Capacitive voltage division power taking device

Abstract

The application discloses a capacitor voltage division type power taking device and belongs to the technical field of power taking. In order to solve the problem of fault early warning lag of a traditional device, a fault risk prediction system is integrated, which comprises the following: a data acquisition module collects environmental temperature, high-voltage arm capacitor temperature, rectifier tube temperature, impedance amplitude, phase angle offset, resonance frequency offset, low-voltage arm voltage before rectification and output power; a temperature field analysis module constructs a temperature field model based on temperature data and outputs temperature field coefficients; an impedance spectrum analysis module outputs impedance spectrum analysis coefficients based on impedance parameters; an electrical state analysis module fuses the above coefficients to construct a power-voltage matching model and outputs matching coefficients; a fault risk assessment module outputs risk coefficients through a risk quantification model; and a judgment module compares each coefficient with a threshold to generate a shutdown maintenance instruction. Through multi-parameter coupling analysis and dynamic correction of the model, the application realizes accurate quantitative prediction of fault risks and significantly improves operation and maintenance efficiency.

Description

Technical Field

[0001] This invention belongs to the technical field of power extraction devices, and particularly relates to a capacitor voltage divider power extraction device. Background Technology

[0002] In power equipment, the stability and reliability of power extraction devices are crucial. Capacitor-divided voltage extraction devices are widely used in high-voltage line monitoring, but they face two major challenges during long-term operation:

[0003] Environmental sensitivity: Sudden temperature changes (ambient / component temperature difference) can easily cause capacitance drift, leading to voltage imbalance;

[0004] Latent faults are difficult to predict: The gradual shift of impedance spectrum parameters (resonant frequency / phase angle) is coupled with abnormal output power, and the traditional threshold method is difficult to quantify the fault risk.

[0005] Existing technologies lack multi-dimensional collaborative analysis models for temperature field distribution, impedance characteristics, and electrical status, resulting in delayed fault prediction and high operation and maintenance costs. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a capacitor voltage divider power extraction device, which solves the aforementioned problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a capacitor voltage divider power extraction device, comprising:

[0008] A fault risk prediction system is used to predict the fault risk of equipment, including:

[0009] The temperature field analysis module constructs a temperature field model based on ambient temperature, high voltage arm capacitor temperature, and rectifier tube temperature, and outputs temperature field coefficients.

[0010] The impedance spectrum analysis module constructs an impedance spectrum analysis model based on impedance amplitude, phase angle offset, and resonant frequency offset, and outputs impedance spectrum analysis coefficients.

[0011] The electrical condition analysis model is based on the output power under impedance spectrum analysis coefficients, temperature field coefficients, and the low-voltage arm voltage before rectification to construct a power-voltage matching model with output power-voltage matching coefficients.

[0012] The fault risk assessment module constructs a risk quantification model based on temperature field coefficient, impedance spectrum analysis coefficient, and power-voltage matching coefficient, and outputs risk quantification coefficient.

[0013] The judgment module compares the temperature field coefficient, impedance spectrum analysis coefficient, power-voltage matching coefficient, and risk quantification coefficient with the corresponding thresholds to obtain judgment information.

[0014] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0015] Further technical solutions include: a data acquisition module for acquiring ambient temperature, high-voltage arm capacitor temperature, rectifier tube temperature, impedance amplitude, phase angle offset, resonant frequency offset, low-voltage arm voltage before rectification, and output power.

[0016] A further technical solution: The steps for comparing the temperature field coefficient, impedance spectrum analysis coefficient, power-voltage matching coefficient, and risk quantification coefficient with their respective thresholds to obtain judgment information are as follows:

[0017] The temperature field coefficient is compared with the preset temperature field coefficient threshold. If the temperature field coefficient is not within the temperature field coefficient threshold, a shutdown and maintenance judgment is generated.

[0018] The impedance spectrum analysis coefficients are compared with the preset impedance spectrum analysis coefficient thresholds. If the impedance spectrum analysis coefficients are not within the impedance spectrum analysis coefficient thresholds, a shutdown and maintenance judgment is generated.

