Voltage control method for high-reliability high-voltage power supply

Abstract

The invention relates to the technical field of voltage control, and particularly discloses a voltage control method for a high-reliability high-voltage power supply, and the method comprises the steps: carrying out the input filtering and power conversion boosting processing of a DC voltage signal provided by a power supply, so as to obtain a high-voltage AC signal; a signal processing technology based on deep learning is adopted to perform CEEMDAN decomposition and intrinsic mode feature extraction on a sampled voltage signal, feature saliency modulation dynamic aggregation is performed on each intrinsic mode after decomposition of the sampled voltage signal to suppress noise and interference in the signal, and a pure sampled voltage signal is obtained. And error calculation and MOS tube driving control are carried out in combination with an external control voltage signal, so that accurate adjustment of the output voltage of the high-voltage power supply is realized. Therefore, the precision of voltage control can be effectively improved, and the adaptability to complex and changeable load conditions and interference environments is enhanced, so that the stability and reliability of a high-voltage power supply system are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of voltage control, and more specifically, to a voltage control method for a high-reliability high-voltage power supply. Background Art

[0002] In the field of power electronics, high-reliability high-voltage power supplies are widely used, covering many key areas such as medical equipment, industrial control, aerospace, and communication equipment. These application scenarios place extremely high demands on the output voltage stability, accuracy, and reliability of the power supply.

[0003] However, traditional voltage control methods, such as analog circuit control or simple digital signal processing (DSP) technology, often fail to achieve ideal control effects when faced with complex and changeable working conditions and external environmental interference. Specifically, the DC voltage signal provided by the power supply is easily affected by various noises and interferences during transmission, including but not limited to electromagnetic interference, line loss, and temperature fluctuations. These adverse factors will reduce the purity and stability of the voltage signal. Although traditional filtering technology can suppress these interferences to a certain extent, the effect is limited and often causes signal distortion or delay, affecting the accuracy and response speed of voltage control.

[0004] In addition, during the boost process of the high-voltage power supply, due to the nonlinear characteristics of power electronic devices and electromagnetic interference during the switching process, a large amount of high-frequency harmonics and noise will be generated, which will further affect the stability and accuracy of the output voltage. Although the traditional rectification and filtering method can remove some high-frequency components, it is not ideal for complex and variable harmonic interference.

[0005] Therefore, an optimized voltage control method for a high-reliability high-voltage power supply is desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a voltage control method for a high-reliability high-voltage power supply, which performs input filtering and power conversion and boost processing on the DC voltage signal provided by the power supply to obtain a high-voltage AC signal, and then, after the high-voltage AC signal is rectified, filtered and sampled, the sampled voltage signal is subjected to CEEMDAN decomposition and intrinsic modal feature extraction using a signal processing technology based on deep learning, and the noise and interference in the signal are suppressed by dynamic aggregation of feature significance modulation of each intrinsic mode after the sampled voltage signal is decomposed, and a pure sampled voltage signal is obtained, and then error calculation and MOS tube drive control are performed in combination with an external control voltage signal to achieve accurate regulation of the output voltage of the high-voltage power supply. In this way, the accuracy of voltage control can be effectively improved, and the adaptability to complex and changeable load conditions and interference environments can be enhanced, thereby significantly improving the stability and reliability of the high-voltage power supply system.

[0007] According to one aspect of the present application, there is provided a voltage control method for a high-reliability high-voltage power supply, comprising:

[0008] Processing an initial DC voltage signal provided by the power supply through an input filter circuit electrically connected to the power supply to obtain a DC voltage signal;

[0009] Performing power conversion and voltage boost processing on the DC voltage signal to obtain a high-voltage AC signal;

[0010] The high voltage AC signal is processed by a rectifier, filter and sampling circuit to output a DC voltage, and the DC voltage is sampled to obtain a sampled voltage signal, and the sampled voltage signal is fed back to the control circuit;

[0011] The control circuit generates an error signal based on the sampled voltage signal and the external control voltage signal, and generates a PWM drive signal based on the error signal, and the PWM drive signal is transmitted to the MOS tube drive circuit;

[0012] The MOS transistor driving circuit drives the MOS transistor based on a PWM driving signal to control the level of the output DC voltage.

[0013] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the control circuit generates an error signal based on the sampled voltage signal and the external control voltage signal, including: performing signal feature extraction based on modal decomposition on the sampled voltage signal to obtain a set of inherent modal semantic feature vectors of the sampled voltage signal; performing dynamic aggregation based on feature energy distribution on the set of inherent modal semantic feature vectors of the sampled voltage signal to obtain a global significant aggregation representation vector of the sampled voltage signal; performing signal reconstruction based on the global significant aggregation representation vector of the sampled voltage signal to obtain a pure sampled voltage signal; and inputting the pure sampled voltage signal and the external control voltage signal into an operator to obtain the error signal.

[0014] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the sampled voltage signal is subjected to signal feature extraction based on modal decomposition to obtain a set of inherent modal semantic feature vectors of the sampled voltage signal, including: performing CEEMDAN decomposition on the sampled voltage signal to obtain a set of inherent modal functions of the sampled voltage signal; and inputting each inherent modal function of the sampled voltage signal in the set of inherent modal functions of the sampled voltage signal into an inherent modal feature extractor based on a 1D-CNN model to obtain a set of inherent modal semantic feature vectors of the sampled voltage signal.

