and real-time ecological environment data;

[0084] S3: Based on the pre-processed real-time GIS data, use the wind farm spatial analysis model to perform wind farm spatial analysis and visualize the real-time wind farm spatial analysis results, including the following steps:

[0085] S3-1: Based on the ArcGIS system and the pre-processed real-time GIS data of the wind farm, a corresponding real-time wind farm spatial 3D simulation model is constructed, which includes the following steps:

[0086] S3-1-1: Project the pre-processed real-time GIS data of the same wind farm into a unified coordinate system;

[0087] Real-time GIS data includes real-time geospatial data, real-time initial wind farm layout planning scheme, real-time wind resource data, real-time terrain data, real-time land use data, and real-time ecological environment data;

[0088] S3-1-2: Create a three-dimensional terrain model using the terrain analysis tool of ArcGIS based on the preprocessed real-time terrain data in the preprocessed real-time GIS data;

[0089] S3-1-3: Based on the preprocessed real-time land use data and preprocessed real-time ecological environment data in the preprocessed real-time GIS data, corresponding layers are superimposed on the 3D terrain of the 3D terrain model to represent different land use types and ecological environment characteristics, thereby obtaining a 3D terrain model after superimposing the layers;

[0090] S3-1-4: Based on the real-time initial wind farm layout plan, wind turbines, towers, etc. are placed in the 3D terrain model after overlaying the layers. Parameters for each wind turbine are set, such as height, blade length, and model, to obtain a 3D terrain model after layout planning.

[0091] S3-1-5: Integrate the pre-processed real-time wind resource data in the pre-processed real-time GIS data into the three-dimensional terrain model after layout planning, set dynamic simulation parameters, simulate the operating status of wind power equipment under different wind speed conditions, and obtain a real-time three-dimensional simulation model of the wind farm space;

[0092] S3-2: Use the wind farm spatial feature extraction module of the wind farm spatial analysis model to extract wind farm spatial features from the real-time wind farm spatial 3D simulation model to obtain real-time wind farm spatial features;

[0093] S3-3: Based on the real-time wind farm spatial characteristics, use the wind farm spatial analysis module of the wind farm spatial analysis model to perform wind farm spatial analysis and obtain real-time wind farm spatial analysis results;

[0094] S3-4: Use the real-time wind farm spatial 3D simulation model to visualize the real-time wind farm spatial analysis results;

[0095] Real-time wind farm spatial analysis results include real-time wind resource distribution map analysis results, real-time wind farm space utilization analysis results, real-time wind farm spatial layout planning score analysis results, real-time wind farm environmental impact analysis results, real-time wind farm power generation efficiency analysis results, etc.

[0096] S4: Based on the real-time wind farm spatial analysis results, use the wind farm layout planning model to perform wind farm layout planning. The visualized real-time wind farm layout planning scheme includes the following steps:

[0097] S4-1: Input the real-time wind farm spatial analysis results and the real-time wind farm spatial characteristics into the wind farm layout planning model, use the format setting module of the wind farm layout planning model to match the real-time wind farm spatial analysis results with the real-time layout planning decision indicators, and set the real-time layout planning solution format based on the real-time layout planning decision indicators;

[0098] Real-time layout planning decision indicators include wind farm location decision indicators, wind power equipment quantity decision indicators, wind power equipment location decision indicators, wind power equipment parameter decision indicators, etc. The real-time layout planning scheme format includes real-time layout planning decisions for several wind power equipment;

[0099] S4-2: Use the impact factor generation module of the wind farm layout planning model to analyze the real-time wind farm spatial characteristics, obtain real-time impact factors, and set the real-time optimization target of the wind farm layout planning model based on the real-time impact factors;

[0100] The real-time influencing factors include real-time layout planning error factor, real-time layout planning accuracy factor, real-time wind resource utilization factor, real-time layout planning cost factor and real-time layout planning efficiency factor;

[0101] S4-3: using the fitness function update module of the wind farm layout planning model, updating the preset fitness function of the wind farm layout planning model according to the real-time optimization target to obtain a real-time fitness function;

[0102] This embodiment takes the real-time layout planning error factor and the real-time layout planning cost factor as an example, and the real-time optimization goal is to minimize the error and cost of the wind farm layout planning;

