New energy power prediction optimization system

Abstract

The invention discloses a new energy power prediction optimization system, which belongs to the technical field of new energy power and comprises an information acquisition module, an environment information data acquisition module, an LSTM network fusion analysis module, a correlation analysis module, a dynamic monitoring module and a monitoring terminal. Wherein a power prediction model of new energy wind power generation is constructed through an LSTM network, power prediction data of wind power generation are generated, power correlation prediction data of wind power generation are generated in combination with a correlation analysis module, then dynamic decision making is performed, and result data of wind power generation power prediction optimization are generated and visually presented. According to the method, the power data in the current environment can be predicted more accurately, more intelligent and automatic operation management can be realized for wind power generation enterprises, the power generation efficiency can be improved, the resource allocation can be optimized, and the safety and stability of the power generation process can be ensured.

Description

Technical Field

[0001] The present invention relates to the field of new energy power technology, and in particular to a new energy power prediction and optimization system. Background Art

[0002] As new energy is deeply integrated into the power system, it is becoming an increasingly important means to solve the problems of energy shortage and environmental pollution. Among them, wind energy is an effectively utilized and important power generation energy source, and the technology for using wind power generation is also constantly deepening and improving. At present, the power prediction of wind power energy is mostly based on the collection of historical data to predict the steady increase or decrease of wind power. At this stage, linear or multivariate linear regression is often used to analyze according to the correlation characteristics of wind power generation, which is not accurate enough and difficult to face the impact of current environment and power generation change data. In addition, the existing technology is difficult to refer to and analyze the correlation changes of power increase peak or decrease peak in certain major months. If the power increase peak or decrease peak exceeds the required safety range, noise hazards and mechanical hazards will occur, which will in turn cause power generation risks and ultimately endanger the safe operation of the power grid. Summary of the invention

[0003] In view of the above problems existing in the existing new energy power prediction technology, the present invention is proposed.

[0004] Therefore, one of the objects of the present invention is to provide a new energy power prediction and optimization system, which utilizes deep learning analysis technology to perform predictive analysis and optimization on the power generation power and environmental information data of wind turbines after layered processing, and at the same time associates historical related change characteristics, so as to accurately control the power development trend of the preset wind turbines in the said month, and can more accurately predict the power data under the current environment, and enable the system to automatically warn according to the current development trend to avoid unexpected events caused by power data changes exceeding the safe peak range.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides a new energy power prediction and optimization system, comprising:

[0007] An information acquisition module is used to acquire the regional location of the wind turbine generator set and the power generation data of the wind turbine generator set in the regional location;

[0008] An environmental information data acquisition module is used to collect environmental information data corresponding to the wind turbine generator set in the regional location, wherein the environmental information data includes wind speed, wind direction, and environmental temperature and humidity;

[0009] An LSTM network fusion analysis module is used to respond to the power generation data of the wind turbine generator set of the information acquisition module and the environmental information data of the environmental information data acquisition module, divide the data sets of the power generation data and the environmental information data into a training set, a verification set and a test set, construct a power prediction model of new energy wind power generation through the LSTM network, and determine the loss function of the power prediction model of new energy wind power generation, optimize the power prediction model of new energy wind power generation according to the loss function, and generate power prediction data of wind power generation; the fusion analysis module includes a division unit, an analysis unit and an optimization unit;

[0010] A correlation analysis module is used to correlate the power prediction data of the current wind power generation with the power prediction data of historical power generation, specifically by determining the month with the most occurrences of the maximum power generation data and the historical minimum power generation data in the power prediction data of historical power generation, and performing correlation analysis after integration, so as to obtain the power generation change characteristic data of the wind turbine generator set under different change trends of the environmental information data, and the fusion analysis module generates the power correlation prediction data of wind power generation in response to the power generation change characteristic data;

[0011] A dynamic monitoring module, for responding to the power-related prediction data of the wind power generation and the power prediction data of the wind power generation, and based on the power prediction data of historical power generation and environmental information data, making dynamic decisions on the power-related prediction data of the wind power generation and the power prediction data of the wind power generation while dynamically monitoring, generating result data of wind power generation power prediction optimization and visually presenting it;

[0012] The monitoring terminal is used to record the communication base station matched with the wind turbine generator set, receive the field data uploaded from the preset area where the wind turbine generator set is located, and update the background data at the same time.

[0013] As a preferred solution of the present invention, wherein: the division unit extracts the change characteristics of the matched power generation data and environmental information data according to the power generation data and the environmental information data, and divides the change characteristics into primary change characteristics, secondary change characteristics and tertiary change characteristics in a step-by-step manner through the division unit, wherein the change characteristic level is positively correlated with the power generation change characteristic;

[0014] The analysis unit analyzes the associated change characteristics of wind speed and wind direction under different levels of change characteristics based on the hierarchical analysis in the LSTM network architecture, and simultaneously analyzes the change probability of the associated change characteristics on the power generation data of the wind turbine generator set, and calculates the power generation change value of the wind turbine generator set under different levels of change characteristics based on the change probability of the power generation data of the wind turbine generator set;

[0015] The optimization unit responds to the power generation change value of the wind turbine generator set, and generates corresponding wind power generation power prediction data after optimizing the power prediction model of the new energy wind power generation through a loss function.

