Wind power prediction method and system based on physical feature enhanced SBLS

Abstract

The invention relates to the technical field of wind power prediction, and discloses a physical feature enhanced SBLS-based wind power prediction method and system. The method comprises the steps of obtaining operation data of a wind turbine generator SCADA system, regarding a constructed wind speed-power dynamic characteristic curve and a decision interval as power dynamic curve knowledge, mapping the power dynamic curve knowledge into physical constraint layer nodes parallel to characteristic mapping layer nodes, and constructing an SBLS network topology containing a physical constraint layer; training the embedded SBLS prediction model containing the physical constraint layer by using a training set, outputting and combining the feature mapping layer, the enhanced mapping layer and the physical constraint layer, and constructing an augmented matrix; analyzing and solving an output weight by using a pseudo-inverse algorithm; and applying the trained SBLS prediction model containing the physical constraint layer to a test set for wind power prediction. According to the method, physical knowledge is explicitly embedded by changing a network topology structure, so that the problem of poor physical interpretability of a standard SBLS is effectively solved, and the training speed is remarkably superior to that of a deep neural network.

Description

Technical Field

[0001] This application belongs to the field of wind power prediction technology, and specifically relates to a wind power prediction method and system based on physical feature enhancement SBLS. Background Technology

[0002] With the continuous growth of global demand for clean energy, the development of renewable energy is showing a rapid trend. In the context of digital operation of wind farms, massive amounts of SCADA data provide the foundation for power prediction. As the scale of wind farms expands, higher demands are placed on the training speed and deployment efficiency of prediction models. Traditional prediction methods often face problems such as high computational resource consumption and long model update cycles, making it difficult to meet the actual needs of wind farms for rapid response and online learning. With the continuous development of machine learning technology, more and more nonlinear models are being introduced into the field of time series prediction. In wind power prediction tasks, many traditional shallow models such as random forests, linear regression, and gradient boosting decision trees are still widely used. In recent years, however, deep learning technology, especially long short-term memory networks and gated recurrent units, has become an indispensable tool in wind power prediction due to its excellent nonlinear modeling capabilities. Although machine learning has achieved significant predictive results in wind power prediction tasks, its models usually contain a large number of parameters, which not only makes the training process more complex but also greatly extends training time and computational costs. To alleviate this problem, Stacked Broad Learning System (SBLS), as an emerging learning architecture, has been applied to wind power prediction tasks. Unlike traditional deep learning methods, SBLS, by constructing a wide network structure rather than a deeply stacked hierarchical structure, can effectively reduce model training time and complexity while maintaining prediction accuracy. However, in practical applications, many studies often overlook the dynamic relationship between wind power and wind speed when constructing SBLS, a characteristic that plays a crucial role in the power output of wind turbines.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To address or at least alleviate one or more of the above problems, a wind power prediction method and system based on physical feature-enhanced SBLS is provided.

[0005] To achieve the above objectives, according to the first aspect of this application, a wind power prediction method based on physically enhanced SBLS is provided, comprising the following steps: Acquire the operating data of the wind turbine SCADA system, including wind speed and power; divide the preprocessed operating data into training set and test set in chronological order; Based on the operational data in the training set, a wind speed-power scatter plot was established. The wind speed interval was discretized using the binning method, that is, the wind speed was divided into partitions with an interval of 1 m / s, and the centroid of each interval was calculated. The centroid was used as the characteristic trend point. The trend point was fitted using the B-spline interpolation method to construct the wind speed-power dynamic characteristic curve. Based on the aforementioned wind speed-power dynamic characteristic curve, a wind speed-power decision interval is constructed. The constructed wind speed-power dynamic characteristic curve and decision interval are regarded as power dynamic curve knowledge, and mapped to physical constraint layer nodes parallel to feature mapping layer nodes, thus obtaining the SBLS prediction model with physical constraint layer, i.e., the SBLS prediction model with physical feature enhancement. The SBLS prediction model with embedded physical constraint layer is trained using the training set. The outputs of the feature mapping layer, enhancement mapping layer, and physical constraint layer are merged to construct an augmented matrix. The output weights are then analytically solved using a pseudo-inverse algorithm. The trained SBLS prediction model with physical constraint layers is applied to the test set for wind power prediction.

