Wind direction and speed monitoring-based wind turbine generator power generation efficiency optimization method and system

Abstract

The application relates to the field of efficiency optimization, and discloses a wind turbine power generation efficiency optimization method and system based on wind direction and wind speed monitoring, which is used for realizing long-term stable and efficient operation of a wind turbine. The method comprises the following steps: obtaining original wind speed and wind direction sequences by using a sensor; generating an enhanced standardized wind speed sequence containing a turbulence intensity feature and a standardized wind direction sequence containing a change amount feature through calculation; outputting a standardized wind speed and wind direction prediction sequence; and improving prediction accuracy and practicability by combining turbulence intensity correction, constructing a continuous and differentiable energy capture efficiency surface, and obtaining a yaw angle and a pitch angle instruction sequence along the gradient direction of the surface, so that global optimization control is realized, and the instruction can also be dynamically adjusted to adapt to changes in wind conditions. The method can generate a wind turbine wind power efficiency evaluation report, and power generation efficiency optimization is carried out according to the wind turbine wind power efficiency evaluation report.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of efficiency optimization, and particularly relates to a wind turbine generator efficiency optimization method and system based on wind direction and speed monitoring. BACKGROUND

[0002] With the continuous growth of global energy demand and the increasing depletion of traditional fossil energy, wind energy as a clean and renewable energy has attracted widespread attention. Wind turbine generators, as the key equipment for converting wind energy into electrical energy, directly affect the economic and environmental benefits of wind energy utilization. Therefore, how to improve the power generation efficiency of wind turbine generators has become an important topic in the field of wind energy research.

[0003] Wind energy capture efficiency is significantly affected by dynamic wind speed and direction. Traditional strategies are difficult to accurately predict changes in wind conditions, resulting in a significant gap between actual power output and theoretical maximum of the unit. Especially in sites with high turbulence intensity and frequent changes in wind direction, how to obtain a wind power efficiency evaluation report to assist in optimizing power generation efficiency is an effective way to improve the power generation efficiency of wind turbine generators.

[0004] Although the existing technology has made certain progress in the optimization of wind turbine generator efficiency, there are still the following shortcomings:

[0005] The existing technology often processes the original wind direction and speed data simply, usually only basic normalization, ignoring the turbulence characteristics of wind speed and the continuity of wind direction changes;

[0006] In terms of wind speed and direction prediction, the existing technology mostly uses traditional statistical methods or simple machine learning models. These methods have low prediction accuracy when dealing with nonlinear and non-stationary wind speed and direction data, and are difficult to meet the actual engineering needs.

[0007] Therefore, we propose a wind turbine generator efficiency optimization method and system based on wind direction and speed monitoring to solve the above problems. SUMMARY

[0008] The present application provides a wind turbine generator efficiency optimization method and system based on wind direction and speed monitoring, which is used to realize long-term stable and efficient operation of wind turbine generators.

[0009] The first aspect of the present application provides a wind turbine power generation efficiency optimization method based on wind direction and wind speed monitoring, which comprises: obtaining an original wind speed sequence and an original wind direction sequence, calling a rated wind speed parameter to process the original wind speed sequence to generate a standardized wind speed sequence, and calling a circular angle constant to process the original wind direction sequence to generate a standardized wind direction sequence; taking the standardized wind speed sequence and the standardized wind direction sequence as input sources, constructing a three-dimensional phase space vector, generating a dynamic triangular mesh topology based on real-time updated historical data flow; locating the current phase space vector in the dynamic triangular mesh topology to extract the historical standardized wind speed and wind direction data of all vertices of the simplex, performing closed calculation on the vertex data, and outputting a standardized wind speed prediction sequence and a standardized wind direction prediction sequence; constructing a continuously differentiable energy capture efficiency surface according to the standardized wind speed prediction sequence and the standardized wind direction prediction sequence as input sources, and analyzing the yaw angle instruction sequence and the pitch angle instruction sequence along the gradient direction of the efficiency surface; generating a wind turbine power generation efficiency evaluation report based on the yaw angle instruction sequence and the pitch angle instruction sequence, and optimizing according to the wind turbine power generation efficiency evaluation report.

[0010] Optionally, in the first implementation manner of the first aspect of the present application, the method comprises: collecting an original wind speed sequence in real time through a wind speed sensor and collecting an original wind direction sequence in real time through a wind direction sensor; segmenting and intercepting the original wind speed sequence based on a preset time window, calculating the statistical variance value of each segment of wind speed data, dividing the statistical variance value by the average wind speed value of the segment to generate a dynamic turbulence intensity factor sequence; calling a preset rated wind speed parameter to perform division calculation on the original wind speed sequence element by element to generate a basic standardized wind speed sequence, and fusing the dynamic turbulence intensity factor sequence as an additional dimension with the basic standardized wind speed sequence to obtain an enhanced standardized wind speed sequence; identifying the angle jump event of adjacent data points in the original wind direction sequence, injecting an angle compensation amount at the jump point to minimize the adjacent angle difference to generate a continuous wind direction sequence; calling a preset circular angle constant to perform division calculation on the continuous wind direction sequence element by element to generate a basic standardized wind direction value, calculating the difference value of adjacent timestamp standardized wind direction values, and binding the wind direction change amount as an additional dimension with the basic standardized wind direction value to obtain a standardized wind direction sequence.

[0011] Optionally, in the second implementation manner of the first aspect of the present application, the method comprises: extracting a basic standardized wind speed value in the enhanced standardized wind speed sequence, extracting a wind direction change amount in the standardized wind direction sequence containing the change amount feature, multiplying the standardized wind speed value of the historical time window by the sine value of the wind direction change amount at the corresponding moment to generate a composite feature sequence; constructing a three-dimensional phase space vector to obtain a three-dimensional phase space point set containing a time marker according to the time stamp; setting a dynamic sliding window to retain the latest N three-dimensional coordinate points, performing Delaunay subdivision calculation on the coordinate point set in the window to generate a dynamically updated tetrahedral mesh topology; inputting the three-dimensional coordinate point at the current moment into the tetrahedral mesh topology, traversing the tetrahedral unit to locate a target simplex containing the point, recording the vertex index set of the target simplex to obtain a simplex vertex index set to which the current point belongs; extracting the historical standardized wind speed value at the corresponding moment according to the vertex index set, synchronously extracting the historical standardized wind direction value at the corresponding moment to generate a simplex vertex historical data packet.

[0012] Optionally, in the third implementation manner of the first aspect of the present application, the method comprises: receiving a simplex vertex index set, retrieving a standardized wind speed value at a corresponding time stamp from a historical database according to the index, synchronously retrieving a standardized wind direction value at the corresponding time stamp to obtain a simplex vertex wind speed data set and a simplex vertex wind direction data set; calculating a long memory feature parameter based on the time distribution of the vertex data, generating a fractional differential weight coefficient sequence according to the long memory feature parameter, and constructing a fractional differential operator template; performing non-iterative convolution calculation according to the simplex vertex wind speed data set to generate a standardized wind speed prediction sequence, performing non-iterative convolution calculation according to the simplex vertex wind direction data set to generate a standardized wind direction prediction sequence, to obtain a primary wind speed prediction sequence and a primary wind direction prediction sequence; extracting a dynamic turbulence intensity factor sequence, generating a correction coefficient matrix according to the current turbulence intensity value, and applying the correction coefficient to the primary wind speed prediction sequence to obtain a corrected standardized wind speed prediction sequence.

[0013] Optionally, in the fourth implementation manner of the first aspect of the present application, the method comprises: preloading a pitch angle-tip speed ratio aerodynamic efficiency coefficient table, calculating a real-time tip speed ratio according to the current standardized wind speed prediction value, and interpolating the dynamic aerodynamic efficiency coefficient sequence in the coefficient table; constructing a three-dimensional energy surface, and fusing the three-dimensional data into a time-varying continuous energy surface according to the time stamp; calculating the partial derivative of the energy surface in the yaw angle dimension and the partial derivative of the energy surface in the pitch angle dimension, and generating a gradient optimization direction vector sequence by combining the sign function with the square root of the gradient modulus; performing proportional integration along the gradient optimization direction vector to output a yaw angle increment instruction sequence and a pitch angle increment instruction sequence, and superimposing the initial attitude angle to generate a yaw angle absolute instruction sequence and a pitch angle absolute instruction sequence; encapsulating the absolute control instruction into a transmission data packet according to the time stamp, and writing it into a first-in-first-out instruction buffer to obtain a control instruction data packet.

[0014] Optionally, in the fifth implementation manner of the first aspect of the present application, the energy capture efficiency value of the energy curve is set as : ; wherein, is a normalized wind speed prediction value; is a current yaw angle of the wind turbine; is a normalized wind direction prediction value; is a decay coefficient; is a dynamic aerodynamic efficiency coefficient.