[0019] The power-voltage matching coefficient is compared with the preset power-voltage matching coefficient threshold. If the power-voltage matching coefficient is not within the power-voltage matching coefficient threshold, a shutdown and maintenance judgment is generated.

[0020] The risk quantification coefficient is compared with the preset risk quantification coefficient threshold. If the risk quantification coefficient is not within the risk quantification coefficient threshold, a shutdown and maintenance judgment is generated.

[0021] Further technical solution: The risk quantification model is expressed as follows:

[0022]

[0023] in, This represents the risk quantification coefficient. Indicates the current power-voltage matching degree. Indicates the ideal power-voltage matching degree. Indicates the standard deviation of power-voltage matching. Represents the impedance spectroscopy analysis coefficients. Represents the temperature field coefficient. This indicates the response weight.

[0024] Further technical solution: Based on impedance spectrum analysis coefficients, temperature field coefficients, and low-voltage arm voltage, construct a power-voltage matching model. The steps for output power-voltage matching coefficients are as follows:

[0025] Based on the impedance spectral analysis coefficients and temperature field coefficients, an efficiency correction model is constructed, and an efficiency correction factor is output. The efficiency correction model is expressed as follows:

[0026]

[0027] in, This represents the efficiency correction factor. Represents the temperature field coefficient. Represents the impedance spectroscopy analysis coefficients. Represents the weight coefficient and ;

[0028] The theoretical power reference model is constructed based on the low-voltage arm voltage before rectification and the equivalent load resistance to output the theoretical power. The theoretical power reference model is expressed as follows:

[0029]

[0030] in, Indicates theoretical power. Indicates the low-voltage arm voltage. Indicates the equivalent resistance. This indicates the rectifier bridge efficiency. This represents the efficiency correction factor;

[0031] Based on the theoretical power and output power, a power-voltage matching model is constructed, and the output power-voltage matching coefficients are determined. The power-voltage matching model is expressed as follows:

[0032]

[0033] in, Indicates power-voltage matching degree. Indicates output power. Indicates the low-voltage arm voltage. Indicates the equivalent resistance. This indicates the rectifier bridge efficiency. This represents the efficiency correction factor.

[0034] Further technical solution: The steps for constructing a temperature field model and outputting the temperature field coefficients based on ambient temperature, high-voltage arm capacitor temperature, and rectifier tube temperature are as follows:

[0035] Import the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature into the temperature gradient model to obtain the temperature gradient value;

[0036] Import the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature into the temperature distribution standard deviation model to obtain the temperature distribution standard deviation.

[0037] The temperature deviation value is obtained by subtracting the reference temperature from the maximum value of the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature.

[0038] The temperature gradient value, the standard deviation of the temperature distribution, and the temperature deviation value are respectively subjected to maximum-min normalization to obtain the current temperature gradient index, the current temperature distribution standard deviation index, and the temperature deviation index.

[0039] A temperature field model is constructed based on the temperature gradient index, the standard deviation index of temperature distribution, and the temperature deviation index, and the output temperature field coefficients are then defined. The temperature field model is expressed as follows:

[0040]

[0041] in, Represents the temperature field coefficient. Indicates the temperature gradient exponent. The standard deviation index of temperature distribution is represented by the index. Indicates the temperature deviation index. Represents the weight coefficient and .

[0042] Further technical solution: The steps for constructing an impedance spectrum analysis model and outputting impedance spectrum analysis coefficients based on impedance amplitude, phase angle offset, and resonant frequency offset are as follows:

[0043] The current impedance amplitude, current phase angle offset, and current resonant frequency offset are subjected to maximum-minimum normalization to obtain the impedance amplitude index, phase angle offset index, and resonant frequency offset index.

