New energy power generation equipment adaptive control system based on multi-scale data fusion

Abstract

The invention discloses a new energy power generation equipment adaptive control system based on multi-scale data fusion, and relates to the technical field of power generation control, and the system comprises the steps: obtaining a multi-source power data set corresponding to each composite fuel type; performing multi-scale feature convolution on the multi-source power data set, and outputting a multi-source high-dimensional data set; fusing the multi-source high-dimensional data set to generate a fused high-dimensional data set; receiving equipment fault information, and generating equipment control parameter items; performing feature dimension reduction on the fused high-dimensional data set, and outputting a fused low-dimensional data set; and training the equipment adaptive control model, and performing adaptive control on the new energy power generation equipment. The technical problems that in the prior art, new energy power generation equipment is poor in adaptability and unstable in power output are solved, and the technical effects that the adaptability of the equipment is remarkably improved, and unstable power output is effectively reduced are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation control, and particularly to an adaptive control system for new energy power generation equipment based on multi-scale data fusion. Background Art

[0002] In the field of new energy power generation, with the rapid development of various new energy power generation technologies, new energy sources such as wind energy, solar energy, and biomass energy have been gradually widely used. However, since these new energy power generation devices usually operate under different environmental conditions and load conditions, their operating states have great uncertainty and complexity. Especially when the device fails or switches the working mode, it is more likely to cause instability of power output. Traditional control systems often have difficulty coping with such a changing operating environment, lacking sufficient adaptability and accuracy, resulting in the stability of the power system being affected.

[0003] The prior art has the technical problems of poor adaptability of new energy power generation equipment and unstable power output. Summary of the Invention

[0004] This application provides an adaptive control system for new energy power generation equipment based on multi-scale data fusion, which is used to solve the technical problems of poor adaptability of new energy power generation equipment and unstable power output in the prior art.

[0005] In view of the above problems, this application provides an adaptive control system for new energy power generation equipment based on multi-scale data fusion. The system includes: A multi-source power data set acquisition module, which is used to obtain new energy fuel information of the new energy power generation equipment, and judge whether the new energy power generation equipment is in a composite fuel power generation mode according to the new energy fuel information. If the new energy power generation equipment is in a composite fuel power generation mode, obtain a multi-source power data set corresponding to each composite fuel type; a multi-source high-dimensional data set output module, which is used to perform multi-scale feature convolution on the multi-source power data set respectively through a multi-scale convolutional network layer, and output a multi-source high-dimensional data set, where the multi-scale feature convolution includes time feature convolution, spatial feature convolution, and type feature convolution; a fused high-dimensional data set generation module, which is used to fuse the multi-source high-dimensional data sets to generate a fused high-dimensional data set; an equipment control parameter item generation module, which is used to receive equipment failure information for the new energy power generation equipment, and generate equipment control parameter items according to the equipment failure information; a fused low-dimensional data set generation module, which is used to perform feature dimensionality reduction on the fused high-dimensional data set by using the t-SNE algorithm, and output a fused low-dimensional data set; an adaptive control module, which is used to train an equipment adaptive control model with the fused low-dimensional data set and the equipment control parameter items, and perform adaptive control on the new energy power generation equipment based on the equipment adaptive control model.

[0006] In a second aspect of the present application, there is provided an electronic device, including a memory and a processor. The memory stores a computer program, and the processor is configured to execute the adaptive control system for new energy power generation equipment based on multi-scale data fusion provided by the present application.

[0007] In a third aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored. The computer program is configured to execute the adaptive control system for new energy power generation equipment based on multi-scale data fusion provided by the present application.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: A multi-source power data set acquisition module, configured to obtain a multi-source power data set corresponding to each composite fuel type; a multi-source high-dimensional data set output module, configured to perform multi-scale feature convolution on the multi-source power data set respectively through a multi-scale convolutional network layer, and output a multi-source high-dimensional data set; a fused high-dimensional data set generation module, configured to fuse the multi-source high-dimensional data sets to generate a fused high-dimensional data set; an equipment control parameter item generation module, configured to receive equipment fault information for the new energy power generation equipment and generate equipment control parameter items; a fused low-dimensional data set generation module, configured to perform feature dimensionality reduction on the fused high-dimensional data set by using the t-SNE algorithm and output a fused low-dimensional data set; an adaptive control module, configured to train an equipment adaptive control model with the fused low-dimensional data set and the equipment control parameter items for adaptive control. It achieves the technical effect of significantly improving the adaptability of the equipment and effectively reducing the instability of power output. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0010] Figure 1 It is a schematic structural diagram of the adaptive control system for new energy power generation equipment based on multi-scale data fusion provided by the embodiments of the present application; Figure 2 It is a schematic flowchart of the multi-source high-dimensional data set output module of the adaptive control system for new energy power generation equipment based on multi-scale data fusion provided by the embodiments of the present application to output a fused low-dimensional data set.

[0011] Figure 3 It is a schematic structural diagram of an electronic device provided by the embodiments of the present application.

[0012] Explanation of the accompanying drawings: multi-source power data set acquisition module 10, multi-source high-dimensional data set output module 20, fused high-dimensional data set generation module 30, equipment control parameter item generation module 40, fused low-dimensional data set generation module 50, adaptive control module 60, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0013] The present application provides an adaptive control system for new energy power generation equipment based on multi-scale data fusion, which is used to solve the technical problems of poor adaptability and unstable power output of new energy power generation equipment in the prior art.

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] Examples, such as Figure 1 As shown, the present application provides an adaptive control system for new energy power generation equipment based on multi-scale data fusion, the system comprising: The multi-source power data set acquisition module 10 is used to obtain the new energy fuel information of the new energy power generation equipment, and determine whether the new energy power generation equipment is a composite fuel power generation mode based on the new energy fuel information. If the new energy power generation equipment is a composite fuel power generation mode, obtain the multi-source power data set corresponding to each composite fuel type.

