Water turbine guide vane signal optimization method and system based on interference resistance

Abstract

The invention discloses a water turbine guide vane signal optimization method and system based on interference resistance, and belongs to the technical field of water turbine signal control, and the method comprises the steps: collecting a water turbine guide vane operation signal, carrying out the preliminary analysis of abnormal factors in the signal, and extracting a key optimization signal; identifying and classifying interference signals in the data according to the extracted key optimization signals; the influence degree of the current signal quality is determined according to different types of interference signals, and key optimization signals are adjusted and optimized in a targeted mode. According to the method, accurate decision and quick response are ensured, interference in signals is identified and classified, so that the influence of environment, mechanical and electrical noise is eliminated, the signal stability is enhanced, the equipment damage risk is reduced, key signals are optimized in a targeted manner, and the operation efficiency and safety are improved; the self-adaptive capacity of the water turbine is improved, the service life of equipment is prolonged, meanwhile, the maintenance cost is reduced, and the obvious practical value is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of water turbine signal control, and in particular to a water turbine guide vane signal optimization method and system based on anti-interference. Background Art

[0002] With the rapid development of renewable energy, hydropower stations, as an important source of clean energy, have gradually received attention for their technical level. As a key equipment in hydropower stations, the performance of turbines directly affects the overall power generation efficiency and economy. In recent years, the regulation technology of turbine guide vanes has made significant progress. Through real-time monitoring and feedback mechanisms, precise control of water flow and pressure changes has been achieved, thereby optimizing the power generation process. However, with the increasing complexity of the operating environment, the signal acquisition of turbine guide vanes faces many challenges. When collecting real-time data, sensors are often interfered by the external environment and internal equipment failures, resulting in unstable signal quality and affecting the decision-making accuracy of the system. Although some existing methods are committed to optimizing and processing signals, they often fail to effectively suppress various interference signals and lack targeted optimization strategies, which restricts the reliability and efficiency of turbine operation.

[0003] The existing technology usually adopts a simple filtering or averaging method when processing turbine guide vane signals. However, this method has limited effect when facing complex and changeable interference sources. For example, factors such as mechanical failures, environmental changes, and electrical noise often cause signal fluctuations. If they are not identified and classified in time, they may lead to system misjudgment, which not only reduces the utilization rate of the signal, but also may cause greater safety risks. In addition, the existing technology lacks systematicity and comprehensiveness in the analysis of interference signals, and fails to form an effective feedback mechanism to adapt to the dynamically changing operating environment. Therefore, the real-time signal optimization of turbine guide vanes urgently needs a new method based on anti-interference mechanism to improve the scientificity and accuracy of signal processing. Summary of the invention

[0004] In view of the above existing problems, in order to solve the above technical deficiencies, the present invention proposes a method for optimizing the guide vane signal of a turbine, which collects the operating signals of the guide vanes of the turbine and its peripheral equipment in real time through a variety of sensors, and conducts in-depth analysis of the abnormal factors in the signals, thereby extracting key optimization signals, which can not only identify different types of interference signals, classify and process them, but also effectively adjust the angle of the guide vanes according to the degree of influence of the interference signals on the operation of the turbine to ensure the stability and safety of the system. The control accuracy of the guide vanes of the turbine is greatly improved, which also helps to improve the power generation efficiency of the entire hydropower system and the service life of the equipment.

[0005] In order to solve the above technical problems, a turbine guide vane signal optimization method based on anti-interference is proposed, including:

[0006] Collect the turbine guide vane operation signal and conduct preliminary analysis on the abnormal factors in the signal to extract the key optimization signal; identify and classify the interference signals in the data based on the extracted key optimization signal; determine the impact of different types of interference signals on the current signal quality, and adjust and optimize the key optimization signal in a targeted manner.

[0007] As a preferred solution of the anti-interference based turbine guide vane signal optimization method described in the present invention, the turbine guide vane operation signal includes real-time signals of the guide vane operation signal obtained by different types of sensors, including the temperature of the guide vane and peripheral equipment, the pressure change when water flows through the guide vane, water flow data, vibration signals, turbine speed information, actual angle of the guide vane and set angle information.