[0015] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the set of intrinsic modal semantic feature vectors of the sampled voltage signals is dynamically aggregated based on the characteristic energy distribution to obtain a global significant aggregation representation vector of the sampled voltage signals, including: determining the initial center vector of the intrinsic modal clustering of the sampled voltage signals based on the characteristic energy distribution of the set of intrinsic modal semantic feature vectors of the sampled voltage signals; and performing explicit dynamic aggregation on the set of intrinsic modal semantic feature vectors of the sampled voltage signals based on the spatial span of the intrinsic modal semantic feature vectors of each sampled voltage signal in the set of intrinsic modal semantic feature vectors of the sampled voltage signals relative to the initial center vector of the intrinsic modal clustering of the sampled voltage signals to obtain the global significant aggregation representation vector of the sampled voltage signals.

[0016] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, based on the characteristic energy distribution of the set of intrinsic modal semantic feature vectors of the sampled voltage signals, the initial center vector of the intrinsic modal clustering of the sampled voltage signals is determined, including: calculating the static energy factors of the intrinsic modal semantic feature vectors of each sampled voltage signal in the set of intrinsic modal semantic feature vectors of the sampled voltage signals to obtain a set of intrinsic modal static energy factors of the sampled voltage signals; selecting the intrinsic modal semantic feature vector of the sampled voltage signal corresponding to the maximum value in the set of intrinsic modal static energy factors of the sampled voltage signals as the initial center vector of the intrinsic modal clustering of the sampled voltage signals.

[0017] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the static energy factor of each sampled voltage signal inherent modal semantic feature vector in the set of the sampled voltage signal inherent modal semantic feature vectors is calculated to obtain a set of sampled voltage signal inherent modal static energy factors, including: calculating the kurtosis of the sampled voltage signal inherent modal semantic feature vector, and inputting the kurtosis into a sigmoid activation function to obtain the sampled voltage signal inherent modal static energy factor.

[0018] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, based on the spatial span of each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal relative to the initial center vector of the inherent modal clustering of the sampled voltage signal, the set of inherent modal semantic feature vectors of the sampled voltage signal is explicitly dynamically aggregated to obtain the global significant aggregation representation vector of the sampled voltage signal, including: based on the spatial span between each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal and the initial center vector of the inherent modal clustering of the sampled voltage signal, and the set of inherent modal static energy factors of the sampled voltage signal, the dynamic aggregation energy factor of each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal is calculated to obtain the set of inherent modal dynamic aggregation energy factors of the sampled voltage signal; based on the set of inherent modal dynamic aggregation energy factors of the sampled voltage signal, the set of inherent modal semantic feature vectors of the sampled voltage signal is significantly modulated and aggregated to obtain the global significant aggregation representation vector of the sampled voltage signal.

[0019] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the dynamic aggregation energy factor of each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal is calculated to obtain a set of inherent modal dynamic aggregation energy factors of the sampled voltage signal, including: taking the square value of the number of feature vectors between the inherent modal semantic feature vector of the sampled voltage signal and the initial center vector of the inherent modal clustering of the sampled voltage signal as the spatial span coefficient, and calculating the weighted ratio between the product of the static energy factor of the inherent modal semantic feature vector of the sampled voltage signal and the static energy factor of the initial center vector of the inherent modal clustering of the sampled voltage signal and the spatial span coefficient to obtain the inherent modal dynamic aggregation energy factor of the sampled voltage signal.

[0020] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, based on the set of inherent modal dynamic aggregation energy factors of the sampled voltage signal, the set of inherent modal semantic feature vectors of the sampled voltage signal is significantly modulated and aggregated to obtain the global significant aggregation representation vector of the sampled voltage signal, including: inputting the set of inherent modal dynamic aggregation energy factors of the sampled voltage signal into a gated mask unit to obtain a set of inherent modal dynamic aggregation weight factors of the sampled voltage signal; and calculating the weighted sum of the set of inherent modal semantic feature vectors of the sampled voltage signal based on the set of inherent modal dynamic aggregation weight factors of the sampled voltage signal to obtain the global significant aggregation representation vector of the sampled voltage signal.

[0021] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, signal reconstruction is performed based on the global significant aggregation representation vector of the sampled voltage signal to obtain a pure sampled voltage signal, including: inputting the global significant aggregation representation vector of the sampled voltage signal into a decoder-based signal reconstruction module to obtain the pure sampled voltage signal.

[0022] Compared with the prior art, the voltage control method for a high-reliability high-voltage power supply provided by the present application performs input filtering and power conversion and boost processing on the DC voltage signal provided by the power supply to obtain a high-voltage AC signal. Then, after the high-voltage AC signal is rectified, filtered and sampled, the sampled voltage signal is subjected to CEEMDAN decomposition and inherent modal feature extraction using a signal processing technology based on deep learning, and each inherent mode after the sampled voltage signal is decomposed is subjected to feature significance modulation and dynamic aggregation to suppress noise and interference in the signal, thereby obtaining a pure sampled voltage signal, and then combined with an external control voltage signal to perform error calculation and MOS tube drive control to achieve accurate regulation of the output voltage of the high-voltage power supply. In this way, the accuracy of voltage control can be effectively improved, and the adaptability to complex and changeable load conditions and interference environments can be enhanced, thereby significantly improving the stability and reliability of the high-voltage power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0024] Figure 1 4 is a flow chart of a voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application.

[0025] Figure 24 is a flowchart of sub-step S4 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application.

[0026] Figure 3 Schematic diagram of data flow of sub-step S4 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application.

[0027] Figure 4 4 is a flowchart of sub-step S41 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application.

