Real-time bandwidth enhancement method for primary frequency modulation DEH side control system

Abstract

The present application belongs to the technical field of power system, and particularly relates to a real-time bandwidth enhancement method of a primary frequency modulation DEH side control system. The method comprises the following steps: step 1, collecting output signals of a digital electro-hydraulic regulating system in real time, performing zero-phase filtering, and obtaining preprocessed signals; step 2, training a support vector prediction model based on historical collected operation parameters of the digital electro-hydraulic regulating system, and setting a mapping rule library of classification results and bandwidth enhancement coefficients, wherein each classification result corresponds to a bandwidth enhancement coefficient in the mapping rule library; step 3, performing feature extraction on the preprocessed signals to capture time-frequency characteristics of the preprocessed signals, and obtaining a frequency correction signal; and step 4, enhancing the bandwidth of the frequency correction signal using the bandwidth enhancement coefficient. The present application can effectively improve the control accuracy and stability of the system, reduce the sensitivity of the system to external interference and noise, and improve the anti-interference ability and adaptability of the system.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a method for enhancing the real-time bandwidth of a primary frequency regulation digital electro-hydraulic control system. Background Technology

[0002] Digital electro-hydraulic control (DEH) systems, as an advanced turbine control system, have been widely used in modern industrial production. DEH systems achieve precise control of turbines through digitalization, offering advantages such as fast response speed and high control accuracy, and are of great significance for improving turbine operating efficiency and reducing energy consumption. This paper will discuss the current development status of DEH systems in the field of turbine control and the challenges they face, based on publicly available technologies.

[0003] The DEH system mainly consists of two parts: an electronic section and a hydraulic section. The electronic section monitors and controls the turbine's operating parameters, while the hydraulic section uses hydraulic actuators to precisely control the turbine's regulating valves. In the electronic section, the computer serves as the primary control device, responsible for real-time acquisition of turbine operating parameters such as vibration, current, and voltage parameters, and processing and analyzing these parameters. Through advanced control algorithms, the computer can monitor the turbine's operating status in real time and adjust control strategies as needed to ensure the turbine's safe and stable operation. Furthermore, the DEH system includes advanced predictive models such as support vector machines and Kalman filters for predicting and analyzing future operating parameters, enabling predictive and optimized control of the turbine's operating state. The hydraulic section mainly consists of hydraulic actuators and regulating valves. The hydraulic actuators adjust the turbine's regulating valves based on control signals output from the electronic section, thereby controlling parameters such as turbine load and speed. Using high-performance hydraulic actuators enables precise turbine control, improving the system's dynamic performance and response speed.

[0004] Despite the numerous advantages of DEH systems in turbine control, several problems and challenges remain in practical applications: System stability and reliability: DEH systems demand extremely high stability and reliability from the turbine. However, in practice, due to system complexity and environmental variations, these requirements are often difficult to guarantee. Especially during long-term operation or under harsh conditions, the system is prone to malfunctions or failures, impacting the safe operation of the turbine. Control accuracy and response speed: The control accuracy and response speed of the DEH system directly affect the turbine's operating efficiency and performance. However, limitations in sensor accuracy and control algorithms often prevent the system from meeting actual requirements, thus restricting turbine performance optimization. Cost and maintenance: DEH systems are costly to build and maintain, especially when high-performance hardware and advanced control algorithms are involved. Furthermore, the maintenance and upkeep of DEH systems require specialized technology and equipment, increasing operating costs and risks. On-site data acquisition and processing issues: DEH systems need to acquire and process large amounts of operational data in real time. However, in the field environment, data acquisition and processing are often affected by environmental conditions, equipment failures, and other factors, which affects the accuracy and timeliness of the data, and consequently affects the control effect and performance optimization of the system. Summary of the Invention

[0005] The main objective of this invention is to provide a real-time bandwidth enhancement method for a primary frequency-modulated (DFH) side control system, specifically a real-time bandwidth enhancement method for a primary frequency-modulated digital electro-hydraulic control system. This method involves real-time acquisition of the output signal of the digital electro-hydraulic control system, smoothing the output signal using a Gaussian filter, and then processing the signal using a zero-phase filter to reduce the time delay effect introduced by the filter. Simultaneously, a resonant filter is used to filter the pre-processed signal, enhancing the signal components within the target frequency band. Furthermore, a nonlinear classification method based on support vector machines is proposed to classify the system operating parameters, and the bandwidth enhancement parameters are adjusted according to the classification results to enhance the bandwidth of the frequency correction signal. Additionally, a Kalman filter prediction model is employed to predict the operating parameters for multiple future time steps, and bandwidth enhancement is performed accordingly to further improve the system's stability and control accuracy.

[0006] To address the aforementioned technical problems, this invention provides a method for enhancing the real-time bandwidth of a primary frequency modulation digital electro-hydraulic control system, the method comprising: Step 1: Real-time acquisition of the output signal of the digital electro-hydraulic control system. A Gaussian filter is used to smooth the output signal, followed by zero-phase filtering to obtain the preprocessed signal. Zero-phase filtering in Step 1 is achieved by filtering the output signal twice: first, a forward filter is applied to obtain an intermediate result; then, a reverse filter is applied to the intermediate result to obtain the preprocessed signal. This ensures that the phase response of the Gaussian filter is close to zero in the frequency domain, thereby reducing the time delay effect introduced by the Gaussian filter. The preprocessed signal in Step 1 is obtained using the following formula: ; in, It is a preprocessed signal. It is the output signal of the digital electro-hydraulic control system. It is a Fourier transform. It is the frequency response of a Gaussian filter; It is the inverse Fourier transform; For time; Step 2: Map the historically collected operating parameters of the digital electro-hydraulic control system to a high-dimensional space using a kernel function, and perform nonlinear classification using a support vector machine to obtain the classification results of the operating parameters. Train a support vector prediction model based on these results. Set a mapping rule base between the classification results and the bandwidth enhancement coefficient. In the mapping rule base, each classification result corresponds to a bandwidth enhancement coefficient. Step 3: Extract features from the preprocessed signal to capture its time-frequency characteristics; set a target frequency band and use a resonant filter within that target frequency band to filter the preprocessed signal, enhancing its signal components within the target frequency band; perform a frequency modulation on the preprocessed signal after filtering by the resonant filter to adjust the frequency range according to the set phase offset parameters and carrier frequency, thus obtaining a frequency correction signal. Step 4: Real-time acquisition of operating parameters of the digital electro-hydraulic control system, inputting the real-time acquired operating parameters into the support vector prediction model; obtaining the classification results of the real-time acquired operating parameters, and based on the correspondence between the classification results in the mapping rule base and the bandwidth enhancement coefficient, obtaining the bandwidth enhancement parameters corresponding to the classification results of the real-time acquired operating parameters; based on the time-frequency characteristics of the preprocessed signal, enhancing the bandwidth of the frequency correction signal using the bandwidth enhancement coefficient.