[0016] As a preferred solution of the present invention, the probability of change of the associated change feature to the power generation data of the wind turbine generator set is analyzed and calculated according to the following formula:

[0017] Among them, P(X) represents the probability of change of wind turbine power generation data;

[0018] In the formula, X represents the power generation data of the wind turbine generator set collected when the wind speed value appears most frequently, K represents the change value of the wind direction angle data at the current wind speed value, ω i represents the weight value of the power generation data of the wind turbine generator set collected for the i-th time at the current wind speed value, m is the total number of items collected for the i-th time, and N represents the mixed weight, which represents the weight of the power generation data of the wind turbine generator set in the data of different wind direction angles under the same wind speed value;

[0019] Based on the change probability of the power generation data of the wind turbine generator set, the power generation change value of the wind turbine generator set under different levels of change characteristics is calculated. Specifically, the change probability of the power generation data of the wind turbine generator set and the different levels of change characteristic data are converted into a serialized form, and the architecture of the LSTM network is defined, including an input layer, three LSTM layers and an output layer using a softmax function. The LSTM model is trained using a training data set, and the verification set is used to adjust the power prediction model parameters of the new energy wind power generation. At the same time, the trained LSTM model is used to analyze the correlation between the level change characteristics of wind speed and wind direction and the power generation. For different wind speed and wind direction level change characteristics, the statistics and confidence intervals of the power generation are calculated respectively. The confidence interval is the confidence interval for calculating the power generation, which is determined by assuming a normal distribution.

[0020] As a preferred solution of the present invention, the loss function adopts MSE loss function or MAE loss function as the standard loss function for regression analysis of the change value of the power generation of the wind turbine generator set.

[0021] As a preferred solution of the present invention, the dynamic monitoring module makes dynamic decisions on the power-related prediction data of the wind power generation and the power prediction data of the wind power generation, specifically, through the Euclidean distance algorithm, calculates the similarity value of the power-related prediction data of the wind power generation and the power prediction data of the wind power generation, makes dynamic decisions based on the similarity value, determines the result data of the wind power generation power prediction optimization and presents it visually in a chart format.

[0022] As a preferred solution of the present invention, wherein: the correlation analysis module obtains the minimum wind speed value data of the month with the most occurrences of the minimum power generation data in the historical power forecast data, and analyzes the unit time period with the most occurrences of the minimum wind speed value data. At the same time, a safety threshold is preset based on the historical minimum safe power generation data within the unit time period, and wind speed value data corresponding to the safety threshold is collected. When the wind speed value data appearing in the unit time period of the month in the future time period is less than the wind speed value data corresponding to the safety threshold, the system determines that the power generation power of the preset wind turbine generator set is lower than the historical minimum safe power generation data, and issues a safety warning. Otherwise, no determination is made.

[0023] As a preferred solution of the present invention, wherein: the correlation analysis module takes the power value data lower than the minimum safe power generation power data in the power prediction data of the historical power generation as a basis, obtains the top three power value data higher than the data, and obtains the wind speed value data change characteristics corresponding to the three-digit power value data to generate a data model, and presets a risk threshold based on the median data of the three-digit power value data. When the power generation power of the preset wind turbine generator set in the future time period is lower than the risk threshold, the system determines that the preset wind turbine generator set is in a risky state, otherwise, it does not make a determination.

[0024] As a preferred solution of the present invention, wherein: the correlation analysis module collects the minimum wind speed value and the maximum wind speed value in the month with the most occurrences of the maximum power generation data in the power prediction data of the historical power generation, and analyzes the change trend of the minimum wind speed value to the maximum wind speed value, and at the same time intercepts the median wind speed value in the change trend, and presets a critical threshold based on the median wind speed value. When the wind speed change value in the future period exceeds the critical threshold in the month, the system determines that the power generation power of the preset wind turbine generator set will exceed the historical maximum power generation data, and issues a warning, otherwise, no determination is made;

[0025] The safe power generation value in the historical maximum power generation data is obtained based on the critical threshold, and the Monte Carlo method is used to sample the safe power generation value after reliability evaluation. At the same time, the sampled data is divided into high-safety power generation value, medium-safety power generation value and low-safety power generation value in a step-by-step manner, and the historical maximum wind speed value data corresponding to the three safety power generation values ​​are collected. When the wind speed value data collected in the future period in the month with the most occurrences of the historical maximum power generation data exceeds the historical maximum wind speed value data corresponding to the medium-safety power generation value, the system determines that the power generation power of the preset wind turbine generator set is converted to the low-safety power generation value, and issues a risk warning.