[0016] Specifically, first, obtain the new energy fuel information of the new energy power generation equipment. New energy power generation equipment can use a variety of different types of new energy fuels, such as solar energy, wind energy, hydropower, biomass energy, etc., or a combination of these energy sources. Through various sensors and monitoring systems, detailed information about the fuel used by the new energy power generation equipment can be obtained. This information includes the type, source, current reserve, supply stability, etc. of the fuel. For example, if it is a solar power generation equipment, it will obtain information such as the current light intensity and the working status of the solar panel; if it is a wind power generation equipment, it will obtain information such as the current wind speed and wind direction; if it is a biomass power generation equipment, it will obtain information such as the supply of biomass fuel and combustion efficiency.

[0017] Determine whether the new energy power generation equipment is in a composite fuel power generation mode based on the obtained new energy fuel information. The composite fuel power generation mode means that the equipment uses two or more different types of new energy fuels for power generation simultaneously. For example, a power generation equipment uses solar energy and wind energy for power generation simultaneously, or combines biomass energy and water energy for use. By analyzing the fuel information, it can be determined whether the equipment is in this composite fuel power generation mode.

[0018] If the new energy power generation equipment is determined to be in a composite fuel power generation mode, then it is necessary to obtain the multi-source power data sets corresponding to each composite fuel type. Since different fuel types will have different impacts on the operation of the equipment, it is necessary to obtain the data related to each fuel type separately. These multi-source power data sets may include electrical parameters, environmental data, equipment status information, etc. related to a specific fuel type. For example, if the equipment uses solar energy and wind energy for power generation simultaneously, then it is necessary to obtain the multi-source power data sets of the solar power generation part and the wind power generation part separately. These data sets will provide an important basis for subsequent analysis and control, so as to better manage and optimize the operation of the composite fuel power generation equipment.

[0019] By obtaining the fuel information of the new energy power generation equipment to determine whether it is in a composite fuel power generation mode and obtaining the corresponding multi-source power data sets, it provides a data basis for optimizing the operation management of the composite fuel power generation equipment and realizes more efficient and stable power generation control.

[0020] The multi-source high-dimensional data set output module 20 is used to perform multi-scale feature convolution on the multi-source power data sets respectively through a multi-scale convolutional network layer, and output a multi-source high-dimensional data set, where the multi-scale feature convolution includes time feature convolution, spatial feature convolution, and type feature convolution.

[0021] Specifically, first of all, the multi-scale convolutional network layer is a neural network structure that can process features of different scales. It can analyze multiple dimensions such as time, space, and data type simultaneously, so as to extract richer and more comprehensive feature information.

[0022] The time feature convolution mainly operates on the time series data in the multi-source power data set. The time series data includes the change of the output power of the equipment over time, the fluctuations of voltage and current, etc. Through the time feature convolution, periodic features and trend features are extracted. For example, it is found that the output power of the equipment shows a periodic change pattern within a day, or shows an upward or downward trend over a period of time. These time features are very important for predicting the future state of the equipment and performing adaptive control.

[0023] Spatial feature convolution focuses on the spatial data in multi-source power datasets. In new energy power generation plants, different devices are distributed at different locations and there are mutual influences among them. Spatial feature convolution can capture such spatial relationships, such as power transmission between different devices, the interaction of electric and magnetic fields, etc. In addition, spatial feature convolution can also consider the differences in the operating states of devices at different geographical locations, such as changes in environmental factors like light intensity and wind speed.

[0024] Type feature convolution performs convolution operations on different types of data in multi-source power datasets. The multi-source power dataset of new energy power generation equipment contains various types of data, such as electrical parameter data, environmental factor data, device status data, etc. Type feature convolution can extract the correlation features between different types of data. For example, it may find the relationship between light intensity and the output power of solar power generation equipment, or the relationship between wind speed and the output power of wind power generation equipment. These correlation features can help better understand the operating mechanism and performance characteristics of the equipment.

[0025] By performing temporal feature convolution, spatial feature convolution, and type feature convolution on the multi-source power dataset, a multi-source high-dimensional dataset can be obtained. This dataset contains richer and more abstract feature information, providing a more powerful basis for subsequent data analysis and adaptive control.

[0026] The fused high-dimensional dataset generation module 30 is used to fuse the multi-source high-dimensional dataset to generate a fused high-dimensional dataset.

[0027] Specifically, the purpose of fusion is to integrate the multi-source high-dimensional data obtained from feature convolutions at different scales. These data have extracted feature information in different dimensions through temporal feature convolution, spatial feature convolution, and type feature convolution respectively. The fusion process is to integrate these scattered information together to form a more comprehensive and representative dataset.

[0028] To achieve fusion, based on the weighted fusion method, according to the importance of different features, a weight is assigned to the high-dimensional features from each source, and then the weighted features are summed. This can highlight the contribution of important features and reduce the impact of unimportant features. During the fusion process, the consistency and compatibility of the data need to be considered. Since different-scale feature convolutions use different processing systems and parameter settings, the resulting high-dimensional data is different. Before fusion, these data need to be preprocessed, such as normalization, standardization, etc., to ensure that the data is on the same scale and within the same range. After the fusion operation, the generated fused high-dimensional dataset contains comprehensive feature information in multiple dimensions such as time, space, and type. This dataset can better reflect the operating status and characteristics of new energy power generation equipment, providing more powerful support for subsequent equipment control and optimization. Fusing the multi-source high-dimensional dataset to generate a fused high-dimensional dataset, integrating feature information in different dimensions, enhancing the ability to reflect the operating status and characteristics of new energy power generation equipment, and providing more powerful support for equipment control and optimization.

[0029] The device control parameter item generation module 40 is configured to receive device fault information for the new energy power generation device and generate device control parameter items according to the device fault information.

[0030] Specifically, when a fault occurs during the operation of the new energy power generation device, the fault detection system will promptly detect the fault and generate corresponding fault information, which includes aspects such as fault type, fault location, and fault severity. For example, it could be the damage of a certain key component, a circuit short circuit, a sensor fault, etc.

[0031] After the new energy power generation device receives this fault information, it will initiate a fault response mechanism. First, the control system of the device will analyze and process the fault information and adopt different coping strategies according to different fault types and severities. For example, if it is a minor fault, the device can continue to operate by adjusting its operating parameters, while arranging maintenance personnel to conduct inspections and repairs; if it is a serious fault, the device needs to be immediately stopped to prevent further damage.