[0007] Furthermore, the operating parameters of the digital electro-hydraulic control system include: vibration parameters, current parameters, and / or voltage parameters; when there is more than one type of operating parameter, the operating parameters are a vector.

[0008] Furthermore, the relationship between the output signal and the input signal of the digital electro-hydraulic control system is expressed by the following equation: ; in, express The second derivative with respect to time; Represents the damping term, where: The damping ratio describes the damping effect of a digital electro-hydraulic control system. It determines the degree of damping in the system and ranges from [value missing]. ;when At that time, the system is undamped, when When the value is greater than 1, the system is critically damped; when the value is greater than 1, it is overdamped. It is the natural frequency, representing the oscillation frequency of the digital electro-hydraulic control system without damping; This represents the input signal of the digital electro-hydraulic control system.

[0009] Furthermore, the support vector machine model in step 2 is represented by the following formula: ; in, It is the weight vector of the classification hyperplane, which determines the direction of the classification hyperplane; It is the bias term of the classification hyperplane, used to adjust the position of the classification hyperplane, and determines the distance between the classification hyperplane and the origin; It is a slack variable. Indicates the first Slack variables for each running parameter; and All are penalty parameters. Control the severity of penalties for misclassification. This controls the degree of regularization of the weight vector; It is the number of runtime parameters; It is a restricted weight vector The norm, by controlling To prevent the support vector machine model from overfitting the training data, thereby improving the generalization ability of the support vector machine model; For the first One operating parameter; For the first One operating parameter The category tag is used to indicate the first The category to which each running parameter belongs; the classification hyperplane is defined as: ; in These are runtime parameters. The nonlinear mapping; the kernel function in step 2 is a Gaussian kernel function. Express it using the following formula: ; in, The mean of the Gaussian kernel function. Let be the variance of the Gaussian kernel function.

[0010] Furthermore, the resonant filter in step 3 is represented by the following formula: ; in, It is the number of resonant peaks in the resonant filter, which is equal to the number of operating parameters; and These are the amplitude, resonant frequency, and damping ratio of each resonance peak; It is the preprocessed signal after being filtered by the resonant filter; Let represent a complex variable, which is a variable in the complex field, representing the frequency in the complex plane. Its real part represents the frequency, and its imaginary part represents the rate of change of the frequency.

[0011] Furthermore, in step 3, the preprocessed signal after being filtered by the resonant filter is frequency-modulated once using the following formula: ; in, For frequency correction signals; Indicates the carrier frequency; Indicates the initial phase of the cosine wave signal; Let be the phase function of the resonant filter.

[0012] Furthermore, the method further includes: using historically acquired operating parameters of the digital electro-hydraulic control system as a training set to train a prediction model based on a Kalman filter; the prediction model predicts the operating parameters for the next time step based on the real-time operating parameters; inputting the operating parameters for the next time step into a support vector prediction model; obtaining the classification result of the operating parameters for the next time step, and obtaining the bandwidth enhancement parameter corresponding to the classification result of the operating parameters for the next time step based on the correspondence between the classification result and the bandwidth enhancement coefficient in the mapping rule base; setting an enhancement interval, after the bandwidth of the frequency correction signal has been enhanced using the bandwidth enhancement coefficient in one time step, during the enhancement interval, the operating parameters are no longer acquired in real time, but the prediction model is used to predict the operating parameters for multiple future time steps, and the bandwidth enhancement coefficients for multiple future time steps are obtained accordingly, and the bandwidth enhancement coefficients are used to enhance the bandwidth of the frequency correction signals corresponding to these future time steps.

[0013] Furthermore, in step 4, the bandwidth of the frequency correction signal is enhanced using a bandwidth enhancement factor based on the time-frequency characteristics of the preprocessed signal, using the following formula: ; in, For frequency correction signal in time At that time, the frequency is The range at the point; This is the bandwidth enhancement factor; This represents the upper limit of the target frequency band. This is the lower limit of the target frequency band; The result obtained by enhancing the bandwidth of the frequency-corrected signal using a bandwidth enhancement factor; The time-frequency characteristics of the preprocessed signal are calculated using the following formula: ; in, These are the mother wavelet basis functions. It is a scale parameter. These are translation parameters; Represents the complex conjugate of the mother wavelet; It is a normalization constant, calculated using the following formula: ; in, It is the Fourier transform of the mother wavelet; ω is the angular frequency.