[0026] As a preferred solution of the present invention, the month with the most number of times exceeding the historical maximum wind speed value data corresponding to the medium safety power generation value is obtained, and the wind speed change trend of the month is analyzed, and the wind direction angle data under the wind speed change trend is collected, and the wind direction angle data is divided into θ 1 ,θ 2 , ..., θ n , where n represents the nth wind direction angle data collected, and the power value data difference of the wind turbine generator set generated power due to the change of different wind direction angle data under the same wind speed value data is analyzed, and the proportion of different wind direction angle data to the power value data difference is calculated as follows:

[0027] δ t vj is the weight of different wind direction angle data in the concentrated period;

[0028] Among them, t represents the concentrated period of the month when the wind direction angle data has the largest change; R vj represents the jth historical maximum wind direction angle data collected in the vth unit period, and R represents the wind speed value data collected in different periods;

[0029] At the same time, based on the wind speed value data collected in different unit time periods, the wind direction angle with the most occurrences corresponding to the data is obtained, and a wind force combination is preset, and the wind force combination is divided into ▽ according to the number of occurrences 1 Wind power combination, 2 Wind power combination and 3 Wind power combination, calculate the weights of different wind power combinations in different unit time periods, as follows:

[0030]

[0031] Among them, ▽ represents the wind force combination, u vIt represents the longest duration wind force combination collected in the vth unit period, and E represents the total number of items in the vth unit period.

[0032] As a preferred solution of the present invention, the change characteristics of the environmental information data are divided according to different unit time periods, the change characteristics of the environmental information data in different unit time periods are obtained, and the environmental information data are divided into several evaluation indicators using a cross-validation method, and a corresponding evaluation report is generated based on the evaluation indicators.

[0033] Beneficial effects of the present invention: The present invention utilizes deep learning analysis technology to perform predictive analysis and optimization on the power generation and environmental information data of the wind turbine generator set after layered processing, and at the same time associates historically related change characteristics, so as to accurately control the power development trend of the preset wind turbine generator set in the said month, and can more accurately predict the power data under the current environment, and enable the system to automatically issue early warnings according to the current development trend to avoid unexpected events caused by power data changes exceeding the safe peak range. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0035] Figure 1 A schematic diagram of the modular structure of a new energy power prediction and optimization system according to an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of the structure of a new energy power prediction and optimization system process according to an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the structure of a wind power combination system process according to an embodiment of the present invention;

[0038] Numbers in the figure: 10 - information acquisition module; 20 - environmental information data acquisition module; 30 - LSTM network fusion analysis module; 40 - correlation analysis module; 50 - dynamic monitoring module; 60 - monitoring terminal. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0040] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0041] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides a new energy power prediction and optimization system, including:

[0042] The information acquisition module 10 is used to obtain the regional location of the wind turbine generator set and the power generation data of the wind turbine generator set in the regional location;

[0043] The environmental information data acquisition module 20 is used to collect environmental information data corresponding to the wind turbine generator set in the regional location, and the environmental information data includes wind speed, wind direction, and environmental temperature and humidity;

[0044] The LSTM network fusion analysis module 30 is used to respond to the power generation data of the wind turbine generator set of the information acquisition module and the environmental information data of the environmental information data acquisition module, divide the data sets of the power generation data and the environmental information data into a training set, a verification set and a test set, build a power prediction model of new energy wind power generation through the LSTM network, and determine the loss function of the power prediction model of new energy wind power generation, optimize the power prediction model of new energy wind power generation according to the loss function, and generate power prediction data of wind power generation; the fusion analysis module includes a division unit, an analysis unit and an optimization unit;

[0045] The correlation analysis module 40 is used to correlate the power prediction data of current wind power generation with the power prediction data of historical power generation, specifically by determining the month with the most occurrences of the maximum power generation data and the historical minimum power generation data in the power prediction data of historical power generation, and performing correlation analysis after integration, so as to obtain the power generation change characteristic data of the wind turbine generator set under different change trends of the environmental information data, and the fusion analysis module responds to the power generation change characteristic data to generate the power correlation prediction data of wind power generation;

[0046] The dynamic monitoring module 50 is used to respond to the power-related prediction data of wind power generation and the power prediction data of wind power generation, and based on the power prediction data of historical power generation and environmental information data, make dynamic decisions on the power-related prediction data of wind power generation and the power prediction data of wind power generation while dynamically monitoring, generate result data of wind power generation power prediction optimization and present them visually;

[0047] The monitoring terminal 60 is used to record the communication base station matched with the wind turbine generator set, receive the field data uploaded from the preset area where the wind turbine generator set is located, and update the background data at the same time.

[0048] Based on the above, it can be known that this embodiment constructs a power prediction model for new energy wind power generation through an LSTM network, generates power prediction data for wind power generation, and combines the correlation analysis module 40 to generate power correlation prediction data for wind power generation, and then makes dynamic decisions, generates wind power generation power prediction optimization result data and presents it visually. The present invention can more accurately predict the power data under the current environment, and can enable wind power generation companies to achieve more intelligent and automated operation management, improve power generation efficiency, optimize resource allocation, and ensure the safety and stability of the power generation process.