[0015] Optionally, in the sixth implementation manner of the first aspect of the present application, the method comprises: calling a yaw angle absolute instruction sequence and a pitch angle absolute instruction sequence to drive the yaw system and the pitch system of the wind turbine to perform actions, synchronously collecting actual output power sequences, actual wind speed sequences and actual wind direction sequences within a preset time period after the actions are performed to generate a unit response data set; taking the corrected normalized wind speed prediction sequence as an input wind speed and taking the normalized wind direction prediction sequence as an input wind direction, in combination with the currently performed yaw angle absolute instruction sequence and the pitch angle absolute instruction sequence, querying a preloaded pitch angle-tip speed ratio aerodynamic efficiency coefficient table to obtain a theoretical output power sequence; aligning the actual output power sequence and the theoretical output power sequence according to time stamps, calculating power capture efficiency values element by element, associating yaw angle instruction values, pitch angle instruction values, corrected normalized wind speed prediction values and normalized wind direction prediction values at corresponding time points to generate a multi-dimensional efficiency evaluation matrix; setting an efficiency threshold interval, scanning the multi-dimensional efficiency evaluation matrix, identifying a continuous time period in which the power capture efficiency values are continuously below a lower threshold, extracting the normalized wind speed prediction sequence, the normalized wind direction prediction sequence, the yaw angle instruction sequence and the pitch angle instruction sequence corresponding to the time period to generate a low-efficiency operation warning data packet; in response to the low-efficiency operation warning data packet, extracting actual wind speed sequences, actual wind direction sequences and actual output power sequences within the warning time period, inversely deducing actual aerodynamic efficiency coefficients based on the relationship between the actual wind speed and the tip speed ratio, comparing the theoretical values of corresponding working points in the preloaded aerodynamic efficiency coefficient table, calculating calibration deviations and updating the coefficient table to generate a calibrated aerodynamic efficiency coefficient table; analyzing the yaw angle instruction distribution and the pitch angle instruction distribution in the high-efficiency operation area of the multi-dimensional efficiency evaluation matrix, counting high-frequency instruction intervals, updating the boundary values of the high-frequency instruction intervals as yaw angle control threshold values and pitch angle control threshold values to generate an optimized control threshold parameter set. The multi-dimensional efficiency evaluation matrix, the low-efficiency operation warning data packet, the calibrated aerodynamic efficiency coefficient table and the optimized control threshold parameter set are packaged to generate a wind turbine wind power efficiency evaluation report.

[0016] Optionally, in the seventh implementation manner of the first aspect of the present application, the sensor anomaly prediction inversion verification is further included, the wind turbine power output measurement value is received in real time, the theoretical wind speed and wind direction interval is reversely calculated based on the current control instruction, the sensor anomaly confidence factor is generated when the measured sensor data continuously exceeds the theoretical interval, the space-time nearest neighbor point set is extracted from the dynamic triangular mesh topology when the anomaly confidence factor exceeds the threshold value, the historical standardized data at the corresponding moment is called to replace the real-time sensor input, and the abnormal condition replacement data packet is obtained, the abnormal condition replacement data packet is input into the fractional order differential joint prediction module, the historical optimal instruction template is directly called by skipping the optimization calculation, the fault-tolerant control instruction stream is generated, the power recovery slope after the execution of the fault-tolerant control instruction is monitored, the normal control mode is switched back when the slope meets the standard, and the abnormal event characteristics are recorded to the fault knowledge base.

[0017] The second aspect of the present application provides a wind turbine power generation efficiency optimization system based on wind direction and wind speed monitoring, which comprises: an acquisition module for acquiring original wind speed sequence and original wind direction sequence, calling the rated wind speed parameter to perform division calculation on the original wind speed sequence to generate a standardized wind speed sequence, and calling the circular angle constant to perform division calculation on the original wind direction sequence to generate a standardized wind direction sequence; a construction module for constructing a three-dimensional phase space vector with the standardized wind speed sequence and the standardized wind direction sequence as input sources, generating a dynamic triangular mesh topology structure based on real-time updated historical data flow; a generation module for locating the current phase space vector in the dynamic triangular mesh topology structure, extracting the historical standardized wind speed and wind direction data of all vertices of the simplex body, performing closed calculation on the vertex data based on the fractional order differential operator, and outputting the standardized wind speed prediction sequence and the standardized wind direction prediction sequence; an analysis module for constructing a continuously differentiable energy capture efficiency surface with the standardized wind speed prediction sequence and the standardized wind direction prediction sequence as input sources, and analyzing the yaw angle instruction sequence and the pitch angle instruction sequence along the gradient direction of the efficiency surface; and a distribution module for generating a wind turbine power efficiency evaluation report based on the yaw angle instruction sequence and the pitch angle instruction sequence, and optimizing according to the wind turbine power efficiency evaluation report.

[0018] The mechanism of the present application is as follows: by constructing a phase space topology structure integrating space-time dynamic characteristics, combining fractional order differential prediction and gradient optimization control, precise coordinated regulation and control of the wind turbine posture under complex wind field conditions are realized.

[0019] Beneficial effects: generate enhanced standardized wind speed sequence and standardized wind direction sequence with varying amount of features, deeply mine data hidden information, provide rich and valuable data for accurate prediction and control, improve data utilization efficiency and application value, better adapt to nonlinear, non-stationary and turbulence-affected wind conditions through the introduction of dynamic turbulence intensity factor and the generation of continuous wind direction sequence, provide reliable data support for stable operation and efficient power generation of wind turbine generators;

[0020] The closed-form calculation of the vertex data with fractional differential operators better captures the nonlinear and long memory characteristics of the data, making the prediction sequence more accurate, providing accurate input for optimization control, and combining dynamic turbulence intensity factor correction results to consider the impact of turbulence on wind speed prediction, making the prediction more realistic and enhancing the practicality and adaptability of the prediction model under different wind conditions;

[0021] An energy capture efficiency surface is constructed to comprehensively reflect the energy capture efficiency of the unit by considering multiple factors, and the gradient analytical instruction sequence along the surface is realized to achieve global optimization control, so that the unit can approach the optimal operating state under various wind conditions, improve power generation efficiency, and real-time generation of yaw angle and pitch angle instruction sequences to enable the unit to respond to changes in wind conditions and adjust its posture, improve its adaptability to complex wind conditions, and improve power generation efficiency and operational stability;

[0022] Synchronous data streams are generated by extracting timestamps, converting instructions into relative increments and compensating for dead zones, generating S-shaped speed planning curves to output smooth driving pulses, ensuring that instructions are real-time, accurate and reliable, enabling smooth operation of the motor, reducing losses and vibrations, and improving service life and efficiency;

[0023] An abnormal sensor prediction inversion verification mechanism is set up, and when an anomaly is detected, historical data is used to replace real-time input to generate fault-tolerant instruction streams, ensuring stable operation of the system and avoiding control failure and power generation efficiency due to sensor problems, improving system reliability and robustness;

[0024] After fault-tolerant control, monitor the power recovery slope, switch back to normal mode when the standard is met, and record the abnormal event characteristics to the fault knowledge base to provide reference for system optimization and maintenance, help researchers improve the system and improve performance and reliability, and achieve long-term stable and efficient operation. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 An embodiment of the wind turbine generator power generation efficiency optimization method based on wind direction and wind speed monitoring in the embodiments of the present application is shown in the figure;

[0026] Figure 2 Another embodiment of the wind turbine generator power generation efficiency optimization method based on wind direction and wind speed monitoring in the embodiments of the present application is shown in the figure;

[0027] Figure 3 FIG. 1 is a schematic diagram of an embodiment of the wind direction and wind speed monitoring-based wind turbine power generation efficiency optimization system in the present application;

[0028] Figure 4 FIG. 2 is a schematic diagram of an embodiment of the wind direction and wind speed monitoring-based wind turbine power generation efficiency optimization device in the present application. DETAILED DESCRIPTION

[0029] The wind direction and wind speed monitoring-based wind turbine power generation efficiency optimization method and system provided by the embodiments of the present application are used to realize long-term stable and efficient operation of wind turbines. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] For ease of understanding, the specific flow of the embodiments of the present application is described below. Please refer to FIG. 1 Figure 1 An embodiment of the wind direction and wind speed monitoring-based wind turbine power generation efficiency optimization method in the present application includes the following steps:

[0031] 101, real-time sensing data acquisition and standardized calculation, obtaining an original wind speed sequence through a wind speed sensor and obtaining an original wind direction sequence through a wind direction sensor; calling a rated wind speed parameter to perform division calculation on the original wind speed sequence to generate a standardized wind speed sequence; calling a circumferential angle constant to perform division calculation on the original wind direction sequence to generate a standardized wind direction sequence;

[0032] It can be understood that the execution subject of the present application can be a wind direction and wind speed monitoring-based wind turbine power generation efficiency optimization system, and can also be a terminal or a server, and the specific implementation is not limited herein. The embodiments of the present application take the server as an example for description.

[0033] It should be noted that the following takes the real-time monitoring of a certain wind farm on May 15, 2025, 10:00-10:05 as an example to specifically describe the implementation process of step 101:

[0034] Sensor data collection, raw wind speed sequence: ultrasonic wind speed sensor (range 0-60 m / s, accuracy ±0.3 m / s) deployed at the top of the wind turbine collects data at a frequency of 1 Hz, obtaining 300 raw wind speed values (unit: m / s) within 5 minutes: ;

[0035] Raw wind direction sequence: synchronously collected ultrasonic wind direction data (range 0-360°, accuracy ±3°): ;

[0036] Wind speed normalization calculation, call rated wind speed parameter: the rated wind speed of this wind turbine is 15 m / s (set according to the model of the wind turbine).

[0037] Perform division calculation: divide each raw wind speed value by the rated wind speed to generate a dimensionless normalized wind speed sequence: example: ; output sequence: ;

[0038] Wind direction normalization calculation, call circumferential angle constant: use the circumferential angle constant 360° (fixed parameter).

[0039] Perform division calculation: divide each raw wind direction value by 360° to generate a normalized wind direction sequence in the interval [0, 1): example: ; output sequence: .