[0008] As a preferred solution of the anti-interference based water turbine guide vane signal optimization method described in the present invention, wherein: the key optimization signal includes establishing a real-time signal value evaluation framework through a Bayesian network model, defining a data vector D to represent real-time signal data streams from different types of sensors;

[0009] The decision vector C represents the real-time decision requirements including guide vane adjustment and energy consumption optimization;

[0010] Use Bayes' theorem to calculate the impact of data on decision changes:

[0011]

[0012] The signal waveform is displayed through a visual interface, and the impact value results are converted into brightness points in the waveform spectrum of the impact display. The brightness threshold is set to 0.7. When the brightness exceeds 0.7, it is considered to be a key optimization signal and is processed and output first;

[0013] When the brightness is less than 0.7 and not more than 0.4, it is considered as a secondary optimization signal and is output after the key optimization signal output is optimized;

[0014] When the brightness is less than 0.4, it is considered as a signal without optimization demand and is screened out;

[0015] Among them, Pr(C|D) represents the influence value of selecting decision requirements based on known real-time data streams, Pr(D|C) is the probability of data D appearing under decision requirement C; Pr(D) is the total probability of data D appearing under all possible decisions, and Pr(C) is the initial expected probability of making decision C.

[0016] As a preferred solution of the anti-interference based turbine guide vane signal optimization method described in the present invention, the identification includes: inputting the obtained key optimization signal into the classification module to identify and classify the interference signal, calculating the average value of the signal, removing the DC component from the signal and then calculating the average value again, extracting the signal characteristics of the standard deviation, kurtosis and skewness, setting a threshold to detect signal abnormalities, identifying the high fluctuation area in the signal, and marking it as an interference signal.

[0017] As a preferred solution of the anti-interference based water turbine guide vane signal optimization method described in the present invention, the classification includes analyzing the value of the signal feature of the signal marked as an interference signal to classify the interference:

[0018] When the standard deviation increases and the skewness is <0, it is classified as type a interference;

[0019] When kurtosis>3 and skewness>0, it is classified as type B interference;

[0020] When the kurtosis is <3 and the signal fluctuates frequently, it is classified as type C interference;

[0021] When there is a continuous deviation from the normal state and the standard deviation is below the normal value but there is a sudden change to a high value, it is classified as a type D interference.

[0022] As a preferred solution of the anti-interference based turbine guide vane signal optimization method described in the present invention, the influence degree includes defining the comprehensive influence degree of each type of interference for different types of interference signals. When the comprehensive influence degree exceeds the set threshold, it is considered that the equipment or environmental factors to which the current interference signal belongs will cause damage to the turbine, and it is necessary to immediately screen out the current interference signal and optimize and adjust the important optimization signals.

[0023] As a preferred solution of the anti-interference based turbine guide vane signal optimization method described in the present invention, the optimization includes calculating the sum of the influence of all categories of interference sources on the signal after the interference is screened out, dividing the comprehensive influence by the sum of all interference influences to obtain a ratio 1, the ratio value represents the relative value of the actual degree of influence on the current system among all interference influences, multiplying the ratio 1 by the gain constant required for signal regulation to obtain a signal regulation index, and dynamically adjusting the turbine guide vane angle according to the product of the signal regulation index and the safety margin.

[0024] Another object of the present invention is to provide a turbine guide vane signal optimization system based on anti-interference, which solves technical problems such as signal interference identification and classification, signal optimization and adjustment, real-time response and decision support, and data-driven dynamic optimization. The system effectively identifies and classifies interference signals from factors such as the environment, mechanical vibration, and electrical noise through algorithms and statistical analysis, thereby improving signal quality and ensuring safe and efficient operation of the turbine. Combined with the real-time signal value assessment framework and Bayesian model, the system can generate a visual interface to provide real-time decision support for operators, flexibly respond to changing working conditions, and ensure the normal operation and long-term durability of the equipment.

[0025] As a preferred solution of the anti-interference based water turbine guide vane signal optimization system described in the present invention, it is characterized by comprising a data extraction module, an identification module and an optimization module;

[0026] The data extraction module acquires data from the sensor in real time, preliminarily analyzes the signal, screens out abnormal factors, extracts key optimization signals, and transmits the key optimization signals to the recognition module;

[0027] The identification module receives the key optimization signal from the data extraction module, calculates the mean, standard deviation, kurtosis and skewness of the signal, identifies the interference signal in the signal, marks the high fluctuation area, classifies the interference signal according to the characteristic value, and transmits the classified interference signal and characteristics to the optimization module;

[0028] The optimization module receives the interference signal classification and influence information from the identification module, calculates the comprehensive influence of each type of interference on the signal quality, screens out the interference signal that causes damage to the turbine, calculates the signal adjustment index and dynamically adjusts the turbine guide vane angle in combination with the safety margin, and outputs the optimization result to the data extraction module for cyclic monitoring and optimization.