[0032] To generate device control parameter items, the control system calculates by combining fault information, the current operating state of the device, and a pre-set fault handling strategy. For example, if the fault is caused by excessive output power, the control system generates a control parameter item to reduce the output power; if it is a fault of a certain sensor, the control algorithm relying on the sensor is adjusted, or a backup sensor is enabled. The generated device control parameter items will be sent to the actuators of the device, such as motors, valves, switches, etc., to adjust the operating state of the device. At the same time, these parameter items will also be recorded for subsequent fault analysis and handling. In this way, effective control measures can be taken in a timely manner when the device fails, reducing the impact of the fault on the device operation and improving the reliability and stability of the device.

[0033] The fused low-dimensional dataset generation module 50 is used to perform feature dimensionality reduction on the fused high-dimensional dataset by using the t-SNE algorithm and output a fused low-dimensional dataset.

[0034] Specifically, the t-SNE algorithm is used to perform feature dimensionality reduction on the fused high-dimensional dataset to output the fused low-dimensional dataset. First of all, the fused high-dimensional dataset usually contains a large amount of feature information. However, high-dimensional data will face problems such as high computational complexity and data sparsity during processing and analysis. Therefore, it is necessary to perform feature dimensionality reduction to simplify the data while retaining important feature information. The t-SNE algorithm is an effective non-linear dimensionality reduction system. Its main idea is to construct probability distributions between data points in high-dimensional space and low-dimensional space respectively, and then find the representation in low-dimensional space by minimizing the difference between these two probability distributions. The working process of the t-SNE algorithm is as follows: In high-dimensional space, Gaussian distribution calculation is performed on each data point in the fused high-dimensional dataset. This means that for each data point, the distance between it and other data points is calculated, and a probability value is determined according to the distance, indicating the similarity degree between this point and other points, thus obtaining the high-dimensional probability distribution matrix. In low-dimensional space, t-distribution calculation is performed on each data point in the fused low-dimensional dataset. Similarly, the distance between each point and other points is calculated, and the probability value is determined to obtain the low-dimensional probability distribution matrix. Then, the KL divergence between the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix is calculated. The KL divergence represents the degree of difference between two probability distributions. The KL divergence is used to construct the objective function, and the goal is to minimize this difference so that the distribution of data points in low-dimensional space is as close as possible to the distribution of data points in high-dimensional space. To minimize the objective function, the gradient descent method is used for optimization. By continuously adjusting the positions of data points in low-dimensional space, the objective function gradually decreases. In each iteration, the moving direction and distance of each data point are calculated according to the gradient of the objective function. As the gradient descent progresses, the distribution of data points in low-dimensional space is gradually adjusted until the objective function converges. When the objective function converges, the fused low-dimensional dataset is obtained. This low-dimensional dataset greatly reduces the dimension of the data while retaining the important feature information in the fused high-dimensional dataset, making it more convenient for subsequent data analysis and processing, such as visualization, clustering, classification, etc. At the same time, it also reduces the consumption of computing resources and improves the efficiency of the algorithm.

[0035] The adaptive control module 60 is used to train the device adaptive control model with the fused low-dimensional dataset and the device control parameter items, and perform adaptive control on the new energy power generation device based on the device adaptive control model.

[0036] Specifically, the support vector machine algorithm is used to implement the training and application of the device adaptive control model. For the processing of the fused low-dimensional dataset and the device control parameter items, the fused low-dimensional dataset is used as the input feature vector, which contains the key information of the new energy power generation device in different aspects. The data after dimensionality reduction is more refined and can highlight important features. The device control parameter items can be used as additional features or part of the labels to guide the training of the model. In the training stage, the support vector machine algorithm aims to find an optimal hyperplane to separate different categories of data points as much as possible while maximizing the classification margin. For the adaptive control problem of the new energy power generation device, different device operating states or control strategies are used as different categories. By learning a large number of fused low-dimensional datasets and the corresponding device control parameter items, the SVM model determines the optimal classification hyperplane, that is, determines the best control strategy for the new energy power generation device in different situations. The SVM algorithm determines the position and direction of the hyperplane by finding the support vectors, that is, those data points closest to the hyperplane. These support vectors are crucial for the performance of the model and determine the classification boundary. During the training process, the SVM algorithm determines the optimal hyperplane parameters by optimizing an objective function to maximize the classification margin and minimize the classification error. Once the training is completed, when a new fused low-dimensional dataset is input, the SVM model quickly performs classification prediction to determine the corresponding device control strategy. For the adaptive control of the new energy power generation device, according to the real-time obtained device operation data, that is, the new fused low-dimensional dataset, the most suitable control parameters are automatically selected to achieve the optimal control of the device.

[0037] In a possible implementation manner, as Figure 2 shown, the fused low-dimensional dataset generation module further includes: Performing Gaussian distribution calculation on each data point in the fused high-dimensional dataset in the high-dimensional space, and outputting a high-dimensional probability distribution matrix.

[0038] Performing t-distribution calculation on each data point in the fused low-dimensional dataset in the low-dimensional space, and outputting a low-dimensional probability distribution matrix.

[0039] Calculating the KL divergence between the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix, and constructing an objective function with the KL divergence, where the KL divergence represents the probability distribution difference between the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix.

[0040] When the objective function converges, output the fused low-dimensional dataset.

[0041] Specifically, first, the fused high-dimensional dataset contains high-dimensional data information obtained through multi-scale feature extraction and fusion. In the high-dimensional space, for each data point in the dataset, it is necessary to calculate its relationship with all other data points. The purpose of using the Gaussian distribution calculation is to determine the probability distribution of each data point in the high-dimensional space. For a specific data point, calculate the distance between it and other data points. The distance is calculated using the common Euclidean distance. Based on these distance values, use the Gaussian distribution function to calculate the similarity probability between this data point and other data points. The Gaussian distribution function has good mathematical properties and can effectively describe the distribution of data points in the high-dimensional space. It takes the distance between data points as input and outputs a probability value between 0 and 1, indicating the degree of similarity between two data points. The closer the distance, the higher the probability value, indicating that the two data points are more similar; the farther the distance, the lower the probability value, indicating that the two data points are less similar. For each data point in the fused high-dimensional dataset, repeat the above calculation process, that is, calculate the distance between it and all other data points, and calculate the similarity probability based on the distance. In this way, a high-dimensional probability distribution matrix can be obtained. The number of rows and columns of this matrix is equal to the number of data points in the dataset. Each element in the matrix represents the similarity probability between two data points in the high-dimensional space.

