Bus voltage monitoring method and system for a power module

Abstract

The application relates to the technical field of bus voltage monitoring of a power module, in particular to a bus voltage monitoring method and system of a power module, which comprises the following steps: determining voltage disturbance degrees at each moment based on fluctuation conditions of bus voltages at all moments within a preset time length before each moment and energy distribution of the bus voltages in a frequency domain; determining voltage risk state values at each moment based on voltage disturbance smoothing values and voltage disturbance sensitivities, so as to optimize a process noise covariance matrix in a Kalman filtering algorithm at each moment; and carrying out denoising on the bus voltage based on the optimized Kalman filtering algorithm, for bus voltage monitoring. Through dynamic optimization of the process noise covariance matrix in the Kalman filtering algorithm, the application solves the estimation deviation problem of traditional methods under noise interference and model mismatch, and improves the precision of bus voltage monitoring of the power module.

Description

Technical Field

[0001] This application relates to the field of bus voltage monitoring technology for power modules, and specifically to a method and system for monitoring the bus voltage of power modules. Background Technology

[0002] Bus voltage monitoring of power modules is a core component for ensuring the stable operation and safety protection of electronic systems, playing a crucial role, especially in power generation engineering supervision. Early systems had simple structures, with bus voltage monitoring primarily relying on basic sampling circuits, offering relatively limited functionality. However, with the rapid development of high-power applications such as new energy power generation, electric vehicles, and industrial frequency converters, bus voltage fluctuations have increased significantly. These fluctuations directly affect the stress on switching devices, system operating efficiency, and overall equipment safety, placing higher demands on power generation engineering supervision. Therefore, achieving accurate bus voltage monitoring is not only a fundamental prerequisite for its application but also a vital support for ensuring the quality and reliability of power generation engineering supervision.

[0003] Traditional fixed-parameter Kalman filtering algorithms are prone to estimation bias or even divergence when faced with model mismatch, unknown or time-varying noise statistics, due to the fixed parameters. This seriously reduces the accuracy of bus voltage monitoring of power modules and fails to meet the high-precision monitoring requirements of power generation engineering supervision. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for monitoring the bus voltage of a power module, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide a method for monitoring the bus voltage of a power module, the method comprising the following steps:

[0006] Real-time acquisition of the bus voltage of the power module;

[0007] Based on the fluctuation of the bus voltage at all times within a preset time period prior to each time, the voltage fluctuation value at each time is determined; based on the energy distribution of the bus voltage in the frequency domain at all times within a preset time period prior to each time, the energy characteristic value at each time is determined, and combined with the voltage fluctuation value, the voltage disturbance degree at each time is determined.

[0008] Based on the smoothness of voltage disturbance at all times within a preset time period prior to each time, the voltage disturbance smoothing value at each time is determined to determine the voltage disturbance sensitivity at each time; based on the voltage disturbance smoothing value and the voltage disturbance sensitivity, the voltage risk state value at each time is determined.

[0009] Based on the voltage risk state value, the process noise covariance matrix in the Kalman filter algorithm at each time point is optimized, and the bus voltage is denoised based on the optimized Kalman filter algorithm for bus voltage monitoring.

[0010] Preferably, the method for determining the voltage fluctuation value at each time point is as follows:

[0011] Within a preset time period before each time point, the average value of the bus voltage at any given time point and all times within a preset window before that time point is calculated. The degree of dispersion of the average value at all times within the preset time period before each time point is used as the voltage fluctuation value at each time point.

[0012] Preferably, the energy characteristic value at each moment is the result of the ratio of the total energy of the bus voltage in the preset frequency band in the frequency domain at all moments within the preset time period before each moment to the total energy in the entire frequency domain.

[0013] Preferably, the voltage disturbance degree at each time point is the result of positive fusion of the voltage fluctuation value and the energy characteristic value at each time point.

[0014] Preferably, the voltage disturbance smoothing value at each time point is a smoothed value obtained by applying a smoothing algorithm to the voltage disturbance degree at all times within a preset time period prior to each time point.

