Wind Farm Intelligent Management System Based on the Internet of Things

Abstract

The present invention discloses an intelligent management system for a wind farm based on the Internet of Things, which relates to the technical field of wind power prediction, and improves the generalization ability of wind power prediction through deep learning and multi-source data fusion; adopts a combined model of a long short-term memory network and a convolutional neural network to extract features from environmental data, and optimizes the time series weights in combination with historical wind power data to reduce the dependence on historical data; aiming at the problem that short-term wind speed fluctuations are difficult to capture, designs a high-frequency wind speed data acquisition and fast-response prediction module, uses lidar wind speed measurement equipment and ultrasonic anemometers to collect high-frequency wind speed data, extracts short-term change features in combination with wavelet transform, and adopts a dual-stream deep neural network to dynamically fuse short-term and long-term information to improve the prediction accuracy of wind speed changes in a short time.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power prediction, and specifically to an intelligent management system for a wind farm based on the Internet of Things. Background Art

[0002] The intelligent management system for a wind farm stems from the development of smart grid and Internet of Things technologies. In the early days, wind farms mainly relied on manual inspections and regular maintenance, making it difficult to achieve precise monitoring and prediction. With the development of sensor technology, cloud computing, big data analysis, and artificial intelligence, the management of wind farms has gradually evolved towards automation and intelligence. In recent years, combined with edge computing and 5G communication, the intelligent management system can achieve remote real-time monitoring, fault prediction, and optimized scheduling, improving the operating efficiency and economy of wind farms.

[0003] However, when the existing intelligent management systems for wind farms are applied to wind power prediction and optimized scheduling, they often have the following technical drawbacks:

[0004] 1. Strong dependence on historical data: Many prediction models rely on historical power generation data. However, the environment of wind farms changes rapidly, which may lead to insufficient generalization ability of the models and reduce prediction accuracy. [[ID=1)]]

[0005] 2. Difficulty in capturing short-term fluctuations: Traditional methods are difficult to accurately depict the instantaneous changes in wind speed, resulting in large prediction errors in power fluctuations within a short period of time and affecting the stability of the power grid.

[0006] 3. Challenges in grid connection security: Wind power fluctuations may cause grid instability, and the existing optimized scheduling strategies are still insufficient in adapting to extreme weather or emergencies, affecting the security and economy of wind power grid connection. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides an intelligent management system for a wind farm based on the Internet of Things, which solves the technical drawbacks mentioned in the background art.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent management system for a wind farm based on the Internet of Things, including a data perception module, a dynamic correction module, an instantaneous modeling module, a security assessment module, and an optimized scheduling module;

[0009] The data perception module uses Internet of Things terminals to collect wind speed, wind direction, turbulence intensity, and unit vibration data in real time, constructs a spatio-temporal data set, and calculates and evaluates the environmental mutation index Hd to trigger a dynamic correction instruction;

[0010] After receiving the dynamic correction instruction, the dynamic correction module uses the sliding time window algorithm to perform timeliness screening on historical power generation data, eliminates invalid data segments, generates a dynamic training set, and updates the prediction model parameters in combination with online learning;

[0011] The instantaneous modeling module extracts turbulence characteristics based on high-frequency sampling data, uses a hybrid model to predict instantaneous power fluctuations and outputs the instantaneous power prediction results; then calculates the fluctuation entropy value Bd; finally, evaluates the fluctuation entropy value Bd to activate the safety warning.

[0012] The safety assessment module is used to monitor the grid frequency deviation, voltage volatility and line load rate, calculate the grid connection risk coefficient Rx in combination with the fluctuation entropy value Bd, and evaluate the system robustness under extreme weather through Monte Carlo simulation to obtain the safety margin Sy.

[0013] The optimal scheduling module dynamically adjusts the unit output according to the instantaneous power prediction results, grid connection risk coefficient Rx and safety margin Sy, and adopts a virtual inertia compensation strategy to provide inertial response during power mutation.

[0014] Preferably, the data perception module includes a collection unit, a fusion calculation unit and a trigger unit.

[0015] The collection unit is used to collect multi-dimensional dynamic data of the wind farm in real time through the Internet of Things terminal, generate a spatio-temporal data set, and transmit it to the central processing platform in real time. By performing spatio-temporal alignment and standardization processing on the wind speed, wind direction, turbulence intensity and unit vibration data, an environmental feature database is constructed.

[0016] Among them, the spatio-temporal data set includes the sequence variation coefficient Sx of the real-time wind speed, the turbulence intensity distribution matrix Tij, the unit vibration spectrum Vk and the environmental temperature and humidity gradient ΔH.