[0049] Specifically, in this embodiment, the division unit extracts the change characteristics of the matched power generation data and environmental information data according to the power generation data and the environmental information data, and divides the change characteristics into primary change characteristics, secondary change characteristics and tertiary change characteristics in a step-by-step manner through the division unit, wherein the change characteristic level is positively correlated with the power generation change characteristic;

[0050] The analysis unit analyzes the correlation change characteristics of wind speed and wind direction under different levels of change characteristics based on the hierarchical analysis in the LSTM network architecture, and analyzes the change probability of the correlation change characteristics on the power generation data of the wind turbine generator set, and calculates the power generation change value of the wind turbine generator set under different levels of change characteristics based on the change probability of the power generation data of the wind turbine generator set;

[0051] The optimization unit responds to the change value of the power generated by the wind turbine generator set, and after optimizing the power prediction model of the new energy wind power generation through the loss function, generates the corresponding wind power generation power prediction data.

[0052] Specifically, in this embodiment, the probability of change of the associated change feature to the power generation data of the wind turbine generator set is analyzed and calculated according to the following formula:

[0053] Among them, P(X) represents the probability of change of wind turbine power generation data;

[0054] In the formula, X represents the power generation data of the wind turbine generator set collected when the wind speed value appears most frequently, K represents the change value of the wind direction angle data at the current wind speed value, ω i represents the weight value of the power generation data of the wind turbine generator set collected for the i-th time at the current wind speed value, m is the total number of items collected for the i-th time, and N represents the mixed weight, which represents the weight of the power generation data of the wind turbine generator set in the data of different wind direction angles under the same wind speed value;

[0055] Based on the change probability of wind turbine power generation data, the change value of wind turbine power generation under different levels of change characteristics is calculated. Specifically, the change probability of wind turbine power generation data and different levels of change characteristic data are converted into serialized form, and the architecture of the LSTM network is defined, including an input layer, three LSTM layers, and an output layer using a softmax function. The LSTM model is trained using a training data set, and the validation set is used to adjust the power prediction model parameters of renewable energy wind power generation. At the same time, the trained LSTM model is used to analyze the correlation between the level change characteristics of wind speed and wind direction and the power generation. For different levels of wind speed and wind direction change characteristics, the statistics and confidence intervals of the power generation are calculated respectively. The confidence interval is the confidence interval for calculating the power generation, which is determined by assuming a normal distribution.

[0056] In this embodiment, the loss function further adopts the MSE loss function or the MAE loss function as the standard loss function for the regression analysis of the change value of the power generation of the wind turbine generator set.

[0057] Preferably, in this embodiment, the dynamic monitoring module 50 makes dynamic decisions on the power-related prediction data of wind power generation and the power prediction data of wind power generation. Specifically, the similarity value of the power-related prediction data of wind power generation and the power prediction data of wind power generation is calculated through the Euclidean distance algorithm, and a dynamic decision is made based on the similarity value to determine the result data of the wind power generation power prediction optimization and visualize it in a chart format.

[0058] Specifically, in this embodiment, the correlation analysis module 40 obtains the minimum wind speed value data of the month with the most occurrences of the minimum power generation data in the power forecast data of historical power generation, and analyzes the unit time period with the most occurrences of the minimum wind speed value data. At the same time, a safety threshold is preset based on the historical minimum safe power generation data in the unit time period, and wind speed value data corresponding to the safety threshold is collected. When the wind speed value data appearing in the unit time period of the month in the future time period is less than the wind speed value data corresponding to the safety threshold, the system determines that the power generation power of the preset wind turbine generator set is lower than the historical minimum safe power generation data, and issues a safety warning. Otherwise, no determination is made.

[0059] Specifically, in this embodiment, the correlation analysis module 40 obtains the top three power value data higher than the minimum safe power generation power data in the power prediction data of historical power generation as a basis, and obtains the wind speed value data change characteristics corresponding to the three-digit power value data to generate a data model, and presets a risk threshold based on the median data of the three-digit power value data. When the power generation power of the preset wind turbine generator set in the future time period is lower than the risk threshold, the system determines that the preset wind turbine generator set is in a risky state, otherwise, it does not make a determination.

[0060] Specifically, in this embodiment, the correlation analysis module 40 collects the minimum wind speed value and the maximum wind speed value in the month with the most occurrences of the maximum power generation data in the power prediction data of historical power generation, and analyzes the change trend of the minimum wind speed value to the maximum wind speed value, and at the same time intercepts the median wind speed value in the change trend, and presets a critical threshold based on the median wind speed value. When the wind speed change value in the month in the future period exceeds the critical threshold, the system determines that the power generation of the preset wind turbine generator set will exceed the historical maximum power generation data, and issues a warning, otherwise, no determination is made;

[0061] The safe power generation value in the historical maximum power generation data is obtained based on the critical threshold, and the Monte Carlo method is used to sample the safe power generation value after reliability assessment. At the same time, the sampled data is divided into high-safety power generation value, medium-safety power generation value and low-safety power generation value in a step-by-step manner, and the historical maximum wind speed value data corresponding to the three safety power generation values ​​are collected. When the wind speed value data collected in the future period in the month with the most occurrences of the historical maximum power generation data exceeds the historical maximum wind speed value data corresponding to the medium-safety power generation value, the system determines that the power generation power of the preset wind turbine generator set is converted to the low-safety power generation value, and issues a risk warning.