[0040] 102、Dynamic phase space topology construction calculation, taking the normalized wind speed sequence and the normalized wind direction sequence as input sources; construct a three-dimensional phase space vector: the first dimension is the current normalized wind speed value, the second dimension is the product of the sine of the historical normalized wind speed value and the wind direction change, and the third dimension is the current normalized wind direction value; based on the real-time updated historical data stream, dynamically generate a triangular mesh topology structure through the Delaunay subdivision algorithm;

[0041] It should be noted that the following takes the real-time monitoring data of a wind farm on May 15, 2025, 10:00-10:05 as an example:

[0042] Input data preparation, normalized wind speed sequence (from step 101): (total of 300 values, sampling frequency 1 Hz);

[0043] Normalized wind direction sequence (from step 101): (unit: circumferential ratio, range [0, 1));

[0044] Three-dimensional phase space vector construction, generate a three-dimensional vector for each real-time data at a time:

[0045] First dimension (current wind speed): directly take the normalized wind speed value at the current time: 10:00:02, ;

[0046] Second dimension (sine product of historical wind speed and wind direction change): take the normalized wind speed at the previous time (historical wind speed); calculate the wind direction change (unit radian) between the current and previous time: ; calculate the product: : 10:00:02, , ;

[0047] Third dimension (current wind direction): directly take the normalized wind direction value. 10:00:02, ;

[0048] An example of the generated three-dimensional vector is shown in Table 1:

[0049] Table 1 Three-dimensional vector

[0050]

[0051] A total of 300 vectors are generated within 5 minutes, forming a dynamic point set.

[0052] Delaunay triangulation is dynamically constructed, and the initial grid is initialized: the first vector (0.613, 0, 0.425) is taken as the starting point, and an initial tetrahedral grid covering the entire data range is generated (three-dimensional Delaunay requires tetrahedrons, but the principle is the same as triangulation).

[0053] Point-by-point insertion and update: new point insertion: insert the new vector (0.633, 0.023, 0.431) at 10:00:02 into the current grid. Local reconstruction: find the tetrahedron containing the new vector; connect the new point with the tetrahedron vertices, and split it into 4 small tetrahedrons; check the circumscribed sphere property: if a tetrahedron's circumscribed sphere contains other points, perform a flip operation to ensure that no point is inside the circumscribed sphere. Dynamic maintenance: every time a new vector is added (0.1 seconds), repeat the above process to update the grid topology in real time.

[0054] Output structure: finally generate an unstructured grid composed of triangular facets (three-dimensional partitioned surface), each triangular facet corresponds to a local region in phase space: at 10:00:05, the grid contains 297 triangular facets, covering all 300 vector points.

[0055] 103. Fractional differential joint prediction calculation, locating the simplex body to which the current phase space vector belongs in the dynamic triangulation mesh topology; extracting the historical normalized wind speed and wind direction data of all vertices of the simplex; performing closed-form calculation on the vertex data based on the fractional differential operator, outputting the normalized wind speed prediction sequence and the normalized wind direction prediction sequence;

[0056] It should be noted that the following takes the real-time data of a certain wind farm on May 15, 2025, 10:00-10:05 as an example:

[0057] Simplex positioning and vertex data extraction, the normalized phase space vector at 10:00:03 is (The second dimension is calculated by the previous time sequence: ).

[0058] Simplex positioning: in the dynamic Delaunay triangulation mesh (containing 297 tetrahedrons) generated in step 102, find the tetrahedron containing V through point positioning algorithm.

[0059] Assuming the positioning result is tetrahedron T, its four vertices correspond to historical time data: vertex 1: vector (0.613, 0, 0.425) at 10:00:00; vertex 2: vector (0.633, 0.023, 0.431) at 10:00:01; vertex 3: vector (0.673, 0.031, 0.439) at 10:00:02; vertex 4: vector (0.580, 0.018, 0.422) at 9:59:58;

[0060] Vertex data extraction: extract the historical normalized wind speed sequence and wind direction sequence corresponding to the four vertices (length of the latest 5 sampling points): wind speed sequence: vertex 1 = [0.58, 0.60, 0.61, 0.62, 0.613], vertex 2 = [0.61, 0.62, 0.63, 0.63, 0.633], etc. Wind direction sequence: vertex 1 = [0.42, 0.421, 0.423, 0.424, 0.425], vertex 2 = [0.428, 0.429, 0.430, 0.430, 0.431], etc.

[0061] Fractional differential closed-form calculation, based on Grünwald-Letnikov fractional differential definition (order ), perform joint prediction on the vertex data of the tetrahedron:

[0062] Estimation calculation: taking wind speed prediction as an example, weighted sum of vertex 1 sequence: , the weights are generated by fractional differential operators (negative correlation with time distance, higher weight for recent data): vertex 1 wind speed prediction = 0.621, vertex 2 = 0.642, vertex 3 = 0.681, vertex 4 = 0.598.

[0063] Correction value fusion: the prediction values of the tetrahedron vertices are fused by weighting the barycentric coordinates of V in the tetrahedron: assuming the barycentric coordinates of V are (0.2, 0.3, 0.4, 0.1); ; similarly, the wind direction prediction value is 0.436 (normalized value) by angle loop processing.

[0064] Prediction sequence output, repeat the above process to generate a normalized prediction sequence of the next 5 seconds as shown in Table 2:

[0065] Table 2 Normalized Prediction Sequence

[0066]

[0067] Fractional differentiation captures the long-term memory characteristics of historical data through non-integer order (0 ) more suitable for nonlinear changes in wind speed / wind direction than integer order models.

[0068] 104、Continuous energy surface gradient optimization calculation, taking the normalized wind speed prediction sequence and the normalized wind direction prediction sequence as input sources; construct a continuous and differentiable energy capture efficiency surface, which is defined by the product of three terms:

[0069] (a) the cube of the normalized wind speed prediction value;

[0070] (b) an exponential decay function with the yaw angle deviation from the predicted wind direction as the variable;

[0071] (c) a table of aerodynamic efficiency coefficients related to the pitch angle;

[0072] Analytically calculate the yaw angle command sequence and the pitch angle command sequence along the gradient direction of the efficiency surface;

[0073] It should be noted that the following takes the real-time data of a certain wind farm on May 15, 2025, 10:00-10:05 as an example:

[0074] Input data preparation, normalized wind speed prediction sequence (from step 103): (dimensionless, normalized based on rated wind speed 15 m / s); normalized wind direction prediction sequence (from step 103): (circumferential proportion, range [0, 1), corresponding to true angles 157.7°, 158.8°);

[0075] Energy capture efficiency surface construction: A surface function is constructed for each future time moment. Taking 10:00:04 as an example: Wind speed cubic term (a): Taking the standardized wind speed prediction value of 0.662, the cubic value is calculated as follows: Physical meaning: Wind energy output is directly proportional to the cube of wind speed, reflecting the basic energy potential.

[0076] Yaw deviation attenuation term (b): Define the exponential attenuation function: ; : Current yaw angle of the wind turbine (assumed to be 30°); The standardized value of the predicted wind direction is 0.438. Actual angle: 157.7°; Deviation angle: (Needs to be normalized to the 0°-180° range);

[0077] Taking the attenuation coefficient k = 0.02 (empirical parameter), calculate: Function: The greater the yaw deviation, the more significant the energy capture attenuation.

[0078] Pitch angle efficiency coefficient (c): Refer to the aerodynamic efficiency coefficient table (example excerpt) as shown in Table 3:

[0079] Table 3 shows the aerodynamic efficiency coefficient.

[0080]

[0081] Assuming the current pitch angle is 5°, the coefficient value obtained from the table is 0.92;

[0082] Final efficiency value calculation: ;

[0083] Gradient optimization calculation: Analytically solve for the optimal control command along the gradient direction of the efficiency surface.

[0084] Yaw Angle Optimization: Computational Efficiency for Yaw Angle partial derivatives Adjust along the gradient ascent direction: Current Command value: (Reduce wind direction deviation);

[0085] Pitch angle optimization: Partial derivative of computational efficiency with respect to pitch angle Adjust along the gradient ascent direction: Current Command value: (Fine-tuning to reduce aerodynamic losses);

[0086] Gradient calculation requires ensuring the differentiability of the surface; exponential decay functions and lookup table interpolation (linear interpolation) must satisfy this condition.

[0087] Output the instruction sequence, repeat the above process to generate instructions for the next 5 seconds as shown in Table 4:

[0088] Table 4 Future 5 seconds instructions

[0089]

[0090] 105、Based on the yaw angle instruction sequence and the pitch angle instruction sequence, a wind turbine wind power efficiency evaluation report is generated, and optimization is performed according to the wind turbine wind power efficiency evaluation report.

[0091] It should be noted that the real-time monitoring data of a certain wind farm on May 15, 2025, 10:00-10:05, based on the yaw angle instruction sequence (31.2° at 10:00:04 and 32.1° at 10:00:05) and the pitch angle instruction sequence (4.8° at 10:00:04 and 4.5° at 10:00:05) generated in step 104, an efficiency evaluation report is generated and optimization is performed.

[0092] Real-time control instruction execution: the wind turbine control system responds to the instruction sequence, dynamically adjusts the yaw system (the yaw angle is adjusted from 30° to 31.2° at 10:00:04) and the pitch system (the pitch angle is adjusted from 5° to 4.8° at 10:00:04).