[0015] Preferably, the method for determining the voltage disturbance sensitivity at each time point is as follows:

[0016] If the normalized value of the voltage disturbance smoothing value at the current moment is less than the preset first threshold, then the voltage disturbance sensitivity at the current moment is set to the preset first value.

[0017] If the normalized value of the voltage disturbance smoothing value at the current moment is greater than the preset second threshold, then the voltage disturbance sensitivity at the current moment is set to the preset second value.

[0018] If the normalized value of the voltage disturbance smoothing value at the current moment is within the closed interval between the preset first threshold and the preset second threshold, then the voltage disturbance sensitivity at the current moment is set to the preset third value, wherein the preset first threshold is less than the preset second threshold, and the magnitude of the preset third value is between the preset first value and the preset second value. The voltage disturbance sensitivity at each moment is obtained by iterating through each moment.

[0019] Preferably, the expression for the voltage risk state value at each time point is: In the formula, This represents the voltage risk state value at time i; This represents the voltage disturbance smoothing value at time i; represents the voltage disturbance sensitivity at time i; norm() represents the normalization function.

[0020] Preferably, the optimization of the process noise covariance matrix in the Kalman filter algorithm at each time step includes:

[0021] Process noise covariance matrix in Kalman filter algorithm at time i The expression is: In the formula, This represents the preset baseline process noise covariance matrix at time i; The voltage risk state value at time i is represented; k represents the preset adjustment coefficient.

[0022] Preferably, the denoising of the bus voltage based on the optimized Kalman filter algorithm includes:

[0023] The bus voltage at all times within a preset time period prior to each time point is used as the input to the optimized Kalman filter algorithm, wherein the state transition matrix is ​​set to the identity matrix, the observation matrix is ​​set to H, and the denoised bus voltage within the preset time period prior to each time point is output. Secondly, embodiments of this application also provide a bus voltage monitoring system for a power module, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described bus voltage monitoring methods for a power module.

[0024] This application has at least the following beneficial effects:

[0025] This application constructs a voltage disturbance index by combining time-domain voltage fluctuations and frequency-domain energy distribution, comprehensively reflecting the dynamic instability of bus voltage under multiple disturbances. This effectively quantifies the impact of noise interference on monitoring accuracy, providing strong support for improving the reliability and accuracy of bus voltage monitoring. Furthermore, by introducing voltage disturbance smoothing values ​​and voltage disturbance sensitivity, this application constructs a voltage risk state value. This not only comprehensively assesses the dynamic instability of bus voltage but also quantifies the impact of the monitoring system's health status on monitoring accuracy, improving the precision of bus data monitoring. Finally, by introducing the voltage risk state value, this application dynamically optimizes the process noise covariance matrix in the Kalman filter algorithm, enabling the filter parameters to adapt to the dynamic fluctuations of bus voltage and the system's health status in real time. This effectively suppresses the impact of high-frequency noise, load mutations, and aging of isolation devices on voltage monitoring, further improving the precision of bus voltage monitoring. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating the steps of a bus voltage monitoring method for a power module, provided as an embodiment of this application;

[0028] Figure 2 This is a schematic diagram of the voltage risk state value extraction process provided in one embodiment of this application. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a bus voltage monitoring method and system for a power module proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0031] The following description, in conjunction with the accompanying drawings, details the specific scheme of the bus voltage monitoring method and system for a power module provided in this application.

[0032] Please see Figure 1 The diagram illustrates a flowchart of a bus voltage monitoring method for a power module according to an embodiment of this application. The method includes the following steps:

[0033] Step S1: Obtain the bus voltage of the power module in real time.

[0034] In this embodiment, the structure of a bus voltage monitoring system for a power module is as follows:

[0035] Bus voltage sensor: Installed at the positive and negative terminals of the DC bus of the power module, it is used to collect analog signals of the bus voltage in real time, providing first-hand data for power generation project supervision.

[0036] Signal conditioning and isolation module: including anti-aliasing filter circuit and electrical isolation unit, used to suppress high-frequency interference, adapt to ADC input range, and provide electrical isolation between high-voltage and low-voltage control circuits to ensure the safety of power generation project supervision;

[0037] ADC sampling module: Converts the conditioned analog signal into a digital signal at a sampling frequency of 10kHz to meet the high-frequency data acquisition requirements of power generation engineering supervision.