[0062] The present embodiment should emphasize that the month with the most historical maximum wind speed data exceeding the medium safety power generation value is obtained, and the wind speed change trend of the month is analyzed. At the same time, the wind direction angle data under the wind speed change trend is collected, and the wind direction angle data is divided into θ 1 ,θ 2 , ..., θ n , where n represents the nth wind direction angle data collected. The power value data difference of the wind turbine generator set generated power due to the change of different wind direction angle data under the same wind speed value data is analyzed, and the proportion of different wind direction angle data to the power value data difference is calculated as follows:

[0063] δ t vj is the weight of different wind direction angle data in the concentrated period;

[0064] in, t Indicates the concentrated period of time in a month when the wind direction angle data changes the most; R vj represents the jth historical maximum wind direction angle data collected in the vth unit period, and R represents the wind speed value data collected in different periods;

[0065] At the same time, based on the wind speed data collected in different unit time periods, the wind direction angle with the most occurrences corresponding to the data is obtained, and the wind force combination is preset, and the wind force combination is divided into ▽ according to the number of occurrences1 Wind power combination, 2 Wind power combination and 3 Wind power combination, calculate the weights of different wind power combinations in different unit time periods, as follows:

[0066]

[0067] Among them, ▽ represents the wind force combination, u v It represents the longest duration wind force combination collected in the vth unit period, and E represents the total number of items in the vth unit period.

[0068] This embodiment divides the change characteristics of environmental information data according to different unit time periods, obtains the change characteristics of environmental information data in different unit time periods, and uses a cross-validation method to divide the environmental information data into several evaluation indicators, and generates a corresponding evaluation report based on the evaluation indicators.

[0069] In summary, the present invention uses advanced deep learning analysis technology to perform refined hierarchical processing on the power generation and related environmental information data of wind turbines. Through this method, we can not only conduct in-depth prediction analysis and optimization, but also associate the change characteristics in historical data, so as to achieve accurate control of the power development trend of wind turbines in specific months.

[0070] This technology can provide more accurate power generation forecasts based on current environmental conditions. More importantly, the system can automatically issue warnings based on the development trend of real-time data and forecast models. This self-warning mechanism can effectively prevent abnormal fluctuations in power generation and ensure that it always remains within a safe peak range, thereby avoiding possible unforeseen risks.

[0071] Through the application of the present invention, wind power generation companies will be able to achieve more intelligent and automated operation management, improve power generation efficiency, optimize resource allocation, and ensure the safety and stability of the power generation process.

[0072] Some code examples:

[0073] Information acquisition module 110

[0074] import numpy as np

[0075] import matplotlib.pyplot as plt

[0076] fromsklearn.datasetsimportmake_classification

[0077] fromsklearn.svmimportSVC

[0078] #Generate GPS satellite positioning navigation data

[0079] X,y=make_classification(n_samples=100,n_features=2,n_redundant=0,n_clusters_per_class=1,random_state=42)

[0080] #Get power data

[0081] svm_linear=SVC(kernel='linear')

[0082] svm_linear.fit(X,y)

[0083] # Generate decision boundary

[0084] plt.figure(figsize=(8,6))

[0085] plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.Paired,marker='o',edgecolors='k')

[0086] ax = plt.gca()

[0087] xlim = ax.get_xlim()

[0088] ylim = ax.get_ylim()

[0089] xx,yy=np.meshgrid(np.linspace(xlim[0],xlim[1],100),np.linspace(ylim[0],ylim[1],100))

[0090] Z=svm_linear.predict(np.c_[xx.ravel(),yy.ravel()]).reshape(xx.shape)

[0091] #Generate decision boundary and support vector data

[0092] plt.contourf(xx,yy,Z,alpha=0.2,cmap=plt.cm.Paired)

[0093] plt.scatter(svm_linear.support_vectors_[:,0],svm_linear.support_vectors_[:,1],s=100,facecolors='none',edgecolors='k')

[0094] plt.title('LinearSVMDecisionBoundary')

[0095] plt.xlabel('Feature1')

[0096] plt.ylabel('Feature2')

[0097] plt.show()

[0098] Data acquisition module 130

[0099] importnumpyasnp

[0100] importmatplotlib.pyplotasplt

[0101] fromsklearn.datasetsimportmake_classification

[0102] fromsklearn.svmimportSVC

[0103] #Get wind speed, wind direction, ambient temperature and humidity data

[0104] X,y=make_classification(n_samples=100,n_features=2,n_redundant=0,n_clusters_per_class=1,random_state=42)

[0105] #Get the historical average power generation data of wind turbines

[0106] svm_poly=SVC(kernel='poly',degree=3)#3rd degree polynomial kernel function

[0107] svm_poly.fit(X,y)

[0108] #Preset algorithm

[0109] plt.figure(figsize=(8,6))

[0110] plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.Paired,marker='o',edgecolors='k')

[0111] #Generate grid data for drawing decision boundaries

[0112] ax = plt.gca()

[0113] xlim = ax.get_xlim()