[0028] Figure 5 4 is a flowchart of sub-step S42 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0030] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0031] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0032] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0033] In view of the above technical problems, the present application proposes an optimized voltage control method for a high-reliability high-voltage power supply, which performs input filtering and power conversion and boost processing on the DC voltage signal provided by the power supply to obtain a high-voltage AC signal, and then, after the high-voltage AC signal is rectified, filtered and sampled, the sampled voltage signal is decomposed by CEEMDAN and the inherent modal features are extracted by using a signal processing technology based on deep learning, and the noise and interference in the signal are suppressed by dynamic aggregation of feature significance modulation of each inherent mode after the decomposition of the sampled voltage signal, and a pure sampled voltage signal is obtained, and then the error calculation and MOS tube drive control are combined with the external control voltage signal to achieve accurate regulation of the output voltage of the high-voltage power supply. In this way, the accuracy of voltage control can be effectively improved, and the adaptability to complex and changeable load conditions and interference environments can be enhanced, thereby significantly improving the stability and reliability of the high-voltage power supply system.

[0034] Figure 1 FIG. 1 is a flow chart of a voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application. Figure 1 As shown, the voltage control method for a high-reliability high-voltage power supply includes the following steps: S1, processing an initial DC voltage signal provided by the power supply through an input filter circuit electrically connected to the power supply to obtain a DC voltage signal; S2, performing power conversion and boost processing on the DC voltage signal to obtain a high-voltage AC signal; S3, processing the high-voltage AC signal through a rectifier filter sampling circuit to output a DC voltage, and sampling the DC voltage to obtain a sampled voltage signal, which is fed back to a control circuit; S4, the control circuit generates an error signal based on the sampled voltage signal and an external control voltage signal, and generates a PWM drive signal based on the error signal, which is transmitted to a MOS tube drive circuit; S5, the MOS tube drive circuit drives the MOS tube based on the PWM drive signal to control the level of the output DC voltage.

[0035] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the step S1 processes the initial DC voltage signal provided by the power supply through an input filter circuit electrically connected to the power supply to obtain a DC voltage signal. It should be understood that when the power supply provides the initial DC voltage signal, it may be affected by factors such as power grid fluctuations and load changes, resulting in instability of the output voltage, and the high-frequency noise and spike voltage in the signal may damage the subsequent power converter and electronic components in the boost circuit. The input filter circuit can effectively filter out high-frequency noise and play a role in smoothing the DC voltage signal, thereby ensuring the stable operation of the subsequent circuit. In an embodiment of the present application, the input filter circuit adopts an LC filter, and the parameters of the inductor and capacitor are reasonably designed to achieve the purpose of suppressing high-frequency noise and reducing voltage fluctuations.

[0036] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, the step S2 performs power conversion and boost processing on the DC voltage signal to obtain a high-voltage AC signal. It should be understood that in application scenarios such as medical equipment, industrial control, and communication equipment, a higher voltage level is usually required to drive a load or perform a specific function. Through power conversion and boost processing, the DC voltage provided by the power supply can be converted into the required high-voltage AC signal to meet the needs of these applications. In addition, during the power transmission and distribution process, high-voltage transmission can effectively reduce line losses, thereby improving energy utilization efficiency.

[0037] In the above-mentioned voltage control method for a high-reliability high-voltage power supply, in step S3, the high-voltage AC signal is processed by a rectifier-filter sampling circuit to output a DC voltage, and the DC voltage is sampled to obtain a sampled voltage signal, and the sampled voltage signal is fed back to the control circuit. It should be understood that AC power will generate electromagnetic interference during transmission and use. The function of the rectifier-filter sampling circuit is to convert the high-voltage AC signal into a DC voltage signal, and remove the high-frequency noise and ripple therein through the filtering link, so as to further improve the quality of the output voltage for subsequent voltage control. In an embodiment of the present application, the rectifier-filter sampling circuit can be implemented by a full-bridge rectifier in combination with a low-pass filter. The full-bridge rectifier can convert the AC signal into a pulsating DC signal, and the low-pass filter further filters the high-frequency components in the pulsating DC signal to obtain a smooth DC voltage output. Then, a portion of the voltage is further extracted from the rectifier-filtered DC voltage as a sampled voltage signal, which is used to reflect the real-time state of the output voltage and provide accurate feedback for subsequent voltage control. The sampled voltage signal is sent back to the control circuit as a feedback signal. The control circuit adjusts the duty cycle of the PWM drive signal according to the difference between the sampled voltage signal and the external control voltage signal (i.e., the error signal), thereby achieving effective control of the MOS tube drive circuit and further achieving the purpose of adjusting the output voltage level.

[0038] In particular, the present application takes into account that during the boost processing of the signal, due to the nonlinear characteristics of the power electronic devices and the electromagnetic interference during the switching process, a large amount of high-frequency harmonics and noise will be generated, and these harmonics and noise will further affect the stability and accuracy of the output voltage. Although the traditional rectification and filtering method can remove some high-frequency components, it is not ideal for complex and variable harmonic interference. At the same time, due to the inevitable parasitic inductance and parasitic capacitance in the circuit, additional high-frequency harmonics will also be generated. Therefore, in order to effectively suppress the influence of these interference components on voltage control, the present application further adopts a signal processing technology based on deep learning to purify the sampled voltage signal.