[0042] Perform t-distribution calculation on each data point in the fused low-dimensional dataset in the low-dimensional space to output a low-dimensional probability distribution matrix. The fused low-dimensional dataset is a dataset after feature dimensionality reduction processing, and its dimension is greatly reduced compared to the original fused high-dimensional dataset. In the low-dimensional space, it is necessary to perform t-distribution calculation on each data point in the dataset. The t-distribution is a probability distribution with specific mathematical forms and properties. For each data point, calculate the distance between it and all other data points. Similar to the high-dimensional space, but in the low-dimensional space, use the t-distribution function to determine the similarity probability between data points based on these distances. The t-distribution has some advantages in processing low-dimensional data. It can better capture the distribution characteristics of data points in the low-dimensional space, especially for those data with complex shapes or uneven distributions. Through the t-distribution calculation, a low-dimensional probability distribution matrix can be obtained. The number of rows and columns of this matrix is also equal to the number of data points in the dataset. Each element in the matrix represents the similarity probability between two data points in the low-dimensional space. By performing t-distribution calculation on each data point in the fused low-dimensional dataset and outputting the low-dimensional probability distribution matrix, we can understand the distribution and similarity relationship of data in the low-dimensional space, which provides an important basis for subsequent comparison of the probability distribution differences between the high-dimensional space and the low-dimensional space, and further optimization of the representation of the low-dimensional space.

[0043] First, the KL divergence is a metric used to measure the difference between two probability distributions. In this step, the high-dimensional probability distribution matrix represents the probability distribution of data in the high-dimensional space, while the low-dimensional probability distribution matrix reflects the probability distribution of data in the low-dimensional space after dimensionality reduction. The process of calculating the KL divergence involves comparing and operating on the corresponding elements in the two probability distribution matrices. For each element in the high-dimensional probability distribution matrix and the corresponding element in the low-dimensional probability distribution matrix, calculations are performed according to the formula of the KL divergence, which measures the amount of information required to transform from one probability distribution to another. By calculating the KL divergence between the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix, the degree of difference between the two probability distributions can be quantitatively determined. This difference reflects the distribution changes of data in the high-dimensional and low-dimensional spaces. If the KL divergence is large, it indicates that the probability distributions in the high-dimensional and low-dimensional spaces are quite different, meaning that a large amount of important information is lost during the dimensionality reduction process. If the KL divergence is small, it means that the low-dimensional space has better retained the probability distribution characteristics in the high-dimensional space. Using the KL divergence to construct the objective function, the goal is to minimize this KL divergence. Through optimization algorithms, the data representation in the low-dimensional space is continuously adjusted to make the low-dimensional probability distribution as close as possible to the high-dimensional probability distribution while retaining the important features of the data. Such an objective function can guide the dimensionality reduction process in a more optimal direction, enabling the fused low-dimensional dataset after dimensionality reduction to reduce the data dimension while maintaining the essential features of the data, facilitating subsequent data analysis and processing.

[0044] The objective function is constructed by calculating the KL divergence between the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix, aiming to make the data distribution in the low-dimensional space as close as possible to that in the high-dimensional space while reducing the data dimension. During the optimization process, the positions of the data points in the low-dimensional space are continuously adjusted to minimize the value of the objective function. As the optimization progresses, the low-dimensional probability distribution matrix gradually approaches the high-dimensional probability distribution matrix. When the objective function converges, it means that the data distribution in the low-dimensional space is close enough to that in the high-dimensional space and reaches a relatively stable state. At this time, the output fused low-dimensional dataset has the following characteristics: First, its dimension is greatly reduced, making it easier to perform subsequent data analysis and processing, such as visualization, clustering, classification, etc., compared to the original fused high-dimensional dataset. Second, although the dimension is reduced, due to the convergence of the objective function, this fused low-dimensional dataset still retains most of the important feature information and can better reflect the essential features of the original data. The fused low-dimensional dataset output when the objective function converges not only achieves data dimensionality reduction but also retains the key information of the original data to a certain extent, providing strong support for subsequent data analysis and applications.

[0045] In a possible implementation manner, the fused low-dimensional dataset generation module further includes: Minimize the objective function by the gradient descent method to make the objective function converge. The gradient calculation formula is as follows: ; Wherein, is the objective function, is the position of the i-th data point in the low-dimensional space. As the gradient descent progresses, the position of the j-th data point in the low-dimensional space, is the data point in the high-dimensional space and the similarity between them, is the data point in the low-dimensional space and the similarity between them, is the data point in the low-dimensional space and the Euclidean distance between them. Based on the gradient descent, the distribution of data points in the low-dimensional space is gradually adjusted until the objective function converges.

[0046] Specifically, the objective function is minimized by the gradient descent method to achieve the convergence of the objective function, thereby obtaining the optimized data distribution in the low-dimensional space. The objective function reflects the degree of difference in data distribution between the high-dimensional space and the low-dimensional space. In the t-SNE algorithm, the core objective is to make the similarity of point pairs in the high-dimensional space and the similarity of point pairs in the low-dimensional space as close as possible, that is, to enable the low-dimensional space to better retain the characteristic information of the high-dimensional space.

[0047] For the position of the i-th data point in the low-dimensional space, it is a key variable in the algorithm. As the gradient descent progresses, is continuously adjusted, and its position is moved in the direction that can reduce the value of the objective function. At the same time, the position of the j-th data point in the low-dimensional space also jointly affects the change of the objective function in this process.