[0114] ylim = ax.get_ylim()

[0115] xx,yy=np.meshgrid(np.linspace(xlim[0],xlim[1],100),np.linspace(ylim[0],ylim[1],100))

[0116] Z=svm_poly.predict(np.c_[xx.ravel(),yy.ravel()]).reshape(xx.shape)

[0117] # draw the decision boundary and support vector

[0118] plt.contourf(xx,yy,Z,alpha=0.2,cmap=plt.cm.Paired)

[0119] plt.scatter(svm_poly.support_vectors_[:,0],svm_poly.support_vectors_[:,1],s=100,facecolors='none',edgecolors='k')

[0120] plt.title('PolynomialSVMDecisionBoundary')

[0121] plt.xlabel('Feature1')

[0122] plt.ylabel('Feature2')

[0123] plt.show()

[0124] Fusion analysis unit 140

[0125] importnumpyasnp

[0126] importmatplotlib.pyplotasplt

[0127] fromsklearn.datasetsimportmake_classification

[0128] fromsklearn.svmimportSVC

[0129] #Generate calculation data

[0130] X,y=make_classification(n_samples=100,n_features=2,n_redundant=0,n_clusters_per_class=1,random_state=42)

[0131] #Analyze the data

[0132] svm_rbf = SVC(kernel = 'rbf', gamma = 0.5) #gamma controls the width of the Gaussian kernel

[0133] svm_rbf.fit(X,y)

[0134] # Generate decision boundary

[0135] plt.figure(figsize=(8,6))

[0136] plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.Paired,marker='o',edgecolors='k')

[0137] #Predict data changes

[0138] ax = plt.gca()

[0139] xlim = ax.get_xlim()

[0140] ylim = ax.get_ylim()

[0141] xx,yy=np.meshgrid(np.linspace(xlim[0],xlim[1],100),np.linspace(ylim[0],ylim[1],100))

[0142] Z=svm_rbf.predict(np.c_[xx.ravel(),yy.ravel()]).reshape(xx.shape)

[0143] plt.contourf(xx,yy,Z,alpha=0.2,cmap=plt.cm.Paired)

[0144] plt.scatter(svm_rbf.support_vectors_[:,0],svm_rbf.support_vectors_[:,1],s=100,facecolors='none',edgecolors='k')

[0145] plt.title('RBFSVMDecisionBoundary')

[0146] plt.xlabel('Feature1')

[0147] plt.ylabel('Feature2')

[0148] plt.show()

[0149] Data integration module 150

[0150] importnumpyasnp

[0151] importmatplotlib.pyplotasplt

[0152] fromsklearn.datasetsimportmake_classification

[0153] fromsklearn.svmimportSVC

[0154] #Integrate data change trends

[0155] X,y=make_classification(n_samples=100,n_features=2,n_redundant=0,n_clusters_per_class=1,random_state=42)

[0156] #Use MonteCarlo method to sample data

[0157] svm_sigmoid=SVC(kernel='sigmoid',coef0=1.0)#coef0 controls the bias of the Sigmoid function

[0158] svm_sigmoid.fit(X,y)

[0159] plt.figure(figsize=(8,6))

[0160] plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.Paired,marker='o',edgecolors='k')

[0161] #Determine data transformation

[0162] ax = plt.gca()

[0163] xlim = ax.get_xlim()

[0164] ylim = ax.get_ylim()

[0165] xx,yy=np.meshgrid(np.linspace(xlim[0],xlim[1],100),np.linspace(ylim[0],ylim[1],100))

[0166] Z=svm_sigmoid.predict(np.c_[xx.ravel(),yy.ravel()]).reshape(xx.shape)

[0167] #Get different trend change characteristics

[0168] plt.contourf(xx,yy,Z,alpha=0.2,cmap=plt.cm.Paired)

[0169] plt.scatter(svm_sigmoid.support_vectors_[:,0],svm_sigmoid.support_vectors_[:,1],s=100,facecolors='none',edgecolors='k')

[0170] plt.title('SigmoidSVMDecisionBoundary')

[0171] plt.xlabel('Feature1')

[0172] plt.ylabel('Feature2')

[0173] plt.show()

[0174] Dynamic monitoring module 160

[0175] importnumpyasnp

[0176] importmatplotlib.pyplotasplt

[0177] fromsklearn.datasetsimportmake_classification

[0178] fromsklearn.svmimportSVC

[0179] #Collect data for a specified month

[0180] X,y=make_classification(n_samples=100,n_features=2,n_redundant=0,n_clusters_per_class=1,random_state=42)

[0181] #Use Laplacian data classifier

[0182] svm_laplacian = SVC(kernel = 'rbf', gamma = 0.5) # gamma controls Laplacian

[0183] svm_laplacian.fit(X,y)

[0184] #Data Fusion

[0185] plt.figure(figsize=(8,6))

[0186] plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.jet,marker='o',edgecolors='k')

[0187] #Generate wind force combination

[0188] ax = plt.gca()

[0189] xlim = ax.get_xlim()

[0190] ylim = ax.get_ylim()