[0029] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for optimizing a turbine guide vane signal based on anti-interference are implemented.

[0030] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for optimizing a turbine guide vane signal based on anti-interference are implemented.

[0031] Beneficial effects of the present invention: The present invention collects the real-time signals of the guide vanes and performs abnormal analysis to effectively identify the key optimization signals, thereby improving the accuracy and response speed of decision-making. In the process of identifying and classifying interference signals, by analyzing the signal characteristics, it is possible to eliminate environmental, mechanical and electrical noise that affects the signal quality, enhance the stability of the signal, reduce the risk of equipment damage, and improve maintenance efficiency. In addition, dynamic analysis and adjustment of the influence of different types of interference signals are performed to achieve accurate optimization of the turbine guide vane angle, thereby significantly improving system performance and safety, and achieving the technical effects of improving operating efficiency, reducing energy loss and reducing the risk of sudden failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0033] Figure 1 An overall flow chart of a method for optimizing a turbine guide vane signal based on anti-interference is provided for one embodiment of the present invention.

[0034] Figure 2 A schematic diagram of a module of a turbine guide vane signal optimization system based on anti-interference is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0038] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0039] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0040] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for optimizing a turbine guide vane signal based on anti-interference, comprising:

[0042] S1: Collect the turbine guide vane operation signal and conduct preliminary analysis on the abnormal factors in the signal to extract the key optimization signal.

[0043] Specifically, the real-time signals of guide vane operation signals obtained through different types of sensors include the temperature of the guide vanes and peripheral equipment, pressure changes when water flows through the guide vanes, water flow data, vibration signals, turbine speed information, actual angle of the guide vanes and set angle information.

[0044] Furthermore, a real-time signal value assessment framework is established through the Bayesian network model, and the data vector D is defined to represent the real-time signal data stream from different types of sensors;

[0045] The decision vector C represents the real-time decision requirements including guide vane adjustment and energy consumption optimization;

[0046] Use Bayes' theorem to calculate the impact of data on decision changes:

[0047]

[0048] The signal waveform is displayed through a visual interface, and the impact value results are converted into brightness points in the waveform spectrum that affects the display:

[0049]

[0050] When the brightness exceeds 0.7, it is considered as a key optimization signal and is processed and output first;

[0051] When the brightness is less than 0.7 and not more than 0.4, it is considered a secondary optimization signal and is output after the key optimization signal is optimized. When the brightness is less than 0.4, it is considered a signal without optimization demand and is screened out.

[0052] Among them, Pr(C|D) represents the influence value of selecting decision requirements based on known real-time data streams, Pr(D|C) is the probability of data D appearing under decision requirement C; Pr(D) is the total probability of data D appearing under all possible decisions, and Pr(C) is the initial expected probability of making decision C.

[0053] S2: Identify and classify interference signals in the data based on the extracted key optimization signals.

[0054] Furthermore, the obtained key optimized signal is input into the classification module to identify and classify the interference signal and calculate the average value of the signal. Recalculate the average value after removing the DC component from the signal The signal characteristics of the standard deviation σ, kurtosis K, and skewness S are extracted as follows:

[0055]

[0056] Among them, N is the total number of sample data, E i 'The i-th sampled data point, i is the variable index, sets the threshold to detect signal anomalies, identifies the high fluctuation area in the signal, and marks it as an interference signal.

[0057] It should be noted that the signal marked as interference signal is analyzed by the value of signal characteristics to classify the interference:

[0058] When σ increases and S<0, it is classified as Class A interference, i.e. environmental interference, which is caused by environmental factors such as temperature changes, humidity, vibration, etc., causing the turbine equipment to operate in a harsh environment, thereby affecting the performance of mechanical components. Long-term existence leads to equipment fatigue and damage. Generally, ventilation, heat insulation or waterproof equipment are strengthened, environmental monitoring sensors are added, environmental changes are fed back in a timely manner, and equipment operating parameters are adjusted.

[0059] When K>3 and S>0, it is classified as Class B interference, that is, mechanical interference, which is caused by mechanical failure or device problems, leading to mechanical failure, increasing maintenance costs, shortening equipment life, affecting equipment performance, causing energy loss and reduced efficiency; regularly carry out equipment maintenance and inspection, especially for key mechanical components.