[0048] The similarity between data points in the high-dimensional space is calculated according to the distribution of data points in the high-dimensional space, and it reflects the degree of closeness of the relationship between data point pairs in the high-dimensional space. While the similarity between data points in the low-dimensional space is calculated based on the position relationship of data points in the low-dimensional space.

[0049] The Euclidean distance between data points and It measures their actual distance in space. Through this distance, the similarity can be further affected in the calculation.

[0050] For a system based on gradient descent, the gradient of the objective function with respect to the position of each data point in the low-dimensional space is calculated each time. This gradient indicates the direction in which the value of the objective function increases fastest at the current position, and what needs to be done is to adjust the position of the data point in the direction in which the value of the objective function decreases. For each data point in the low-dimensional space , its gradient direction and magnitude at the current position are calculated according to the gradient calculation formula. If the value of the objective function is large, it means that the data distribution in the current low-dimensional space is quite different from that in the high-dimensional space. At this time, the gradient will guide the data point to move in the direction that can reduce this difference.

[0051] As the gradient descent is continuously carried out, the data point distribution in the low-dimensional space is gradually adjusted. In each iteration, the value of the objective function will change. If the value of the objective function is gradually decreasing, it means that the data distribution in the low-dimensional space is getting closer and closer to the data distribution in the high-dimensional space. When the objective function converges, it means that the data distribution in the low-dimensional space has reached a stable state. At this time, the data distribution difference between the high-dimensional space and the low-dimensional space is the smallest, and an optimized fused low-dimensional data set is obtained. While retaining the important features of the high-dimensional space, this data set reduces the data dimension, providing convenience for subsequent data analysis and processing. By minimizing the objective function through the gradient descent method, adjusting the positions of the data points in the low-dimensional space, minimizing the data distribution difference between the high-dimensional space and the low-dimensional space, an optimized fused low-dimensional data set is obtained, which is convenient for subsequent data analysis and processing and retains the important features of the high-dimensional space.

[0052] In a possible implementation manner, the fused low-dimensional data set generation module further includes: Performing a correlation analysis on the fused high-dimensional data set with the device control parameter items to determine the relevant high-dimensional data set in the fused high-dimensional data set.

[0053] Using the t-SNE algorithm to retain the relevant high-dimensional data set and perform feature dimensionality reduction on the remaining fused high-dimensional data set, and outputting an updated fused low-dimensional data set.

[0054] Specifically, correlation analysis is performed on the fused high-dimensional dataset using device control parameter items. The device control parameter items are generated based on the fault information of new energy power generation equipment, and they are closely related to the operation status and control of the equipment. The purpose of the correlation analysis is to determine which data in the fused high-dimensional dataset has a strong association with the device control parameter items. Through various correlation analysis systems, such as calculating the Pearson correlation coefficient, Spearman correlation coefficient, etc., the linear or non-linear relationship between each feature in the fused high-dimensional dataset and the device control parameter items can be measured. If the correlation coefficient of a certain feature with the device control parameter item is high, it indicates that this feature greatly affects the device control decision, and then the data point where this feature is located is considered to be part of the relevant high-dimensional dataset. After correlation analysis, the relevant high-dimensional dataset in the fused high-dimensional dataset is determined. This relevant high-dimensional dataset contains those data points that are closely related to the device control parameter items, and they have important reference value for the adaptive control of the device.

[0055] The t-SNE algorithm is used to retain the relevant high-dimensional dataset and perform feature dimensionality reduction on the remaining fused high-dimensional dataset, and an updated fused low-dimensional dataset is output. The t-SNE algorithm has been used to perform feature dimensionality reduction on the fused high-dimensional dataset in the previous steps. Here, the t-SNE algorithm is used again to further reduce the dimensionality of the data while retaining important information, and the relevant high-dimensional dataset is separately retained because these data are closely related to device control and their importance cannot be lost during the dimensionality reduction process. For the remaining fused high-dimensional dataset, that is, the part excluding the relevant high-dimensional dataset, the t-SNE algorithm is applied again for feature dimensionality reduction. The t-SNE algorithm finds the representation in the low-dimensional space by constructing the probability distribution between data points in the high-dimensional space and the low-dimensional space and minimizing the difference between these two probability distributions. In this process, the noise and redundant information in the data can be effectively removed while the main features of the data are retained. After being processed by the t-SNE algorithm, an updated fused low-dimensional dataset is obtained. This dataset not only contains the retained relevant high-dimensional dataset but also reduces the dimensionality of the remaining data through dimensionality reduction processing, making the data easier to perform subsequent analysis and processing. The updated fused low-dimensional dataset can better serve the adaptive control of new energy power generation equipment and provide more accurate information support for the efficient operation and optimization of the equipment.

[0056] In a possible implementation manner, the multi-source high-dimensional dataset output module further includes: Among them, the temporal feature convolution includes performing a convolution operation on the time series data of the multi-source power dataset to extract periodic features and trend features.

[0057] The spatial feature convolution includes performing a convolution operation on the spatial data of the multi-source power dataset, including the association between different devices and the operating states of devices at different locations.

[0058] The type feature convolution includes performing a convolution operation on different types of data in the multi-source power dataset to extract the association features between different types of data.

[0059] Specifically, the time feature convolution mainly operates on the time series data of the multi-source power dataset. The time series data includes the variation of the output power of devices over time, the fluctuations of voltage and current, etc. By performing a convolution operation on these time series data, periodic features and trend features can be extracted. The periodic feature refers to the repetitive pattern presented by the data in time. For example, the output power of new energy generation devices shows periodic changes within a day, with higher power during the day and lower power at night. Through the time feature convolution, this periodic change pattern can be captured, providing important reference information for the control and optimization of devices. The trend feature reflects the change trend of the data over a long time range. For example, the output power of a device may show an upward or downward trend as the device ages or due to changes in environmental factors. The time feature convolution can extract this trend feature, helping to predict the future state of the device and taking corresponding maintenance measures in advance.