[0044] An impedance spectrum analysis model is constructed based on the impedance magnitude exponent, phase angle offset exponent, and resonant frequency offset exponent, and the impedance spectrum analysis coefficients are output. The impedance spectrum analysis model is expressed as follows:

[0045]

[0046] in, Represents the impedance spectroscopy analysis coefficients. Indicates the impedance magnitude index. Indicates the phase angle offset index. Indicates the resonant frequency offset index. Represents the weight coefficient and .

[0047] A further technical solution: The temperature gradient model is expressed as follows:

[0048]

[0049] in, Indicates the temperature gradient value. This indicates the surface temperature of the high-voltage arm capacitor. Indicates ambient temperature. This indicates the temperature of the rectifier tube.

[0050] A further technical solution: The temperature distribution standard deviation model is expressed as follows:

[0051]

[0052] in, Indicates the standard deviation of temperature distribution. Indicates the number of terms. Indicates the temperature at each point. This represents the arithmetic mean of the temperatures at each point.

[0053] This invention provides a capacitor voltage divider power extraction device, which has the following advantages compared with the prior art:

[0054] 1. This invention quantifies temperature distribution characteristics through a temperature field model and combines dynamic correlation analysis of impedance spectrum parameters and power-voltage matching degree to construct a multi-dimensional risk quantification assessment model. This model effectively identifies potential fault risks under the synergistic effect of temperature drift and impedance parameter degradation. It has the advantage of achieving multi-dimensional synergistic degradation risk quantification assessment and early warning of potential faults through comprehensive temperature field distribution characteristics and dynamic correlation analysis of impedance spectrum parameters. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the fault risk prediction system of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0058] Please see Figure 1 According to one embodiment of the present invention, a capacitor voltage divider power extraction device includes:

[0059] A fault risk prediction system is used to predict the fault risk of equipment, including:

[0060] The data acquisition module is used to acquire ambient temperature, high-voltage arm capacitor temperature, rectifier tube temperature, impedance amplitude, phase angle offset, resonant frequency offset, low-voltage arm voltage before rectification, and output power.

[0061] The temperature field analysis module constructs a temperature field model based on ambient temperature, high voltage arm capacitor temperature, and rectifier tube temperature, and outputs temperature field coefficients.

[0062] The impedance spectrum analysis module constructs an impedance spectrum analysis model based on impedance amplitude, phase angle offset, and resonant frequency offset, and outputs impedance spectrum analysis coefficients.

[0063] The electrical condition analysis model is based on the output power under impedance spectrum analysis coefficients, temperature field coefficients, and the low-voltage arm voltage before rectification to construct a power-voltage matching model with output power-voltage matching coefficients.

[0064] The fault risk assessment module constructs a risk quantification model based on temperature field coefficient, impedance spectrum analysis coefficient, and power-voltage matching coefficient, and outputs risk quantification coefficient.

[0065] The judgment module compares the temperature field coefficient, impedance spectrum analysis coefficient, power-voltage matching coefficient, and risk quantification coefficient with the corresponding thresholds to obtain judgment information.

[0066] Among them, the temperature field coefficient is a quantitative indicator reflecting the temperature distribution state of the device, output by the temperature field model. Specifically, it can be achieved by a weighted combination of normalized temperature gradient values, temperature distribution standard deviation, and temperature deviation values, used to characterize the impact of temperature field anomalies on device stability. The impedance spectrum analysis coefficient is a quantitative indicator reflecting changes in impedance characteristics, output by the impedance spectrum analysis model. Specifically, it can be achieved by a weighted combination of normalized impedance amplitude shift, phase angle shift, and resonant frequency shift, used to identify whether the gradual shift of impedance parameters exceeds the safe range. The power-voltage matching coefficient is the ratio of theoretical power to actual output power output by the power-voltage matching model. Specifically, it can be calculated using the low-voltage arm voltage before rectification, equivalent resistance, and efficiency correction factor, used to detect anomalies in power-voltage matching. The risk quantification coefficient is a comprehensive evaluation indicator output by the risk quantification model. Specifically, it can be achieved by combining an exponential function with the product of power-voltage matching, impedance spectrum analysis coefficient, and temperature field coefficient, used to quantify the overall failure risk level of the device.