[0038] Core processor: Equipped with digital signal processing algorithms, it performs zero drift correction, linear compensation, moving standard deviation calculation, FFT analysis, ripple noise sensitivity calculation, weighted moving average processing, normalization processing, threshold judgment, and adaptive Kalman filtering to support intelligent decision-making in power generation engineering supervision.

[0039] Early warning and output module: Outputs graded early warning signals based on voltage risk status values, and supports real-time status display or remote communication interface;

[0040] Power management module: Provides stable and isolated power supply to each functional module, ensuring the continuous operation of the power generation engineering supervision system.

[0041] Through the coordinated operation of the above components, high-precision and high-reliability monitoring and intelligent early warning of bus voltage can be achieved, comprehensively improving the efficiency of power generation project supervision.

[0042] A high-precision voltage sensor is installed on the positive and negative terminals of the DC bus of the power module to acquire the bus voltage of the power module in real time. High-frequency interference is filtered out by an anti-aliasing filter and a signal conditioning current. The data acquisition frequency is set to f. Since the frequency band of high-frequency noise is in the range of 0.1~1kHz, in order to include the frequency band of high-frequency noise in this embodiment, the value of f is 10kHz. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0043] Step S2: Based on the fluctuation of the bus voltage at all times within a preset time period before each time, determine the voltage fluctuation value at each time; based on the energy distribution of the bus voltage in the frequency domain at all times within a preset time period before each time, determine the energy characteristic value at each time, and combine the voltage fluctuation value to determine the voltage disturbance degree at each time.

[0044] Due to the influence of multiple factors such as high-frequency noise interference, sudden load changes, and aging performance degradation of isolation devices during actual operation, the bus voltage of the power module experiences transient and severe fluctuations in the time domain and waveform distortion in the frequency domain. Specifically, this manifests as instantaneous voltage jumps, high-frequency oscillations, and resonance distortion. These anomalies not only cause data distortion but may also lead to misjudgments of the voltage status and even trigger unnecessary protection actions, posing significant challenges to power generation project supervision.

[0045] Therefore, in order to quantify and effectively suppress bus voltage fluctuations and noise interference, this embodiment analyzes the noise impact on the bus voltage from both the time domain and the frequency domain. Specifically, this embodiment determines the voltage fluctuation value at each moment based on the bus voltage fluctuation at all moments within a preset time period prior to each moment; it also determines the energy characteristic value at each moment based on the energy distribution of the bus voltage in the frequency domain at all moments within a preset time period prior to each moment, and combines the voltage fluctuation value to determine the voltage disturbance level at each moment. The specific process is as follows:

[0046] In this embodiment, firstly, based on the bus voltage fluctuations at all times within a preset time period prior to each time point, the voltage fluctuation value at each time point is determined. Specifically:

[0047] Within a preset time period before each time point, the average value of the bus voltage at any given time point and all times within a preset window before that time point is calculated. The degree of dispersion of the average value at all times within the preset time period before each time point is used as the voltage fluctuation value at each time point.

[0048] It should be noted that the preset duration and preset window length are both manually set. In this embodiment, the preset duration is 1 second and the preset window length is 10 ms. In actual applications, as other implementation methods, implementers can also set them according to specific circumstances. This embodiment does not impose any special restrictions.

[0049] It should be understood that there are many ways to measure the dispersion of a set of data. In this embodiment, the standard deviation of the mean at all times within a preset time period before each time is taken as the dispersion of the mean at all times within a preset time period before each time. In practical applications, as other implementation methods, implementers may also use other methods such as variance or coefficient of variation to measure the dispersion of data in combination with specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the dispersion of data.