[0191] xx,yy=np.meshgrid(np.linspace(xlim[0],xlim[1],100),np.linspace(ylim[0],ylim[1],100))

[0192] Z=svm_laplacian.predict(np.c_[xx.ravel(),yy.ravel()]).reshape(xx.shape)

[0193] #Generate data unit time period

[0194] plt.contourf(xx,yy,Z,alpha=0.2,cmap=plt.cm.jet)

[0195] plt.scatter(svm_laplacian.support_vectors_[:,0],svm_laplacian.support_vectors_[:,1],s=100,facecolors='none',edgecolors='limegreen',linewidths=2)

[0196] plt.title('LaplacianSVMDecisionBoundary',fontsize=14)

[0197] plt.xlabel('Feature1',fontsize=12)

[0198] plt.ylabel('Feature2',fontsize=12)

[0199] plt.show()

[0200] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0201] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0202] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0203] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0204] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0205] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0206] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0207] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A new energy power prediction and optimization system, characterized in that: include: An information acquisition module (10) is used to acquire the regional location of the wind turbine generator set and the power generation data of the wind turbine generator set in the regional location; An environmental information data acquisition module (20) is used to acquire environmental information data corresponding to the wind turbine generator set in the regional location, wherein the environmental information data includes wind speed, wind direction, and environmental temperature and humidity; An LSTM network fusion analysis module (30) is used to respond to the power generation data of the wind turbine generator set of the information acquisition module and the environmental information data of the environmental information data acquisition module, divide the data sets of the power generation data and the environmental information data into a training set, a verification set and a test set, construct a power prediction model of new energy wind power generation through the LSTM network, and determine the loss function of the power prediction model of new energy wind power generation, optimize the power prediction model of new energy wind power generation according to the loss function, and generate power prediction data of wind power generation; the fusion analysis module includes a division unit, an analysis unit and an optimization unit; A correlation analysis module (40) is used to correlate the current wind power generation power prediction data with the historical power generation power prediction data, specifically by determining the month with the most occurrences of the maximum power generation data and the historical minimum power generation data in the historical power generation power prediction data, and performing an integrated correlation analysis on the change trend of the environmental information data matching the month, so as to obtain the power generation change characteristic data of the wind turbine generator set under different change trends of the environmental information data, and the fusion analysis module generates the wind power generation power correlation prediction data in response to the power generation change characteristic data; A dynamic monitoring module (50) is used to respond to the wind power generation power correlation prediction data and the wind power generation power prediction data, and based on the historical power generation power prediction data and environmental information data, perform dynamic decision-making on the wind power generation power correlation prediction data and the wind power generation power prediction data while dynamically monitoring, generate wind power generation power prediction optimization result data and visualize the result data; The monitoring terminal (60) is used to record the communication base station matched with the wind turbine generator set, receive the field data uploaded from the area where the preset wind turbine generator set is located, and update the background data at the same time.

2. A new energy power prediction and optimization system as claimed in claim 1, characterized in that: The division unit extracts the change characteristics of the matched power generation data and environmental information data according to the power generation data and the environmental information data, and divides the change characteristics into primary change characteristics, secondary change characteristics and tertiary change characteristics in a step-by-step manner through the division unit, wherein the change characteristic level is positively correlated with the power generation change characteristic; The analysis unit analyzes the associated change characteristics of wind speed and wind direction under different levels of change characteristics based on the hierarchical analysis in the LSTM network architecture, and simultaneously analyzes the change probability of the associated change characteristics on the power generation data of the wind turbine generator set, and calculates the power generation change value of the wind turbine generator set under different levels of change characteristics based on the change probability of the power generation data of the wind turbine generator set; The optimization unit responds to the power generation change value of the wind turbine generator set, and generates corresponding wind power generation power prediction data after optimizing the power prediction model of the new energy wind power generation through a loss function.

3. A new energy power prediction and optimization system as claimed in claim 2, characterized in that: The probability of change of the associated change characteristics to the power generation data of the wind turbine generator set is analyzed and calculated according to the following formula: Among them, P(X) represents the probability of change of wind turbine power generation data; In the formula, X represents the power generation data of the wind turbine generator set collected when the wind speed value appears most frequently, K represents the change value of the wind direction angle data at the current wind speed value, ω i represents the weight value of the power generation data of the wind turbine generator set collected for the i-th time at the current wind speed value, m is the total number of items collected for the i-th time, and N represents the mixed weight, which represents the weight of the power generation data of the wind turbine generator set in the data of different wind direction angles under the same wind speed value; Based on the change probability of the power generation data of the wind turbine generator set, the power generation change value of the wind turbine generator set under different levels of change characteristics is calculated. Specifically, the change probability of the power generation data of the wind turbine generator set and the different levels of change characteristic data are converted into a serialized form, and the architecture of the LSTM network is defined, including an input layer, three LSTM layers and an output layer using a softmax function. The LSTM model is trained using a training data set, and the verification set is used to adjust the power prediction model parameters of the new energy wind power generation. At the same time, the trained LSTM model is used to analyze the correlation between the level change characteristics of wind speed and wind direction and the power generation. For different wind speed and wind direction level change characteristics, the statistics and confidence intervals of the power generation are calculated respectively. The confidence interval is the confidence interval for calculating the power generation, which is determined by assuming a normal distribution.