[0006] To achieve the above objectives, according to a second aspect of this application, a wind power prediction system based on Physical Feature Enhancement (SBLS) is provided, the wind power prediction system comprising: The acquisition module is used to acquire the operating data of the wind turbine SCADA system, including wind speed and power; the preprocessed operating data is divided into training set and test set in chronological order; The module is used to build a wind speed-power scatter plot based on the running data in the training set. The wind speed interval is discretized by binning, that is, the wind speed is divided into intervals of 1 m / s, and the centroid of each interval is calculated and used as the feature trend point. The trend point is fitted by B-spline interpolation method to construct the wind speed-power dynamic characteristic curve. Based on the aforementioned wind speed-power dynamic characteristic curve, a wind speed-power decision interval is constructed. The training module is used to treat the constructed wind speed-power dynamic characteristic curve and decision interval as power dynamic curve knowledge, and map them into physical constraint layer nodes parallel to the feature mapping layer nodes to obtain the SBLS prediction model with physical constraint layer. The SBLS prediction model with embedded physical constraint layer is trained using the training set. The outputs of the feature mapping layer, enhancement mapping layer, and physical constraint layer are merged to construct an augmented matrix. The output weights are then analytically solved using a pseudo-inverse algorithm. The application module is used to apply the trained SBLS prediction model with physical constraint layers to the test set for wind power prediction.

[0007] To achieve the above objectives, according to a third aspect of this application, a computer-readable storage medium is provided storing a computer program, which, when executed by a processor, is used to implement the wind power prediction method based on physical feature enhancement SBLS as described above.

[0008] By adopting the above technical solution, this application has the following beneficial effects compared with the prior art: This application introduces a method for constructing wind speed-power dynamic characteristic curves and wind speed-power decision intervals. First, a wind speed-power scatter plot is established with wind speed as the horizontal axis and power as the vertical axis. Then, the continuous wind speed domain is discretized using a binning method, and the centroid of each interval is extracted as a representative trend point. The wind speed-power dynamic characteristic curve is obtained by fitting these trend points using B-spline interpolation. Then, all characteristic trend points are traversed, and the deviation of data points near the trend points from the power curve is calculated. Based on these deviations, MAD is used to identify upper and lower boundary points, and then B-spline interpolation is used to fit the upper and lower boundaries. Points within the upper and lower boundaries are considered as decision intervals. This invention treats the wind speed-power dynamic characteristic curve and wind speed-power decision interval as knowledge of the power dynamic operation curve. This provides a reliable prior foundation for subsequent embedding of SBLS prediction models and constructing SBLS with physical constraint layers.

[0009] In this application, a dedicated physical constraint layer is constructed to explicitly embed the extracted dynamic operating curve knowledge into the network topology of the stacked width learning system. Compared with traditional SBLS prediction models without a physical constraint layer, this method not only significantly enhances the model's fitting ability and generalization performance when dealing with the complex nonlinear and fluctuating conditions of wind power, but also effectively suppresses the irrationality of prediction results through physical constraints, thereby improving the accuracy and physical interpretability of the prediction results.

[0010] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. Attached Figure Description