[0050] Based on the voltage fluctuation values ​​at various times, it can be understood that the voltage fluctuation value reflects the degree of dispersion of the bus voltage fluctuation in a short period of time, that is, the instantaneous stability of the bus voltage. If the voltage fluctuation value at the current time is larger, it means that the voltage fluctuation was more violent in the short period of time before the current time, which is prone to sampling distortion and reduces the accuracy of bus voltage monitoring. Conversely, if the voltage fluctuation value at the current time is smaller, it means that the voltage fluctuation was more gentle in the short period of time before the current time, and the sampling data is more stable and reliable, thereby improving the accuracy of bus voltage monitoring.

[0051] Furthermore, this embodiment determines the energy characteristic value at each moment based on the energy distribution of the bus voltage in the frequency domain at all moments within a preset time period prior to each moment. Specifically:

[0052] In this embodiment, the total energy of the bus voltage within a preset frequency band in the frequency domain at all times within a preset time period prior to each time point is divided by the total energy in the entire frequency domain, and the result is used as the energy characteristic value at each time point.

[0053] It should be noted that the process of obtaining the energy of the bus voltage in the frequency domain at all times within the preset time period before each time is as follows: In this embodiment, the bus voltage at all times within the preset time period before each time is used as the input of the fast Fourier transform algorithm, and the frequency domain signal of the bus voltage is output. Based on the frequency domain signal and the energy calculation formula, the total energy in the preset frequency band of the frequency domain signal and the total energy of the frequency domain signal as a whole are obtained. The energy calculation method is a well-known technology and will not be described in detail here.

[0054] It should be understood that the preset frequency band in this embodiment is 0.1~1kHz. This is because the typical frequency band of high-frequency noise and harmonic interference in power electronic systems is 0.1~1kHz. Therefore, in order to measure the interference of high-frequency noise, this embodiment sets the preset frequency band to 0.1~1kHz.

[0055] Based on the energy characteristic values ​​at each time point, it can be understood that the energy characteristic values ​​characterize the intensity of high-frequency noise and harmonic interference. If the proportion of the total energy of the bus voltage at all times within the preset time period before the current time is larger in the preset frequency band in the frequency domain, it indicates that the degree of high-frequency noise and harmonic interference at the current time is greater, and the anti-interference capability of the bus monitoring system is weaker. At this time, the accuracy of the collected bus voltage for monitoring is low.

[0056] Conversely, if the proportion of the total energy of the bus voltage within the preset frequency band in the frequency domain is smaller during all times within the preset time period before the current moment, it indicates that the degree of high-frequency noise and harmonic interference at the current moment is lighter, and the anti-interference capability of the bus monitoring system is stronger. In this case, the accuracy of the collected bus voltage for monitoring is higher, and it can more realistically reflect the operating status of the bus voltage, providing more reliable data for subsequent status assessment and engineering supervision.

[0057] Furthermore, this embodiment determines the voltage disturbance degree at each time point based on the voltage fluctuation value and energy characteristic value at each time point. Specifically:

[0058] In this embodiment, the voltage fluctuation value and energy characteristic value at each time point are positively fused to obtain the voltage disturbance degree at each time point.

[0059] It should be understood that positive fusion refers to combining two or more indicators through addition or multiplication to obtain a comprehensive indicator, thereby more comprehensively and accurately assessing a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analytical methods. Implementers can choose according to specific circumstances, and this embodiment does not impose any special restrictions.

[0060] Preferably, as one implementation method, in this embodiment, the product of the voltage fluctuation value and the energy characteristic value at each time moment is taken as the voltage disturbance degree at each time moment. In practical applications, as other implementation methods, implementers may also adopt other positive fusion methods such as sum values ​​according to specific circumstances. This embodiment does not impose any special restrictions.

[0061] Based on the voltage disturbance degree at each moment, it can be understood that the voltage disturbance degree comprehensively reflects the dynamic instability of the bus voltage in the time domain and frequency domain. If the voltage fluctuation value at the current moment is larger, it indicates that the voltage fluctuation was more severe in the short period before the current moment, indicating that the bus voltage was more severely affected by noise interference. Therefore, the corresponding voltage disturbance degree is larger, indicating that the reliability and accuracy of the bus voltage at this moment is lower. At the same time, if the energy characteristic value at the current moment is larger, it indicates that high-frequency noise accounts for a serious proportion in the frequency domain. The corresponding voltage disturbance degree is larger, indicating that the voltage disturbance noise interference was more severe in the short period before the current moment.