[0093] 10:00:05 continues to execute the instruction: yaw angle 32.1°, pitch angle 4.5°.

[0094] Efficiency evaluation index calculation (take 10:00:04 as an example): pre-execution efficiency: based on step 104, the original efficiency value is:

[0095] ;

[0096] Post-execution efficiency: the actual capture efficiency after adjustment is increased to 0.236 (the measured value).

[0097] Improvement rate:

[0098] ;

[0099] The evaluation report is generated as shown in Table 5:

[0100] Table 5 Evaluation report

[0101]

[0102] In the embodiment of the present application, the original wind speed and wind direction data are converted into dimensionless or standardized sequences of specific intervals through division operation with the rated wind speed and the constant of the circumferential angle. This processing method eliminates the influence of the data dimension and range difference, makes the data of different sources and scales comparable, provides a unified and standardized data basis for subsequent complex calculation and analysis, and improves the accuracy and stability of the model; the three-dimensional phase space vector is constructed and the Delaunay subdivision algorithm is used to dynamically generate the triangular mesh topology, which can capture the dynamic change characteristics of wind speed and wind direction in real time, and convert complex multivariate time series data into intuitive topological structure. Through this structure, the relationship between wind speed and wind direction at different times can be more accurately described, which provides more rich information for subsequent prediction and optimization, and helps to improve the prediction accuracy and optimization effect; the fractional differential operator is used for closed calculation of the historical data, realizing the joint prediction of the standardized wind speed and wind direction. The fractional differential has the characteristic of non-integer order, which can capture the long-term memory characteristics of the historical data, and compared with the traditional integer order model, it is more suitable for the nonlinear change of wind speed and wind direction. Through joint prediction, the mutual influence between wind speed and wind direction is considered, which improves the prediction accuracy and reliability, and provides a more accurate basis for subsequent optimization control.

[0103] Please refer to Figure 2 Another embodiment of the wind turbine generator power generation efficiency optimization method based on wind direction and wind speed monitoring in the embodiment of the present application includes:

[0104] 201, real-time sensing data acquisition and standardized calculation, obtaining the original wind speed sequence through the wind speed sensor, and obtaining the original wind direction sequence through the wind direction sensor; calling the rated wind speed parameter to perform division calculation on the original wind speed sequence to generate a standardized wind speed sequence; calling the constant of the circumferential angle to perform division calculation on the original wind direction sequence to generate a standardized wind direction sequence;

[0105] In particular, the original sensor data stream capture, real-time acquisition of the original wind speed sequence by the wind speed sensor; real-time acquisition of the original wind direction sequence by the wind direction sensor; product: time-synchronized original wind speed sequence and original wind direction sequence; dynamic turbulence intensity factor calculation, segmenting and intercepting the original wind speed sequence based on a preset time window; calculating the statistical variance value of each segment of wind speed data; dividing the statistical variance value by the average wind speed value of the segment to generate a dynamic turbulence intensity factor sequence; product: dynamic turbulence intensity factor sequence; enhanced standardized wind speed sequence generation, calling a preset rated wind speed parameter; performing element-by-element division calculation on the original wind speed sequence to generate a basic standardized wind speed sequence; fusing the dynamic turbulence intensity factor sequence as an additional dimension with the basic standardized wind speed sequence; product: enhanced standardized wind speed sequence containing turbulence intensity characteristics; wind direction continuity correction, identifying angle jump events of adjacent data points in the original wind direction sequence; injecting an angle compensation amount at the jump point to minimize the adjacent angle difference; generating a physically continuous corrected wind direction sequence; product: continuous wind direction sequence; composite standardized wind direction sequence generation, calling a preset circular angle constant; performing element-by-element division calculation on the continuous wind direction sequence to generate a basic standardized wind direction value; calculating the difference value of adjacent timestamp standardized wind direction values; binding the wind direction change amount as an additional dimension with the basic standardized wind direction value; product: standardized wind direction sequence containing change amount characteristics.

[0106] It should be noted that the data is derived from the actual monitoring (sampling frequency 1 Hz) of a certain 2MW wind turbine within 60 seconds:

[0107] Original sensor data stream capture, wind speed sensor acquires original wind speed sequence (unit: m / s): (T0 to T5 seconds); wind direction sensor acquires original wind direction sequence (unit: °): ; product: time-synchronized original data (each 1 second a pair of wind speed-wind direction).

[0108] Dynamic turbulence intensity factor calculation, preset time window: 30 seconds (example takes the first 6 data points). Calculation process: segment wind speed mean: m / s; statistical variance: ; turbulence intensity factor: ; product: dynamic turbulence intensity factor sequence [0.316] (output one factor every 30 seconds).

[0109] Enhanced standardized wind speed sequence generation, preset rated wind speed parameter: 15 m / s (wind turbine rated wind speed). Basic standardized wind speed: original wind speed ÷ rated wind speed; ; fuse turbulence intensity: generate a two-dimensional sequence with factor 0.316 as an additional dimension:

[0110] Product: Enhanced normalized wind speed sequence (including turbulence features). Wind direction continuity correction; Problem: The original wind direction sequence exhibits an angle jump at time T2. The actual change is only 4°, but it spans the 360° boundary. Correction method: Detect the jump point (time T2): Injection compensation: Increase the wind direction by 360° at point T2, correcting to 362° (to minimize the difference between adjacent points). (Difference of only 4°). Corrected sequence: Product: Physically continuous corrected wind direction sequence.

[0111] Composite normalized wind direction sequence generation, with a circular angle constant of 360°. Basic normalized wind direction: corrected wind direction ÷ 360°. Wind direction change: Calculate the difference in standardized wind direction between adjacent moments. ; Binding additional dimensions: generating a two-dimensional sequence: Product: Standardized wind direction sequence containing variation characteristics.

[0112] Data flow is represented as shown in Table 6:

[0113] Table 6 Data Flow

[0114]

[0115] The turbulence intensity factor (0.316) has been incorporated into the normalized wind speed sequence, forming an enhanced feature.

[0116] 202. Dynamic phase space topology construction calculation, using the standardized wind speed sequence and standardized wind direction sequence as input sources; constructing a three-dimensional phase space vector: the first dimension is the current standardized wind speed value, the second dimension is the sine product of the historical standardized wind speed value and the wind direction change, and the third dimension is the current standardized wind direction value; based on the real-time updated historical data stream, a triangular mesh topology structure is dynamically generated through the Delaunay partitioning algorithm;

[0117] Specifically, the phase space dimension compression calculation extracts the basic standardized wind speed value in the enhanced standardized wind speed sequence; the wind direction change amount in the standardized wind direction sequence containing the change amount feature is extracted; the standardized wind speed value of the historical time window is multiplied by the sine value of the wind direction change amount at the corresponding moment to generate a composite feature value; the product is a composite feature sequence; three-dimensional phase space vector construction, the first dimension is assigned the basic standardized wind speed value at the current moment; the second dimension is assigned the composite feature value at the delayed moment; the third dimension is assigned the basic standardized wind direction value at the current moment; assemble the three-dimensional coordinate point set according to the timestamp; the product is a three-dimensional phase space point set containing a time marker; dynamic triangular mesh topology generation, set the dynamic sliding window to retain the latest N three-dimensional coordinate points; perform Delaunay subdivision calculation on the coordinate point set in the window; generate a mesh topology structure composed of tetrahedral units; the product is a dynamically updated tetrahedral mesh topology; real-time position simplex positioning, input the three-dimensional coordinate point at the current moment into the tetrahedral mesh topology; traverse the tetrahedral unit to locate the target simplex containing the point; record the vertex index set of the target simplex; the product is the simplex vertex index set to which the current point belongs; historical data association binding, extract the historical standardized wind speed value at the corresponding moment according to the vertex index set; synchronously extract the historical standardized wind direction value at the corresponding moment; generate a historical data set bound to the simplex vertex; the product is a simplex vertex historical data package.

[0118] It should be noted that the following is based on a 2MW wind turbine 60 seconds of standardized wind speed sequence [0.55, 0.57, 0.82, 0.78, 0.65, 0.56] (T0-T5 seconds) and standardized wind direction sequence [0.986, 0.994, 1.006, 0.997, 0.992, 0.983], to illustrate the implementation process of dynamic phase space topology construction:

[0119] Phase space dimension compression calculation, input: standardized wind speed sequence: ; Standardized wind direction change sequence (difference between adjacent wind directions): (T1-T5); Composite feature value generation: historical time window is 1 second (delayed 1 second), calculation: ; T2 moment: ; T3 moment: ; Product: composite feature sequence (T1-T5);

[0120] Three-dimensional phase space vector construction, coordinate point assembly (timestamp T2 as an example): first dimension (current wind speed): T2 wind speed value 0.57; second dimension (composite feature value delayed 1 second): T1 composite feature 0.0066; third dimension (current wind direction): T2 wind direction value 0.994; three-dimensional coordinate point: (0.57, 0.0066, 0.994);

[0121] All coordinate point set (T2-T5) as shown in Table 7:

[0122] Table 7 All coordinate point set (T2-T5)

[0123]

[0124] Dynamic triangulation mesh topology generation, dynamic sliding window: keep the latest 4 points (T2-T5); Delaunay triangulation: input 4 three-dimensional points into the triangulation algorithm to generate a tetrahedral mesh. Topology structure: T2, T3, T4, T5 as vertices to form a tetrahedral unit, vertex index set .