[0103] The formula of the real-time fitness function is:

[0104] Fit(P)=min[W1AX(P)+W2AC(P)]

[0105] Where Fit(P) is the real-time fitness function; AX(P) is the real-time layout planning error function; AC(P) is the real-time layout planning cost function; P is the ISGA individual; W1, W2 are the first weight value and the second weight value;

[0106] S4-4: using the initial solution generation module of the wind farm layout planning model, encoding the real-time layout planning scheme format into individual vectors of the wind farm layout planning model, and performing initial solution generation based on the individual vectors to obtain a plurality of initial solutions; the initial solutions correspond to the initial real-time layout planning schemes;

[0107] S4-5: Using the iterative update module of the wind farm layout planning model, several initial solutions are iteratively updated according to the real-time fitness function, and the optimal solution with the best real-time fitness value is output, including the following steps:

[0108] S4-5-1: Use the real-time fitness function to obtain the initial real-time fitness value of each initial ISGA individual in the initial ISGA population, and take the initial ISGA individual with the lowest real-time fitness value as the leader goose;

[0109] S4-5-2: Entering the exploration phase, the leader goose rotation mechanism, the calling guidance mechanism, and the dynamic reverse mechanism are introduced to iteratively update the initial ISGA population to obtain an updated ISGA population and retain the optimal individual;

[0110] The leader goose rotation mechanism selects a new leader goose in each iteration based on the fitness value of the ISGA individuals. This mechanism can prevent the leader goose from falling into the local optimum too early and enhance the global search capability of the algorithm.

[0111] The formula is:

[0112]

[0113] Where, P i t+1 Be the leader of an update; is the third-to-last initial ISGA individual in the initial ISGA population with the highest fitness value at the tth and t+1th iterations; is the fifth-to-last initial ISGA individual in the initial ISGA population with the highest fitness value at the tth iteration; t is the current iteration number; is the optimal individual; a is the first weight factor; rand is the random number generation function;

[0114] The calling guidance mechanism uses the sound wave propagation attenuation model to adjust the position update of the individual ISGA according to the distance between the individual and the leader goose. The position update of the ISGA individual that is closer is more affected by the leader goose, and it can quickly approach the optimal solution. The position update of the ISGA individual that is farther away is less affected by the leader goose, and it can maintain a certain exploration ability. This mechanism can avoid excessive aggregation or dispersion of the group and improve the local search accuracy of the algorithm.

[0115] The formula is:

[0116]

[0117] Where, It is an updated ISGA individual; is the initial ISGA individual of the tth iteration; is the sound intensity received by the initial ISGA individual; is the sound intensity parameter; L WA is the initial sound intensity; L low is the minimum acceptable sound intensity; a" is the convergence factor; is the initial ISGA individual with the farthest distance; r' is a random parameter; B(d) is the Brownian motion function; d is the Brownian motion parameter; is the XOR processing symbol;

[0118]

[0119] Where a" is the convergence factor; tanh(.) is the hyperbolic tangent function; t is the current number of iterations; t max is the maximum number of iterations; a max 、a min are the maximum and minimum values of the convergence factor, respectively; λ is the decreasing rate parameter, k' is the decreasing period parameter, λ = -2π, k' = π;

[0120] Dynamic reverse mechanism, which dynamically reverses the initial ISGA individuals to improve the diversity of exploration directions and avoid falling into local optimality;

[0121] The formula is:

[0122]

[0123] Where, is a reverse ISGA individual updated once; γ is the decreasing inertia coefficient; L max 、L min are the maximum and minimum values of the vector space respectively;

[0124] The leader goose of one update, several ISGA individuals of one update and several reverse ISGA individuals of one update are integrated to obtain an ISGA population of one update, and the ISGA individual with the lowest fitness value is retained as the optimal individual;

[0125] S4-5-3: Entering the development stage, introducing the abnormal boundary strategy and Gaussian mutation mechanism, performing a second update on the once-updated ISGA population, obtaining a second-updated ISGA population, and retaining the optimal individual;