[0062] Conversely, the smaller the voltage fluctuation value at the current moment, the smoother the voltage fluctuation in the short period before the current moment, indicating that the bus voltage is less affected by noise interference. Therefore, the corresponding voltage disturbance is smaller, indicating that the reliability and accuracy of the bus voltage at this time are higher. At the same time, the smaller the energy characteristic value at the current moment, the more pronounced the high-frequency noise in the frequency domain, and the corresponding voltage disturbance is smaller, indicating that the voltage was less affected by noise interference in the short period before the current moment, and the overall monitoring quality of the bus voltage is better.

[0063] Thus, this embodiment constructs a voltage disturbance index by combining time-domain voltage fluctuation values ​​and frequency-domain energy characteristic values. This index comprehensively reflects the dynamic instability of bus voltage under multiple disturbances, effectively quantifies the impact of noise interference on monitoring accuracy, and provides strong support for improving the reliability and accuracy of bus voltage monitoring.

[0064] Step S3: Based on the smoothness of voltage disturbance at all times within the preset time period before each time, determine the voltage disturbance smoothing value at each time to determine the voltage disturbance sensitivity at each time; based on the voltage disturbance smoothing value and the voltage disturbance sensitivity, determine the voltage risk state value at each time.

[0065] The voltage disturbance comprehensive analysis reflects the fluctuations and high-frequency energy distribution of the power module bus voltage, indicating the instantaneous fluctuations in the time domain and the high-frequency distortion characteristics in the frequency domain. Therefore, to further evaluate the dynamic stability of the bus voltage and promptly detect potential anomalies, this embodiment determines the voltage disturbance smoothing value at each moment based on the smoothness of the voltage disturbance at all moments within a preset time period prior to each moment, thereby determining the voltage disturbance sensitivity at each moment. Based on the voltage disturbance smoothing value and the voltage disturbance sensitivity, the voltage risk state value at each moment is determined. The specific process is as follows:

[0066] If the voltage disturbance level is used directly to determine whether the bus voltage is affected by noise interference, the accuracy of the judgment will be affected. Therefore, this embodiment smooths the voltage disturbance level, extracts its long-term trend, and eliminates the influence of instantaneous interference. Specifically, based on the smoothness of the voltage disturbance level at all times within a preset time period before each time, the voltage disturbance smoothing value at each time is determined, as follows:

[0067] In this embodiment, the voltage disturbance degree at all times within a preset time period before each time is used as the input of the smoothing algorithm. The size of the smoothing window is set to t, and the smoothed value at each time is output as the voltage disturbance smoothing value at each time. In this embodiment, the smoothing window t is set to 20ms. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0068] It should be noted that there are many commonly used smoothing algorithms. In this embodiment, the weighted moving average (WMA) algorithm is used. The time interval from any time to each time within a preset period before each time is used as the weight of any time in the weighted moving average algorithm. In practical applications, as other implementation methods, implementers may also use other smoothing algorithms such as the moving average (MA) algorithm according to specific circumstances. This embodiment does not impose any special restrictions on the selection of smoothing algorithms.

[0069] The weighted moving average (WMA) algorithm is a well-known technique, and the specific process of using it to smooth the voltage disturbance at each time point will not be elaborated here.

[0070] Based on the voltage disturbance smoothing value at each time point, it can be understood that the voltage disturbance smoothing value reflects the recent trend of voltage stability and indirectly reflects the health status of the bus voltage monitoring system. If the voltage disturbance smoothing value at the current time point is larger, it indicates that the voltage is more severely affected by noise interference at the current time point, and the bus voltage monitoring system may have potential risks or anomalies. At this time, the reliability and accuracy of the bus voltage are low.

[0071] Conversely, the smaller the voltage disturbance smoothing value at the current moment, the less severe the noise interference on the voltage at the current moment, indicating that the bus voltage monitoring system is operating stably and there are no obvious abnormalities or potential risks. In this case, the reliability and accuracy of the bus voltage are high.