[0125] Real-time position simplex positioning, new point positioning (T6 time new point (0.56, -0.0058, 0.983)): add new point to the window, remove the earliest point T2, and update the window to T3-T6 point set. After re-triangulation, the new point is located in the tetrahedron Product: target simplex vertex index set .

[0126] Historical data association binding, vertex data extraction (take the simplex positioned at T6 as an example) as shown in Table 8:

[0127] Table 8 Vertex data

[0128]

[0129] Product: simplex vertex historical data package (binding wind speed, wind direction time series).

[0130] 203、Fractional differential joint prediction calculation, positioning the simplex to which the current phase space vector belongs in the dynamic triangulation mesh topology structure; extracting the historical normalized wind speed and wind direction data of all vertices of the simplex; performing closed calculation on the vertex data based on the fractional differential operator, outputting the normalized wind speed prediction sequence and the normalized wind direction prediction sequence;

[0131] In particular, the simplex vertex data extraction receives a simplex vertex index set; the corresponding timestamp standardized wind speed value is called from the historical database according to the index; the corresponding timestamp standardized wind direction value is synchronously called; the product: simplex vertex wind speed data set, simplex vertex wind direction data set; fractional order differential operator construction, long memory characteristic parameter is calculated based on the time distribution of vertex data; the fractional order differential weight coefficient sequence is generated according to the long memory characteristic parameter; the differential operator template containing the time decay characteristic is constructed; the product: fractional order differential operator template; closed form prediction calculation execution, input the simplex vertex wind speed data set into the differential operator template; execute non-iterative convolution calculation to generate standardized wind speed prediction sequence; input the simplex vertex wind direction data set into the same operator template; execute non-iterative convolution calculation to generate standardized wind direction prediction sequence; the product: primary wind speed prediction sequence, primary wind direction prediction sequence; turbulence intensity dynamic correction, extract dynamic turbulence intensity factor sequence; generate correction coefficient matrix according to the current turbulence intensity value; apply correction coefficient to the primary wind speed prediction sequence; the product: corrected standardized wind speed prediction sequence; the prediction result integration output, the corrected standardized wind speed prediction sequence and the primary wind direction prediction sequence are aligned according to the timestamp; packaged as joint prediction data package; the product: standardized wind speed and wind direction joint prediction package.

[0132] It should be noted that the following is based on the results of the dynamic phase space topology construction of a certain 2MW wind turbine within 60 seconds (simplex vertex index set corresponding to timestamps T3-T6), which illustrates the implementation process of the fractional order differential joint prediction calculation steps, and the specific data is as follows:

[0133] Simplex vertex data extraction, input: simplex vertex index set ;

[0134] The historical data is shown in Table 9:

[0135] Table 9 Historical data

[0136]

[0137] Product: wind speed data set ; wind direction data set ;

[0138] Fractional order differential operator construction, long memory characteristic analysis: according to the time distribution of vertex data (T3-T6 interval 1 second), the time decay weight is calculated. It is assumed that the influence of historical data on current prediction decays with distance, and the decay coefficient is set to (T6 time weight is the highest, T3 is the lowest).

[0139] Differential operator template generation: normalize the decay coefficient to weight sequence , construct convolution template. Product: fractional order differential operator template ;

[0140] Closed-form prediction computation, wind speed prediction computation: wind speed dataset convolved with operator template:

[0141] ; output primary wind speed prediction value at T7: 0.74;

[0142] Wind direction prediction computation: wind direction dataset convolved with same template:

[0143] ; output primary wind direction prediction value at T7: 0.998;

[0144] Product: primary prediction sequence (T7 time instant);

[0145] Turbulence intensity dynamic correction, input: current turbulence intensity factor (value 0.15 at T6 time instant);

[0146] Correction factor generation: turbulence intensity positively correlates with wind speed fluctuation, set correction function as . ; wind speed prediction correction: corrected T7 wind speed prediction value: 0.64; product: corrected wind speed prediction sequence [0.64] (T7 time instant);

[0147] Prediction result integration output, data alignment: corrected wind speed 0.64 and primary wind direction 0.998 at T7 time instant are bound by time stamp.

[0148] Joint prediction package generation as table 10:

[0149] Table 10 Joint prediction package

[0150]

[0151] 204、Continuous energy surface gradient optimization computation, with the normalized wind speed prediction sequence and the normalized wind direction prediction sequence as input sources; construct a continuously differentiable energy capture efficiency surface, which is defined by three products:

[0152] (a) the cube of the normalized wind speed prediction value;

[0153] (b) an exponential decay function with the yaw angle and the predicted wind direction deviation as variables;

[0154] (c) a table of aerodynamic efficiency coefficients related to the pitch angle;

[0155] Analytically compute the yaw angle command sequence and the pitch angle command sequence along the gradient direction of the efficiency surface;

[0156] Specifically, the aerodynamic efficiency coefficient is dynamically mapped, a preloaded pitch angle-tip speed ratio aerodynamic efficiency coefficient table is obtained; a real-time tip speed ratio is calculated according to a current standardized wind speed prediction value; a dynamic aerodynamic efficiency coefficient sequence is obtained by interpolation in the coefficient table; and a product is the dynamic aerodynamic efficiency coefficient sequence; a three-dimensional energy surface is constructed, a first dimension is a cubic operation result of a standardized wind speed prediction sequence; a second dimension is a negative exponential function value with a yaw angle and a predicted wind direction deviation as independent variables; a third dimension is the dynamic aerodynamic efficiency coefficient sequence; the three-dimensional data is fused into a continuous energy surface according to a time stamp; a product is a time-varying continuous energy surface; a sign function gradient is analyzed, a partial derivative of the energy surface in the yaw angle dimension is calculated; a partial derivative of the energy surface in the pitch angle dimension is calculated; an optimization direction vector is generated by combining a sign function and a gradient modulus square root; a product is a gradient optimization direction vector sequence; a continuous control instruction is generated, a proportional integral is performed along the gradient optimization direction vector; a yaw angle increment instruction sequence and a pitch angle increment instruction sequence are output; an initial attitude angle is superimposed to generate absolute control instructions; products are a yaw angle absolute instruction sequence and a pitch angle absolute instruction sequence; instruction flow buffering output is performed, the absolute control instructions are packaged into transmission data packets according to the time stamp; the transmission data packets are written into a first-in-first-out instruction buffer; and a product is a time-aligned control instruction data packet.

[0157] It should be noted that the following is based on a 2MW wind turbine 60-second standardized wind speed prediction sequence [0.85, 0.82, 0.78] (T0-T2 seconds) and a standardized wind direction prediction sequence [0.98, 0.95, 0.93] (T0-T2 seconds), to illustrate the implementation process of the continuous energy surface gradient optimization step:

[0158] The aerodynamic efficiency coefficient is dynamically mapped, and a preloaded aerodynamic efficiency coefficient table (pitch angle vs. tip speed ratio) is as shown in Table 11:

[0159] Table 11 Aerodynamic efficiency coefficient

[0160]

[0161] Real-time tip speed ratio calculation: ; T0 moment: standardized wind speed prediction value m / s; assuming impeller radius 40m, rotating speed ;

[0162] Interpolation to obtain aerodynamic efficiency coefficient: interpolation at pitch angle 5°, tip speed ratio 0.94 Dynamic aerodynamic efficiency coefficient 0.32; product: dynamic aerodynamic efficiency coefficient sequence (T0-T2);

[0163] Three-dimensional construction of energy surface, set the energy capture efficiency value of the energy surface to :

[0164] ;

[0165] wherein, is the normalized wind speed prediction value; is the current wind turbine yaw angle (sensor real-time measurement, normalized: 350° / 360°); is the normalized wind direction prediction value; is the decay coefficient (preset constant, 100 in the case, calibrated by wind turbine aerodynamic characteristics); is the dynamic aerodynamic efficiency coefficient;

[0166] First dimension (wind speed cubed): T0 moment: ;

[0167] Second dimension (yaw deviation index decay): current wind turbine yaw angle: 350° (normalized value ); predicted wind direction deviation: ; negative exponential function: ;

[0168] Third dimension (aerodynamic efficiency coefficient): 0.32; three-dimensional energy value: ;

[0169] All energy surface points (T0-T2) are shown in Table 12:

[0170] Table 12 All energy surface points (T0-T2)

[0171]

[0172] Symbol function gradient analysis, yaw angle dimension gradient: calculate the partial derivative of the energy surface at T0 point to the yaw angle: fine-tune the yaw angle (+0.1°) → new deviation | (350.1 / 360)-0.98|=0.0078→ new index value e^(-0.78)≈0.458→ new energy value 0.614×0.458×0.32≈0.090; ;

[0173] Pitch angle dimension gradient: fine-tune the pitch angle (+0.1°) → new aerodynamic coefficient (table interpolation) 0.325→ new energy value 0.614×0.449×0.325≈0.089; partial derivative=(0.089-0.088) / 0.1=0.01;

[0174] Gradient optimization direction vector: symbol function combined gradient modulus square root: [sign(0.02)×√(0.02), sign(0.01)×√(0.01)]≈[0.141, 0.100]; product: gradient vector sequence [(0.141, 0.100)] (T0 moment);

[0175] Continuous control instruction generation, proportional integral generation of incremental instruction: yaw angle increment: ; pitch angle increment: ; superimposed initial attitude angle: current yaw angle 350°→ absolute instruction 350.0705°; current pitch angle 5°→ absolute instruction 5.050°;

[0176] Product as shown in Table 13:

[0177] Table 13 Product

[0178]

[0179] Instruction stream cache output, instruction encapsulation: encapsulate the absolute instruction at T0-T2 time according to the time stamp as shown in Table 14:

[0180] Table 14 Instruction stream cache output

[0181]

[0182] Write buffer: store in first-in first-out queue in time sequence, wait for execution.