[0126] Abnormal boundary strategy, calculates the difference between the fitness value of each updated ISGA individual and the average fitness value of the group. For ISGA individuals whose fitness value is much higher than the group average, their position update method will be adjusted, such as using Gaussian mutation mechanism, larger step size or smaller step size. This mechanism can help individuals avoid falling into local optimality and improve the convergence speed and accuracy of the algorithm;

[0127] The formula is:

[0128]

[0129] Where, is the second updated ISGA individual; is an updated ISGA individual; Fit(*) is the fitness function; Fit avg is the average fitness value of the group; is the ISGA individual with the highest fitness value; a' and e are the second and third weight factors; G(1,1) is the Gaussian mutation mechanism parameter;

[0130] S4-5-4: If the number of iterations is greater than or equal to the iteration number threshold or the real-time fitness value of the optimal individual meets the requirements, that is, it is greater than the upper limit of the fitness threshold or less than the lower limit of the fitness threshold, then the optimal individual is output as the optimal solution;

[0131] S4-6: Use the solution vector analysis module of the wind farm layout planning model to analyze the individual vectors of the optimal solution and obtain the optimal real-time layout planning solution;

[0132] S4-7: Use the real-time wind farm spatial 3D simulation model to visualize the optimal real-time layout planning scheme, including the following steps:

[0133] S4-7-1: Setting decision indicators based on the real-time wind farm location in the optimal real-time layout planning solution, adjusting the wind farm location of the real-time wind farm spatial three-dimensional simulation model, and obtaining the real-time wind farm spatial three-dimensional simulation model after the wind farm location adjustment;

[0134] S4-7-2: Based on the real-time wind turbine equipment quantity decision in the optimal real-time layout planning solution, adjust the number of wind turbines in the real-time wind farm spatial three-dimensional simulation model, including adding or deleting corresponding wind turbines, to obtain the real-time wind farm spatial three-dimensional simulation model after the wind turbine equipment quantity is adjusted;

[0135] S4-7-3: Adjust the wind turbine equipment positions in the real-time wind farm spatial three-dimensional simulation model based on the real-time wind turbine equipment location decision in the optimal real-time layout planning solution, and obtain the real-time wind farm spatial three-dimensional simulation model after the wind turbine equipment positions are adjusted;

[0136] S4-7-4: Based on the real-time wind power equipment parameter decision in the optimal real-time layout planning solution, adjust the wind power equipment parameters of the real-time wind farm spatial three-dimensional simulation model to obtain the real-time wind farm spatial three-dimensional simulation model after the wind power equipment parameters are adjusted;

[0137] S4-7-5: Visualize the real-time three-dimensional wind farm spatial simulation model after adjusting the wind power equipment parameters. By combining the three-dimensional simulation model, an intuitive display of the optimal real-time layout planning scheme is achieved.

[0138] Example 2:

[0139] like Figure 2As shown, this embodiment provides a wind farm layout planning system based on an optimization algorithm, which is used to implement a wind farm layout planning method. The system includes a model building unit, a real-time data acquisition unit, a wind farm space analysis unit, and a wind farm layout planning unit connected in sequence;

[0140] Model building unit, used to build wind farm spatial analysis model and wind farm layout planning model based on ArcGIS system using GeoAI and optimization algorithm;

[0141] A real-time data acquisition unit is used to collect real-time GIS data of the wind farm, pre-process the real-time GIS data to obtain pre-processed real-time GIS data, and import the pre-processed real-time GIS data into the ArcGIS system;

[0142] The wind farm spatial analysis unit is used to perform wind farm spatial analysis based on pre-processed real-time GIS data using a wind farm spatial analysis model, and visualize the obtained real-time wind farm spatial analysis results;

[0143] The wind farm layout planning unit is used to perform wind farm layout planning based on the real-time wind farm spatial analysis results and use the wind farm layout planning model to visualize the real-time wind farm layout planning scheme.