[0039] Figure 2 4 is a flowchart of sub-step S4 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application. Figure 3 FIG. 4 is a data flow diagram of sub-step S4 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application. Figure 2 and Figure 3As shown, the step S4 includes the steps of: S41, performing signal feature extraction based on modal decomposition on the sampled voltage signal to obtain a set of inherent modal semantic feature vectors of the sampled voltage signal; S42, performing dynamic aggregation based on feature energy distribution on the set of inherent modal semantic feature vectors of the sampled voltage signal to obtain a global significant aggregation representation vector of the sampled voltage signal; S43, performing signal reconstruction based on the global significant aggregation representation vector of the sampled voltage signal to obtain a pure sampled voltage signal; S44, inputting the pure sampled voltage signal and the external control voltage signal into an operator to obtain the error signal.

[0040] Specifically, in step S41, the sampled voltage signal is subjected to signal feature extraction based on modal decomposition to obtain a set of intrinsic modal semantic feature vectors of the sampled voltage signal. Figure 4 FIG. 4 is a flowchart of sub-step S41 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application. Figure 4 As shown, the step S41 includes the steps of: S411, performing CEEMDAN decomposition on the sampled voltage signal to obtain a set of intrinsic modal functions of the sampled voltage signal; S412, inputting each intrinsic modal function of the sampled voltage signal in the set of intrinsic modal functions of the sampled voltage signal into an intrinsic modal feature extractor based on a 1D-CNN model to obtain a set of intrinsic modal semantic feature vectors of the sampled voltage signal.

[0041] More specifically, in step S411, CEEMDAN is performed on the sampled voltage signal to obtain a set of intrinsic mode functions of the sampled voltage signal. It should be understood that in order to more finely analyze the frequency characteristics of the sampled voltage signal, the present application uses CEEMDAN (Complete Set Empirical Mode Decomposition with Adaptive Noise) technology to decompose the sampled voltage signal into a series of intrinsic mode functions (IMFs) to obtain a set of intrinsic mode functions of the sampled voltage signal. Among them, each intrinsic mode function of the sampled voltage signal represents the information of the sampled voltage signal within a specific frequency range, which helps to reveal the intrinsic structure of the signal and identify the interference components in the signal. It is worth mentioning that CEEMDAN is an improved empirical mode decomposition (EMD) method, which is very suitable for processing nonlinear and non-stationary signals. It adds adaptive white noise to the sampled voltage signal, performs multiple EMD (Empirical Mode Decomposition) decompositions, and finally takes an average to obtain a more stable decomposition result, which can effectively avoid the occurrence of modal aliasing, thereby improving the accuracy and stability of the decomposition.

[0042] More specifically, in step S412, each of the sampled voltage signal intrinsic modal functions in the set of the sampled voltage signal intrinsic modal functions is input into an intrinsic modal feature extractor based on a 1D-CNN model to obtain a set of the sampled voltage signal intrinsic modal semantic feature vectors. It should be understood that in order to further learn the characteristic patterns of the intrinsic modal functions of each sampled voltage signal, the present application adopts a 1D-CNN model as an intrinsic modal feature extractor, processes each sampled voltage signal intrinsic modal function separately, captures the local dependencies in the sampled voltage signal intrinsic modal function through a one-dimensional convolution operation, and mines the frequency distribution characteristic patterns of each intrinsic modal function to obtain a set of sampled voltage signal intrinsic modal semantic feature vectors, thereby providing a data basis for subsequent intrinsic modal feature modulation aggregation. It is worth mentioning that the 1D-CNN model can effectively extract local features in the input signal, and it can capture the key patterns and features in the function by convolving the sliding convolution kernel on the sampled voltage signal intrinsic modal function. In addition, through convolution operations, 1D-CNN can reduce the dimension of the input signal while retaining important information, effectively reducing the computational complexity of subsequent processing and improving the operating efficiency of the model.

[0043] Specifically, the step S42 dynamically aggregates the set of intrinsic modal semantic feature vectors of the sampled voltage signal based on the characteristic energy distribution to obtain a global significant aggregation representation vector of the sampled voltage signal. It should be understood that in order to effectively suppress the noise and interference in the sampled voltage signal, the present application proposes a dynamic aggregation method based on characteristic energy distribution, which can dynamically adjust the contribution weight of each intrinsic modal semantic feature vector of the sampled voltage signal in the aggregation process by performing feature energy significance evaluation on each intrinsic modal semantic feature vector and a specially designed dynamic weight allocation mechanism, thereby achieving the enhancement of important features and the suppression of noise components, thereby effectively extracting pure voltage signal features. Among them, Figure 5 FIG. 4 is a flowchart of sub-step S42 of the voltage control method for a high-reliability high-voltage power supply according to an embodiment of the present application. Figure 5 As shown, the step S42 includes the steps of: S421, determining the initial center vector of the inherent modal clustering of the sampled voltage signal based on the characteristic energy distribution of the set of the inherent modal semantic feature vectors of the sampled voltage signal; S422, based on the spatial span of each inherent modal semantic feature vector of the sampled voltage signal in the set of the inherent modal semantic feature vectors of the sampled voltage signal relative to the initial center vector of the inherent modal clustering of the sampled voltage signal, performing explicit dynamic aggregation on the set of the inherent modal semantic feature vectors of the sampled voltage signal to obtain the global significant aggregation representation vector of the sampled voltage signal.

[0044] More specifically, the step S421 further includes: calculating the static energy factor of each sampled voltage signal inherent modal semantic feature vector in the set of the sampled voltage signal inherent modal semantic feature vectors to obtain a set of sampled voltage signal inherent modal static energy factors; selecting the sampled voltage signal inherent modal semantic feature vector corresponding to the maximum value in the set of sampled voltage signal inherent modal static energy factors as the initial center vector of the sampled voltage signal inherent modal clustering.