[0183] 205、Based on the yaw angle instruction sequence and the pitch angle instruction sequence, a wind turbine wind power efficiency evaluation report is generated, and optimization is performed according to the wind turbine wind power efficiency evaluation report.

[0184] Specifically, the yaw angle absolute instruction sequence and the pitch angle absolute instruction sequence in the control instruction data packet are called to drive the yaw system and the pitch system of the wind turbine to perform actions, and the actual output power sequence, the actual wind speed sequence and the actual wind direction sequence within a preset time period after the actions are performed are synchronously collected to generate a unit response data set; the corrected normalized wind speed prediction sequence is taken as the input wind speed, the normalized wind direction prediction sequence is taken as the input wind direction, the preloaded pitch angle-tip speed ratio aerodynamic efficiency coefficient table is queried in combination with the currently executed yaw angle absolute instruction sequence and the pitch angle absolute instruction sequence, and the theoretical output power sequence is calculated; the actual output power sequence and the theoretical output power sequence are aligned according to the time stamp, the power capture efficiency value (actual power / theoretical power) is calculated element by element, the yaw angle instruction value, the pitch angle instruction value, the corrected normalized wind speed prediction value and the normalized wind direction prediction value at the corresponding moment are associated, and a multi-dimensional efficiency evaluation matrix is generated; an efficiency threshold interval is set, the multi-dimensional efficiency evaluation matrix is scanned, the continuous time period in which the power capture efficiency value continuously falls below the lower threshold value is identified, the normalized wind speed prediction sequence, the normalized wind direction prediction sequence, the yaw angle instruction sequence and the pitch angle instruction sequence corresponding to the time period are extracted, and a low-efficiency operation warning data packet is generated; in response to the low-efficiency operation warning data packet, the actual wind speed sequence, the actual wind direction sequence and the actual output power sequence within the warning period are extracted, the actual aerodynamic efficiency coefficient is back calculated based on the relationship between the actual wind speed and the tip speed ratio, the theoretical value of the corresponding working condition point in the preloaded aerodynamic efficiency coefficient table is compared, the calibration deviation is calculated and the coefficient table is updated, and a calibrated aerodynamic efficiency coefficient table is generated; the yaw angle instruction distribution and the pitch angle instruction distribution in the high-efficiency operation region (efficiency value ≥ upper threshold value) in the multi-dimensional efficiency evaluation matrix are analyzed, the high-frequency instruction interval is counted, the boundary values of the high-frequency instruction interval are updated as the yaw angle control threshold value and the pitch angle control threshold value, and an optimized control threshold parameter set is generated. The multi-dimensional efficiency evaluation matrix, the low-efficiency operation warning data packet, the calibrated aerodynamic efficiency coefficient table and the optimized control threshold parameter set are packaged to generate a wind turbine wind power efficiency evaluation report, the calibrated aerodynamic efficiency coefficient table is fed back to the aerodynamic efficiency coefficient calling link, and the optimized control threshold parameter set is fed back to the gradient optimization direction vector generation link.

[0185] It should be noted that the yaw angle and the pitch angle instruction sequence (60 second window) generated by the execution step 204 of a certain 2MW wind turbine, the actual operation data are synchronously collected, the control effect is evaluated and the parameters are optimized.

[0186] The unit response data is collected, and the instructions are executed: the yaw angle instruction: [350.07°, 350.13°, 350.20°] (T0-T2 seconds); the pitch angle instruction: [5.05°, 5.09°, 5.14°];

[0187] Actual output: Actual power: [820kW, 795kW, 770kW]; Actual wind speed: [8.5m / s, 8.2m / s, 7.8m / s]; Actual wind direction: [352°, 349°, 351°];

[0188] Theoretical power calculation, input predicted data: Normalized wind speed prediction: [0.85, 0.82, 0.78]; Normalized wind direction prediction: [0.98, 0.95, 0.93];

[0189] Query aerodynamic efficiency table (pitch angle vs tip speed ratio): T0 moment: pitch angle 5.05°, tip speed ratio 5.2 → interpolated efficiency coefficient 0.315; Theoretical power formula: Theoretical power = (wind speed 3 ) × aerodynamic coefficient × attenuation function; Output theoretical power sequence: [865kW, 830kW, 798kW];

[0190] Efficiency evaluation matrix generation, capture efficiency value: Actual power / Theoretical power → [94.8%, 95.8%, 96.5%];

[0191] Correlation parameter matrix as Table 15:

[0192] Table 15 Correlation parameter matrix

[0193]

[0194] Low efficiency alarm and parameter calibration, alarm trigger: When efficiency is continuously < 95% (such as T0), extract corresponding period data.

[0195] Back-propagate actual aerodynamic efficiency: T0 actual power 820kW → back-propagated aerodynamic coefficient 0.302 (original theoretical value 0.315); Calibration deviation: -4.1% → update aerodynamic table corresponding position coefficient to 0.302.

[0196] High efficiency interval optimization, statistics of high efficiency area (efficiency ≥ 96%) instruction distribution: yaw angle high frequency interval: 350.13°-350.20°; pitch angle high frequency interval: 5.09°-5.14°;

[0197] Update control threshold: feedback the above interval boundary to the gradient optimization module, constraint subsequent instruction generation.

[0198] Output evaluation report and feedback, report content: multi-dimensional efficiency matrix; low efficiency alarm package: T0 period data + calibration deviation amount; optimization control threshold: yaw angle [350.13°, 350.20°], pitch angle [5.09°, 5.14°];

[0199] The calibrated aerodynamic table is pushed to the aerodynamic efficiency calling module of step 204; the optimization threshold is pushed to the gradient direction vector generation link to narrow the search range.

[0200] 206, also including sensor anomaly prediction inversion verification:

[0201] Real-time receive wind turbine power output measurement; based on the current control instruction, reverse calculation of theoretical wind speed and wind direction interval; when the measured sensor data continuously exceeds the theoretical interval, generate an anomaly marker; product: sensor anomaly confidence factor;

[0202] Emergency call of phase space historical data, start when the anomaly confidence factor exceeds the threshold; extract the spatiotemporal nearest neighbor point set from the dynamic triangular mesh topology; call the historical standardized data at the corresponding time to replace the real-time sensor input; product: anomaly condition replacement data package;

[0203] Control instruction flow reconstruction, input the replacement data package into the fractional order differential joint prediction module; skip the optimization calculation and directly call the historical optimal instruction template; generate a fault-tolerant control instruction sequence; product: fault-tolerant control instruction stream;

[0204] Perform state closed-loop feedback, monitor the power recovery slope after executing the fault-tolerant control instruction; when the slope meets the standard, switch back to the normal control mode; record the abnormal event characteristics to the fault knowledge base; product: control mode switching signal, fault feature archive.

[0205] It should be noted that the following is based on the running scene of a 2MW wind turbine within 60 seconds (sampling frequency 1Hz), which illustrates the implementation process of the sensor anomaly prediction inversion verification step (step 206). Assuming that after the current control instruction is generated, the actual power output of the wind turbine deviates from the theoretical expectation, the specific implementation is as follows:

[0206] Theoretical interval inversion and anomaly marker, input: T0-T5 second control instruction: yaw angle instruction sequence [350.5°, 351.2°, 352.0°], pitch angle instruction sequence [5.1°, 5.3°, 5.6°]; measured power sequence: [1.52, 1.48, 1.25, 1.30, 1.42, 1.38] MW;

[0207] Theoretical interval calculation (take T2 time as an example): according to the aerodynamic efficiency coefficient table and the instruction, the theoretical wind speed should be 8.0-8.5m / s (standardized wind speed 0.53-0.57), and the theoretical wind direction should be 352-356° (standardized wind direction 0.978-0.989).

[0208] Anomaly detection: T2 measured data: wind speed 7.2 m / s (normalized 0.48), wind direction 358° (normalized 0.994), both exceed the theoretical interval. Three consecutive overruns (T2-T4) → trigger anomaly flag, generate confidence factor 0.92 (threshold = 0.9).

[0209] Historical data replacement and fault-tolerant control, phase space emergency call: Extract the spatiotemporal nearest neighbor points from the dynamic topology (T1, T3, T4 time data), form a replacement data package as shown in Table 16:

[0210] Table 16 Replacement data package

[0211]

[0212] Fault-tolerant instruction generation: input the replacement data into the prediction module, skip the optimization calculation, and directly call the historical optimal instruction template (T3 time instruction: yaw angle 352.0°, pitch angle 5.6°). Output fault-tolerant instruction sequence: [352.0°, 5.6°] (T6 time).

[0213] Closed-loop feedback and mode switching, power recovery monitoring: after executing the fault-tolerant instruction, T6-T8 seconds power output: [1.55, 1.58, 1.60] MW → recovery slope 0.8% / s (calculation: (1.60-1.55) / 1.55 x 100% ÷ 3 seconds). Slope exceeds threshold 0.5% / s → trigger switching signal, return to normal control mode.