[0017] Preferably, the fusion calculation unit is used to perform multi-scale fusion calculation on the spatio-temporal data set by taking the dispersion of the sequence variation coefficient Sx of the real-time wind speed, the dispersion of the turbulence intensity distribution matrix Tij and the energy entropy of the unit vibration spectrum Vk to obtain the environmental mutation index Hd. The specific calculation formula is as follows:

[0018] ;

[0019] In the formula, represents the dispersion of the turbulence intensity distribution matrix Tij, represents the energy entropy of the unit vibration spectrum Vk; w1, w2 and w3 respectively correspond to the contribution weights of the sequence variation coefficient Sx of the real-time wind speed, the dispersion of the turbulence intensity distribution matrix Tij and the energy entropy of the unit vibration spectrum Vk, and w1 + w2 + w3 = 1.

[0020] Preferably, the trigger unit is used to preset an adaptive threshold J and compare and evaluate it with the environmental mutation index Hd to determine whether it is necessary to start the model correction mechanism. The specific determination logic is as follows:

[0021] When the environmental mutation index Hd ≥ the adaptive threshold J, it indicates that the current environmental mutation degree exceeds the adaptation range of the model, and a dynamic correction instruction is generated and transmitted to the dynamic correction module;

[0022] When the environmental mutation index Hd < the adaptive threshold J, it indicates that the current environmental state is within the tolerance of the prediction model, and the system default operation mode is maintained.

[0023] Preferably, after receiving the dynamic correction instruction, the dynamic correction module uses the sliding time window algorithm to perform timeliness screening on the historical power generation data, dynamically eliminates the invalid data segments beyond the time window range, and generates a dynamic training set based on the screened valid data; then, in combination with the online learning mechanism, the dynamic training set is used to update the parameters of the prediction model in real time to dynamically adjust the prediction accuracy and adaptability of the model.

[0024] Preferably, the instantaneous modeling module includes a feature prediction unit and an entropy value evaluation unit;

[0025] The feature prediction unit extracts turbulence features based on high-frequency sampling data, including the instantaneous wind speed fluctuation amplitude, spectral distribution characteristics, and spatial correlation coefficient; uses a hybrid model to model the turbulence features, predicts the instantaneous power fluctuation, and outputs the instantaneous power prediction result;

[0026] A hybrid modeling method combining a physical model and data-driven is adopted, and the specific prediction formula is as follows:

[0027]

[0028] In the formula, Ppred represents the predicted value of the instantaneous power, Pphy represents the prediction component of the physical model based on hydrodynamic calculations, v represents the wind speed, ρ represents the air density, Pdata represents the prediction component of the data-driven model based on the turbulence feature Xturb, α and β are the weight coefficients of the physical model and the data-driven model respectively, and ϵ is the correction error term.

[0029] Preferably, the entropy value evaluation unit is used to calculate the fluctuation entropy value Bd according to the predicted value Ppred of the instantaneous power, and evaluate the fluctuation entropy value Bd to activate the safety warning mechanism; among them, the information entropy algorithm is used to calculate the fluctuation entropy value Bd, and the specific calculation formula is as follows:

[0030] ;

[0031] In the formula, represents the probability distribution of the instantaneous power fluctuation in the i-th amplitude interval;

[0032] Preset the first safety threshold Bdth-1 and the second safety threshold Bdth-2, compare and evaluate them with the fluctuation entropy value Bd, and activate the safety warning; and the first safety threshold Bdth-1 is greater than the second safety threshold Bdth-2. The specific evaluation content is as follows:

[0033] When the fluctuation entropy value Bd ≥ the first safety threshold Bdth-1, it is determined as the first abnormal fluctuation, triggering a first-level safety warning, and the following operations are performed:

[0034] Immediately start the emergency load reduction control strategy of the fan, limit the output power to the safe range; if the fluctuation continues to intensify, trigger the forced shutdown protection; simultaneously send a high-level alarm signal to the monitoring system to prompt manual intervention;

[0035] When the second safety threshold Bdth-2 ≤ the fluctuation entropy value Bd < the first safety threshold Bdth-1, it is determined as the second abnormal fluctuation, triggering a second-level safety warning, and the following operations are performed:

[0036] Automatically adjust the dynamic damping parameter or pitch rate of the fan to suppress power oscillation;

[0037] Real-time optimize the prediction model parameters to enhance the adaptability to short-term fluctuations;

[0038] Generate a warning log and mark the abnormal period for subsequent analysis;

[0039] When the fluctuation entropy value Bd < the second safety threshold Bdth-2, it is determined as the normal fluctuation range, maintain the current operating state of the fan, and continuously monitor the change trend of the entropy value.

[0040] Preferably, the safety assessment module includes a real-time monitoring unit and a risk simulation unit;

[0041] The real-time monitoring unit is used to continuously collect the grid frequency deviation Ra, voltage volatility Rb, and line load rate Rc and perform normalization processing, and combine with the fluctuation entropy value Bd output by the instantaneous modeling module to fit and calculate the grid connection risk coefficient Rx;

[0042] The specific calculation formula of the grid connection risk coefficient Rx is:

[0043] ;

[0044] In the formula, all parameters participate in the calculation with equal weight. When any parameter increases abnormally, the grid connection risk coefficient Rx rises, reflecting the risk accumulation effect.