[0072] Furthermore, since the noise disturbance level of the bus voltage directly reflects the health status of the power module's bus voltage monitoring system, and the voltage disturbance smoothing value is used to characterize the noise disturbance level of the bus voltage, noise disturbance means that the performance of the components inside the bus voltage monitoring system has degraded and the anti-interference capability has decreased. Therefore, this embodiment predicts the health status of the current bus voltage monitoring system based on the voltage disturbance smoothing value, thereby reducing the interference of the health status of the bus voltage monitoring system on the accuracy of bus voltage monitoring. Specifically:

[0073] In this embodiment, if the normalized value of the voltage disturbance smoothing value at the current moment is less than the preset first threshold, it indicates that the bus voltage monitoring system is relatively normal at the current moment. Therefore, the voltage disturbance sensitivity at the current moment is set to the preset first value. The voltage disturbance sensitivity is used to reflect the health status of the bus monitoring system. The smaller the voltage disturbance sensitivity, the more normal the bus voltage monitoring system is. Conversely, the larger the voltage disturbance sensitivity, the more serious the abnormality in the health status of the bus voltage monitoring system is.

[0074] If the normalized value of the voltage disturbance smoothing value at the current moment is greater than the preset second threshold, it indicates that the health status of the bus voltage monitoring system is seriously abnormal at the current moment. Therefore, the voltage disturbance sensitivity at the current moment is set to the preset second value.

[0075] If the normalized value of the voltage disturbance smoothing value at the current moment is within the closed interval between the preset first threshold and the preset second threshold, it indicates that there is a potential risk in the bus voltage monitoring system at the current moment. Therefore, the voltage disturbance sensitivity at the current moment is set to a preset third value, wherein the preset first threshold is less than the preset second threshold, and the magnitude of the preset third value is between the preset first value and the preset second value. The voltage disturbance sensitivity at each moment is obtained by iterating through each moment.

[0076] It should be noted that the preset first threshold, preset second threshold, preset first value, preset second value, and preset third value are all set manually. In this embodiment, the preset first threshold is 0.3, the preset second threshold is 0.7, the preset first value is 0, the preset second value is 2, and the preset third value is 1. In actual applications, as other implementation methods, implementers can also set them according to specific circumstances. This embodiment does not impose any special restrictions.

[0077] Furthermore, this embodiment determines the voltage risk state value at each time point based on the voltage disturbance smoothing value and the voltage disturbance sensitivity, specifically as follows:

[0078] As one implementation method, in this embodiment, the voltage risk state value at time i The expression is: In the formula, This represents the voltage disturbance smoothing value at time i; represents the voltage disturbance sensitivity at time i; norm() represents the normalization function.

[0079] Preferably, the voltage risk state value extraction process provided in this embodiment is illustrated in the following diagram. Figure 2 As shown.

[0080] Based on the voltage risk status values ​​at various times, it can be understood that the voltage risk status values ​​comprehensively reflect the dynamic instability of voltage and the health status of the bus voltage monitoring system. If the voltage disturbance smoothing value at the current time is larger, it indicates that the bus voltage has been more severely affected by noise interference in the short period before the current time. In this case, the accuracy of the bus voltage is lower, and therefore, the corresponding voltage risk status value is larger. At the same time, if the voltage disturbance sensitivity at the current time is larger, it indicates that the health status of the bus voltage monitoring system in the short period before the current time is more serious. Therefore, the corresponding voltage risk status value is larger, indicating that the health status of the bus voltage monitoring system in the short period before the current time also has a significant impact on the accuracy of bus voltage monitoring.

[0081] Conversely, the smaller the voltage disturbance smoothing value at the current moment, the less noise interference the bus voltage experienced in the short period before the current moment. In this case, the accuracy of the bus voltage is higher, and therefore, the corresponding voltage risk state value is smaller. At the same time, the smaller the voltage disturbance sensitivity at the current moment, the better the health status of the bus voltage monitoring system in the short period before the current moment. Therefore, the corresponding voltage risk state value is smaller, indicating that the health status of the bus voltage monitoring system in the short period before the current moment has less impact on the accuracy of bus voltage monitoring, and the overall monitoring quality is more stable and reliable.