[0067] Specifically, the temperature field coefficient threshold is set as the allowable temperature distribution fluctuation range. When the temperature field coefficient exceeds this range, it indicates an abnormal temperature gradient or local overheating risk within the device, triggering a shutdown and maintenance signal to prevent capacitance drift. The impedance spectrum analysis coefficient threshold is set as the safety boundary for impedance parameters. When this coefficient exceeds the threshold, it indicates that the resonant frequency or phase angle shift has affected the device's impedance characteristics, requiring immediate shutdown to prevent the latent fault from worsening. The power-voltage matching coefficient threshold is set as the critical value for power output matching. When this coefficient deviates from the threshold range, it indicates that voltage imbalance has led to abnormal electrical conditions, requiring shutdown and maintenance to restore the power-voltage matching relationship. The risk quantification coefficient threshold is set as the upper limit of the comprehensive risk level. When this coefficient exceeds the threshold, it indicates that the synergistic effect of multiple parameters has placed the device in a high-risk state, requiring mandatory shutdown for systematic investigation. The independent judgment of these four thresholds forms a hierarchical early warning mechanism. Any abnormal parameter can trigger a maintenance decision, avoiding the risk of missed detections due to single-dimensional judgment.

[0068] Compared to existing technologies, traditional methods rely solely on single-parameter threshold judgments, such as monitoring only output power or ambient temperature, failing to distinguish the coupled effects of abnormal temperature field distribution and impedance characteristic shifts. This solution, however, identifies early signs of abnormal temperature gradients and resonant frequency shifts by comparing independent thresholds for temperature field coefficients and impedance spectrum analysis coefficients. Furthermore, by combining power-voltage matching coefficient threshold judgments, it distinguishes the direct correlation between voltage imbalance and power anomalies. Finally, it achieves coordinated risk assessment of multiple parameters through risk quantification coefficient thresholds. The problem in existing technologies where the lack of coordinated analysis of temperature field, impedance spectrum, and electrical status leads to fault warnings lagging behind the actual risk accumulation process is addressed in this solution through a four-layer threshold judgment mechanism.

[0069] Through the above technical solutions, this application can identify the risk of capacitance drift caused by uneven temperature distribution in advance, accurately detect latent faults caused by gradual shifts in impedance spectrum parameters, promptly determine voltage imbalance caused by abnormal power-voltage matching, and achieve collaborative risk assessment of multi-dimensional parameters through a risk quantification model. When any dimension parameter exceeds the safety threshold, a shutdown and maintenance command is immediately triggered, avoiding the fault omissions or misjudgments caused by single-parameter judgment in traditional methods, and significantly improving the timeliness and accuracy of shutdown decisions.

[0070] Preferably, the steps for constructing a temperature field model and outputting temperature field coefficients based on ambient temperature, high-voltage arm capacitor temperature, and rectifier tube temperature are as follows:

[0071] The current ambient temperature, current high-voltage arm capacitor temperature, and current rectifier tube temperature are imported into the temperature gradient model to obtain the temperature gradient value. The temperature gradient model is expressed as follows:

[0072]

[0073] in, Indicates the temperature gradient value. This indicates the surface temperature of the high-voltage arm capacitor. Indicates ambient temperature. Indicates the rectifier tube temperature;

[0074] The current ambient temperature, current high-voltage arm capacitor temperature, and current rectifier tube temperature are imported into the temperature distribution standard deviation model to obtain the temperature distribution standard deviation. The temperature distribution standard deviation model is expressed as follows:

[0075]

[0076] in, Indicates the standard deviation of temperature distribution. Indicates the number of terms. Indicates the temperature at each point. This represents the arithmetic mean of the temperatures at all points.

[0077] The temperature deviation value is obtained by subtracting the reference temperature from the maximum value of the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature.