[0060] The spatial feature convolution focuses on the spatial data of the multi-source power dataset. In a new energy power generation field, different devices are distributed at different locations and there are mutual influences between them. The spatial feature convolution can capture this spatial relationship and extract useful feature information. On the one hand, the spatial feature convolution can consider the association between different devices. For example, there are mutual influences of airflows between multiple wind power generation devices, or there is a power transmission relationship between solar power generation devices and energy storage devices. By performing a convolution operation on the spatial data, the association features between these devices can be extracted, providing a basis for the coordinated control of devices. On the other hand, the spatial feature convolution can also consider the differences in the operating states of devices at different locations. For example, environmental factors such as light intensity and wind speed may be different at different geographical locations, which will lead to differences in the output power and operating efficiency of new energy generation devices at different locations. The spatial feature convolution can capture this spatial difference, providing a reference for the layout optimization and operation management of devices.

[0061] The type feature convolution performs convolution operations on different types of data in the multi-source power dataset. The multi-source power dataset of new energy power generation equipment contains various types of data, such as electrical parameter data, environmental factor data, equipment status data, etc. By performing convolution operations on different types of data, the correlation features between different types of data can be extracted. For example, the relationship between light intensity and the output power of solar power generation equipment, or the relationship between wind speed and the output power of wind power generation equipment can be discovered. These correlation features can help better understand the operation mechanism and performance characteristics of the equipment, providing more comprehensive information support for the control and optimization of the equipment. In summary, through time feature convolution, spatial feature convolution, and type feature convolution, rich feature information can be extracted from the multi-source power dataset, providing strong support for subsequent data analysis and equipment adaptive control.

[0062] In a possible implementation manner, the adaptive control module further includes: Obtain multiple groups of multi-source power datasets of the new energy power generation equipment in multiple working modes, where the multiple working modes include a load mode, a non-load mode, an energy storage mode, and a fault mode.

[0063] Obtain multiple groups of fused low-dimensional datasets corresponding to the multiple groups of multi-source power datasets respectively.

[0064] Train a multi-mode-device adaptive control model according to the multiple groups of fused low-dimensional datasets, and perform mode adaptive control on the new energy power generation equipment with the multi-mode-device adaptive control model.

[0065] Specifically, first, obtain multiple groups of multi-source power datasets of the new energy power generation equipment in multiple working modes. New energy power generation equipment usually has different working modes, which reflect the state of the equipment under different operating conditions. Here, the multiple working modes include a load mode, a non-load mode, an energy storage mode, and a fault mode. In the load mode, the new energy power generation equipment is supplying power to an external load, and its output power, voltage, and other parameters will be adjusted according to the load demand. In the non-load mode, the equipment is in an idle state or only supplies power to the internal system. At this time, the operating parameters are different from those in the load mode. The energy storage mode usually involves storing excess electric energy for subsequent use. The operation method of the equipment in this mode also has its characteristics, while the fault mode is a special state when the equipment fails. At this time, the data contains fault feature information. By collecting the multi-source power datasets of the equipment in these different working modes, the operating state and data characteristics of the equipment in various situations can be comprehensively understood. The multi-source power dataset includes data sources in multiple aspects such as the electrical parameters of the equipment, environmental data, and equipment status information.

[0066] Obtain multiple groups of fused low-dimensional datasets corresponding to multiple groups of multi-source power datasets. For each group of obtained multi-source power datasets, they are processed through the previous steps, including multi-scale feature convolution, fusion, dimensionality reduction using the t-SNE algorithm, etc., and finally the corresponding fused low-dimensional datasets are obtained. These fused low-dimensional datasets retain the important feature information in the original multi-source power datasets, but the dimensions are greatly reduced, making it more convenient for subsequent analysis and processing. Each group of fused low-dimensional datasets reflects the operating characteristics of new energy power generation equipment under specific working modes.

[0067] Use the support vector machine algorithm to train a multi-mode-device adaptive control model based on multiple groups of fused low-dimensional datasets, and use this model to perform mode adaptive control on new energy power generation equipment.

[0068] Multiple groups of fused low-dimensional datasets are obtained by performing multi-scale feature extraction, fusion, and dimensionality reduction on multiple groups of multi-source power datasets of new energy power generation equipment under different working modes. These datasets not only retain the key feature information but also reduce the data dimensions, providing an efficient data basis for model training.

[0069] Divide multiple groups of fused low-dimensional datasets into a training set and a test set. Usually, it can be divided according to a certain ratio, such as 70% as the training set and 30% as the test set. This can ensure that the model has enough data to learn the rules during the training process, and at the same time, it can be verified on the test set to evaluate the performance and generalization ability of the model.

[0070] Determine the kernel function of the support vector machine. Common kernel functions include linear kernel function, polynomial kernel function, radial basis function (RBF), etc. The RBF kernel function performs well in dealing with non-linear problems. It can map the data into a high-dimensional space, thus better capturing the complex relationships in the data. Select appropriate kernel function parameters, such as the bandwidth parameter of the RBF kernel function. This parameter is crucial for the performance of the model. The optimal parameter value can be found through systems such as cross-validation. Cross-validation can further divide the training set into multiple subsets, and train and validate on different subset combinations to find the best parameter settings.

[0071] Use the training set data to train the support vector machine model, taking the samples in the fused low-dimensional dataset as the input and the corresponding device control parameters as the output target.

[0072] The support vector machine finds a hyperplane such that most of the sample points in the training dataset are as close as possible to this hyperplane, while ensuring that the hyperplane has a certain generalization ability. During the training process, the model will find those support vectors that play a key role in determining the hyperplane. These support vectors are part of the sample points in the training dataset, and they are located on or near the boundary of the hyperplane.

[0073] By optimizing the sequential minimal optimization algorithm to solve the parameters of the support vector machine model, the parameters of the support vector machine can be solved quickly, improving the training efficiency.