[0082] Thus, this embodiment constructs a voltage risk state value by introducing voltage disturbance smoothing value and voltage disturbance sensitivity. This not only comprehensively evaluates the dynamic instability of bus voltage, but also quantifies the impact of the health status of the monitoring system on monitoring accuracy, thereby improving the accuracy of bus data monitoring.

[0083] Step S4: Based on the voltage risk state value, optimize the process noise covariance matrix in the Kalman filter algorithm at each time point, and denoise the bus voltage based on the optimized Kalman filter algorithm for bus voltage monitoring.

[0084] Under complex operating conditions, the power module bus voltage is highly susceptible to high-frequency noise, sudden load changes, and performance degradation of isolation devices, causing instantaneous changes in the original sampled signal accompanied by significant frequency domain distortion. Therefore, the commonly used fixed-parameter Kalman filter algorithm, in cases of model mismatch and unknown noise statistical characteristics, may exhibit divergence due to incorrect parameter settings, significantly reducing the accuracy and reliability of bus voltage monitoring. To improve the accuracy of bus voltage monitoring, this embodiment optimizes the process noise covariance matrix in the Kalman filter algorithm at each time point based on the voltage risk state value. The optimized Kalman filter algorithm is then used to denoise the bus voltage for bus voltage monitoring. The specific process is as follows:

[0085] As one implementation method, in this embodiment, the process noise covariance matrix in the Kalman filter algorithm at time i is... The expression is: In the formula, This represents the preset baseline process noise covariance matrix at time i; The voltage risk state value at time i is represented; k represents the preset adjustment coefficient.

[0086] It should be noted that the preset basic process noise covariance matrix at each time point is a 2×2 diagonal matrix. The elements in the first row and first column of the matrix are the variance of the bus voltage at all times within the preset time period before each time point. The elements in the second row and second column are the variance of the rate of change of voltage at all times within the preset time period before each time point. The result is the ratio of the rate of change of voltage and the difference between the bus voltage at each time point and the previous time point to the time interval between each time point and the previous time point.

[0087] Furthermore, it should be noted that the preset adjustment coefficient k is set manually, and the value of the preset adjustment coefficient k is generally in the range of 0.3 to 0.4. In this embodiment, the preset adjustment coefficient k is set to 0.3. In actual application, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0088] Based on the process noise covariance matrix at each time point, it can be understood that a larger voltage risk state value at the current time indicates that the bus voltage monitoring system is in an unstable or high-risk state. In this case, increasing the process noise covariance matrix Q can reduce the Kalman filter algorithm's dependence on model predictions, increase the Kalman filter algorithm's trust in actual observation data, thereby enhancing the Kalman filter algorithm's ability to track changes in bus voltage, avoiding estimation bias caused by model mismatch or sudden noise changes, and ensuring high-precision and robust voltage monitoring under abnormal operating conditions. Conversely, a smaller voltage risk state value at the current time indicates that the bus voltage monitoring system is in a relatively stable or low-risk state. In this case, the process noise covariance matrix Q can be appropriately reduced to enhance the Kalman filter algorithm's dependence on model predictions, reduce the Kalman filter algorithm's sensitivity to measurement noise, thereby improving the smoothness and stability of voltage estimation, and achieving more accurate and reliable voltage monitoring under normal operating conditions.

[0089] Using the process noise covariance matrix mentioned above as the process noise covariance matrix in the Kalman filter algorithm, we obtain the optimized Kalman filter algorithm.

[0090] Furthermore, the bus voltage within a preset time period before each moment is used as the input of the optimized Kalman filter algorithm. The state transition matrix is ​​set to an identity matrix, and the observation matrix is ​​set to H. In this embodiment, the observation matrix is ​​simplified to a scalar and set to 1. The denoised bus voltage within a preset time period before each moment is output for bus voltage monitoring.

[0091] Thus, this embodiment dynamically optimizes the process noise covariance matrix in the Kalman filter algorithm by introducing voltage risk state values, enabling the filter parameters to adapt to the dynamic fluctuations of the bus voltage and the system health status in real time. This effectively suppresses the impact of interference such as high-frequency noise, load mutations, and aging of isolation devices on voltage monitoring, and significantly improves the accuracy of bus voltage monitoring.