[0144] The present invention discloses a wind farm layout planning method and system based on an optimization algorithm, which fully considers the actual needs of the wind farm and its impact on the environment, realizes scientific and reasonable layout planning, and significantly improves the power generation efficiency and economic benefits of the wind farm; GIS data includes multi-faceted and multi-dimensional data such as geographic space, wind resources, terrain, land and ecological environment, and wind farm layout planning is carried out based on GIS data, comprehensively considering multiple influencing factors, thereby improving the adaptability of wind farm layout planning to complex and changeable natural environments; using the ArcGIS system and advanced artificial intelligence and swarm intelligence optimization algorithms, the dependence on manual experience is greatly reduced, the labor cost and workload are reduced, and through the automated spatial analysis mechanism and automatic layout planning mechanism, the intelligent automated layout planning of the wind farm is realized, meeting the needs of efficient and accurate planning of modern wind farms.

[0145] The present invention is not limited to the above optional embodiments. Anyone can derive various other forms of products based on the teachings of the present invention. The above specific embodiments should not be construed as limiting the scope of protection of the present invention. The scope of protection of the present invention shall be based on the scope defined in the claims, and the description can be used to interpret the claims.

Claims

1. A wind farm layout planning method based on an optimization algorithm, characterized by: The steps include: Based on the ArcGIS system, using GeoAI and optimization algorithms, we built a wind farm spatial analysis model and a wind farm layout planning model; Collect real-time GIS data of the wind farm, pre-process the real-time GIS data to obtain pre-processed real-time GIS data, and import the pre-processed real-time GIS data into the ArcGIS system; Based on the pre-processed real-time GIS data, the wind farm spatial analysis model is used to perform wind farm spatial analysis and visualize the real-time wind farm spatial analysis results. Based on the real-time wind farm spatial analysis results, the wind farm layout planning model is used to carry out wind farm layout planning, and the real-time wind farm layout planning scheme obtained is visualized.

2. The wind farm layout planning method based on an optimization algorithm according to claim 1, characterized in that: Based on the ArcGIS system, using GeoAI and optimization algorithms, we constructed a wind farm spatial analysis model and a wind farm layout planning model, which includes the following steps: Collect and pre-process several historical GIS data of different wind farms to obtain several pre-processed historical GIS data, and then import the pre-processed historical GIS data into the ArcGIS system; Based on the ArcGIS system, a corresponding historical wind farm spatial 3D simulation model is constructed according to the pre-processed historical GIS data of each wind farm; Use the deep learning and spatial data analysis fusion algorithm in the GeoAI tool to build an initial wind farm spatial analysis model; Based on several historical wind farm spatial three-dimensional simulation models, the initial wind farm spatial analysis model is optimized and trained to obtain the final wind farm spatial analysis model, and several historical wind farm spatial characteristics and several historical wind farm spatial analysis results are generated; Use the swarm intelligence optimization algorithm in the GeoAI tool to build an initial wind farm layout planning model; According to several historical wind farm spatial characteristics and several historical wind farm spatial analysis results, the initial wind farm layout planning model is optimized and trained to obtain the final wind farm layout planning model.

3. The wind farm layout planning method based on an optimization algorithm according to claim 2, characterized in that: The wind farm spatial analysis model is constructed based on the 3D-DBN-GAT algorithm, and includes a wind farm spatial feature extraction module constructed based on the 3D-DBN algorithm and a wind farm spatial analysis module constructed based on the GAT algorithm.

4. The wind farm layout planning method based on an optimization algorithm according to claim 3, characterized in that: Based on several historical wind farm spatial three-dimensional simulation models, the initial wind farm spatial analysis model is optimized and trained to obtain a final wind farm spatial analysis model, and several historical wind farm spatial features and several historical wind farm spatial analysis results are generated, including the following steps: Setting a corresponding real wind farm spatial analysis result for each historical wind farm spatial three-dimensional simulation model, and dividing several historical wind farm spatial three-dimensional simulation models into a model test set and a model training set; Input the model training set, optimize the initial wind farm spatial analysis model and obtain the optimized wind farm spatial analysis model; Input the model test set to test the optimized wind farm spatial analysis model and obtain several historical wind farm spatial analysis results; Compare and statistically analyze several historical wind farm spatial analysis results obtained from the model test with several corresponding real wind farm spatial analysis results to obtain the model test accuracy; If the test accuracy of several models is greater than the accuracy threshold, the final wind farm spatial analysis model is output, and several historical wind farm spatial features and several historical wind farm spatial analysis results are generated.