[0078] The temperature gradient value, the standard deviation of the temperature distribution, and the temperature deviation value are respectively subjected to maximum-min normalization to obtain the current temperature gradient index, the current temperature distribution standard deviation index, and the temperature deviation index.

[0079] A temperature field model is constructed based on the temperature gradient index, the standard deviation index of temperature distribution, and the temperature deviation index, and the output temperature field coefficients are then defined. The temperature field model is expressed as follows:

[0080]

[0081] in, Represents the temperature field coefficient. Indicates the temperature gradient exponent. The standard deviation index of temperature distribution is represented by the index. Indicates the temperature deviation index. Represents the weight coefficient and The The higher the value, the higher the risk.

[0082] The temperature gradient model, calculated by averaging the absolute values ​​of the temperature differences between the high-voltage arm capacitor, rectifier tube, and ambient temperature, reflects the risk of localized temperature abrupt changes. This can be achieved by using temperature sensors to collect temperature data at various points, and is used to identify capacitance drift caused by temperature differences between components. The temperature distribution standard deviation model assesses the overall temperature field uniformity by calculating the dispersion of three temperature points. This can be calculated using the statistical standard deviation formula, and is used to detect voltage imbalances caused by abnormal temperature distribution. The temperature deviation value compares the highest temperature with a reference temperature to reflect the overall deviation. This can be calculated using a preset reference temperature threshold, and is used to capture the impact of extreme temperatures on system stability. Maximum-minimum normalization maps parameters with different dimensions to a unified numerical range. This can be achieved using a linear transformation formula to eliminate dimensional differences, making the temperature gradient, distribution standard deviation, and deviation value comparable. The temperature field coefficient model characterizes the overall temperature state using linear weights.

[0083] Specifically, after the ambient temperature, high-voltage arm capacitor temperature, and rectifier tube temperature are simultaneously collected, the temperature gradient value among the three is first calculated. For example, when the temperature difference between the surface temperature of the high-voltage arm capacitor and the ambient temperature reaches 15℃, this parameter will increase significantly. Then, the standard deviation of the temperature distribution is calculated. If the three temperature points show a significant discrete distribution, such as an abnormal increase in the rectifier tube temperature causing the standard deviation to exceed 2℃, it indicates a deterioration in the uniformity of the temperature field. Simultaneously, the highest temperature among the three is extracted and compared with a preset reference temperature. For example, when the rectifier tube temperature reaches 85℃ while the reference temperature is 70℃, the temperature deviation value will be recorded as a 15℃ offset. These three parameters are then normalized and converted into exponential values ​​within the range of 0-1. For example, the temperature gradient value is processed to become a gradient exponent of 0.8. Finally, the gradient exponent, standard deviation exponent, and deviation exponent are combined according to preset weights using a temperature field coefficient model to comprehensively quantify the abnormal state of the temperature field.

[0084] Compared to existing technologies, traditional methods only monitor a single temperature parameter or use a fixed threshold for judgment, which cannot effectively identify uneven temperature field distribution and gradient abrupt changes. This solution uses multi-dimensional temperature parameter fusion modeling to capture three types of features simultaneously: local temperature difference, overall dispersion, and extreme offset. For example, existing technologies may only compare ambient temperature and component temperature, while this solution additionally introduces rectifier tube temperature to construct a triangular temperature relationship, which more accurately reflects the thermal field distribution characteristics of the voltage divider circuit.

[0085] Through the above technical solutions, this application can quantitatively assess abnormal temperature field conditions and provide early warnings of capacitance drift risks caused by sudden changes or uneven distribution of temperature gradients. For example, when the temperature difference between the high-voltage arm capacitor and the rectifier tube continues to widen, the increase in the temperature gradient exponent will trigger an early warning mechanism, preventing abnormal output power caused by voltage imbalance. Simultaneously, through normalization processing and a weighted combination model, the problem of evaluation bias caused by inconsistent dimensions of multiple temperature parameters is solved. For instance, by unifying the units of Celsius and standard deviation into dimensionless exponents, the impact of different temperature characteristics on system stability can be accurately compared.