[0045] Preferably, the risk simulation unit is used to simulate the dynamic response of the power grid under fault conditions and output the system safety margin Sy by generating the joint probability distribution of wind speed and load under extreme weather scenarios based on the Monte Carlo method; when performing the Monte Carlo simulation, the joint probability density function f(v, P) of wind speed and load is used as the input, and the system instability probability Ps is calculated through N random samplings. The safety margin Sy is defined as:

[0046] ;

[0047] where N ≥ 10000 times, and the action time sequence constraints of the protection device are considered in each simulation.

[0048] Preferably, it receives in real time the power prediction results output by the instantaneous modeling module, the grid connection risk coefficient Rx calculated by the safety assessment module, and the safety margin Sy, and generates the unit output command through a dynamic optimization algorithm; when a power mutation is detected, the virtual inertia compensation strategy is activated, and the virtual inertia coefficient Kv is dynamically adjusted according to the grid frequency change rate dt, and inertial power compensation is achieved through additional torque control. At the same time, the coupling relationship between the grid connection risk coefficient Rx and the safety margin Sy is combined to constrain the output adjustment amplitude to form the final control signal.

[0049] The present invention provides a wind farm intelligent management system based on the Internet of Things, which has the following beneficial effects:

[0050] (1) The wind farm intelligent management system based on the Internet of Things improves the generalization ability of wind power prediction by introducing deep learning methods and multi-source data fusion mechanisms; specifically, a model combining a long short-term memory network and a convolutional neural network is used to perform deep feature extraction on environmental feature data including wind speed Fsd, temperature Wdj, humidity Sds, and air pressure Qyl, and modeling is carried out in combination with historical wind power data (Fdl); at the same time, the attention mechanism is used to optimize the time series weight allocation, reduce the over-reliance on historical data, and improve the adaptability to environmental changes, thus solving the problem of strong dependence on historical data in traditional methods.

[0051] (2) In order to solve the problem that short-term wind speed fluctuations are difficult to capture, the wind farm intelligent management system based on the Internet of Things designs a high-frequency wind speed data acquisition and fast response prediction module; first, lidar wind speed measurement equipment and ultrasonic anemometers are used to collect high-frequency wind speed data, and the wavelet transform is combined to decompose the wind speed signal to extract short-term change features; secondly, the long-term trend and short-term fluctuation information are processed respectively based on a two-stream deep neural network, and the prediction weights are adaptively adjusted through a dynamic fusion mechanism to improve the sensitivity to wind speed changes within a short time, thereby reducing the short-term power prediction error.

[0052] (3) The wind farm intelligent management system based on the Internet of Things combines wind power optimization scheduling with extreme weather response mechanism to improve the safety of wind power grid connection. First, a wind farm power scheduling model is constructed based on the adaptive reinforcement learning algorithm. The parameters including the current wind power Fdl, grid load Wfh, standby power capacity Bdr and weather anomaly index Tyyz are used as input to optimize the active power output. Second, an extreme weather recognition network is used to classify abnormal meteorological events including sudden storms, lightning and low temperature icing, and the scheduling system is linked to adjust the wind power output curve to ensure the stability and economy of wind power grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a schematic diagram of the framework structure of the wind farm intelligent management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] Example 1

[0056] See also Figure 1 , the present invention provides an intelligent management system for wind farms based on the Internet of Things, including a data perception module, a dynamic correction module, a transient modeling module, a safety assessment module and an optimization scheduling module;

[0057] The data perception module collects wind speed, wind direction, turbulence intensity and unit vibration data in real time through the Internet of Things terminal, constructs a spatiotemporal data set, and calculates and evaluates the environmental mutation index Hd, triggering dynamic correction instructions;

[0058] After receiving the dynamic correction instruction, the dynamic correction module uses a sliding time window algorithm to screen the historical power generation data for timeliness, remove invalid data segments, generate a dynamic training set, and update the prediction model parameters in combination with online learning;

[0059] The transient modeling module extracts turbulence characteristics based on high-frequency sampling data, uses a hybrid model to predict instantaneous power fluctuations, and outputs the instantaneous power prediction results; then calculates the fluctuation entropy value Bd; finally, the fluctuation entropy value Bd is evaluated and a safety warning is activated;

[0060] A safety assessment module is used to monitor the grid frequency deviation, voltage volatility, and line load rate, calculate the grid connection risk coefficient Rx in combination with the fluctuation entropy value Bd, and evaluate the system robustness under extreme weather through Monte Carlo simulation to obtain the safety margin Sy; the optimal scheduling module dynamically adjusts the generator output according to the instantaneous power prediction result, grid connection risk coefficient Rx, and safety margin Sy, and adopts a virtual inertia compensation strategy to provide inertial response during power mutation.