[0045] In a specific example of the present application, the static energy factor of each sampled voltage signal intrinsic modal semantic feature vector in the set of the sampled voltage signal intrinsic modal semantic feature vector is calculated to obtain a set of sampled voltage signal intrinsic modal static energy factors, including: calculating the kurtosis of the sampled voltage signal intrinsic modal semantic feature vector, and inputting the kurtosis into a sigmoid activation function to obtain the sampled voltage signal intrinsic modal static energy factor. That is, the static energy factor is calculated based on the kurtosis of each sampled voltage signal intrinsic modal semantic feature vector to evaluate its stability and importance in the feature space, and the sampled voltage signal intrinsic modal semantic feature vector corresponding to the largest static energy factor is selected as the initial center vector of the sampled voltage signal intrinsic modal clustering, so as to determine the cluster core of the global frequency distribution of the sampled voltage signal, thereby providing a stable reference point for subsequent feature aggregation.

[0046] More specifically, the step S422 further includes: based on the spatial span between each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal and the initial center vector of the inherent modal clustering of the sampled voltage signal, and the set of inherent modal static energy factors of the sampled voltage signal, calculating the dynamic aggregation energy factor of each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal to obtain a set of inherent modal dynamic aggregation energy factors of the sampled voltage signal; based on the set of inherent modal dynamic aggregation energy factors of the sampled voltage signal, performing significance modulation aggregation on the set of inherent modal semantic feature vectors of the sampled voltage signal to obtain the global significant aggregation representation vector of the sampled voltage signal.

[0047] In a specific example of the present application, the dynamic aggregation energy factor of each sampled voltage signal inherent modal semantic feature vector in the set of the sampled voltage signal inherent modal semantic feature vectors is calculated to obtain a set of sampled voltage signal inherent modal dynamic aggregation energy factors, including: taking the square value of the number of feature vectors between the sampled voltage signal inherent modal semantic feature vector and the initial center vector of the sampled voltage signal inherent modal clustering as the spatial span coefficient, and calculating the weighted ratio between the product of the static energy factor of the sampled voltage signal inherent modal semantic feature vector and the static energy factor of the initial center vector of the sampled voltage signal inherent modal clustering and the spatial span coefficient to obtain the sampled voltage signal inherent modal dynamic aggregation energy factor.

[0048] In a specific example of the present application, based on the set of intrinsic modal dynamic aggregation energy factors of the sampled voltage signal, the set of intrinsic modal semantic feature vectors of the sampled voltage signal is significantly modulated and aggregated to obtain the global significant aggregation representation vector of the sampled voltage signal, including: inputting the set of intrinsic modal dynamic aggregation energy factors of the sampled voltage signal into a gated mask unit to obtain a set of intrinsic modal dynamic aggregation weight factors of the sampled voltage signal; and calculating the weighted sum of the set of intrinsic modal semantic feature vectors of the sampled voltage signal based on the set of intrinsic modal dynamic aggregation weight factors of the sampled voltage signal to obtain the global significant aggregation representation vector of the sampled voltage signal.

[0049] That is, by considering the degree of proximity of the feature space of each sampled voltage signal intrinsic modal semantic feature vector to the core of the global frequency distribution cluster of the sampled voltage signal and the difference in the importance of the intrinsic attributes, its contribution to the global features of the sampled voltage signal can be more accurately measured. Then, based on the gated mask mechanism, the obtained dynamic aggregation energy factor is nonlinearly transformed and screened to generate corresponding weights, so as to perform weighted aggregation on the set of the sampled voltage signal intrinsic modal semantic feature vectors, thereby enhancing the expression of important features and suppressing the influence of irrelevant or noise features, so as to obtain a global significant aggregation representation vector of the sampled voltage signal.

[0050] Accordingly, the step S42 includes: processing the set of intrinsic modal semantic feature vectors of the sampled voltage signal using the following feature dynamic aggregation formula to obtain a global significant aggregation representation vector of the sampled voltage signal, wherein the feature dynamic aggregation formula is:

[0051] X={x1,x2,...,x i ,...,x n}

[0052]

[0053] xc =x m

[0054]

[0055] w si =mask(w i )

[0056]

[0057] Wherein, X represents the set of intrinsic modal semantic feature vectors of the sampled voltage signal, x1, x2, x i 、x m and x n represent the first, second, i-th, m-th and n-th sampled voltage signal intrinsic modal semantic feature vectors in the set of the sampled voltage signal intrinsic modal semantic feature vectors respectively, and the value of n is the number of the sampled voltage signal intrinsic modal semantic feature vectors, represents the eigenvalue of the jth position in the intrinsic modal semantic feature vector of the i-th sampled voltage signal, μ i and σ i 4 denote the feature mean and the square of the feature variance of the intrinsic modal semantic feature vector of the i-th sampled voltage signal, respectively; E{·} denotes the expected value of the calculation set; sigmoid denotes the sigmoid activation function; represents the static energy factor of the intrinsic modal semantic feature vector of the i-th sampled voltage signal, argmax represents the index corresponding to the maximum value, m represents the index of the largest static energy factor in the set of intrinsic modal static energy factors of the sampled voltage signal, x c represents the initial center vector of the intrinsic mode clustering of the sampled voltage signal, represents the static energy factor of the initial center vector of the inherent modal clustering of the sampled voltage signal, a and b are different weight parameters, Count(x i →x m ) represents the number of feature vectors between the i-th sampled voltage signal intrinsic modal semantic feature vector and the sampled voltage signal intrinsic modal clustering initial center vector, represents the dynamic aggregation energy factor of the intrinsic modal semantic feature vector of the i-th sampled voltage signal, w i represents the dynamic aggregation energy factor of the intrinsic modal of the i-th normalized sampled voltage signal, mask(·) represents mask processing, θ is the preset mask threshold, and w si represents the inherent modal dynamic aggregation weight factor of the i-th sampled voltage signal, and V represents the global significant aggregation representation vector of the sampled voltage signal.