[0074] Use the test set data to evaluate the trained support vector machine model, and calculate indicators such as the mean squared error and mean absolute error of the model on the test set to evaluate the prediction accuracy and generalization ability of the model. If the performance of the model on the test set is poor, it is possible to consider adjusting the kernel function parameters and increasing the training data to improve the model. When the new energy power generation equipment is operating, the working mode of the equipment is monitored in real time. According to the current working mode of the equipment, the corresponding fused low-dimensional data set is selected. Input this fused low-dimensional data set into the trained multi-mode-device adaptive control model. The model will predict the control parameters suitable for this mode according to the input data and the current working mode. These control parameters can include adjustment values of electrical parameters such as the output power, voltage, and current of the equipment, or changes in the operating mode and control strategy of the equipment. Finally, send the predicted control parameters to the control system of the new energy power generation equipment, and the control system will execute the corresponding control operations. In this way, the multi-mode-device adaptive control model can automatically adjust the control parameters according to the real-time working mode of the equipment, realizing the mode adaptive control of the new energy power generation equipment. Using the support vector machine algorithm can effectively train the multi-mode-device adaptive control model and perform mode adaptive control on the new energy power generation equipment with this model. Such a system can improve the efficiency, stability, and reliability of the equipment in different working modes, providing strong support for the intelligent control of new energy power generation equipment.

[0075] In a possible implementation manner, the adaptive control module further includes: Obtain the real-time multi-source power data set of the new energy power generation equipment.

[0076] According to the real-time multi-source power data set, output a real-time fused low-dimensional data set.

[0077] Input the real-time fused low-dimensional data set into the device adaptive control model to obtain adaptive control parameters that meet the preset power demand, and perform adaptive control on the new energy power generation equipment with the adaptive control parameters.

[0078] Specifically, obtain the real-time multi-source power data set of the new energy power generation equipment. This data set contains various information of the equipment at the current moment, such as electrical parameters (voltage, current, power, etc.), environmental data (light intensity, wind speed, temperature, etc.), and the status information of the equipment. Through sensors and monitoring devices, these data can be collected in real time to timely understand the operation of the equipment.

[0079] Based on the real-time multi-source power dataset, a real-time fusion low-dimensional dataset is output. This process is similar to the previous processing of multiple groups of multi-source power datasets, but it targets real-time data. Through operations such as multi-scale feature extraction, fusion, and dimensionality reduction, the high-dimensional real-time multi-source power dataset is transformed into a low-dimensional fusion low-dimensional dataset. The advantage of this is that it reduces the complexity of the data, facilitating subsequent analysis and processing, while also being able to retain the key feature information in the data.

[0080] The real-time fusion low-dimensional dataset is input into the device adaptive control model. This model is trained with multiple groups of fusion low-dimensional datasets and can predict the adaptive control parameters that meet the preset power demand based on the input data. For example, if the preset power demand is to maintain a stable output power under certain load conditions, then the model will predict appropriate control parameters based on the information in the real-time fusion low-dimensional dataset, such as adjusting electrical parameters such as the output voltage and current of the device, or changing the operating mode of the device. Once the adaptive control parameters are obtained, these parameters can be used to perform adaptive control on the new energy power generation device, and the control system will adjust the operating state of the device according to these parameters to achieve the goal of meeting the preset power demand. Through this real-time adaptive control, the efficiency, stability, and reliability of the new energy power generation device can be improved, better adapting to different operating conditions and power demands, constituting a complete real-time adaptive control process. Through the real-time monitoring and control of the new energy power generation device, it is ensured that the device can operate efficiently and stably to meet various power demands.

[0081] In a possible implementation manner, the adaptive control module further includes: If the new energy power generation device is in a composite fuel power generation mode, analyze the data correlation of each mode in the composite fuel power generation mode and output a correlation matrix.

[0082] Learn the model parameters of the multi-scale convolutional network layer based on the correlation matrix.

[0083] Specifically, when the new energy power generation equipment is in the combined fuel power generation mode, the Pearson correlation coefficient algorithm is used to analyze the data correlation of each mode and output the correlation matrix. The Pearson correlation coefficient is an index to measure the linear correlation degree between two variables. For different modes in the combined fuel power generation mode, first, the multi-source power data sets of each mode are sorted out. For each pair of different modes, the Pearson correlation coefficients between each characteristic variable in their data sets are calculated respectively. For example, for the power output time series data under two modes, they are regarded as two variable sets, and the linear correlation degree of these two time series is determined by calculating the Pearson correlation coefficient. If the correlation coefficient is close to 1, it indicates that there is a strong positive correlation between the two modes in terms of the change of power output over time, that is, their change trends are very similar; if the correlation coefficient is close to -1, it means there is a strong negative correlation, that is, when the power output of one mode increases, the power output of the other mode tends to decrease; if it is close to 0, it shows that the linear correlation is weak, and the relationship between the two modes in the power output time series is not obvious. For other characteristic variables, such as fuel consumption rate, equipment temperature, voltage and current, etc., the Pearson correlation coefficients are also calculated one by one. By comprehensively analyzing the correlation coefficients of each characteristic variable under different modes, a correlation matrix is constructed, and each element in the matrix represents the Pearson correlation coefficient value of two specific modes on a certain characteristic variable. Through the Pearson correlation coefficient algorithm, the data correlation between different combined fuel power generation modes is accurately quantified, providing an important basis for the model parameter learning of the subsequent multi-scale convolutional network layer and the adaptive control of the new energy power generation equipment.

[0084] The correlation matrix provides important guiding information for the parameter learning of the multi-scale convolutional network layer. This matrix reflects the data correlation between various modes in the composite fuel power generation mode. By analyzing the values in the matrix, the similarity and association of different modes on different features can be understood. For the learning of convolutional weights, the correlation matrix is used to adjust the weight distribution on different feature channels. If two modes have a high correlation on a certain feature, then on the corresponding feature channel, the convolutional weight is appropriately increased to better capture and utilize this correlation. In terms of the learning of the feature convolution scale, the correlation matrix also provides valuable reference. Different modes may exhibit correlations at different time, space, and type feature scales. If the correlation matrix indicates that certain modes have a high correlation at a specific time scale, then the scale parameter of the time feature convolution in the multi-scale convolutional network layer is adjusted to make it more suitable for processing the data of these related modes. Similarly, the scale parameters of the spatial feature convolution and the type feature convolution can also be adjusted accordingly according to the correlation matrix. The specific learning process adopts optimization algorithms such as gradient descent. By calculating the gradients of the convolutional weights and the feature convolution scale with respect to the loss function, the parameters are updated according to the direction and magnitude of the gradients. When calculating the loss function, the correlation information between the modes reflected by the correlation matrix is considered, so that the network can better adapt to the characteristics of the composite fuel power generation mode during the learning process, and improve the processing ability and accuracy of different mode data.