[0061] In this embodiment, the stability and safety of wind power grid connection are improved through the collaborative work of each module; the data perception module uses the Internet of Things terminal to collect the wind speed Wf, wind direction Wfx, turbulence intensity Wt, and generator vibration data Vd in real time, constructs a spatio-temporal data set, and calculates and evaluates the environmental mutation index Hd to trigger a dynamic correction instruction to ensure the timeliness and reliability of the data.

[0062] After receiving the dynamic correction instruction, the dynamic correction module uses the sliding time window algorithm to perform timeliness screening on the historical power generation data, eliminates the invalid data segment, generates a dynamic training set, and updates the prediction model parameters in combination with online learning, thereby enhancing the adaptability and generalization ability of the model.

[0063] The instantaneous modeling module extracts the turbulence characteristics based on the high-frequency sampling data, uses the hybrid model to predict the instantaneous power fluctuation and outputs the instantaneous power prediction result Pg, then calculates the fluctuation entropy value Bd, evaluates the fluctuation entropy value Bd to activate the safety warning, and realizes the accurate prediction and control of the short-term power fluctuation.

[0064] The safety assessment module is used to monitor the grid frequency deviation Fv, voltage volatility Vw, and line load rate Ll, calculate the grid connection risk coefficient Rx in combination with the fluctuation entropy value Bd, and at the same time evaluate the system robustness under extreme weather through Monte Carlo simulation to obtain the safety margin Sy to ensure the grid connection stability of the wind farm in a complex environment.

[0065] The optimal scheduling module dynamically adjusts the generator output according to the instantaneous power prediction result Pg, grid connection risk coefficient Rx, and safety margin Sy, and adopts a virtual inertia compensation strategy to provide inertial response during power mutation, improving the adaptability and regulation ability of the wind farm in the power grid.

[0066] Embodiment 2

[0067] The data perception module includes a collection unit, a fusion calculation unit, and a trigger unit.

[0068] The collection unit is used to collect the multi-dimensional dynamic data of the wind farm in real time through the Internet of Things terminal, generate a spatio-temporal data set, and transmit it to the central processing platform in real time. By performing spatio-temporal alignment and standardization processing on the wind speed, wind direction, turbulence intensity, and generator vibration data, an environmental feature database is constructed.

[0069] ​

[0070] Among them, the spatio-temporal data set includes the sequence coefficient of variation Sx of the real-time wind speed, the turbulence intensity distribution matrix Tij, the unit vibration spectrum Vk, and the environmental temperature and humidity gradient ΔH.

[0071] The fusion calculation unit is used to obtain the environmental mutation index Hd through multi-scale fusion calculation of the sequence coefficient of variation Sx of the real-time wind speed, the dispersion degree of the turbulence intensity distribution matrix Tij, and the energy entropy of the unit vibration spectrum Vk based on the spatio-temporal data set. The specific calculation formula is as follows:

[0072] ;

[0073] In the formula, represents the dispersion degree of the turbulence intensity distribution matrix Tij, represents the energy entropy of the unit vibration spectrum Vk; w1, w2, and w3 respectively correspond to the contribution weights of the sequence coefficient of variation Sx of the real-time wind speed, the dispersion degree of the turbulence intensity distribution matrix Tij, and the energy entropy of the unit vibration spectrum Vk, and w1 + w2 + w3 = 1.

[0074] The trigger unit is used to preset an adaptive threshold J and compare it with the environmental mutation index Hd for evaluation to determine whether to start the model correction mechanism. The specific determination logic is as follows:

[0075] If the environmental mutation index Hd ≥ the adaptive threshold J, it means that the current environmental mutation degree exceeds the model adaptation range, and a dynamic correction instruction is generated and transmitted to the dynamic correction module;

[0076] If the environmental mutation index Hd < the adaptive threshold J, it means that the current environmental state is within the tolerance of the prediction model, and the system default operation mode is maintained.

[0077] In this embodiment, is the standard deviation of the wind speed sequence within the sliding time window, is the mean value of the wind speed sequence within the sliding time window; , M×N represents the dimension of the spatial grid division of the wind farm; The global mean value of the turbulence intensity matrix; , pk represents the proportion of the energy of the kth frequency band in the total energy; K represents the total number of frequency bands of the spectrum analysis;

[0078] The data perception module undertakes the functions of real-time perception, fusion calculation and early warning triggering of multi-dimensional dynamic data of the wind farm in the system, ensuring that environmental changes can be captured in time and fed back to subsequent modules to optimize the adaptability and reliability of the prediction model; the acquisition unit obtains the wind speed Wf, wind direction Wfx, turbulence intensity Wt and unit vibration data Vd in real time through the Internet of Things terminal, constructs a spatio-temporal data set, and performs spatio-temporal alignment and standardization processing on the data to form an environmental feature database, so as to improve the comparability and calculation efficiency of the data; among them, the spatio-temporal data set includes the sequence coefficient of variation Sx of the real-time wind speed, which is used to measure the stability of the wind speed over time and reflect the short-term fluctuation characteristics of wind power output; the turbulence intensity distribution matrix Tij, which is used to describe the spatial distribution and dispersion degree of turbulence in the wind farm and is crucial for the load fluctuation of wind turbines; the unit vibration spectrum Vk, which is used to analyze the vibration characteristics of the wind turbine under different operating states, so as to identify potential mechanical failures or abnormal working conditions; the environmental temperature and humidity gradient ΔH, which is used to evaluate the impact of external meteorological conditions on the operating efficiency and material fatigue loss of the fan;