[0011] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application. The illustrative embodiments and descriptions of the application are used to explain the application, but do not constitute an undue limitation of the application. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0012] In the attached diagram: Figure 1This is a flowchart illustrating the wind power prediction method based on Physical Feature Enhancement (SBLS) in this specific embodiment. Figure 2 This is a scatter plot of wind speed-power on the training set in this specific embodiment; Figure 3 This is a schematic diagram of the power dynamic characteristic curve identified based on B-spline interpolation in this specific embodiment; Figure 4 This is a schematic diagram of the wind speed-power decision interval constructed based on the MAD method in this specific embodiment; Figure 5 This is a prediction fitting curve of the SBLS model containing the physical feature layer in this specific embodiment. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0014] Please see Figure 1 This application provides a wind power prediction method based on Physical Feature Enhancement (SBLS), comprising the following steps: Acquire the operating data of the wind turbine SCADA system, including wind speed and power; divide the preprocessed operating data into training set and test set in chronological order; Based on the operational data in the training set, a wind speed-power scatter plot was established. The wind speed interval was discretized using the binning method, that is, the wind speed was divided into partitions with an interval of 1 m / s, and the centroid of each interval was calculated. The centroid was used as the characteristic trend point. The trend point was fitted using the B-spline interpolation method to construct the wind speed-power dynamic characteristic curve. Based on the aforementioned wind speed-power dynamic characteristic curve, a wind speed-power decision interval is constructed. The constructed wind speed-power dynamic characteristic curve and decision interval are regarded as power dynamic curve knowledge, and mapped to physical constraint layer nodes parallel to feature mapping layer nodes, thus obtaining the SBLS prediction model with physical constraint layer, i.e., the SBLS prediction model with physical feature enhancement. The SBLS prediction model with embedded physical constraint layer is trained using the training set. The outputs of the feature mapping layer, enhancement mapping layer, and physical constraint layer are merged to construct an augmented matrix. The output weights are then analytically solved using a pseudo-inverse algorithm. The trained SBLS prediction model with physical constraint layers is applied to the test set for wind power prediction.

[0015] In this embodiment, the power generation of wind turbines is used as the prediction target, and a physical feature enhanced SBLS prediction model is established. First, a wind speed-power scatter plot is established with wind speed as the independent variable and output power as the dependent variable. Based on the changes in wind speed, the wind speed intervals are discretized using a binning method. Then, the centroid of each wind speed interval is calculated, serving as the characteristic trend point for that interval. By determining the wind speed and power values ​​at 95% of the maximum power, the rated power and rated wind speed are determined, and a constant power range is set after the rated wind speed to ensure that these points reflect the actual variation between wind speed and power. Next, all trend points are arranged in ascending order according to wind speed, and B-spline interpolation is used to fit these points, generating a smooth power dynamic characteristic curve. Based on this curve, a wind speed-power decision interval is further constructed to define the upper and lower limits of power prediction. By analyzing the power residuals near the characteristic trend points, the MAD method is used to evaluate the distribution of the residuals, thereby determining the upper and lower limits of power prediction. These upper and lower limit boundary points are also fitted using B-spline interpolation to construct decision intervals that conform to physical laws. This invention treats the wind speed-power dynamic characteristic curve and decision interval as dynamic power curve knowledge. Finally, this dynamic power curve knowledge is embedded into the SBLS model, forming a physical constraint layer. During the training process of the SBLS model, external physical laws are introduced, thereby adding physical constraints during data learning. In this way, the SBLS model can effectively combine data-driven learning with prior physical knowledge, improving the model's predictive ability while ensuring that the prediction results conform to the physical characteristics of the wind turbine in actual operation. This invention provides a more accurate solution for wind power prediction that conforms to actual operating laws by integrating physical knowledge and data learning. By introducing the dynamic characteristics of wind speed-power and physically meaningful decision intervals, prediction accuracy can be effectively improved, and the model's prediction results can better match the actual operating characteristics of wind turbines.

[0016] This embodiment employs a wind power prediction method based on Physical Feature Enhancement (SBLS), as shown in the flowchart below. Figure 1 As shown, the specific steps include: Step 1: Obtain operational data from the SCADA system of an actual generator unit, including key parameters such as wind speed and power. Raw data often contains missing or outlier values. To ensure high-quality input data, this embodiment employs a rapid cleaning strategy based on global statistical distribution. Specific steps include: for wind speed data, using the quartile method for outlier detection to ensure data stability; for other key data such as power and pitch angle, using the 3σ method for outlier detection to avoid the impact of extreme data on the model; and uniformly correcting missing and outlier values ​​using ridge regression extrapolation to ensure data continuity and the integrity of the time series.

[0017] Step two involves dividing the processed data into training and testing sets in chronological order. Specifically, the first 80% of the samples are used as the training set to build knowledge of the power dynamic curve and train the prediction model; while the remaining 20% ​​of the samples are used as the testing set to evaluate the generalization ability and actual prediction performance of the proposed model.