[0086] Preferably, the steps for constructing the impedance spectrum analysis model and outputting the impedance spectrum analysis coefficients based on the impedance amplitude, phase angle offset, and resonant frequency offset are as follows:

[0087] The current impedance amplitude, current phase angle offset, and current resonant frequency offset are subjected to maximum-minimum normalization to obtain the impedance amplitude index, phase angle offset index, and resonant frequency offset index.

[0088] An impedance spectrum analysis model is constructed based on the impedance magnitude exponent, phase angle offset exponent, and resonant frequency offset exponent, and the impedance spectrum analysis coefficients are output. The impedance spectrum analysis model is expressed as follows:

[0089]

[0090] in, Represents the impedance spectroscopy analysis coefficients. Indicates the impedance magnitude index. Indicates the phase angle offset index. Indicates the resonant frequency offset index. Represents the weight coefficient and The The higher the value, the higher the risk.

[0091] The maximum-minimum normalization process involves linearly transforming the original data to the [0,1] interval. This can be achieved using the formula (current value - minimum value) / (maximum value - minimum value), eliminating numerical differences between parameters of different dimensions and improving the objectivity of the impedance spectrum characterization. The impedance spectrum analysis coefficients are comprehensive indicators that integrate impedance amplitude, phase angle shift, and resonant frequency shift. These can be achieved through weighted summation and are used to quantify the contribution of impedance anomalies to fault risk. The weighting coefficients represent the proportion of each normalization index in the model. These can be determined using empirical values ​​or optimization algorithms and are used to dynamically adjust the sensitivity weights of each parameter according to actual operating conditions, meeting the fault detection needs of different scenarios.

[0092] Specifically, by normalizing the impedance amplitude, phase angle offset, and resonant frequency offset, parameters with different dimensions are converted into dimensionless exponents, making the changes in impedance amplitude, the degree of phase angle offset, and the magnitude of resonant frequency offset comparable. The impedance amplitude exponent reflects the overall trend of the impedance characteristics of the capacitor voltage divider arm, the phase angle offset exponent characterizes the stability of the system's resonant state, and the resonant frequency offset exponent indicates the abnormal offset direction of the circuit matching degree. Subsequently, the three exponents are weighted and summed using weighting coefficients to generate impedance spectrum analysis coefficients. The constraints of the weighting coefficients ensure that the model can dynamically adjust the contribution ratio of each parameter according to the actual operating conditions. For example, in high-temperature environments, the weight of the phase angle offset exponent can be increased to capture the resonant instability characteristics caused by temperature. This coefficient maps multidimensional impedance spectrum parameters into a single quantitative index, providing a calculable intermediate variable for subsequent risk quantification models, and solving the problem that traditional methods cannot analyze the coupling relationship between impedance characteristics and electrical state.

[0093] Compared to existing technologies, which typically assess fault risk using a single impedance parameter threshold or static weighting, it is difficult to detect latent faults caused by the coordinated changes of multiple parameters. This proposed solution, however, eliminates parameter dimension differences through normalization and, combined with a dynamic weighting mechanism, can more sensitively identify the gradual coupling characteristics of impedance amplitude, phase angle shift, and resonant frequency shift, thereby achieving early warning of latent faults.

[0094] Through the above technical solution, this application can effectively solve the problem of delayed fault warning caused by the gradual shift of impedance spectrum parameters and abnormal coupling of output power in capacitor voltage divider power extraction devices. By synergistic analysis and dynamic weight fusion of multi-dimensional impedance characteristic parameters, the originally dispersed impedance characteristic anomalies are transformed into quantifiable comprehensive coefficients, which significantly improves the accuracy and reliability of fault risk assessment and provides an operable technical means for early warning of hidden faults in high-voltage line monitoring.