[0079] The fusion calculation unit performs multi-scale fusion calculation based on the spatio-temporal data set, using the sequence coefficient of variation Sx of the real-time wind speed, the dispersion degree of the turbulence intensity distribution matrix Tij and the energy entropy of the unit vibration spectrum Vk to obtain the environmental mutation index Hd, so as to quantify the environmental dynamic changes of the wind farm. Among them, the dispersion degree of the turbulence intensity distribution matrix Tij measures the inhomogeneity of the turbulence intensity, and the energy entropy of the unit vibration spectrum Vk is used to quantify the complexity of the vibration signal. The combination of the two can accurately describe the operating stability of the wind farm; the triggering unit conducts a comparative evaluation of the environmental mutation index Hd based on the adaptive threshold J, and generates a dynamic correction instruction and transmits it to the dynamic correction module according to the evaluation content, ensuring that the prediction model can be adaptively adjusted for abnormal working conditions.

[0080] Embodiment 3

[0081] After receiving the dynamic correction instruction, the dynamic correction module uses the sliding time window algorithm to perform timeliness screening on historical power generation data, dynamically eliminates the invalid data segments outside the time window range, and generates a dynamic training set based on the screened valid data; then combines the online learning mechanism and uses the dynamic training set to update the parameters of the prediction model in real time to dynamically adjust the prediction accuracy and adaptability of the model.

[0082] The instantaneous modeling module includes a feature prediction unit and an entropy value evaluation unit;

[0083] The feature prediction unit extracts turbulence features based on high-frequency sampling data, including the instantaneous wind speed fluctuation amplitude, spectral distribution characteristics and spatial correlation coefficient; uses a hybrid model to model the turbulence features, predicts the instantaneous power fluctuation, and outputs the instantaneous power prediction result;

[0084] A hybrid modeling method combining physical models and data-driven approaches is adopted, and the specific prediction formula is as follows:

[0085]

[0086] Where Ppred represents the predicted value of instantaneous power, Pphy represents the predicted component of the physical model based on hydrodynamic calculations, v represents the wind speed, ρ represents the air density, Pdata represents the predicted component of the data-driven model based on the turbulence feature Xturb, α and β are the weight coefficients of the physical model and the data-driven model respectively, and ϵ is the correction error term.

[0087] The entropy value evaluation unit is used to calculate the fluctuation entropy value Bd based on the predicted value of instantaneous power Ppred, and evaluate the fluctuation entropy value Bd to activate the safety warning mechanism; among them, the information entropy algorithm is used to calculate the fluctuation entropy value Bd, and the specific calculation formula is as follows:

[0088] ;

[0089] Where represents the probability distribution of the instantaneous power fluctuation in the i-th amplitude interval;

[0090] The preset first safety threshold Bdth-1 and the second safety threshold Bdth-2 are compared and evaluated with the fluctuation entropy value Bd to activate the safety warning; and the first safety threshold Bdth-1 is greater than the second safety threshold Bdth-2, and the specific evaluation content is as follows:

[0091] When the fluctuation entropy value Bd ≥ the first safety threshold Bdth-1, it is determined as the first abnormal fluctuation, triggering a first-level safety warning, and the following operations are performed:

[0092] Immediately start the emergency load reduction control strategy of the fan, limit the output power to the safe range; if the fluctuation continues to intensify, trigger the forced shutdown protection; simultaneously send a high-level alarm signal to the monitoring system to prompt manual intervention;

[0093] When the second safety threshold Bdth-2 ≤ the fluctuation entropy value Bd < the first safety threshold Bdth-1, it is determined as the second abnormal fluctuation, triggering a second-level safety warning, and the following operations are performed:

[0094] Automatically adjust the dynamic damping parameters or pitch rate of the fan to suppress power oscillation;

[0095] Real-time optimize the parameters of the prediction model to enhance the adaptability to short-term fluctuations;

[0096] Generate a warning log and mark the abnormal period for subsequent analysis;

[0097] When the fluctuation entropy value Bd < the second safety threshold Bdth-2, it is determined to be within the normal fluctuation range, the current operating state of the fan is maintained, and the change trend of the entropy value is continuously monitored.