[0018] Step 3: Based on the training data obtained in Step 2, construct a scatter plot with wind speed as the independent variable and power as the dependent variable, as shown below. Figure 2 As shown, the wind speed intervals are discretized using a binning method, that is, the wind speed is divided into sections with an interval of 1 m / s, and the centroid of each interval is calculated and used as the characteristic trend point. Then, the B-spline interpolation method is used to fit these trend points to obtain a continuous and smooth wind speed-power dynamic characteristic curve, as shown. Figure 3 As shown, this characterizes the nonlinear mapping relationship between wind speed and power.

[0019] In this embodiment, the method used to identify feature trend points refers to: A wind speed-power scatter plot was created with wind speed and power as independent variables. The wind speed was then divided into zones with intervals of 1 m / s. The centroid of each zone was calculated. Specifically, the x-axis of the characteristic trend point represents the average wind speed of the current zone, and the y-axis represents the average power value of the zone. The calculated centroids were used as characteristic trend points, and B-spline interpolation was used to fit these trend points to obtain the wind speed-power dynamic characteristic curve.

[0020] In this embodiment, the B-spline interpolation method is as follows: Feature trend points identified on the training set The wind speed-power dynamic characteristic curve is fitted using the B-spline interpolation method. The B-spline interpolation method is defined as follows: Given B-spline basis functions Basis functions of order 0: ; Basis functions of order 1: ; Basis functions of order 2: ; Basis functions of order 3: ; Wind speed-power dynamic characteristic curve fitted by B-spline interpolation: ; in, It is the first Wind speed values ​​at each characteristic trend point It is the power value of the i-th key trend point. , , and They are respectively in the wind speed range [ , B-spline basis functions of orders 0, 1, 2, and 3 on [ ] It is the fitted wind speed-power dynamic characteristic curve, representing the theoretical power at a given wind speed v.

[0021] Step four: Based on the wind speed-power dynamic characteristic baseline curve constructed in step (3), this embodiment further constructs the wind speed-power decision interval as follows: Figure 4 As shown. The specific steps are as follows: First, calculate the deviation distance between the nearby data points and the dynamic characteristic curve for each characteristic trend point; then, use the MAD method to perform robustness analysis on these deviations, determine the upper and lower bounds of the residuals, and obtain the decision points; finally, use the B-spline interpolation method to fit the upper and lower bounds to form continuous upper and lower boundaries. The area between the upper and lower boundaries is the wind speed-power decision interval, used to limit the reasonable range of power prediction.

[0022] In this embodiment, the wind speed-power decision interval construction method refers to: Based on the wind speed-power dynamic characteristic curve constructed above, the deviation distance from the dynamic characteristic curve to the data points near the selected trend point is calculated for each characteristic trend point. The MAD method is used to perform robustness analysis on these deviations to find the upper and lower boundary points. Then, the B-spline interpolation method is used again to fit the upper and lower boundaries to obtain the upper and lower boundaries. The points within the boundaries are regarded as the decision interval.

[0023] MAD stands for: Calculate the residuals: ; Median residual: ; Calculate the absolute deviation: ; Calculate MAD: ; Calculate the upper and lower boundaries: ; ; in, It is the first Power values ​​near a characteristic trend point yes The power value on the corresponding power dynamic characteristic curve, It is the power deviation between the actual power and the power dynamic characteristic curve. (·) is the median function. It is the median of the residuals. It is the absolute deviation of the median. It is the first The upper boundary point corresponding to each characteristic trend point It is the first The lower boundary point corresponding to each characteristic trend point.

[0024] Step five: Building upon step four, this embodiment treats the identified wind speed-power dynamic characteristic curve and decision interval as power dynamic curve knowledge and embeds them into the SBLS prediction model, resulting in an SBLS with a physical constraint layer, i.e., a SBLS with enhanced physical features. To enhance the physical interpretability of the model, a physical constraint layer is introduced into the SBLS, ensuring that the model's prediction results conform to the actual operating characteristics of wind turbine units.