[0014] The real-time bandwidth enhancement method for a primary frequency-modulated digital electro-hydraulic control system of the present invention has the following beneficial effects: First, by acquiring and processing the output signal of the digital electro-hydraulic control system in real time, and using Gaussian and zero-phase filters to smooth the signal, the present invention effectively reduces the time delay effect introduced by the filters, thereby improving the accuracy and real-time performance of signal processing. During turbine operation, accurate acquisition and rapid processing of the output signal are crucial to ensuring system stability and reliability. The method of the present invention enables more precise acquisition of real-time information on the turbine's operating status, providing reliable data support for subsequent control strategies. Second, the present invention proposes a signal enhancement method based on a resonant filter. By filtering the pre-processed signal at a specific frequency band, the signal components within the target frequency band are enhanced, further improving the system's ability to identify and respond to signals in the target frequency band. In turbine control, the accuracy and stability of the frequency correction signal are crucial for ensuring the normal operation of the turbine. The method of the present invention effectively improves the system's processing accuracy of the frequency correction signal, reduces the system's sensitivity to external interference and noise, and enhances the system's anti-interference capability and stability. Furthermore, this invention employs a nonlinear classification method based on support vector machines to classify system operating parameters and adjust bandwidth enhancement parameters according to the classification results, thereby enhancing the bandwidth of the frequency correction signal. Intelligent classification and analysis of system operating parameters through machine learning algorithms enables more accurate identification of system behavior under different operating states, providing a more refined and personalized strategy for real-time system control. Simultaneously, dynamic adjustment of bandwidth enhancement parameters allows for real-time adjustment of the frequency correction signal, enabling the system to better adapt to different operating conditions and environments, improving its adaptability and flexibility. In addition, this invention proposes a Kalman filter-based prediction model to predict operating parameters for multiple future time steps and enhance bandwidth accordingly. Predicting system operating parameters allows for more accurate prediction of future system operating states and trends, providing a more reliable reference for real-time system control. Furthermore, using a Kalman filter model for system operating parameter prediction offers good real-time performance and accuracy, enabling timely detection of changes in system operating states and the implementation of corresponding control measures to ensure stable system operation. Attached Figure Description

[0015] Figure 1 A schematic flowchart of a method for enhancing the real-time bandwidth of a primary frequency modulation digital electro-hydraulic regulation system provided in an embodiment of the present invention. Detailed Implementation

[0016] The method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0017] Example 1: Reference Figure 1 A method for enhancing the real-time bandwidth of a primary frequency modulation digital electro-hydraulic control system, namely, a method for enhancing the real-time bandwidth of a primary frequency modulation digital electro-hydraulic control system, the method comprising: Step 1: Real-time acquisition of the output signal of the digital electro-hydraulic control system, smoothing of the output signal using a Gaussian filter, and zero-phase filtering to obtain the pre-processed signal; The purpose of signal smoothing is to remove noise from a signal while preserving its main features. Here, a Gaussian filter is used for smoothing. A Gaussian filter is a linear smoothing filter whose kernel is a two-dimensional Gaussian function. The filtering operation is equivalent to convolving the signal with the Gaussian function. This type of filter achieves smoothing in the time domain while also exhibiting good characteristics in the frequency domain. Specifically, the Gaussian filter performs a weighted average on each point of the signal, with the weights determined by the value of the Gaussian function at that point. This effectively reduces the influence of noise while preserving the main components of the signal.

[0018] Zero-phase filtering is a filtering method that prevents phase shift in the filtered signal. Phase shift can affect the timing information of a signal, so in some applications it is necessary to maintain the relative position of the signals. Zero-phase filtering can be achieved by filtering the signal twice: first, a forward filter is applied, and then the result is filtered inversely. This cancels out the phase shift introduced during the filtering process, thus maintaining the relative position of the signals. The output signal of a digital electrohydraulic control system may be affected by various interferences and noises, such as electromagnetic interference and system errors. Signal smoothing uses Gaussian filters to smooth the signal, removing noise components and improving signal quality and stability. Zero-phase filtering can eliminate the phase delay introduced by filtering, keeping the various parts of the signal consistent in the time domain. This helps improve the timing consistency and stability of the signal, making it easier for subsequent processing algorithms to recognize and analyze.

[0019] Step 2: Map the historically collected operating parameters of the digital electro-hydraulic control system to a high-dimensional space using a kernel function, and perform nonlinear classification using a support vector machine to obtain the classification results of the operating parameters. Train a support vector prediction model based on these results. Set a mapping rule base between the classification results and the bandwidth enhancement coefficient. In the mapping rule base, each classification result corresponds to a bandwidth enhancement coefficient. In digital electro-hydraulic control systems, the system's operating parameters may change with time and operating conditions, significantly impacting system performance and stability. Step 2 first maps historically acquired operating parameters to a high-dimensional space using a kernel function, then utilizes a support vector machine (SVM) to perform nonlinear classification of these parameters. This allows for effective feature extraction of the system's operating state and classification of different operating states in high-dimensional space, providing a basis for subsequent bandwidth enhancement decisions. A support vector prediction model is established by classifying historically acquired operating parameters using SVM. This model can predict the classification results of unknown operating parameters, i.e., predict the current operating state of the system. This enables timely identification and monitoring of the system's operating state, providing real-time parameter guidance for subsequent bandwidth enhancement. In the mapping rule base, bandwidth enhancement coefficients corresponding to different classification results are determined based on the SVM classification results and pre-defined mapping rules. Thus, during system operation, the bandwidth enhancement coefficient can be accurately determined based on the classification results obtained from the real-time acquired operating parameters predicted by the prediction model. This coefficient reflects the degree of bandwidth enhancement under the current system operating state and can be dynamically adjusted according to the actual system conditions, thereby improving system stability and performance.