[0098] In this embodiment, after receiving the dynamic correction instruction, the dynamic correction module performs timeliness screening on historical power generation data through the sliding time window algorithm, eliminates the invalid data segments outside the time window range, to ensure the timeliness and accuracy of the training data, and generates a dynamic training set based on the screened valid data, combines with the online learning mechanism to update the parameters of the prediction model in real time, and improves the prediction accuracy and adaptability of the model; the instantaneous modeling module includes a feature prediction unit and an entropy value evaluation unit. Among them, the feature prediction unit extracts the turbulence feature Xturb based on high-frequency sampling data, including the instantaneous wind speed fluctuation amplitude Xamp, the spectral distribution characteristic Xfreq, and the spatial correlation coefficient Xcorr, to accurately describe the instantaneous change characteristics of the wind speed, and uses a hybrid model for modeling, and calculates the instantaneous power prediction value Ppred by combining the physical model prediction component Pphy and the data-driven model prediction component Pdata. Among them, Pphy provides physical constraints based on the hydrodynamic calculation of the wind speed v and the air density ρ, and Pdata uses the change trend of Xturb for data-driven prediction. α and β are the weight coefficients of the two models, and the correction error term ϵ is used to compensate for the prediction error to improve the accuracy and robustness of the instantaneous power prediction; the entropy value evaluation unit calculates the fluctuation entropy value Bd based on Ppred to quantify the fluctuation degree of the instantaneous power. Among them, Bd is calculated using the information entropy algorithm, which reflects the probability distribution of power fluctuations in different amplitude intervals, and is compared and evaluated with the first safety threshold Bdth-1 and the second safety threshold Bdth-2 to activate the safety warning mechanism; when Bd ≥ Bdth-1, a first-level safety warning is triggered, the emergency load reduction control strategy of the fan is started, the output power is limited to the safe range, forced shutdown protection is performed if necessary, and a high-level alarm signal is sent to the monitoring system to prompt manual intervention to ensure the safe operation of the fan; when Bdth-2 ≤ Bd < Bdth-1, a second-level safety warning is triggered, the power oscillation is suppressed by adjusting the dynamic damping parameter or the pitch rate of the fan, and at the same time, the parameters of the prediction model are optimized to enhance the short-term fluctuation adaptability, and a warning log is generated to record the abnormal period for subsequent analysis; when Bd < Bdth-2, it is determined to be within the normal fluctuation range, the current operating state of the fan is maintained, and the change trend of the entropy value is continuously monitored to ensure the long-term stable operation of the system.

[0099] Embodiment 4

[0100] The safety assessment module includes a real-time monitoring unit and a risk simulation unit;

[0101] The real-time monitoring unit is used to continuously collect the grid frequency deviation Ra, voltage volatility Rb, and line load rate Rc, perform normalization processing, and combine with the fluctuation entropy value Bd output by the instantaneous modeling module to fit and calculate the grid connection risk coefficient Rx;

[0102] The specific calculation formula for the grid connection risk coefficient Rx is:

[0103] ;

[0104] In the formula, all parameters participate in the calculation with equal weights. When any parameter increases abnormally, the grid connection risk coefficient Rx will rise, reflecting the risk accumulation effect.

[0105] The risk simulation unit is used to simulate the dynamic response of the power grid under fault conditions and output the system safety margin Sy based on the Monte Carlo method by generating the wind speed-load joint probability distribution under extreme weather scenarios; when performing the Monte Carlo simulation, the wind speed-load joint probability density function f(v,P) is used as the input, and the system instability probability Ps is calculated through N random samplings. The safety margin Sy is defined as:

[0106] ;

[0107] Where N ≥ 10000 times, and each simulation considers the action time sequence constraints of the protection device.

[0108] Receives in real time the power prediction results output by the instantaneous modeling module, the grid connection risk coefficient Rx and the safety margin Sy calculated by the safety assessment module, and generates the unit output command through the dynamic optimization algorithm; when a power mutation is detected, activates the virtual inertia compensation strategy, dynamically adjusts the virtual inertia coefficient Kv according to the grid frequency change rate dt, realizes the inertial power compensation through the additional torque control, and at the same time combines the coupling relationship between the grid connection risk coefficient Rx and the safety margin Sy to constrain the output adjustment amplitude to form the final control signal.

[0109] In this embodiment, the safety assessment module includes a real-time monitoring unit and a risk simulation unit, which are used to comprehensively evaluate the safety of wind power grid connection; the real-time monitoring unit continuously collects the grid frequency deviation Ra, voltage volatility Rb, and line load rate Rc, and performs normalization processing on them to ensure the comparability of different physical quantities, and combines with the fluctuation entropy value Bd output by the instantaneous modeling module to comprehensively fit and calculate the grid connection risk coefficient Rx. The calculation formula of the grid connection risk coefficient Rx ensures that all parameters participate in the calculation with equal weights, so as to accurately reflect the stable state of the grid connection;

[0110] The risk simulation unit uses the Monte Carlo method to simulate the dynamic response of the power grid under fault conditions by generating the joint probability distribution f(v, P) of wind speed and load in extreme weather scenarios, and outputs the system safety margin Sy. The Monte Carlo simulation calculates the system instability probability Ps through N≥10000 random samplings, and considers the action timing constraints of protection devices in each simulation to ensure that Sy can accurately evaluate the safety margin of the power grid in extreme cases;