5. The wind farm layout planning method based on optimization algorithm according to claim 4, characterized in that: The wind farm layout planning model is constructed based on the ISGA algorithm, and the wind farm layout planning model includes a format setting module, an impact factor generation module, a fitness function update module, an initial solution generation module, an iterative update module and a solution vector analysis module which are connected in sequence.

6. The wind farm layout planning method based on optimization algorithm according to claim 5, characterized in that: Based on several historical wind farm spatial characteristics and several historical wind farm spatial analysis results, the initial wind farm layout planning model is optimized and trained to obtain the final wind farm layout planning model, including the following steps: Setting preset layout planning decision indicators for each historical wind farm spatial analysis result to obtain several historical layout planning scheme formats; Analyze the spatial characteristics of each historical wind farm to obtain several historical influencing factors. Based on these historical influencing factors, set several historical optimization goals and obtain several historical fitness functions. The current historical layout planning scheme format is encoded into the individual vectors of the initial wind farm layout planning model, and based on the current historical fitness function and the current individual vectors, the initial wind farm layout planning model is optimized and trained to obtain the optimized wind farm layout planning model; Traverse all historical fitness functions of each historical layout planning scheme format, repeat the previous step, continue to train the optimized wind farm layout planning model, and obtain the final wind farm layout planning model.

7. The wind farm layout planning method based on optimization algorithm according to claim 6, characterized in that: Based on the pre-processed real-time GIS data, the wind farm spatial analysis model is used to perform wind farm spatial analysis and visualize the real-time wind farm spatial analysis results, including the following steps: Based on the ArcGIS system, a corresponding real-time wind farm spatial 3D simulation model is constructed according to the pre-processed real-time GIS data of the wind farm; Use the wind farm spatial feature extraction module of the wind farm spatial analysis model to extract wind farm spatial features from the real-time wind farm spatial three-dimensional simulation model to obtain real-time wind farm spatial features; According to the real-time wind farm spatial characteristics, the wind farm spatial analysis module of the wind farm spatial analysis model is used to perform wind farm spatial analysis to obtain real-time wind farm spatial analysis results; Use a real-time wind farm spatial 3D simulation model to visualize the real-time wind farm spatial analysis results.

8. The wind farm layout planning method based on optimization algorithm according to claim 7, characterized in that: Based on the real-time wind farm spatial analysis results, the wind farm layout planning model is used to carry out wind farm layout planning. The visualized real-time wind farm layout planning scheme includes the following steps: Inputting the real-time wind farm spatial analysis results and the real-time wind farm spatial characteristics into the wind farm layout planning model, using the format setting module of the wind farm layout planning model to match the real-time wind farm spatial analysis results with real-time layout planning decision indicators, and setting the real-time layout planning scheme format according to the real-time layout planning decision indicators; Using the impact factor generation module of the wind farm layout planning model, the real-time wind farm spatial characteristics are analyzed to obtain real-time impact factors. Based on the real-time impact factors, the real-time optimization goals of the wind farm layout planning model are set; Using the fitness function update module of the wind farm layout planning model, the preset fitness function of the wind farm layout planning model is updated according to the real-time optimization target to obtain a real-time fitness function; Using an initial solution generation module of a wind farm layout planning model, encoding a real-time layout planning scheme format into individual vectors of the wind farm layout planning model, and generating initial solutions based on the individual vectors to obtain a plurality of initial solutions; the initial solutions corresponding to the initial real-time layout planning schemes; Using the iterative update module of the wind farm layout planning model, several initial solutions are iteratively updated according to the real-time fitness function, and the optimal solution with the best real-time fitness value is output; Use the solution vector analysis module of the wind farm layout planning model to analyze the individual vectors of the optimal solution and obtain the optimal real-time layout planning solution; Use a real-time 3D wind farm spatial simulation model to visualize the optimal real-time layout planning solution.

9. A wind farm layout planning system based on an optimization algorithm, used to implement the wind farm layout planning method according to any one of claims 1 to 8, characterized in that: The system comprises a model building unit, a real-time data acquisition unit, a wind farm space analysis unit and a wind farm layout planning unit which are connected in sequence.

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