[0060] When K<3 and the signal fluctuates frequently, it is classified as Class C interference, that is, electrical noise interference. Due to the instability of electrical equipment or power supply, signal transmission is interfered with, affecting the accuracy of the signal, resulting in instability of the control system and affecting the overall performance of the system. Introduce filters in the electrical system to reduce the impact of electrical noise, optimize power management, and ensure voltage stabilization and stable power supply of electrical equipment.

[0061] When there is a continuous deviation from the normal state and σ is below the normal value but a sudden change to a high value occurs, it is classified as Class D interference, that is, signal transmission interference, which leads to data loss or signal distortion, misleading system decision-making, and causing instability and sudden failure of the control system; check and replace aging or damaged signal transmission media, and use shielded cables to enhance the anti-interference ability of the signal transmission path.

[0062] The high-value mutation is a point where σ remains below the normal value but suddenly becomes higher than the normal value.

[0063] After different types of interference signals are identified, the interference source is located and the device that sends the interference signal is resolved. At the same time, the impact value of the interference device or interference environment is calculated based on the interference signal, and the position of the turbine guide vane is adjusted.

[0064] S3: Determine the impact of different types of interference signals on the current signal quality, and make targeted adjustments to optimize the key signals.

[0065] Furthermore, for different types of interference signals, the comprehensive impact of each type of interference is defined as complex :

[0066]

[0067] When I complex ≥I th When the current interference signal is detected, it is considered that the equipment or environmental factors to which the current interference signal belongs will cause damage to the turbine, and it is necessary to immediately filter out the current interference signal and optimize and adjust the important optimization signal;

[0068] Among them, M is all interference categories, j is the variable index, g j is the weight of the relative importance of the j-th interference signal to the signal quality, dis jis the distance between the jth interference signal and the signal acquisition position, λ is the attenuation factor that controls the intensity of interference on the signal, m is the number of signal features, m includes standard deviation, kurtosis and skewness; α k is the correction coefficient of the k-th signal feature, V k is the measurement value of the k-th signal feature. exp ensures the rapid decline of the influence, making the influence of close-range interference sources more significant. log helps to linearize the relationship to make the influence calculation more stable and avoid numerical problems caused by too small indicators. th Impact threshold.

[0069] It should be noted that the sum of the influence of all types of interference sources is calculated for the signal after the interference is screened out, and the comprehensive influence is divided by the sum of all interference influences to obtain ratio 1. The ratio value represents the relative value of the actual degree of influence on the current system among all interference influences. The ratio 1 is multiplied by the gain constant required for signal adjustment to obtain the signal adjustment index. The turbine guide vane angle is dynamically adjusted according to the product of the signal adjustment index and the safety margin.

[0070] Embodiment 2, the second embodiment of the present invention, is different from the first two embodiments in that:

[0071] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0075] Example 3, reference Figure 2 , which is the third embodiment of the present invention, and provides a turbine guide vane signal optimization system based on anti-interference, including a data extraction module 10, an identification module 20 and an optimization module 30;

[0076] The data extraction module 10 acquires data from the sensor in real time, preliminarily analyzes the signal, screens out abnormal factors, extracts key optimization signals, and transmits the key optimization signals to the recognition module 20;

[0077] The identification module 20 receives the key optimization signal from the data extraction module 10, calculates the mean, standard deviation, kurtosis and skewness of the signal, identifies the interference signal in the signal, marks the high fluctuation area, classifies the interference signal according to the characteristic value, and transmits the classified interference signal and characteristics to the optimization module 30;

[0078] The optimization module 30 receives the interference signal classification and influence information from the identification module 20, calculates the comprehensive influence of each type of interference on the signal quality, screens out the interference signal that causes damage to the turbine, calculates the signal adjustment index and dynamically adjusts the turbine guide vane angle in combination with the safety margin, and outputs the optimization result to the data extraction module 10 for cyclic monitoring and optimization.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing guide vane signals of a hydraulic turbine based on anti-interference, characterized in that: include, Collect turbine guide vane operation signals and conduct preliminary analysis on abnormal factors in the signals to extract key optimization signals; Identify and classify interference signals in the data based on the extracted key optimization signals; Determine the impact of different types of interference signals on the current signal quality, and make targeted adjustments to optimize the key signals.

2. The method for optimizing the guide vane signal of a water turbine based on anti-interference according to claim 1, characterized in that: The turbine guide vane operation signal includes real-time signals of the guide vane operation signal obtained through different types of sensors, including the temperature of the guide vane and peripheral equipment, pressure changes when water flows through the guide vane, water flow data, vibration signals, turbine speed information, actual angle of the guide vane and set angle information.