[0111] The system receives the power prediction result Ppred from the instantaneous modeling module, the grid connection risk coefficient Rx and the safety margin Sy calculated by the safety assessment module in real time, and generates the unit output command through the dynamic optimization algorithm. When a power mutation (|dt|≥threshold) is detected, the virtual inertia compensation strategy is activated, and the virtual inertia coefficient Kv is dynamically adjusted according to the power grid frequency change rate dt to achieve inertial power compensation through additional torque control. At the same time, the coupling relationship between Rx and Sy is combined to constrain the output adjustment amplitude to form the final control signal, so as to optimize the dynamic response ability of wind power grid connection while ensuring the stability of the power grid.

[0112] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent management system for a wind farm based on the Internet of Things, characterized in that: It includes a data perception module, a dynamic correction module, an instantaneous modeling module, a safety assessment module, and an optimization scheduling module; The data perception module collects wind speed, wind direction, turbulence intensity, and unit vibration data in real time through Internet of Things terminals, constructs a spatio-temporal data set, calculates and evaluates the environmental mutation index Hd, and triggers a dynamic correction instruction; After receiving the dynamic correction instruction, the dynamic correction module uses a sliding time window algorithm to perform timeliness screening on historical power generation data, eliminates invalid data segments, generates a dynamic training set, and updates the prediction model parameters by combining online learning; The instantaneous modeling module extracts turbulence characteristics based on high-frequency sampling data, uses a hybrid model to predict instantaneous power fluctuations and outputs instantaneous power prediction results; a hybrid modeling method combining a physical model and data-driven is adopted, and the specific prediction formula is as follows: ； In the formula, Ppred represents the predicted value of instantaneous power, Pphy represents the predicted component of the physical model calculated based on fluid dynamics, v represents the wind speed, ρ represents the air density, Pdata represents the predicted component of the data-driven model based on the turbulence characteristics Xturb, α and β are the weight coefficients of the physical model and the data-driven model respectively, and ϵ is the correction error term; Then calculate the fluctuation entropy value Bd; finally, evaluate the fluctuation entropy value Bd, activate the safety warning and perform different operations accordingly, including the first-level safety warning, the second-level safety warning, and the third-level safety warning; The safety assessment module is used to monitor the grid frequency deviation, voltage volatility, and line load rate, calculate the grid connection risk coefficient Rx in combination with the fluctuation entropy value Bd, and evaluate the system robustness under extreme weather through Monte Carlo simulation to obtain the safety margin Sy; The optimization scheduling module dynamically adjusts the unit output according to the instantaneous power prediction result, the grid connection risk coefficient Rx, and the safety margin Sy, and adopts a virtual inertia compensation strategy to provide inertial response during power mutation.

2. The intelligent management system for a wind farm based on the Internet of Things according to claim 1, wherein: The data perception module includes a collection unit, a fusion calculation unit, and a trigger unit; The collection unit is used to collect multi-dimensional dynamic data of the wind farm in real time through Internet of Things terminals, generate a spatio-temporal data set, and transmit it to the central processing platform in real time. By performing spatio-temporal alignment and standardization processing on the wind speed, wind direction, turbulence intensity, and unit vibration data, an environmental feature database is constructed; Among them, the spatio-temporal data set includes the sequence variation coefficient Sx of the real-time wind speed, the turbulence intensity distribution matrix Tij, the unit vibration spectrum Vk, and the environmental temperature and humidity gradient ΔH.

3. The Internet of Things-based intelligent management system for a wind farm according to claim 2, characterized in that: The fusion calculation unit is used to perform multi-scale fusion calculation on the sequence variation coefficient Sx of the real-time wind speed, the dispersion of the turbulence intensity distribution matrix Tij, and the energy entropy of the unit vibration spectrum Vk based on the spatio-temporal data set to obtain the environmental mutation index Hd. The specific calculation formula is as follows: ； In the formula, represents the dispersion of the turbulent intensity distribution matrix Tij, represents the energy entropy of the unit vibration spectrum Vk; w1, w2, and w3 respectively correspond to the contribution weights of the sequence variation coefficient Sx of the real-time wind speed, the dispersion of the turbulent intensity distribution matrix Tij, and the energy entropy of the unit vibration spectrum Vk, and w1 + w2 + w3 = 1.

4. The intelligent management system for a wind farm based on the Internet of Things according to claim 3, characterized in that: The trigger unit is used to preset an adaptive threshold J and compare it with the environmental mutation index Hd to judge whether it is necessary to start the model correction mechanism. The specific determination logic is as follows: When the environmental mutation index Hd ≥ the adaptive threshold J, it indicates that the current environmental mutation degree exceeds the adaptation range of the model, and a dynamic correction instruction is generated and transmitted to the dynamic correction module; When the environmental mutation index Hd < the adaptive threshold J, it indicates that the current environmental state is within the tolerance of the prediction model, and the system default operation mode is maintained.