[0058] Specifically, the step S43 performs signal reconstruction based on the global significant aggregation representation vector of the sampled voltage signal to obtain a pure sampled voltage signal. In a specific example of the present application, the step S43 includes: inputting the global significant aggregation representation vector of the sampled voltage signal into a decoder-based signal reconstruction module to obtain the pure sampled voltage signal. That is, the decoder structure is used to decode the global significant aggregation representation vector of the sampled voltage signal, and map it back to the original space of the voltage signal to restore the original signal form. In the technical solution of the present application, the decoder includes multiple layers of fully connected layers and activation functions, which can gradually reconstruct the waveform of the voltage signal through layer-by-layer nonlinear transformations, while retaining key information such as the amplitude, frequency and phase of the signal, thereby obtaining a pure sampled voltage signal.

[0059] In a preferred example of the present application, based on the source frequency domain distribution differences of the set of intrinsic modal functions of the sampled voltage signal obtained by CEEMDAN decomposition of the sampled voltage signal, after being extracted by an intrinsic modal feature extractor based on a 1D-CNN model, each intrinsic modal semantic feature vector of the sampled voltage signal in the set of intrinsic modal semantic feature vectors of the sampled voltage signal will also have differences in the distribution of encoded features, resulting in different field dynamic aggregation modes of the feature distribution under the differences in the distribution of encoded features during dynamic aggregation of features, causing the global significant aggregation representation vector of the sampled voltage signal to have a complex aggregation space structure. Therefore, when the global significant aggregation representation vector of the sampled voltage signal is input into a decoder-based signal reconstruction module to obtain a pure sampled voltage signal, it is expected to improve the decoding generation convergence and generalization effects of the global significant aggregation representation vector of the sampled voltage signal under a complex aggregation space structure.

[0060] Based on this, the present application considers optimizing the global significant aggregation representation vector of the sampled voltage signal when the global significant aggregation representation vector of the sampled voltage signal is input into a decoder-based signal reconstruction module. The optimization process includes: calculating the sum of the absolute values ​​of each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal to obtain a first global significant aggregation spatial structure value of the sampled voltage signal; calculating the square root of the sum of the squares of each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal to obtain a second global significant aggregation spatial structure value of the sampled voltage signal; multiplying each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal with the first global significant aggregation spatial structure value of the sampled voltage signal and the second global significant aggregation spatial structure value of the sampled voltage signal to obtain a first global significant aggregation structure reference value and a second global significant aggregation structure reference value of the sampled voltage signal corresponding to each eigenvalue; multiplying each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal with the first global significant aggregation spatial structure value of the sampled voltage signal and the second global significant aggregation spatial structure value of the sampled voltage signal to obtain a first global significant aggregation structure reference value and a second global significant aggregation structure reference value of the sampled voltage signal corresponding to each eigenvalue; multiplying each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal with the sampled voltage signal The length of the global significant aggregation representation vector of the sampled voltage signal is multiplied by the square root of the length to obtain a first sampled voltage signal global significant aggregation scale transformation value and a second sampled voltage signal global significant aggregation scale transformation value corresponding to each eigenvalue; the first sampled voltage signal global significant aggregation structure reference value is divided by the difference between the first sampled voltage signal global significant aggregation spatial structure value and the first sampled voltage signal global significant aggregation scale transformation value to obtain a first sampled voltage signal global significant aggregation transformation adjustment value; the second sampled voltage signal global significant aggregation structure reference value is divided by the difference between the second sampled voltage signal global significant aggregation spatial structure value and the second sampled voltage signal global significant aggregation scale transformation value to obtain a second sampled voltage signal global significant aggregation transformation adjustment value; the weighted sum of the first sampled voltage signal global significant aggregation transformation adjustment value and the second sampled voltage signal global significant aggregation transformation adjustment value is calculated to obtain each eigenvalue of the optimized sampled voltage signal global significant aggregation representation vector.

[0061] Accordingly, the optimization process of the global significant aggregation representation vector of the sampled voltage signal is expressed by the following optimization formula:

[0062] v 1i =(α×v i ) / (α-L×v i )

[0063]

[0064]

[0065] v i ∈V∈R 1×L

[0066] v1i ∈V1∈R 1×L

[0067] v 2i ∈V2∈R 1×L

[0068] V'=V1⊕(ω⊙V2)

[0069] Wherein, V represents the global significant aggregation representation vector of the sampled voltage signal, v i represents the ith eigenvalue of the global significant aggregation representation vector of the sampled voltage signal, L represents the length of the global significant aggregation representation vector of the sampled voltage signal, R represents a real number set, α represents the global significant aggregation spatial structure value of the first sampled voltage signal, β represents the global significant aggregation spatial structure value of the second sampled voltage signal, v 1i represents the first sampled voltage signal global significant aggregation scale transformation value corresponding to the i-th eigenvalue in the sampled voltage signal global significant aggregation representation vector, V1 is the first sampled voltage signal global significant aggregation transformation adjustment vector composed of the first sampled voltage signal global significant aggregation scale transformation values, v 2i represents the second sampled voltage signal global significant aggregation scale transformation value corresponding to the i-th eigenvalue in the sampled voltage signal global significant aggregation representation vector, V2 is the second sampled voltage signal global significant aggregation transformation adjustment vector composed of each of the second sampled voltage signal global significant aggregation scale transformation values, ω is the weight hyperparameter, ⊕ represents position point addition, ⊙ represents position point multiplication, and V′ represents the optimized sampled voltage signal global significant aggregation representation vector.