[0025] In this embodiment, the knowledge of the power dynamic curve refers to: Wind speed-power dynamic characteristic curve: ; Wind speed-power decision range: Upper boundary: ; Lower boundary: ; in, and These are the dynamic operating curves corresponding to wind speeds, and the power values ​​corresponding to the upper and lower decision boundaries v, respectively. It's the wind speed value. It's the cut-in wind speed. That is the rated wind speed. These are the turning wind speeds corresponding to the upper and lower boundary curves; It is the rated power of the wind speed-power dynamic characteristic curve.

[0026] Step six: Train the SBLS model with the physical constraint layer using the training set. Combine the outputs of the feature mapping layer, augmentation mapping layer, and physical constraint layer to construct an augmented matrix. Analyze the output weights analytically using a pseudo-inverse algorithm, avoiding the tedious iterative training process.

[0027] In this embodiment, SBLS with a physical constraint layer refers to: Feature mapping layer: ; ; Enhanced mapping layer: ; ; Physical constraint layer: ; ; Augmented matrix: ; ; Output result: ; in, ; Final prediction result: ; in, It is the input feature matrix. These are the activation functions for the feature mapping layer and the enhanced feature layer; in this embodiment, ReLU is used. It is the output matrix of the feature mapping layer of the k-th layer. It is the weight matrix of the feature mapping layer of the k-th layer. It is the bias term of the feature mapping layer of the k-th layer. It is the output matrix of the k-th enhancement mapping layer. It is the weight matrix of the k-th layer of the enhancement mapping layer. It is the bias term of the enhanced feature layer of the k-th layer. It is the output of the physical constraint layer. It is the weight of the power dynamic characteristic curve. It is the weighting of the wind speed-power range. It is the augmented matrix of the k-th layer. This is the output prediction result of the k-th layer. It is the regression parameter matrix of the k-th layer. It is the regularization term of the k-th layer, which prevents overfitting. It is the true power value of the training set. This is the final prediction result; each layer is stacked together based on residual cascades.

[0028] Step seven, finally, evaluate the performance of the prediction results. RMSE, MAE, and R² metrics are used to quantitatively assess the model's prediction accuracy.

[0029] In this embodiment, RMSE, MAE, R 2 It means RMSE definition: ; MAE Definition: ; R 2 definition: ; Where m is the amount of data in the test set. Is the model in the first Power prediction values ​​at each time step. It is the first The actual power value at each time step It is the average power value on the test set.

[0030] Step 8: The SBLS prediction model with physical feature layer is compared and analyzed with the standard SBLS prediction model, as well as with the deep learning BiGRU. The evaluation metrics and training time of the three are shown in Table 1.

[0031] Table 1: Quantitative Evaluation Table of Performance Indicators for Standard SBLS and SBLS with Physical Feature Layer

[0032] As shown in Table 1, this method demonstrates a significant performance improvement compared to BiGRU and standard SBLS. Regarding accuracy, thanks to the constraints of the physical mechanism, this method achieves the lowest RMSE and the highest R-value. 2 It even outperforms deep neural networks. In terms of efficiency, this method perfectly inherits the fast computation characteristics of SBLS, taking only 8.97 seconds, with only a negligible increase in computational overhead compared to standard SBLS. This proves that this method is a lightweight solution that combines high accuracy and high efficiency.

[0033] After verifying the embedding of dynamic operating mechanism knowledge into the model, this method significantly improves prediction accuracy, generalization ability, and physical interpretability while ensuring the computational efficiency and training speed of the model, thus proving the feasibility and superiority of the method in this embodiment.