[0020] Kernel functions are a technique that maps data from a low-dimensional space to a high-dimensional space, making the data easier to separate in that space. In this step, the historically acquired operating parameters of the digital electro-hydraulic control system are mapped to a high-dimensional space, making the original data easier for a Support Vector Machine (SVM) classifier to separate in that space. The choice of kernel function has a significant impact on classification performance; common kernel functions include linear kernels, polynomial kernels, and radial basis function kernels. Support Vector Machines are a commonly used machine learning algorithm for pattern recognition and classification tasks. The basic idea is to find a hyperplane in a high-dimensional space that clearly separates data points of different classes, maximizing the margin between the hyperplane and the nearest data point. By solving the optimization problem of the Support Vector Machine, an optimal classification hyperplane can be obtained, thus achieving data classification.

[0021] Step 3: Extract features from the preprocessed signal to capture its time-frequency characteristics; set a target frequency band and use a resonant filter within that target frequency band to filter the preprocessed signal, enhancing its signal components within the target frequency band; perform a frequency modulation on the preprocessed signal after filtering by the resonant filter to adjust the frequency range according to the set phase offset parameters and carrier frequency, thus obtaining a frequency correction signal. Feature extraction is the process of extracting useful information for problem-solving from a raw signal. Here, the preprocessed signal undergoes feature extraction to capture its time-frequency characteristics. Various methods can be used for feature extraction, such as time-domain analysis, frequency-domain analysis, and wavelet transform. After feature extraction, a target frequency band needs to be defined, typically a specific frequency range within the system. To enhance the signal components of the preprocessed signal within the target frequency band, a resonant filter can be used. A resonant filter selectively amplifies signals within a specific frequency range while suppressing signals in other frequency ranges, thus highlighting the signal components within the target frequency band. In step 3, the resonant filter is used to enhance the signal components of the preprocessed signal within the target frequency band. The principle here is that the resonant filter selectively enhances the signal components within the target frequency band, thereby increasing the bandwidth of the signal within that band. Specifically, when the resonant frequency matches the signal frequency within the target frequency band, the resonant filter generates a resonant response, increasing the signal amplitude within the target frequency band. Thus, through the action of the resonant filter, the signal components of the preprocessed signal within the target frequency band are enhanced, thereby increasing the signal bandwidth.

[0022] Step 4: Real-time acquisition of operating parameters of the digital electro-hydraulic control system, inputting the real-time acquired operating parameters into the support vector prediction model; obtaining the classification results of the real-time acquired operating parameters, and based on the correspondence between the classification results in the mapping rule base and the bandwidth enhancement coefficient, obtaining the bandwidth enhancement parameters corresponding to the classification results of the real-time acquired operating parameters; based on the time-frequency characteristics of the preprocessed signal, enhancing the bandwidth of the frequency correction signal using the bandwidth enhancement coefficient.

[0023] In step 2, a support vector prediction model has been established to classify and predict the operating parameters of the digital electro-hydraulic control system. By learning patterns and rules from historical data, the support vector prediction model can classify and predict unknown data. Based on the mapping rule base established in step 2, the classification results of the real-time acquired operating parameters can be correlated with bandwidth enhancement parameters. This correlation allows for the determination of appropriate bandwidth enhancement parameters based on different operating states and characteristics. Based on the classification results of the real-time acquired operating parameters and the corresponding bandwidth enhancement parameters, the bandwidth of the frequency correction signal can be adjusted in real time. Adjusting the bandwidth enhancement parameters according to different operating states and needs allows the system to selectively enhance signals within different frequency ranges, thereby optimizing the system's control performance.

[0024] Example 2: The operating parameters of the digital electro-hydraulic control system include vibration parameters, current parameters and / or voltage parameters; when there is more than one type of operating parameter, the operating parameters are a vector.

[0025] Specifically, when the number of parameter types in the runtime parameters exceeds one, these parameters are organized into a vector. A vector is a data structure that can store multiple values ​​or parameters simultaneously, possessing both direction and magnitude. Here, the vector of runtime parameters can be viewed as points in a multi-dimensional space, where each dimension represents a parameter type, and the vector's values ​​represent the numerical values ​​of the corresponding parameters. By combining multiple parameters into a vector, it becomes easier to describe and analyze the overall state of the system.

[0026] Example 3: Zero-phase filtering in step 1 is achieved by filtering the output signal twice. First, the output signal is forward filtered to obtain an intermediate result. Then, the intermediate result is reverse filtered to obtain a preprocessed signal, so that the phase response of the Gaussian filter is close to zero in the frequency domain, thereby reducing the time delay effect introduced by the Gaussian filter. In step 1, the preprocessed signal is obtained using the following formula: ; in, It is a preprocessed signal. It is the output signal of the digital electro-hydraulic control system. It is a Fourier transform. It is the frequency response of a Gaussian filter; It is the inverse Fourier transform; For time.

[0027] Specifically, firstly, regarding the input signal A Fourier transform is performed to convert the signal to the frequency domain. This step converts the signal from the time domain to a frequency domain representation, making the signal's components at different frequencies visible. The spectrum obtained after the Fourier transform represents the energy distribution of the signal at different frequencies. The Fourier transform result of the input signal is then multiplied point-by-point with the frequency response of a Gaussian filter. This product represents the filtering effect of the input signal in the frequency domain. The frequency response of a Gaussian filter typically follows a Gaussian distribution, exhibiting a wide bandpass characteristic in the frequency domain, capable of smoothing signals and removing noise. An inverse Fourier transform is performed on the product result to convert it back to the time domain. This step converts the filtered frequency domain signal back to a time domain representation. Since the inverse transform reverses the frequency domain transformation, it preserves the phase information of the input signal, compensating for the phase delay introduced during filtering and achieving zero-phase filtering. The final result is... This is the preprocessed signal after zero-phase filtering. This signal undergoes smoothing and denoising in the time domain while preserving the phase information of the input signal. Compared to general filtering, zero-phase filtering does not introduce additional phase delay, thus maintaining the signal's shape and phase characteristics in the time domain.