3. The method for optimizing the guide vane signal of a water turbine based on anti-interference according to claim 2, characterized in that: The key optimization signal includes establishing a real-time signal value evaluation framework through a Bayesian network model, defining a data vector D to represent real-time signal data streams from different types of sensors; The decision vector C represents the real-time decision requirements including guide vane adjustment and energy consumption optimization; Use Bayes' theorem to calculate the impact of data on decision changes: The signal waveform is displayed through a visual interface, and the impact value results are converted into brightness points in the waveform spectrum of the impact display. The brightness threshold is set to 0.

7. When the brightness exceeds 0.7, it is considered to be a key optimization signal and is processed and output first; When the brightness is less than 0.7 and not more than 0.4, it is considered as a secondary optimization signal and is output after the key optimization signal output is optimized; When the brightness is less than 0.4, it is considered as a signal without optimization demand and is screened out; Among them, Pr(C|D) represents the influence value of selecting decision requirements based on known real-time data streams, Pr(D|C) represents the probability of data D appearing under decision requirement C; Pr(D) is the total probability of data D appearing under all possible decisions, and Pr(C) is the initial expected probability of making decision C.

4. The method for optimizing the guide vane signal of a water turbine based on anti-interference according to claim 3, characterized in that: The identification includes inputting the obtained key optimized signal into a classification module to identify and classify the interference signal, calculating the average value of the signal, removing the DC component from the signal and then calculating the average value again, extracting the signal characteristics of the standard deviation, kurtosis and skewness, setting a threshold to detect signal anomalies, identifying the high fluctuation area in the signal, and marking it as an interference signal.

5. The method for optimizing the guide vane signal of a water turbine based on anti-interference according to claim 4, characterized in that: The classification includes analyzing the value of the signal feature of the signal marked as an interference signal to classify the interference: When the standard deviation increases and the skewness is <0, it is classified as type a interference; When kurtosis>3 and skewness>0, it is classified as type B interference; When the kurtosis is <3 and the signal fluctuates frequently, it is classified as type C interference; When there is a continuous deviation from the normal state and the standard deviation is below the normal value but there is a sudden change to a high value, it is classified as a type D interference.

6. The method for optimizing the guide vane signal of a water turbine based on anti-interference according to claim 5, characterized in that: The impact degree includes defining the comprehensive impact degree of each type of interference for different types of interference signals. When the comprehensive impact degree exceeds the set threshold, it is considered that the equipment or environmental factors to which the current interference signal belongs will cause damage to the turbine, and the current interference signal needs to be immediately screened out and important optimization signals need to be optimized and adjusted.

7. The method for optimizing the guide vane signal of a water turbine based on anti-interference according to claim 6, characterized in that: The optimization includes calculating the sum of the influence of all categories of interference sources on the signal after the interference is screened out, dividing the comprehensive influence by the sum of all interference influences to obtain a ratio 1, where the ratio value represents the relative value of the actual degree of influence on the current system among all interference influences, multiplying the ratio 1 by the gain constant required for signal regulation to obtain a signal regulation index, and dynamically adjusting the turbine guide vane angle according to the product of the signal regulation index and the safety margin.

8. A system using the anti-interference based water turbine guide vane signal optimization method according to any one of claims 1 to 7, characterized in that: It includes data extraction module, identification module and optimization module; The data extraction module acquires data from the sensor in real time, preliminarily analyzes the signal, screens out abnormal factors, extracts key optimization signals, and transmits the key optimization signals to the recognition module; The identification module receives the key optimization signal from the data extraction module, calculates the mean, standard deviation, kurtosis and skewness of the signal, identifies the interference signal in the signal, marks the high fluctuation area, classifies the interference signal according to the characteristic value, and transmits the classified interference signal and characteristics to the optimization module; The optimization module receives the interference signal classification and influence information from the identification module, calculates the comprehensive influence of each type of interference on the signal quality, screens out the interference signal that causes damage to the turbine, calculates the signal adjustment index and dynamically adjusts the turbine guide vane angle in combination with the safety margin, and outputs the optimization result to the data extraction module for cyclic monitoring and optimization.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for optimizing a water turbine guide vane signal based on anti-interference are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for optimizing a water turbine guide vane signal based on anti-interference are implemented as described in any one of claims 1 to 7.

Images