[0034] This application also provides a wind power prediction system based on Physical Feature Enhancement (SBLS), the wind power prediction system comprising: The acquisition module is used to acquire the operating data of the wind turbine SCADA system, including wind speed and power; the preprocessed operating data is divided into training set and test set in chronological order; The module is used to build a wind speed-power scatter plot based on the running data in the training set. The wind speed interval is discretized by binning, that is, the wind speed is divided into intervals of 1 m / s, and the centroid of each interval is calculated and used as the feature trend point. The trend point is fitted by B-spline interpolation method to construct the wind speed-power dynamic characteristic curve. Based on the aforementioned wind speed-power dynamic characteristic curve, a wind speed-power decision interval is constructed. The training module is used to treat the constructed wind speed-power dynamic characteristic curve and decision interval as power dynamic curve knowledge, and map them into physical constraint layer nodes parallel to the feature mapping layer nodes to obtain the SBLS prediction model with physical constraint layer. The SBLS prediction model with embedded physical constraint layer is trained using the training set. The outputs of the feature mapping layer, enhancement mapping layer, and physical constraint layer are merged to construct an augmented matrix. The output weights are then analytically solved using a pseudo-inverse algorithm. The application module is used to apply the trained SBLS prediction model with physical constraint layers to the test set for wind power prediction.

[0035] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the wind power prediction method based on physical feature enhancement SBLS as described above.

[0036] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has disclosed preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-mentioned technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. The implementation schemes in the above embodiments can also be further combined or replaced. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of this application.

Claims

1. A wind power prediction method based on Physical Feature Enhancement (SBLS), characterized in that, Includes the following steps: Acquire the operating data of the wind turbine SCADA system, including wind speed and power; divide the preprocessed operating data into training set and test set in chronological order; Based on the operational data in the training set, a wind speed-power scatter plot was established. The wind speed interval was discretized using the binning method, that is, the wind speed was divided into intervals of 1 m / s, and the centroid of each interval was calculated. The centroid was used as the characteristic trend point. The trend points were fitted using the B-spline interpolation method to construct the wind speed-power dynamic characteristic curve; Based on the aforementioned wind speed-power dynamic characteristic curve, a wind speed-power decision interval is constructed. The constructed wind speed-power dynamic characteristic curve and decision interval are regarded as power dynamic curve knowledge. The power dynamic curve knowledge is mapped to physical constraint layer nodes parallel to the feature mapping layer nodes, resulting in an SBLS prediction model with physical constraint layer, i.e., a physical feature enhanced SBLS prediction model. The SBLS prediction model with the embedded physical constraint layer is trained using the training set. The outputs of the feature mapping layer, the enhancement mapping layer, and the physical constraint layer are merged to construct an augmented matrix. The output weights are then analytically solved using a pseudo-inverse algorithm. The trained SBLS prediction model with physical constraint layers is applied to the test set for wind power prediction.

2. The method according to claim 1, characterized in that, The preprocessing includes outlier detection using the quartile method for wind speed data and the 3σ method for power data; missing values ​​and outliers are corrected using ridge regression extrapolation to ensure data continuity and the integrity of the time series.

3. The method according to claim 2, characterized in that, The method of fitting trend points using B-spline interpolation to construct the wind speed-power dynamic characteristic curve includes: Feature trend points identified on the training set The wind speed-power dynamic characteristic curve is fitted using the B-spline interpolation method; the B-spline interpolation method is defined as follows: Given B-spline basis functions: Basis functions of order 0: ； Basis functions of order 1: ； Basis functions of order 2: ； Basis functions of order 3: ； Wind speed-power dynamic characteristic curve fitted by B-spline interpolation: ； in, It is the first Wind speed values ​​at each characteristic trend point , , , They represent the first , , , Wind speed values ​​at each characteristic trend point It is the power value of the i-th key trend point. , , and They are respectively in the wind speed range [ , B-spline basis functions of orders 0, 1, 2, and 3 on [ ] It is the fitted wind speed-power dynamic characteristic curve, representing the theoretical power at a given wind speed v.

4. The method according to claim 3, characterized in that, The wind speed-power decision range includes: Based on the constructed wind speed-power dynamic characteristic curve, the deviation distance from the dynamic characteristic curve to the data points near the selected trend point is calculated for each characteristic trend point. The MAD method is used to perform robustness analysis on the deviation to find the upper and lower boundary points. Then, the B-spline interpolation method is used to fit the upper and lower boundaries to obtain the upper and lower boundaries. The area within the upper and lower boundaries is the wind speed-power decision interval.