[0028] Example 4: The relationship between the output signal and the input signal of the digital electro-hydraulic control system is represented by the following equation: ; in, express The second derivative with respect to time; Represents the damping term, where: The damping ratio describes the damping effect of a digital electro-hydraulic control system. It determines the degree of damping in the system and ranges from [value missing]. ;when At that time, the system is undamped, when When the value is greater than 1, the system is critically damped; when the value is greater than 1, it is overdamped. It is the natural frequency, representing the oscillation frequency of the digital electro-hydraulic control system without damping; This represents the input signal of the digital electro-hydraulic control system.

[0029] Specifically, this equation describes the relationship between the output signal and the input signal of the digital electro-hydraulic control system; it is a second-order linear differential equation with constant coefficients. In this equation, Indicates the output signal Regarding time The second derivative represents the acceleration of the output signal, i.e., the rate of change of the output signal. This term describes the inertial characteristics of the system, that is, the speed at which the output signal responds to changes in the input signal. Represents the damping term, where This is the damping ratio, used to describe the damping effect of a digital electro-hydraulic control system. The damping ratio determines the degree of damping in the system, ranging from... .when When the system is undamped; when When the value is less than 1, the system is critically damped; when it is greater than 1, it is overdamped. This is the natural frequency, representing the oscillation frequency of the system without damping. This term describes the damped characteristics of the system, that is, the variation of the output signal is affected by damping. Indicates the output signal Multiplying by the square of the natural frequency represents the system's restoring force. Natural frequency This describes the oscillation frequency of the system in the undamped state, while This indicates the magnitude of the system's restoring force. This term describes the system's inertial characteristics, that is, the degree to which the output signal recovers after being subjected to an external force. Finally, This represents the input signal of the digital electro-hydraulic control system, i.e., the system's control signal. This signal can be any type of input, such as the valve opening, voltage, or current, used to control the system's operating state. This equation describes the dynamic relationship between the output signal and the input signal of the digital electro-hydraulic control system. The system's output signal is affected by damping, inertia, and external control signals; these factors collectively determine the system's response characteristics and dynamic behavior.

[0030] Example 5: The support vector machine model in step 2 is represented by the following formula: ; in, It is the weight vector of the classification hyperplane, which determines the direction of the classification hyperplane; It is the bias term of the classification hyperplane, used to adjust the position of the classification hyperplane, and determines the distance between the classification hyperplane and the origin; It is a slack variable. Indicates the first Slack variables for each running parameter; and All are penalty parameters. Control the severity of penalties for misclassification. This controls the degree of regularization of the weight vector; It is the number of runtime parameters; It is a restricted weight vector The norm, by controlling To prevent the support vector machine model from overfitting the training data, thereby improving the generalization ability of the support vector machine model; For the first One operating parameter; For the first One operating parameter The category tag is used to indicate the first The category to which each running parameter belongs; the classification hyperplane is defined as: ; in These are runtime parameters. The nonlinear mapping; the kernel function in step 2 is a Gaussian kernel function. Express it using the following formula: ; in, The mean of the Gaussian kernel function. Let be the variance of the Gaussian kernel function.

[0031] Specifically, in this embodiment, SVM is applied to a digital electrohydraulic control system to classify its operating parameters. The basic idea of ​​SVM is to find an optimal hyperplane in the feature space that separates samples of different classes while maximizing the margin, making the classification more accurate and reliable. First, let's look at the optimization objective of the Support Vector Machine model. This objective is to determine the parameters of the classification hyperplane by minimizing a loss function, making the classification results more accurate. Specifically, the optimization objective consists of three parts: the first part is the weight vector of the classification hyperplane. The first part is the square of the norm, used to maximize the margin, that is, to maximize the distance between the classification hyperplane and the support vectors (the sample points closest to the classification hyperplane); the second part is a penalty term for misclassified samples, used to control the degree of misclassification, so that the number of misclassified samples is minimized; the third part is a regularization term, used to prevent the model from overfitting and maintain the model's generalization ability. The penalty parameter is adjusted... and regularization parameters First, we can balance the penalty for misclassification with the model's complexity to achieve optimal classification performance. Second, constraints define the decision boundary and the range of slack variables for the support vector machine model. These constraints ensure the correctness of the classification hyperplane and its sensitivity to training samples. Specifically, the constraints require that the margin of all training samples be greater than or equal to 1 minus the corresponding slack variable, and that the slack variable be nonnegative. These constraints enable the classification hyperplane to correctly partition the training samples and allow for a certain degree of misclassification. Finally, the kernel function defines the mapping from the input feature space to a high-dimensional feature space. In Example 5, we used a Gaussian kernel function, which maps the input features to an infinite-dimensional feature space, thus making non-linearly separable problems linearly separable in the feature space. The parameters of the Gaussian kernel function include the mean of the Gaussian kernel function. and variance These parameters determine the smoothness of the feature space. By adjusting these parameters, we can control how well the model fits the input data, thereby achieving better classification performance.

[0032] Example 6: The resonant filter in step 3 is represented by the following formula: ; in, It is the number of resonant peaks in the resonant filter, which is equal to the number of operating parameters; and These are the amplitude, resonant frequency, and damping ratio of each resonance peak; It is the preprocessed signal after being filtered by the resonant filter; Let represent a complex variable, which is a variable in the complex field, representing the frequency in the complex plane. Its real part represents the frequency, and its imaginary part represents the rate of change of the frequency.