[0070] That is, with respect to the spatial structure information of the feature set of the global significant aggregation representation vector of the sampled voltage signal in the high-dimensional space, a scale-based frame transformation of each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal is performed by taking the class-norm spatial structured representation of the global significant aggregation representation vector of the sampled voltage signal as a reference window, and a spatial structure-based frame attention weight adjustment of each eigenvalue of the global significant aggregation representation vector of the sampled voltage signal is implemented to ensure the spatial transformation invariance of the global significant aggregation representation vector of the sampled voltage signal under the interaction of feature space, thereby improving the convergence and generalization effects of decoding and generating the feature set of the global significant aggregation representation vector of the sampled voltage signal under the complex spatial structure representation, and improving the signal quality of the pure sampled voltage signal obtained by inputting it into the signal reconstruction module based on the decoder.

[0071] Specifically, in step S44, the clean sampled voltage signal and the external control voltage signal are input into the operator to obtain the error signal. In a specific example of the present application, the operator performs a differential operation on the clean sampled voltage signal and the external control voltage signal to calculate the difference between the two, thereby obtaining the error signal. The error signal is then sent to the PWM controller, which adjusts the duty cycle of the PWM waveform according to the size and direction of the error signal, and transmits the generated PWM drive signal to the MOS tube drive circuit to control the switching state of the MOS tube.

[0072] In the voltage control method for a high-reliability high-voltage power supply, in step S5, the MOS tube driving circuit drives the MOS tube based on the PWM driving signal to control the level of the output DC voltage. It should be understood that the MOS tube, as a high-speed switching device, has a low on-resistance and a fast switching speed, and is very suitable for accurately controlling the output voltage of the high-voltage power supply. Under the control of the PWM driving signal, the MOS tube driving circuit can adjust the on-time of the MOS tube according to the pulse width of the PWM driving signal, thereby controlling the magnitude of the output voltage. Since the switching frequency of the MOS tube can be very high, it is possible to achieve rapid adjustment of the output voltage, respond to load changes and external interference in real time, thereby ensuring the stability and accuracy of the output voltage, and providing strong technical support for the stable operation of the power system.

[0073] In summary, the voltage control method for a high-reliability high-voltage power supply based on the embodiment of the present application is explained, which performs input filtering and power conversion and boost processing on the DC voltage signal provided by the power supply to obtain a high-voltage AC signal, then, after the high-voltage AC signal is rectified, filtered and sampled, the sampled voltage signal is subjected to CEEMDAN decomposition and inherent modal feature extraction using a signal processing technology based on deep learning, and the noise and interference in the signal are suppressed by dynamic aggregation of feature significance modulation of each inherent mode after the decomposition of the sampled voltage signal, and a pure sampled voltage signal is obtained, and then error calculation and MOS tube drive control are performed in combination with an external control voltage signal to achieve accurate regulation of the output voltage of the high-voltage power supply. In this way, the accuracy of voltage control can be effectively improved, and the adaptability to complex and changeable load conditions and interference environments can be enhanced, thereby significantly improving the stability and reliability of the high-voltage power supply system.

[0074] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0076] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0077] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0078] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A voltage control method for a high reliability high voltage power supply, characterized in that: include: Processing an initial DC voltage signal provided by the power supply through an input filter circuit electrically connected to the power supply to obtain a DC voltage signal; Performing power conversion and voltage boost processing on the DC voltage signal to obtain a high-voltage AC signal; The high voltage AC signal is processed by a rectifier, filter and sampling circuit to output a DC voltage, and the DC voltage is sampled to obtain a sampled voltage signal, and the sampled voltage signal is fed back to the control circuit; The control circuit generates an error signal based on the sampled voltage signal and the external control voltage signal, and generates a PWM drive signal based on the error signal, and the PWM drive signal is transmitted to the MOS tube drive circuit; The MOS tube driving circuit drives the MOS tube based on the PWM driving signal to control the level of the output DC voltage; Among them, the control circuit generates an error signal based on the sampled voltage signal and the external control voltage signal, including: performing signal feature extraction based on modal decomposition on the sampled voltage signal to obtain a set of inherent modal semantic feature vectors of the sampled voltage signal; performing dynamic aggregation based on feature energy distribution on the set of inherent modal semantic feature vectors of the sampled voltage signal to obtain a global significant aggregation representation vector of the sampled voltage signal; performing signal reconstruction based on the global significant aggregation representation vector of the sampled voltage signal to obtain a pure sampled voltage signal; and inputting the pure sampled voltage signal and the external control voltage signal into an operator to obtain the error signal.

2. The voltage control method for a high-reliability high-voltage power supply according to claim 1, characterized in that: The sampled voltage signal is subjected to signal feature extraction based on modal decomposition to obtain a set of intrinsic modal semantic feature vectors of the sampled voltage signal, including: Performing CEEMDAN decomposition on the sampled voltage signal to obtain a set of intrinsic mode functions of the sampled voltage signal; Each of the sampled voltage signal intrinsic modal functions in the set of the sampled voltage signal intrinsic modal functions is respectively input into an intrinsic modal feature extractor based on a 1D-CNN model to obtain a set of the sampled voltage signal intrinsic modal semantic feature vectors.