[0214] Fault feature recording: anomaly event features: sustained low wind speed + reverse jump, record to knowledge base (marked as "wind direction sensor zero drift fault"). Data flow example as shown in Table 17:

[0215] Table 17 Data flow example

[0216]

[0217] In the embodiment of the present application, through multi-dimensional data standardization and enhancement processing, dynamic phase space topology construction, fractional differential joint prediction and continuous energy surface gradient optimization, etc., accurate prediction of wind speed and direction and optimal adjustment of fan posture are realized, so that the fan is always in the best power generation state, and the power generation efficiency is improved; the sensor anomaly prediction inversion verification mechanism can monitor sensor data anomalies in real time, enable fault tolerance control in time, avoid fan operation anomalies caused by sensor failure, and enhance the stability and reliability of the system; the dynamic phase space topology construction and continuous energy surface gradient optimization method fully utilizes historical data and real-time data, mines the internal relationship between data, improves the utilization value of data, and provides more accurate basis for fan control; the sensor anomaly prediction inversion verification mechanism can timely discover sensor failure and record abnormal event features to the fault knowledge base, providing reference for operation and maintenance personnel, helping to develop maintenance plans in advance and reduce operation and maintenance costs; various factors such as wind speed, wind direction and turbulence intensity are considered comprehensively, so that the system can adapt to complex and variable wind conditions and realize efficient operation of the fan under different wind conditions.

[0218] The wind turbine power generation efficiency optimization method based on wind direction and speed monitoring in the embodiment of the present application is described above, and the wind turbine power generation efficiency optimization system based on wind direction and speed monitoring in the embodiment of the present application is described below, please refer to Figure 3 The wind turbine power generation efficiency optimization system based on wind direction and speed monitoring in the embodiment of the present application includes: an acquisition module 301, configured to acquire an original wind speed sequence and an original wind direction sequence, call a rated wind speed parameter to perform division calculation on the original wind speed sequence to generate a standardized wind speed sequence, and call a circular angle constant to perform division calculation on the original wind direction sequence to generate a standardized wind direction sequence; a construction module 302, configured to take the standardized wind speed sequence and the standardized wind direction sequence as input sources, construct a three-dimensional phase space vector, and generate a dynamic triangular mesh topology structure based on real-time updated historical data flow; a generation module 303, configured to locate a current phase space vector in the dynamic triangular mesh topology structure, extract historical standardized wind speed and wind direction data of all vertices of the simplex body, perform closed calculation on the vertex data based on a fractional differential operator, and output a standardized wind speed prediction sequence and a standardized wind direction prediction sequence; an analysis module 304, configured to take the standardized wind speed prediction sequence and the standardized wind direction prediction sequence as input sources, construct a continuously differentiable energy capture efficiency surface, and analyze a yaw angle instruction sequence and a pitch angle instruction sequence along the gradient direction of the efficiency surface; and a distribution module 305, configured to generate a fan wind power efficiency evaluation report based on the yaw angle instruction sequence and the pitch angle instruction sequence, and optimize according to the fan wind power efficiency evaluation report.

[0219] In the embodiment of the present application, the acquisition module performs standardization processing on the original wind speed and wind direction sequence, eliminates the dimensional difference, makes different data comparable and unified, provides more accurate and reasonable data basis for subsequent analysis and processing, and helps to improve the accuracy of system overall prediction and optimization; the construction module generates a dynamic triangular mesh topology based on the real-time updated historical data stream, can reflect the change characteristics and trend of wind speed and wind direction in real time, is more adaptable to complex and changeable wind field environment compared with a static model, and improves the adaptability and processing efficiency of the system to different wind conditions; the generation module performs closed calculation on the vertex data by using a fractional differential operator, the fractional differential operator has memory and genetic characteristics, can better capture the historical information and dynamic change characteristics of the data, and compared with a traditional integer order differential operator, can output more accurate standardized wind speed and wind direction prediction sequence, and provides a reliable basis for subsequent optimization; the analysis module constructs a continuously differentiable energy capture efficiency surface, and obtains the yaw angle and pitch angle instruction sequence along the gradient direction of the efficiency surface, can find the optimal direction of energy capture efficiency, makes the fan posture adjustment more accurate, effectively improves the power generation efficiency of the wind turbine, ensures that the fan can quickly and accurately respond according to the real-time wind condition, realizes efficient operation, and improves the power generation capacity and energy utilization rate.

[0220] The above Figure 3 The wind turbine power generation efficiency optimization system based on wind direction and wind speed monitoring in the embodiment of the present application is described in detail from the perspective of modular functional entities, and the wind turbine power generation efficiency optimization device based on wind direction and wind speed monitoring in the embodiment of the present application is described in detail from the perspective of hardware processing.

[0221] Figure 4 Fig. 4 is a structural schematic diagram of a wind turbine power generation efficiency optimization device based on wind direction and wind speed monitoring provided by the embodiment of the present application. The wind turbine power generation efficiency optimization device 400 based on wind direction and wind speed monitoring can have great differences due to different configurations or performances, and can include one or more processors (central processing units, CPU) 410 (for example, one or more processors) and a memory 420, and one or more storage media 430 (for example, one or more mass storage devices) storing application programs 433 or data 432. The memory 420 and the storage media 430 can be temporary storage or persistent storage. The programs stored in the storage media 430 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the wind turbine power generation efficiency optimization device 400 based on wind direction and wind speed monitoring. Furthermore, the processor 410 can be configured to communicate with the storage media 430, and execute the series of instruction operations in the storage media 430 on the wind turbine power generation efficiency optimization device 400 based on wind direction and wind speed monitoring.

[0222] The wind direction and wind speed monitoring based wind turbine power generation efficiency optimization device 400 can also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input / output interfaces 460, and / or one or more operating systems 431, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that, Figure 4 The illustrated wind direction and wind speed monitoring based wind turbine power generation efficiency optimization device structure does not constitute a limitation to the wind direction and wind speed monitoring based wind turbine power generation efficiency optimization device, and can include more or fewer components than illustrated, or combine certain components, or different component arrangements.

[0223] The present application also provides a wind direction and wind speed monitoring based wind turbine power generation efficiency optimization device, which includes a memory and a processor, the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to make the processor execute the steps of the wind direction and wind speed monitoring based wind turbine power generation efficiency optimization method in each of the above embodiments.

[0224] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, and the instructions make the computer execute the steps of the wind direction and wind speed monitoring based wind turbine power generation efficiency optimization method when the instructions are run on the computer.

[0225] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0226] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0227] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the same; even though the present application has been described in detail with reference to the foregoing embodiments, those ordinarily skilled in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the power generation efficiency of wind turbine units based on wind direction and wind speed monitoring, characterized in that, The wind turbine power generation efficiency optimization method based on wind direction and wind speed monitoring includes: Obtain the original wind speed sequence and the original wind direction sequence. Use the rated wind speed parameter to process the original wind speed sequence to generate a standardized wind speed sequence. Use the circumferential angle constant to process the original wind direction sequence to generate a standardized wind direction sequence. Using the standardized wind speed sequence and standardized wind direction sequence as input sources, a three-dimensional phase space vector is constructed. The first dimension of the three-dimensional phase space vector is the current standardized wind speed value, the second dimension is the sine product of the historical standardized wind speed value and the wind direction change, and the third dimension is the current standardized wind direction value. Based on the real-time updated historical data stream, a dynamic triangular mesh topology structure is generated. In the dynamic triangular mesh topology, locate the simplex to which the current phase space vector belongs, extract the historical standardized wind speed and wind direction data of all vertices of the simplex, perform closed-form calculation on the vertex data, and output the standardized wind speed prediction sequence and the standardized wind direction prediction sequence. Based on the standardized wind speed prediction sequence and the standardized wind direction prediction sequence as input sources, a continuously differentiable energy capture efficiency surface is constructed. The energy capture efficiency surface is defined by the cube of the standardized wind speed prediction value, the exponential decay function of the yaw angle and the predicted wind direction deviation, and the aerodynamic efficiency coefficient related to the pitch angle. The yaw angle command sequence and the pitch angle command sequence are obtained analytically along the gradient direction of the efficiency surface. Based on the yaw angle command sequence and the pitch angle command sequence, a wind turbine power efficiency evaluation report is generated, and optimization is performed based on the wind turbine power efficiency evaluation report.

2. The method for optimizing the power generation efficiency of wind turbines based on wind direction and speed monitoring according to claim 1, characterized in that, include: The raw wind speed sequence is collected in real time by a wind speed sensor, and the raw wind direction sequence is collected in real time by a wind direction sensor. The original wind speed sequence is segmented based on a preset time window. The statistical variance of each segment of wind speed data is calculated. The statistical variance is divided by the average wind speed of that segment to generate a dynamic turbulence intensity factor sequence. The preset rated wind speed parameters are called, and the original wind speed sequence is divided element by element to generate a basic normalized wind speed sequence. The dynamic turbulence intensity factor sequence is added as an additional dimension and fused with the basic normalized wind speed sequence to obtain the enhanced normalized wind speed sequence as the normalized wind speed sequence. Identify angle jump events between adjacent data points in the original wind direction sequence, inject angle compensation at the jump points to minimize the difference between adjacent angles, and generate a continuous wind direction sequence. The system calls a preset circumferential angle constant, performs a division operation on each element of the continuous wind direction sequence to generate a basic standardized wind direction value, calculates the difference between the basic standardized wind direction values ​​of adjacent timestamps, and binds the wind direction change as an additional dimension to the basic standardized wind direction value to obtain a standardized wind direction sequence.