[0033] Specifically, a resonant filter is a frequency-selective filter whose structure consists of multiple resonant peaks. Each resonant peak is characterized by an amplitude... Resonance frequency Damping ratio Confirmed. Amplitude Indicates the magnitude of the resonance peak and the resonance frequency. The center frequency of the resonance peak and the damping ratio are indicated. This describes the attenuation degree of the resonant peak. The denominator in the formula represents the frequency domain response of the resonant filter, i.e., the transfer function of the second-order low-pass filter. This transfer function is the sum of a quadratic term and a linear term, corresponding to the resonant frequency and damping ratio of the resonant peak, respectively. This form of transfer function can describe the frequency characteristics and attenuation characteristics of the resonant peak. This represents the input signal, which is filtered by a resonant filter to obtain the output signal. The input signal is affected by each resonance peak; its spectrum is amplified near the resonant frequency of the peak and suppressed at other frequencies. The summation term in the formula represents the contribution of all resonance peaks, which are weighted and superimposed on the input signal. The number of resonance peaks is determined by the parameter... The number of operating parameters determines the output of each resonant peak. The output of each resonant peak is determined by its corresponding amplitude, resonant frequency, and damping ratio. By adjusting the parameters of each resonant peak, the frequency and amplitude characteristics of the resonant filter can be controlled.

[0034] Example 7: In step 3, the preprocessed signal after being filtered by the resonant filter is frequency-modulated once using the following formula: ; in, For frequency correction signals; representing carrier frequency; Indicates the initial phase of the cosine wave signal; Let be the phase function of the resonant filter.

[0035] Specifically, firstly, in the formula This represents the frequency correction signal after one frequency modulation. This signal consists of two parts: one part is the pre-processed signal after filtering by the resonant filter. The other part is a cosine wave signal. These two parts correspond to the original component and the frequency-modulated component of the signal, respectively. In the cosine wave signal part, This represents the angular frequency of the cosine wave signal, where The carrier frequency determines the fundamental frequency of the signal. This angular frequency changes over time. This change produces a frequency modulation effect. The initial phase of the cosine wave signal... Decided on the time The phase angle at that time adjusts the initial phase of the frequency-modulated signal. In the phase function part of the resonant filter, The law governing the phase variation of a resonant filter over time is described. This is achieved by calculating the third derivative of the phase function. We can obtain the rapid rate of change of the phase. This rapid rate of change is then compared with the preprocessed signal. Multiplication achieves phase modulation of the preprocessed signal. This phase modulation can change the phase of the signal in the time domain, thus affecting the signal's frequency. Therefore, the first term in the formula... This represents the signal components after cosine frequency modulation, while the second term... This represents the signal component modulated by the phase of the resonant filter. By combining these two parts, we obtain the frequency-corrected signal after one frequency modulation process. This frequency correction signal It can be applied to digital electro-hydraulic control systems to adjust the system's operating frequency to adapt to different operating conditions and requirements. By adjusting the carrier frequency, initial phase, and phase function, we can achieve flexible adjustment of the frequency and phase of the preprocessed signal, thereby realizing precise control and regulation of the system output.

[0036] Example 8: The method further includes: using historically acquired operating parameters of the digital electro-hydraulic control system as a training set to train a prediction model based on a Kalman filter; the prediction model predicts the operating parameters for the next time step based on the real-time operating parameters; inputting the operating parameters for the next time step into the support vector prediction model; obtaining the classification result of the operating parameters for the next time step; and obtaining the bandwidth enhancement parameter corresponding to the classification result of the operating parameters for the next time step based on the correspondence between the classification result and the bandwidth enhancement coefficient in the mapping rule base; setting an enhancement interval; after the bandwidth of the frequency correction signal has been enhanced using the bandwidth enhancement coefficient in one time step, during the enhancement interval, the operating parameters are no longer acquired in real time, but the prediction model is used to predict the operating parameters for multiple future time steps, and the bandwidth enhancement coefficients for multiple future time steps are obtained accordingly; and using these bandwidth enhancement coefficients, the bandwidth of the frequency correction signal corresponding to these future time steps is enhanced.

[0037] Specifically, the Kalman filter is a recursive filter used to estimate the optimal solution for the system state. In Example 8, the historically acquired operating parameters of the digital electro-hydraulic control system are first used as a training set to train a prediction model using a Kalman filter. Based on the system's dynamic and observation models, the Kalman filter recursively updates the estimated system state to predict the operating parameters for the next time step. During training, the system's state transition equations, observation equations, and noise model need to be considered to ensure the filter can accurately estimate the system state. After the Kalman filter model is trained, the real-time operating parameters are input into the support vector machine (SVM) prediction model. SVM is a supervised learning algorithm used for classification and regression tasks. In Example 8, SVM is used to predict the operating parameters for the next time step. Through the trained prediction model, SVM can accurately predict future operating parameters based on patterns learned from historical data. Based on the classification results of the operating parameters for the next time step obtained from the SVM prediction model, corresponding bandwidth enhancement parameters are searched in the mapping rule base. These bandwidth enhancement parameters are used to adjust the bandwidth of the frequency correction signal to meet the system's performance requirements. According to a pre-defined mapping rule, the predicted operating parameters are mapped to corresponding bandwidth enhancement coefficients, thereby enhancing the bandwidth of the frequency correction signal. To reduce the real-time acquisition load of the system, an enhancement interval strategy is adopted in Example 8. After the bandwidth enhancement of the frequency correction signal is completed in one time step, the operating parameters are no longer acquired in real time during the enhancement interval. Instead, a prediction model is used to predict the operating parameters for multiple future time steps. This reduces the real-time acquisition load of the system and improves the system efficiency.