[0085] Embodiment 2, based on the same inventive concept as the new energy power generation equipment adaptive control system based on multi-scale data fusion in the foregoing embodiment, provides an electronic device in this embodiment. Figure 3 It is a schematic structural diagram of the electronic device provided in Embodiment 2 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The displayed electronic device is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present invention. As Figure 3 shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking one processor 21 as an example, the processor 21, the memory 22, the input device 23, and the output device 24 in the electronic device can be connected through a bus or other means. Figure 3 Taking the connection through the bus as an example.

[0086] Embodiment 3. Based on the same inventive concept as the adaptive control system for new energy power generation equipment based on multi-scale data fusion in the foregoing embodiments, this embodiment provides a computer-readable storage medium, which can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the adaptive control system for new energy power generation equipment based on multi-scale data fusion in the embodiments of the present application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory, that is, implements the above-mentioned adaptive control system for new energy power generation equipment based on multi-scale data fusion.

[0087] It should be noted that the above-mentioned sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0088] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0089] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. Adaptive control system for renewable energy power generation equipment based on multi-scale data fusion, characterized in that: The system comprises: A multi-source power data set acquisition module is used to obtain new energy fuel information of new energy power generation equipment, and determine whether the new energy power generation equipment is a composite fuel power generation mode according to the new energy fuel information. If the new energy power generation equipment is a composite fuel power generation mode, obtain a multi-source power data set corresponding to each composite fuel type; A multi-source high-dimensional data set output module, used to perform multi-scale feature convolution on the multi-source power data set through a multi-scale convolutional network layer, and output a multi-source high-dimensional data set, wherein the multi-scale feature convolution includes time feature convolution, space feature convolution and type feature convolution; A fused high-dimensional data set generation module, used to fuse the multi-source high-dimensional data sets to generate a fused high-dimensional data set; An equipment control parameter item generating module, used for the new energy power generation equipment to receive equipment fault information and generate equipment control parameter items according to the equipment fault information; A fused low-dimensional data set generation module is used to perform feature dimensionality reduction on the fused high-dimensional data set using a t-SNE algorithm, and output a fused low-dimensional data set; An adaptive control module is used to train an equipment adaptive control model using the fused low-dimensional data set and the equipment control parameter items, and to perform adaptive control on the new energy power generation equipment based on the equipment adaptive control model.

2. The system according to claim 1, characterized in that The fused low-dimensional data set generation module also includes: Performing Gaussian distribution calculation on each data point in the fused high-dimensional data set in a high-dimensional space, and outputting a high-dimensional probability distribution matrix; Performing t-distribution calculation on each data point in the fused low-dimensional data set in a low-dimensional space, and outputting a low-dimensional probability distribution matrix; Calculating the KL divergence of the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix, and constructing an objective function with the KL divergence, wherein the KL divergence represents the difference in probability distribution between the high-dimensional probability distribution matrix and the low-dimensional probability distribution matrix; When the objective function converges, a fused low-dimensional data set is output.

3. The system according to claim 2, characterized in that The fused low-dimensional data set generation module also includes: ； in, is the objective function, is the position of the i-th data point in the low-dimensional space. As the gradient descends, The position of the jth data point in the low-dimensional space, is a data point in high-dimensional space and The similarity between is a data point in low-dimensional space and The similarity between is a data point in low-dimensional space and The Euclidean distance between them is calculated based on gradient descent, and the distribution of data points in the low-dimensional space is gradually adjusted until the objective function converges.

4. The system according to claim 1, characterized in that The fused low-dimensional data set generation module also includes: Performing correlation analysis on the fused high-dimensional data set using the device control parameter items to determine related high-dimensional data sets in the fused high-dimensional data set; The t-SNE algorithm is used to retain the relevant high-dimensional data sets to perform feature dimensionality reduction on the remaining fused high-dimensional data sets, and an updated fused low-dimensional data set is output.

5. The system according to claim 1, wherein: The multi-source high-dimensional data set output module also include: Wherein, the time feature convolution includes performing a convolution operation on the time series data of the multi-source power data set to extract periodic features and trend features; The spatial feature convolution includes performing a convolution operation on the spatial data of the multi-source power data set, including associations between different devices and operating states of the devices at different locations; The type feature convolution includes performing a convolution operation on different types of data in the multi-source power data set to extract correlation features between different types of data.

6. The system according to claim 1, characterized in that The adaptive control module also includes: Acquire multiple groups of multi-source power data sets of the new energy power generation equipment under multiple working modes, wherein the multiple working modes include a load mode, a non-load mode, an energy storage mode, and a fault mode; Acquire multiple groups of fused low-dimensional data sets corresponding to the multiple groups of multi-source power data sets respectively; A multi-mode-device adaptive control model is trained according to the multiple groups of fused low-dimensional data sets, and the multi-mode-device adaptive control model is used to perform mode adaptive control on the new energy power generation equipment.

7. The system according to claim 1, characterized in that The adaptive control module also includes: Acquire a real-time multi-source power data set of the new energy power generation equipment; Outputting a real-time fused low-dimensional data set according to the real-time multi-source power data set; The real-time fused low-dimensional data set is input into the device adaptive control model to obtain adaptive control parameters that meet the preset power demand, and the new energy power generation equipment is adaptively controlled with the adaptive control parameters.

8. The system of claim 1, wherein: The adaptive control module also includes: If the new energy power generation equipment is a composite fuel power generation mode, analyzing the data correlation of each mode in the composite fuel power generation mode and outputting a correlation matrix; Model parameters of the multi-scale convolutional network layer are learned based on the correlation matrix.

9. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; A processor, used to implement the adaptive control system for new energy power generation equipment based on multi-scale data fusion as described in any one of claims 1 to 8 when executing the executable instructions stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an adaptive control system for new energy power generation equipment based on multi-scale data fusion as described in any one of claims 1 to 8 is implemented.

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