[0095] Preferably, the steps for constructing the power-voltage matching model and output power-voltage matching coefficients based on the impedance spectrum analysis coefficients, temperature field coefficients, and low-voltage arm voltage are as follows:

[0096] Based on the impedance spectral analysis coefficients and temperature field coefficients, an efficiency correction model is constructed, and an efficiency correction factor is output. The efficiency correction model is expressed as follows:

[0097]

[0098] in, This represents the efficiency correction factor. Represents the temperature field coefficient. Represents the impedance spectroscopy analysis coefficients. Represents the weight coefficient and ;

[0099] The theoretical power reference model is constructed based on the low-voltage arm voltage before rectification and the equivalent load resistance to output the theoretical power. The theoretical power reference model is expressed as follows:

[0100]

[0101] in, Indicates theoretical power. Indicates the low-voltage arm voltage. Indicates the equivalent resistance. This indicates the rectifier bridge efficiency. This represents the efficiency correction factor;

[0102] Based on the theoretical power and output power, a power-voltage matching model is constructed, and the output power-voltage matching coefficients are determined. The power-voltage matching model is expressed as follows:

[0103]

[0104] in, Indicates power-voltage matching degree. Indicates output power. Indicates the low-voltage arm voltage. Indicates the equivalent resistance. This indicates the rectifier bridge efficiency. This represents the efficiency correction factor. A value greater than 1 indicates an abnormal load. A value less than 1 indicates uneven pressure distribution.

[0105] The efficiency correction factor refers to a parameter that dynamically adjusts the system efficiency using the temperature field coefficient and impedance spectral analysis coefficients. Specifically, it can be implemented using a linear weighted model, with weighting coefficients... and It can be adjusted according to actual operating conditions to reflect the coupling effect of abnormal temperature gradients and impedance parameter deviations on system efficiency. Theoretical power refers to the theoretical reference power calculated based on the low-voltage arm voltage and equivalent resistance. Specifically, the theoretical formula can be dynamically corrected using rectifier bridge efficiency and efficiency correction factors to establish a power calculation benchmark that integrates temperature field and impedance spectrum characteristics. Power-voltage matching degree refers to the ratio of actual output power to the corrected theoretical power, which can be calculated to quantitatively assess the degree of deviation between the electrical state and the theoretical reference.

[0106] Specifically, firstly, an efficiency correction model is used to weight the temperature field coefficient and impedance spectrum analysis coefficients to dynamically correct the system efficiency parameters, eliminating the impact of uneven temperature distribution and impedance characteristic deviations on theoretical power calculations. Then, based on the corrected efficiency factor and low-voltage arm voltage parameters, combined with equivalent resistance and rectifier bridge efficiency, a theoretical power benchmark reflecting real-time operating conditions is generated. Finally, the power-voltage matching degree is calculated by the ratio of actual output power to theoretical power, transforming previously latent electrical anomalies into quantifiable matching coefficients, providing a dynamic data foundation for subsequent fault risk assessment.

[0107] Compared to existing technologies, traditional methods typically rely on a single electrical parameter threshold for fault diagnosis, such as monitoring only the absolute value changes in output voltage or power. This proposed solution, however, constructs a multi-dimensional collaborative model that couples temperature field distribution, impedance spectrum characteristics, and electrical state parameters. By introducing an efficiency correction factor into theoretical power calculations, the assessment of power-voltage matching can reflect the combined effects of abnormal temperature gradients and impedance parameter shifts, thus resolving the misjudgment problem caused by neglecting physical field coupling effects in traditional methods.

[0108] Through the above technical solution, this application achieves dynamic quantitative evaluation of the power-voltage matching degree of capacitor voltage divider power extraction devices, which can accurately identify latent faults caused by uneven temperature field distribution or impedance characteristic deviation. By establishing a corrected theoretical power benchmark, the sensitivity of actual output power anomaly detection is improved, providing a reliable quantitative basis for fault risk early warning and effectively avoiding the early warning lag problem of the traditional single threshold method.