[0038] Example 9: In step 4, the bandwidth of the frequency correction signal is enhanced using a bandwidth enhancement factor based on the time-frequency characteristics of the preprocessed signal, using the following formula: ; first, Indicates time and frequency The amplitude of the frequency correction signal at this location. This is the original signal to be enhanced, which may have a small amplitude or require further enhancement in certain frequency ranges. The time-frequency characteristics of the preprocessed signal are represented by wavelet transform. Wavelet transform is an analytical method that can provide local features of a signal at different times and frequencies. and These are the scaling and translation parameters of the wavelet transform, used to adjust the resolution and position of the wavelet basis functions in time and frequency. Next, This is the bandwidth enhancement factor, used to adjust the degree of enhancement. It can be adjusted according to system requirements and real-time conditions to achieve precise control over the bandwidth of the frequency correction signal. In this formula, It is a function that changes over time and can be adjusted according to the system state or a pre-defined strategy. and These are the upper and lower limits of the target frequency band, used to limit the range of enhancement. Only signals within this frequency range will be enhanced, ensuring the effectiveness and accuracy of the enhancement operation. Finally, in the formula... This is the enhancement portion of the frequency-corrected signal bandwidth. This portion is based on the upper and lower limits of the target frequency band and the bandwidth enhancement factor. The frequency correction signal is enhanced within the target frequency band. Within the target frequency band, changes in the enhancement factor will result in an increase or decrease in signal amplitude, thereby adjusting the bandwidth of the frequency correction signal.

[0039] in, For the frequency correction signal, the frequency is at time . The range at the point; This is the bandwidth enhancement factor; This represents the upper limit of the target frequency band. This is the lower limit of the target frequency band; The result obtained by enhancing the bandwidth of the frequency-corrected signal using a bandwidth enhancement factor; The time-frequency characteristics of the preprocessed signal are calculated using the following formula: ; Specifically, in the formula This represents the time-frequency characteristics of the preprocessed signal, where and These are the scaling and translation parameters in wavelet transform. These two parameters determine the resolution and position of the wavelet basis functions in time and frequency, thus affecting the representation of the preprocessed signal in the time and frequency domains. In the formula, This is a preprocessed signal, representing the signal that requires time-frequency analysis. This signal may be the output signal of a digital electrohydraulic control system or a preprocessed signal. It is the complex conjugate of the mother wavelet, which is the complex conjugate of the wavelet basis functions after translation and scaling on the time axis. Wavelet basis functions are special functions with localization properties, capable of describing the local characteristics of a signal in the time-frequency domain. The integral operation in the formula represents the integral of the preprocessed signal... Integrating the product with the complex conjugate wavelet basis functions over the entire real number domain, this process essentially analyzes the local features of the signal at different scales and locations, yielding its time-frequency domain representation. This is the normalization constant, introduced to ensure that the wavelet transform is a unit operator. Calculating the normalization constant involves the Fourier transform of the wavelet basis functions and integration based on the properties of the Fourier transform. Its purpose is to ensure the scaling consistency and stability of the wavelet transform, enabling the correct representation and analysis of the time-frequency characteristics of the preprocessed signal.

[0040] in, These are the mother wavelet basis functions. It is a scale parameter. These are translation parameters; Represents the complex conjugate of the mother wavelet; It is a normalization constant, calculated using the following formula: ; in, It is the Fourier transform of the mother wavelet; ω is the angular frequency.

[0041] Specifically, firstly, in the formula It is the normalization constant of the wavelet basis functions, which ensures that the wavelet transform is a unit operator. The calculation of the normalization constant involves the Fourier transform of the wavelet basis functions and the integration calculation based on the properties of the Fourier transform. It is the frequency domain representation of wavelet basis functions, and its Fourier transform. This describes the relationship between wavelet basis functions in the time and frequency domains. In the formulas, we take the absolute value of the Fourier transform of the wavelet basis functions to ensure that the calculation results in the frequency domain are non-negative. The normalization factor represents the Fourier transform, introduced to maintain the amplitude consistency of the wavelet transform. When calculating the normalization constants of the wavelet basis functions, we need to consider the influence of this normalization factor to ensure the accuracy of the normalization constants. The integral operation in the formula represents dividing the absolute value of the Fourier transform of the wavelet basis functions by the frequency. The integration is performed over the entire real number domain. This process essentially normalizes the amplitude of the wavelet basis functions in the frequency domain to ensure the scaling consistency and stability of the wavelet transform. This is the pre-coefficient of the normalization constant, introduced to ensure the normalization of the wavelet basis functions under scaling transformation. The scaling parameter of the wavelet basis functions is used... Taking this into account, we can ensure that the calculation of the normalization constant is scale-dependent on the wavelet basis functions, thus ensuring that the wavelet transform has the same normalization at different scales.

[0042] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function and achieve substantially the same result according to substantially the same method falls within the scope of the present invention. Therefore, the scope of the present invention is defined only by the appended claims.

Claims

1. A method for enhancing the real-time bandwidth of a primary frequency modulation DEH-side control system, characterized in that, The method includes: Step 1: Real-time acquisition of the output signal of the digital electro-hydraulic control system. A Gaussian filter is used to smooth the output signal, followed by zero-phase filtering to obtain the preprocessed signal. Zero-phase filtering in Step 1 is achieved by filtering the output signal twice: first, a forward filter is applied to obtain an intermediate result; then, a reverse filter is applied to the intermediate result to obtain the preprocessed signal. This ensures that the phase response of the Gaussian filter is close to zero in the frequency domain, thereby reducing the time delay effect introduced by the Gaussian filter. The preprocessed signal in Step 1 is obtained using the following formula: ； in, It is a preprocessed signal. It is the output signal of the digital electro-hydraulic control system. It is a Fourier transform. It is the frequency response of a Gaussian filter; It is the inverse Fourier transform; For time; Step 2: Map the historically collected operating parameters of the digital electro-hydraulic control system to a high-dimensional space using a kernel function, and perform nonlinear classification using a support vector machine to obtain the classification results of the operating parameters. Train a support vector prediction model based on these results. Set a mapping rule base between the classification results and the bandwidth enhancement coefficient. In the mapping rule base, each classification result corresponds to a bandwidth enhancement coefficient. Step 3: Extract features from the preprocessed signal to capture its time-frequency characteristics; set a target frequency band and use a resonant filter within that target frequency band to filter the preprocessed signal, enhancing its signal components within the target frequency band; perform a frequency modulation on the preprocessed signal after filtering by the resonant filter to adjust the frequency range according to the set phase offset parameters and carrier frequency, thus obtaining a frequency correction signal. Step 4: Real-time acquisition of operating parameters of the digital electro-hydraulic control system, inputting the real-time acquired operating parameters into the support vector prediction model; obtaining the classification results of the real-time acquired operating parameters, and based on the correspondence between the classification results in the mapping rule base and the bandwidth enhancement coefficient, obtaining the bandwidth enhancement parameters corresponding to the classification results of the real-time acquired operating parameters; based on the time-frequency characteristics of the preprocessed signal, enhancing the bandwidth of the frequency correction signal using the bandwidth enhancement coefficient.

2. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 1, characterized in that, The operating parameters of a digital electro-hydraulic control system include vibration parameters, current parameters, and / or voltage parameters; when there is more than one type of operating parameter, the operating parameters are a vector.

3. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 2, characterized in that, The relationship between the output signal and the input signal of a digital electro-hydraulic control system is expressed by the following equation: ； in, express The second derivative with respect to time; Represents the damping term, where: The damping ratio describes the damping effect of a digital electro-hydraulic control system. It determines the degree of damping in the system and ranges from [value missing]. ;when At that time, the system is undamped, when When the value is greater than 1, the system is critically damped; when the value is greater than 1, it is overdamped. It is the natural frequency, representing the oscillation frequency of the digital electro-hydraulic control system without damping; This represents the input signal of the digital electro-hydraulic control system.

4. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 3, characterized in that, The support vector machine model in step 2 is represented by the following formula: ； in, It is the weight vector of the classification hyperplane, which determines the direction of the classification hyperplane; It is the bias term of the classification hyperplane, used to adjust the position of the classification hyperplane, and determines the distance between the classification hyperplane and the origin; It is a slack variable. Indicates the first Slack variables for each running parameter; and All are penalty parameters. Control the severity of penalties for misclassification. This controls the degree of regularization of the weight vector; It is the number of runtime parameters; It is a restricted weight vector The norm, by controlling To prevent the support vector machine model from overfitting the training data, thereby improving the generalization ability of the support vector machine model; For the first One operating parameter; For the first One operating parameter The category tag is used to indicate the first The category to which each running parameter belongs; the classification hyperplane is defined as: ； in These are runtime parameters. The nonlinear mapping; the kernel function in step 2 is a Gaussian kernel function. Express it using the following formula: ； in, The mean of the Gaussian kernel function. Let be the variance of the Gaussian kernel function.

5. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 4, characterized in that, The resonant filter in step 3 is represented by the following formula: ； in, It is the number of resonant peaks in the resonant filter, which is equal to the number of operating parameters; and These are the amplitude, resonant frequency, and damping ratio of each resonance peak; It is the preprocessed signal after being filtered by the resonant filter; Let represent a complex variable, which is a variable in the complex field, representing the frequency in the complex plane. Its real part represents the frequency, and its imaginary part represents the rate of change of the frequency.

6. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 5, characterized in that, In step 3, the preprocessed signal after being filtered by the resonant filter is frequency-modulated once using the following formula: ； in, For frequency correction signals; Indicates the carrier frequency; Indicates the initial phase of the cosine wave signal; Let be the phase function of the resonant filter.

7. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 6, characterized in that, The method further includes: using historically acquired operating parameters of the digital electro-hydraulic control system as a training set to train a prediction model based on a Kalman filter; the prediction model predicts the operating parameters for the next time step based on the real-time operating parameters; inputting the operating parameters for the next time step into a support vector prediction model; obtaining the classification result of the operating parameters for the next time step, and obtaining the bandwidth enhancement parameter corresponding to the classification result of the operating parameters for the next time step based on the correspondence between the classification result and the bandwidth enhancement coefficient in the mapping rule base; setting an enhancement interval, after the bandwidth of the frequency correction signal has been enhanced using the bandwidth enhancement coefficient in one time step, during the enhancement interval, the operating parameters are no longer acquired in real time, but the prediction model is used to predict the operating parameters for multiple future time steps, and the bandwidth enhancement coefficients for multiple future time steps are obtained accordingly, and the bandwidth enhancement coefficients are used to enhance the bandwidth of the frequency correction signals corresponding to these future time steps.

8. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 6, characterized in that, In step 4, the bandwidth of the frequency correction signal is enhanced using a bandwidth enhancement factor based on the time-frequency characteristics of the preprocessed signal, using the following formula: ； in, For frequency correction signal in time At that time, the frequency is The range at the point; This is the bandwidth enhancement factor; This represents the upper limit of the target frequency band. This is the lower limit of the target frequency band; The result obtained by enhancing the bandwidth of the frequency-corrected signal using a bandwidth enhancement factor; The time-frequency characteristics of the preprocessed signal are calculated using the following formula: ； in, These are the mother wavelet basis functions. It is a scale parameter. These are translation parameters; Represents the complex conjugate of the mother wavelet; It is a normalization constant, calculated using the following formula: ； in, It is the Fourier transform of the mother wavelet; ω is the angular frequency.

9. The real-time bandwidth enhancement method for a primary frequency modulation DEH-side control system as described in claim 8, characterized in that, In the formula It is the normalization constant of the mother wavelet basis function. It is the frequency domain representation of the mother wavelet basis functions, and its Fourier transform. The relationship between the mother wavelet basis functions in the time and frequency domains is described. The absolute value of the Fourier transform of the mother wavelet basis functions is taken to ensure that the calculation result in the frequency domain is non-negative. The normalization factor of the Fourier transform is represented by the integral operation, which represents the absolute value of the Fourier transform of the mother wavelet basis function divided by the frequency. The integration is performed over the entire real number field. It is the pre-coefficient of the normalization constant.

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