3. The voltage control method for a high-reliability high-voltage power supply according to claim 2, characterized in that: The set of intrinsic modal semantic feature vectors of the sampled voltage signal is dynamically aggregated based on feature energy distribution to obtain a global significant aggregation representation vector of the sampled voltage signal, including: Determining an initial center vector of the intrinsic modal clustering of the sampled voltage signal based on a characteristic energy distribution of a set of intrinsic modal semantic feature vectors of the sampled voltage signal; Based on the spatial span of each inherent modal semantic feature vector of the sampled voltage signal in the set of inherent modal semantic feature vectors of the sampled voltage signal relative to the initial center vector of the inherent modal clustering of the sampled voltage signal, the set of inherent modal semantic feature vectors of the sampled voltage signal is explicitly dynamically aggregated to obtain a global significant aggregation representation vector of the sampled voltage signal.

4. The voltage control method for a high-reliability high-voltage power supply according to claim 3, characterized in that: Determining an initial center vector of the intrinsic modal clustering of the sampled voltage signal based on the characteristic energy distribution of the set of intrinsic modal semantic feature vectors of the sampled voltage signal comprises: Calculating the static energy factor of each sampled voltage signal intrinsic modal semantic feature vector in the set of sampled voltage signal intrinsic modal semantic feature vectors to obtain a set of sampled voltage signal intrinsic modal static energy factors; The sampled voltage signal intrinsic modal semantic feature vector corresponding to the maximum value in the set of the sampled voltage signal intrinsic modal static energy factors is selected as the sampled voltage signal intrinsic modal clustering initial center vector.

5. The voltage control method for a high-reliability high-voltage power supply according to claim 4, characterized in that: Calculating the static energy factor of each sampled voltage signal intrinsic modal semantic feature vector in the set of sampled voltage signal intrinsic modal semantic feature vectors to obtain a set of sampled voltage signal intrinsic modal static energy factors, including: The kurtosis of the intrinsic modal semantic feature vector of the sampled voltage signal is calculated, and the kurtosis is input into a sigmoid activation function to obtain the intrinsic modal static energy factor of the sampled voltage signal.

6. The voltage control method for a high-reliability high-voltage power supply according to claim 5, characterized in that: Based on the spatial span of each sampled voltage signal intrinsic modal semantic feature vector in the set of the sampled voltage signal intrinsic modal semantic feature vectors relative to the initial center vector of the sampled voltage signal intrinsic modal clustering, the set of the sampled voltage signal intrinsic modal semantic feature vectors is explicitly dynamically aggregated to obtain the sampled voltage signal global significant aggregation representation vector, including: Based on the spatial span between each sampled voltage signal inherent modal semantic feature vector in the set of the sampled voltage signal inherent modal semantic feature vectors and the initial center vector of the sampled voltage signal inherent modal clustering, and the set of the sampled voltage signal inherent modal static energy factors, the dynamic aggregation energy factor of each sampled voltage signal inherent modal semantic feature vector in the set of the sampled voltage signal inherent modal semantic feature vectors is calculated to obtain a set of sampled voltage signal inherent modal dynamic aggregation energy factors; Based on the set of dynamic aggregation energy factors of the sampled voltage signal intrinsic modalities, the set of semantic feature vectors of the sampled voltage signal intrinsic modalities is aggregated by saliency modulation to obtain a global saliency aggregation representation vector of the sampled voltage signal.

7. The voltage control method for a high-reliability high-voltage power supply according to claim 6, characterized in that: Calculating the dynamic aggregation energy factor of each sampled voltage signal intrinsic modal semantic feature vector in the set of sampled voltage signal intrinsic modal semantic feature vectors to obtain a set of sampled voltage signal intrinsic modal dynamic aggregation energy factors, including: The square value of the number of feature vectors between the inherent modal semantic feature vector of the sampled voltage signal and the initial center vector of the inherent modal clustering of the sampled voltage signal is used as the spatial span coefficient, and the weighted ratio between the product of the static energy factor of the inherent modal semantic feature vector of the sampled voltage signal and the static energy factor of the initial center vector of the inherent modal clustering of the sampled voltage signal and the spatial span coefficient is calculated to obtain the inherent modal dynamic aggregation energy factor of the sampled voltage signal.

8. The voltage control method for a high-reliability high-voltage power supply according to claim 7, characterized in that: Based on the set of dynamic aggregation energy factors of the intrinsic modalities of the sampled voltage signals, a set of intrinsic modal semantic feature vectors of the sampled voltage signals is subjected to saliency modulation aggregation to obtain a global saliency aggregation representation vector of the sampled voltage signals, including: Inputting the set of sampled voltage signal intrinsic modal dynamic aggregation energy factors into a gated mask unit to obtain a set of sampled voltage signal intrinsic modal dynamic aggregation weight factors; The weighted sum of the set of intrinsic modal semantic feature vectors of the sampled voltage signal is calculated based on the set of intrinsic modal dynamic aggregation weight factors of the sampled voltage signal to obtain the global significant aggregation representation vector of the sampled voltage signal.

9. The voltage control method for a high-reliability high-voltage power supply according to claim 8, characterized in that: Signal reconstruction is performed based on the global significant aggregation representation vector of the sampled voltage signal to obtain a pure sampled voltage signal, including: The global significant aggregation representation vector of the sampled voltage signal is input into a decoder-based signal reconstruction module to obtain the clean sampled voltage signal.

Images