4. A new energy power prediction and optimization system as claimed in claim 2, characterized in that: The loss function uses the MSE loss function or the MAE loss function as the standard loss function for regression analysis of the power generation change value of the wind turbine generator set.

5. A new energy power prediction and optimization system as claimed in claim 1, characterized in that: The dynamic monitoring module (50) makes a dynamic decision on the power-related prediction data of the wind power generation and the power prediction data of the wind power generation, specifically, by using a Euclidean distance algorithm to calculate a similarity value between the power-related prediction data of the wind power generation and the power prediction data of the wind power generation, makes a dynamic decision based on the similarity value, determines the result data of the wind power generation power prediction optimization, and presents it visually in a chart format.

6. A new energy power prediction and optimization system as claimed in claim 5, characterized in that: The association analysis module (40) obtains the minimum wind speed value data of the month with the most occurrences of the minimum power generation data in the power prediction data of the historical power generation, and analyzes the unit time period with the most occurrences of the minimum wind speed value data. At the same time, a safety threshold is preset based on the historical minimum safe power generation data in the unit time period, and wind speed value data corresponding to the safety threshold is collected. When the wind speed value data appearing in the unit time period of the month in the future time period is less than the wind speed value data corresponding to the safety threshold, the system determines that the power generation power of the preset wind turbine generator set is lower than the historical minimum safe power generation data, and issues a safety warning. Otherwise, no determination is made.

7. A new energy power prediction and optimization system as claimed in claim 6, characterized in that: The association analysis module (40) obtains the first three digits of power value data higher than the minimum safe power generation data in the power prediction data of the historical power generation based on the power value data lower than the minimum safe power generation data, and obtains the wind speed value data change characteristics corresponding to the three digits of power value data to generate a data model, and presets a risk threshold based on the median data of the three digits of power value data. When the power generation power of the preset wind turbine generator set in the future period is lower than the risk threshold, the system determines that the preset wind turbine generator set is in a risk state, otherwise, no determination is made.

8. A new energy power prediction and optimization system as claimed in claim 7, characterized in that: The correlation analysis module (40) collects the minimum wind speed value and the maximum wind speed value in the month with the most occurrences of the maximum power generation data in the power prediction data of the historical power generation, and analyzes the change trend of the minimum wind speed value toward the maximum wind speed value, and at the same time intercepts the median wind speed value in the change trend, and presets a critical threshold value based on the median wind speed value. When the wind speed change value in the future period in the month exceeds the critical threshold value, the system determines that the power generation of the preset wind turbine generator set will exceed the historical maximum power generation data, and issues a warning. Otherwise, no determination is made. The safe power generation value in the historical maximum power generation data is obtained based on the critical threshold, and the Monte Carlo method is used to sample the safe power generation value after reliability evaluation. At the same time, the sampled data is divided into high-safety power generation value, medium-safety power generation value and low-safety power generation value in a step-by-step manner, and the historical maximum wind speed value data corresponding to the three safety power generation values ​​are collected. When the wind speed value data collected in the future period in the month with the most occurrences of the historical maximum power generation data exceeds the historical maximum wind speed value data corresponding to the medium-safety power generation value, the system determines that the power generation power of the preset wind turbine generator set is converted to the low-safety power generation value, and issues a risk warning.

9. A new energy power prediction and optimization system as claimed in claim 8, characterized in that: Obtain the month with the most times exceeding the historical maximum wind speed value data corresponding to the medium safety power generation value, and analyze the wind speed change trend of the month, and at the same time collect the wind direction angle data under the wind speed change trend, and divide the wind direction angle data into θ1, θ2, ..., θ n , where n represents the nth wind direction angle data collected, and the power value data difference of the wind turbine generator set generated power due to the change of different wind direction angle data under the same wind speed value data is analyzed, and the proportion of different wind direction angle data to the power value data difference is calculated as follows: δ t vj is the weight of different wind direction angle data in the concentrated period; Among them, t represents the concentrated period of the month when the wind direction angle data has the largest change; R vj represents the jth historical maximum wind direction angle data collected in the vth unit period, and R represents the wind speed value data collected in different periods; At the same time, based on the wind speed value data collected in different unit time periods, the wind direction angle with the most occurrences corresponding to the data is obtained, and the wind force combination is preset, and the wind force combination is divided into Wind power combination, Wind power combination and Wind power combination, calculate the weights of different wind power combinations in different unit time periods, as follows: in, represents the wind force combination, u v It represents the longest duration wind force combination collected in the vth unit period, and E represents the total number of items in the vth unit period.

10. A new energy power prediction and optimization system as claimed in claim 8, characterized in that: The change characteristics of the environmental information data are divided according to different unit time periods, the change characteristics of the environmental information data in different unit time periods are obtained, and the environmental information data are divided into several evaluation indicators using a cross-validation method, and a corresponding evaluation report is generated according to the evaluation indicators.

Images