3. The method for optimizing the power generation efficiency of wind turbines based on wind direction and speed monitoring according to claim 2, characterized in that, include: Extract the basic standardized wind speed value from the enhanced standardized wind speed sequence, extract the wind direction change from the standardized wind direction sequence, and multiply the standardized wind speed value of the preset time window with the sine value of the wind direction change at the corresponding time to generate a composite feature sequence. Construct a three-dimensional phase space vector, where the first dimension is the current basic normalized wind speed value, the second dimension is the value of the composite feature sequence, and the third dimension is the current basic normalized wind direction value. Assemble the vectors according to the timestamps to obtain a three-dimensional phase space point set with timestamps. Set a dynamic sliding window to retain the latest N 3D coordinate points, perform Delaunay subdivision calculation on the coordinate point set within the window, and generate a dynamically updated tetrahedral mesh topology; Input the current three-dimensional coordinate point into the tetrahedral mesh topology, traverse the tetrahedral cells to locate the target simplex containing the point, record the vertex index set of the target simplex, and obtain the vertex index set of the simplex to which the current point belongs; Based on the vertex index set, extract the historical standardized wind speed value at the corresponding time, and simultaneously extract the historical standardized wind direction value at the corresponding time to generate a simplex vertex historical data package.

4. The method for optimizing the power generation efficiency of wind turbine units based on wind direction and wind speed monitoring according to claim 3, characterized in that, include: Receive the vertex index set of a monolith, retrieve the standardized wind speed value of the corresponding timestamp from the historical database according to the index, and simultaneously retrieve the standardized wind direction value of the corresponding timestamp to obtain the vertex wind speed dataset and the vertex wind direction dataset of the monolith. Calculate long-memory feature parameters based on the temporal distribution of vertex data, generate a fractional-order differential weight coefficient sequence based on the long-memory feature parameters, and construct a fractional-order differential operator template. A primary wind speed prediction sequence is generated by performing non-iterative convolution calculations based on the vertex wind speed dataset of a single-shape, and a primary wind direction prediction sequence is generated by performing non-iterative convolution calculations based on the vertex wind direction dataset of a single-shape. Extract the dynamic turbulence intensity factor sequence, generate a correction coefficient matrix based on the current turbulence intensity value, apply the correction coefficient to the primary wind speed prediction sequence to obtain the corrected standardized wind speed prediction sequence as the standardized wind speed prediction sequence, and use the primary wind direction prediction sequence as the standardized wind direction prediction sequence.

5. The method for optimizing the power generation efficiency of wind turbine units based on wind direction and wind speed monitoring according to claim 4, characterized in that, include: Preload the pitch angle-tip speed ratio aerodynamic efficiency coefficient table, calculate the real-time tip speed ratio based on the current corrected standardized wind speed prediction value, and interpolate the coefficient table to obtain the dynamic aerodynamic efficiency coefficient sequence. A three-dimensional energy surface is constructed, where the first dimension is the cube of the standardized wind speed prediction value, the second dimension is the exponential decay function with the yaw angle and the deviation of the predicted wind direction as variables, and the third dimension is the dynamic aerodynamic efficiency coefficient sequence. The three-dimensional data are fused into a time-varying continuous energy surface according to the timestamp. Calculate the partial derivatives of the energy surface in the yaw angle dimension and the partial derivatives of the energy surface in the pitch angle dimension. Use the sign function and the square root of the gradient magnitude to generate a gradient optimization direction vector sequence. Perform proportional integration along the gradient optimization direction vector to output the yaw angle increment command sequence and the pitch angle increment command sequence, and generate the yaw angle absolute command sequence and the pitch angle absolute command sequence as the yaw angle command sequence and the pitch angle command sequence. The absolute control commands are encapsulated into transmission data packets according to timestamps and written into the first-in-first-out command buffer to obtain the control command data packets.

6. The method for optimizing the power generation efficiency of wind turbine units based on wind direction and wind speed monitoring according to claim 5, characterized in that, Set the energy capture efficiency value of the energy surface to [value]. : ； in, This is the corrected standardized wind speed forecast; This is the current yaw angle of the wind turbine; Standardized wind direction forecast; The attenuation coefficient; This is the dynamic aerodynamic efficiency coefficient.

7. The method for optimizing the power generation efficiency of wind turbine units based on wind direction and wind speed monitoring according to claim 5, characterized in that, include: Call the absolute command sequence of yaw angle and absolute command sequence of pitch angle to drive the yaw system and pitch system of the wind turbine to perform actions, and simultaneously collect the actual output power sequence, actual wind speed sequence and actual wind direction sequence within a preset time period after the actions are performed to generate the unit response dataset. Using the corrected standardized wind speed prediction sequence as the input wind speed and the standardized wind direction prediction sequence as the input wind direction, combined with the currently executed yaw angle absolute command sequence and pitch angle absolute command sequence, the preloaded pitch angle-tip speed ratio aerodynamic efficiency coefficient table is queried to obtain the theoretical output power sequence. Align the actual output power sequence with the theoretical output power sequence by timestamp, calculate the power capture efficiency value element by element, and associate the yaw angle absolute command value, pitch angle absolute command value, corrected standardized wind speed prediction value and standardized wind direction prediction value at the corresponding time to generate a multi-dimensional efficiency evaluation matrix. Set an efficiency threshold range, scan the multi-dimensional efficiency evaluation matrix, identify continuous time periods when the power capture efficiency value is consistently lower than the lower limit threshold, extract the standardized wind speed prediction sequence, standardized wind direction prediction sequence, yaw angle absolute command sequence and pitch angle absolute command sequence corresponding to the time period, and generate an inefficient operation alarm data packet. In response to the inefficient operation alarm data packet, the actual wind speed sequence, actual wind direction sequence and actual output power sequence within the alarm period are extracted. Based on the relationship between actual wind speed and tip speed ratio, the actual aerodynamic efficiency coefficient is inversely calculated. The theoretical values ​​of the corresponding operating points are compared with those in the preloaded aerodynamic efficiency coefficient table. The calibration deviation is calculated and the coefficient table is updated to generate a calibrated aerodynamic efficiency coefficient table. Analyze the absolute yaw angle command distribution and absolute pitch angle command distribution in the high-efficiency operating region of the multidimensional efficiency evaluation matrix, statistically analyze the high-frequency command interval, update the boundary values ​​of the high-frequency command interval to the yaw angle control threshold and the pitch angle control threshold, and generate an optimized control threshold parameter set; The multidimensional efficiency evaluation matrix, inefficient operation alarm data package, calibrated aerodynamic efficiency coefficient table, and optimized control threshold parameter set are encapsulated to generate a wind turbine power efficiency evaluation report.

8. The method for optimizing the power generation efficiency of wind turbine units based on wind direction and wind speed monitoring according to claim 7, characterized in that, It also includes sensor anomaly prediction inversion verification: Real-time reception of wind turbine power output measurement values, reverse calculation of theoretical wind speed and direction range based on current control commands, and generation of sensor anomaly confidence factor when measured sensor data continuously exceeds the theoretical range; When the anomaly confidence factor exceeds the threshold, it is activated to extract the spatiotemporal nearest neighbor set from the dynamic triangular mesh topology, call the historical standardized data of the corresponding time to replace the real-time sensor input, and obtain the abnormal operating condition replacement data packet. Based on abnormal operating conditions, the replacement data packet skips the optimization calculation and directly calls the historical best instruction template to generate a fault-tolerant control instruction flow; Monitor the power recovery slope after the fault-tolerant control command is executed. When the slope reaches the target, switch back to normal control mode and record the abnormal event characteristics to the fault knowledge base.

9. A wind turbine power generation efficiency optimization system based on wind direction and wind speed monitoring, characterized in that, The wind turbine power generation efficiency optimization system based on wind direction and wind speed monitoring includes: The acquisition module is used to acquire the original wind speed sequence and the original wind direction sequence, call the rated wind speed parameter to perform division calculation on the original wind speed sequence to generate a standardized wind speed sequence, and call the circular angle constant to perform division calculation on the original wind direction sequence to generate a standardized wind direction sequence. The construction module is used to construct a three-dimensional phase space vector with the standardized wind speed sequence and the standardized wind direction sequence as input sources. The first dimension of the three-dimensional phase space vector is the current standardized wind speed value, the second dimension is the sine product of the historical standardized wind speed value and the wind direction change, and the third dimension is the current standardized wind direction value. Based on the real-time updated historical data stream, a dynamic triangular mesh topology structure is generated. The generation module is used to locate the simplex to which the current phase space vector belongs in the dynamic triangular mesh topology, extract the historical standardized wind speed and wind direction data of all vertices of the simplex, perform closed-form calculation on the vertex data based on fractional differential operators, and output standardized wind speed prediction sequence and standardized wind direction prediction sequence. The analysis module is used to construct a continuously differentiable energy capture efficiency surface using the standardized wind speed prediction sequence and the standardized wind direction prediction sequence as input sources. The energy capture efficiency surface is defined by the cube of the standardized wind speed prediction value, the exponential decay function of the yaw angle and the predicted wind direction deviation, and the aerodynamic efficiency coefficient related to the pitch angle. The yaw angle command sequence and the pitch angle command sequence are obtained by analysis along the gradient direction of the efficiency surface. The allocation module is used to generate a wind turbine power efficiency evaluation report based on the yaw angle command sequence and the pitch angle command sequence, and to optimize the wind turbine power efficiency evaluation report.

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