5. The method according to claim 4, characterized in that, The MAD method includes: Calculate the residuals: ； Median residual: ； Calculate the absolute deviation: ； Calculate MAD: ； Calculate the upper and lower boundaries: ； ； in, It is the first Power values ​​near a characteristic trend point yes The power value on the corresponding power dynamic characteristic curve, It is the first …the actual power and the power deviation on the power dynamic characteristic curve corresponding to n characteristic trend points. (·) is the median function. It is the median of the residuals. It is the absolute deviation of the median. It is the first The upper boundary point corresponding to each characteristic trend point It is the first The lower boundary point corresponding to each characteristic trend point.

6. The method according to claim 3, characterized in that, The constructed wind speed-power dynamic characteristic curve and decision interval are considered as power dynamic curve knowledge; whereby... Wind speed-power dynamic characteristic curve: ； The wind speed-power decision range includes: Upper boundary: ； Lower boundary: ； in, and These are the dynamic operating curves corresponding to wind speeds, and the power values ​​corresponding to the upper and lower decision boundaries v, respectively. It's the wind speed value. It's the cut-in wind speed. That is the rated wind speed. , , , These are the turning wind speeds corresponding to the upper and lower boundary curves; It is the rated power of the wind speed-power dynamic characteristic curve.

7. The method according to claim 6, characterized in that, The SBLS prediction model with a physical constraint layer is: Feature layer mapping: ； ； Enhancement layer mapping: ； ； Physical constraint layer: ； ； Augmented matrix: ； ； Output result: ； Final prediction result: ； in, It is the input feature matrix. It consists of wind speed data from the training set. The matrix, These are the activation functions of the feature mapping layer and the enhanced feature layer. It is the output matrix of the feature mapping layer of the k-th layer. It is the weight matrix of the feature mapping layer of the k-th layer. It is the bias term of the feature mapping layer of the k-th layer. It is the output matrix of the k-th enhancement mapping layer. It is the weight matrix of the k-th layer of the enhancement mapping layer. It is the bias term of the enhanced feature layer of the k-th layer. It is the output of the physical constraint layer. It is the weight of the power dynamic characteristic curve. It is the weighting of the wind speed-power range. It is the augmented matrix of the k-th layer. This is the output prediction result of the k-th layer. This is the regression parameter matrix of the k-th layer, obtained by using ridge regression to find the pseudo-inverse. , It is the true power value of the training set. This is the final prediction result; each layer is stacked together based on residual cascades. It represents a matrix with N rows and p columns.

8. A wind power prediction system based on Physical Feature Enhancement-Simplified SBLS, characterized in that, The wind power prediction system includes: The acquisition module is used to acquire the operating data of the wind turbine SCADA system, including wind speed and power; the preprocessed operating data is divided into training set and test set in chronological order; The module is used to build a wind speed-power scatter plot based on the running data in the training set. The wind speed interval is discretized by binning, that is, the wind speed is divided into intervals of 1 m / s, and the centroid of each interval is calculated and used as the feature trend point. The trend point is fitted by B-spline interpolation method to construct the wind speed-power dynamic characteristic curve. Based on the aforementioned wind speed-power dynamic characteristic curve, a wind speed-power decision interval is constructed. The training module is used to treat the constructed wind speed-power dynamic characteristic curve and decision interval as power dynamic curve knowledge, and to map the power dynamic curve knowledge as physical constraint layer nodes parallel to the feature mapping layer nodes, so as to obtain the SBLS prediction model with physical constraint layer, that is, the SBLS prediction model with physical feature enhancement. The SBLS prediction model with the embedded physical constraint layer is trained using the training set. The outputs of the feature mapping layer, the enhancement mapping layer, and the physical constraint layer are merged to construct an augmented matrix. The output weights are then analytically solved using a pseudo-inverse algorithm. The application module is used to apply the trained SBLS prediction model with physical constraint layers to the test set for wind power prediction.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it is used to implement the wind power prediction method based on physical feature enhancement SBLS as described in any one of claims 1-7.

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