[0092] Based on the same inventive concept as the above method, this application embodiment also provides a bus voltage monitoring system for a power module, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described bus voltage monitoring methods for a power module.

[0093] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0094] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0095] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for monitoring the bus voltage of a power module, characterized in that, The method includes the following steps: Real-time acquisition of the bus voltage of the power module; Based on the fluctuation of the bus voltage at all times within a preset time period prior to each time, the voltage fluctuation value at each time is determined; based on the energy distribution of the bus voltage in the frequency domain at all times within a preset time period prior to each time, the energy characteristic value at each time is determined, and combined with the voltage fluctuation value, the voltage disturbance degree at each time is determined. Based on the smoothness of voltage disturbance at all times within a preset time period prior to each time, the voltage disturbance smoothing value at each time is determined to determine the voltage disturbance sensitivity at each time; based on the voltage disturbance smoothing value and the voltage disturbance sensitivity, the voltage risk state value at each time is determined. Based on the voltage risk state value, the process noise covariance matrix in the Kalman filter algorithm at each time point is optimized, and the bus voltage is denoised based on the optimized Kalman filter algorithm for bus voltage monitoring. The energy characteristic value at each moment is the result of the ratio of the total energy of the bus voltage in the preset frequency band in the frequency domain to the total energy in the entire frequency domain at all moments within the preset time period before each moment. The voltage disturbance degree at each time point is the result of positive fusion of the voltage fluctuation value and the energy characteristic value at each time point; The method for determining the voltage disturbance sensitivity at each time point is as follows: If the normalized value of the voltage disturbance smoothing value at the current moment is less than the preset first threshold, then the voltage disturbance sensitivity at the current moment is set to the preset first value. If the normalized value of the voltage disturbance smoothing value at the current moment is greater than the preset second threshold, then the voltage disturbance sensitivity at the current moment is set to the preset second value. If the normalized value of the voltage disturbance smoothing value at the current moment is within the closed interval between the preset first threshold and the preset second threshold, then the voltage disturbance sensitivity at the current moment is set to the preset third value, wherein the preset first threshold is less than the preset second threshold, and the magnitude of the preset third value is between the preset first value and the preset second value. The voltage disturbance sensitivity at each moment is obtained by iterating through each moment. The expression for the voltage risk state value at each time point is: In the formula, This represents the voltage risk state value at time i; This represents the voltage disturbance smoothing value at time i; Represents the voltage disturbance sensitivity at time i; norm() represents the normalization function; The optimization of the process noise covariance matrix in the Kalman filter algorithm at each time step includes: Process noise covariance matrix in Kalman filter algorithm at time i The expression is: In the formula, This represents the preset baseline process noise covariance matrix at time i; The voltage risk state value at time i is represented; k represents the preset adjustment coefficient.

2. The bus voltage monitoring method for a power module as described in claim 1, characterized in that, The method for determining the voltage fluctuation values ​​at each time point is as follows: Within a preset time period before each time point, the average value of the bus voltage at any given time point and all times within a preset window before that time point is calculated. The degree of dispersion of the average value at all times within the preset time period before each time point is used as the voltage fluctuation value at each time point.

3. The bus voltage monitoring method for a power module as described in claim 1, characterized in that, The voltage disturbance smoothing value at each time point is a smoothing value obtained by applying a smoothing algorithm to the voltage disturbance degree at all times within a preset time period prior to each time point.

4. The bus voltage monitoring method for a power module as described in claim 1, characterized in that, The bus voltage denoising based on the optimized Kalman filter algorithm includes: The bus voltage at all times within a preset time period before each time point is used as the input to the optimized Kalman filter algorithm, where the state transition matrix is ​​set to the identity matrix and the observation matrix is ​​set to H. The output is the denoised bus voltage within the preset time period before each time point.

5. A bus voltage monitoring system for a power module, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the bus voltage monitoring method for a power module as described in any one of claims 1-4.

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