5. The intelligent management system for a wind farm based on the Internet of Things according to claim 4, characterized in that: After receiving the dynamic correction instruction, the dynamic correction module uses the sliding time window algorithm to perform timeliness screening on historical power generation data, dynamically eliminates the invalid data segments beyond the time window range, and generates a dynamic training set based on the screened valid data; then, combined with the online learning mechanism, the dynamic training set is used to update the parameters of the prediction model in real time to dynamically adjust the prediction accuracy and adaptability of the model.

6. The intelligent management system for a wind farm based on the Internet of Things according to claim 5, wherein: The instantaneous modeling module includes a feature prediction unit and an entropy value evaluation unit; The feature prediction unit extracts turbulence features based on high-frequency sampling data, including the instantaneous wind speed fluctuation amplitude, spectral distribution characteristics, and spatial correlation coefficient; uses a hybrid model to model the turbulence features, predicts the instantaneous power fluctuation, and outputs the instantaneous power prediction result.

7. The intelligent management system for a wind farm based on the Internet of Things according to claim 6, characterized in that: The entropy value evaluation unit is used to calculate the fluctuation entropy value Bd according to the instantaneous power prediction value Ppred, and evaluate the fluctuation entropy value Bd to activate the safety warning mechanism; among them, the information entropy algorithm is used to calculate the fluctuation entropy value Bd, and the specific calculation formula is as follows: ； wherein, represents the probability distribution of the instantaneous power fluctuation in the i-th amplitude interval; Preset the first safety threshold Bdth-1 and the second safety threshold Bdth-2, compare and evaluate them with the fluctuation entropy value Bd to activate the safety warning; and the first safety threshold Bdth-1 is greater than the second safety threshold Bdth-2, and the specific evaluation content is as follows: When the fluctuation entropy value Bd ≥ the first safety threshold Bdth-1, it is determined as the first abnormal fluctuation, triggering a first-level safety warning, and performing the following operations: Immediately start the emergency load reduction control strategy of the fan, limit the output power to the safe range; if the fluctuation continues to intensify, trigger the forced shutdown protection; synchronously send a high-level alarm signal to the monitoring system to prompt manual intervention; When the second safety threshold Bdth-2 ≤ the fluctuation entropy value Bd < the first safety threshold Bdth-1, it is determined as the second abnormal fluctuation, triggering a second-level safety warning, and performing the following operations: Automatically adjust the dynamic damping parameter or pitch rate of the fan to suppress power oscillation; Optimize the parameters of the prediction model in real time to enhance the adaptability to short-term fluctuations; Generate a warning log and mark the abnormal period for subsequent analysis; When the fluctuation entropy value Bd < the second safety threshold Bdth-2, it is determined as the normal fluctuation range, maintain the current operating state of the fan, and continuously monitor the change trend of the entropy value.

8. The intelligent management system for a wind farm based on the Internet of Things according to claim 7, characterized in that: The safety assessment module includes a real-time monitoring unit and a risk simulation unit; The real-time monitoring unit is used to continuously collect the grid frequency deviation Ra, voltage volatility Rb, and line load rate Rc and perform normalization processing, and combine the fluctuation entropy value Bd output by the instantaneous modeling module to fit and calculate the grid connection risk coefficient Rx; The specific calculation formula of the grid connection risk coefficient Rx is: ； In the formula, all parameters participate in the calculation with equal weights. When any parameter increases abnormally, the grid connection risk coefficient Rx rises, reflecting the risk accumulation effect.

9. The intelligent management system for a wind farm based on the Internet of Things according to claim 8, wherein: The risk simulation unit is used to simulate the dynamic response of the power grid under fault conditions based on the Monte Carlo method by generating the joint probability distribution of wind speed and load in extreme weather scenarios, and output the system safety margin Sy. When performing the Monte Carlo simulation, the joint probability density function f(v, P) of wind speed and load is used as the input, and the system instability probability Ps is calculated through N random samplings. The safety margin Sy is defined as: ； where N ≥ 10,000 times, and the action time sequence constraints of the protection device are considered in each simulation.

10. The intelligent management system for a wind farm based on the Internet of Things according to claim 9, characterized in that: It receives in real time the power prediction results output by the instantaneous modeling module, the grid connection risk coefficient Rx calculated by the safety assessment module, and the safety margin Sy, and generates the unit output command through the dynamic optimization algorithm. When a power mutation is detected, the virtual inertia compensation strategy is activated, and the virtual inertia coefficient Kv is dynamically adjusted according to the grid frequency change rate dt, and the inertial power compensation is realized through additional torque control. At the same time, the coupling relationship between the grid connection risk coefficient Rx and the safety margin Sy is combined to constrain the output adjustment amplitude to form the final control signal.

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