[0109] Preferably, the risk quantification model is expressed as:

[0110]

[0111] in, This represents the risk quantification coefficient. Indicates the current power-voltage matching degree. Indicates the ideal power-voltage matching degree. Indicates the standard deviation of power-voltage matching. Represents the impedance spectroscopy analysis coefficients. Represents the temperature field coefficient. Indicates the response weight, the The higher the value, the lower the risk.

[0112] Among them, the risk quantification coefficient refers to a quantitative indicator for dynamically assessing the risk of device failure through mathematical modeling. Specifically, it can be implemented by combining an exponential function with weight parameters, and is used to comprehensively reflect the superimposed impact of power-voltage matching deviation, impedance characteristic shift and temperature field anomaly on risk.

[0113] The current power-voltage matching degree refers to the ratio of actual output power to theoretical power. It can be calculated through a power-voltage matching model and is used to characterize whether the matching state between power and voltage deviates from the ideal range.

[0114] Ideal power-voltage matching degree refers to the optimal matching state of power and voltage under standard operating conditions. It can be determined through experimental calibration or historical data statistics and serves as a benchmark value for judging the degree of deviation in risk quantification models.

[0115] The standard deviation of power-voltage matching refers to the degree of dispersion of power-voltage matching. It can be calculated from historical operating data and is used to adjust the model's sensitivity to deviations in matching.

[0116] Impedance spectrum analysis coefficients are quantitative parameters constructed from impedance amplitude, phase angle offset, and resonant frequency offset. Specifically, they can be calculated using normalization and a linear weighted model to reflect the degree of abnormal offset in impedance characteristics.

[0117] The temperature field coefficient is a quantitative parameter constructed from the temperature gradient, distribution standard deviation, and temperature deviation. Specifically, it can be calculated using normalization and a nonlinear weighted model, and is used to characterize the non-uniformity of the temperature field distribution.

[0118] Response weights refer to the exponential weighting coefficients of each parameter in the model, used to adjust the contribution of impedance spectroscopy coefficients and temperature field coefficients to risk quantification.

[0119] Specifically, this technical solution uses a risk quantification model combining an exponential function and weighting parameters to uniformly quantify the impact of power-voltage mismatch deviation, impedance characteristics, and temperature field distribution. First, the exponential term... The deviation of the current power-voltage matching degree from the ideal value is reflected by a Gaussian function. Deviation At this time, the exponential term decays, the risk coefficient decreases, thus capturing the direct impact of power-voltage mismatch anomalies on fault risk. Secondly, and We introduce weighted exponential forms for the impedance spectral analysis coefficients and temperature field coefficients, respectively, and then use response weights. The contribution of both parameters to risk quantification is adjusted. Impedance spectrum analysis coefficients reflect anomalous shifts in impedance characteristics such as resonant frequency and phase angle, while temperature field coefficients characterize temperature field inhomogeneities such as temperature gradient and standard deviation. The exponential form of both can amplify or suppress their nonlinear effects on the final risk coefficient. Finally, the model integrates three types of parameters—power-voltage matching deviation, impedance characteristic shift, and temperature field anomaly—through a product form, overcoming the limitations of traditional single-threshold judgments and achieving synergistic analysis of multi-dimensional parameters.

[0120] Compared to existing technologies, traditional methods rely on single threshold judgments or linear weighted models, which cannot effectively quantify the coupling effects of gradual impedance characteristic shifts, uneven temperature field distribution, and power-voltage mismatch anomalies. For example, existing technologies typically set fixed thresholds for temperature and impedance parameters, but ignore the dynamic impact of the nonlinear superposition of these three factors on fault risk. Our proposed solution, however, uses a nonlinear combination of exponential functions and weighting parameters to dynamically adjust the sensitivity of different parameters and utilizes a Gaussian function to capture the statistical characteristics of mismatch deviations, thereby more accurately identifying latent fault risks.

[0121] Through the above technical solution, this application solves the problem of delayed prediction of latent fault risks in capacitor voltage divider power extraction devices caused by gradual shifts in impedance characteristics, uneven temperature field distribution, and abnormal coupling of power-voltage matching, which is difficult to quantify using traditional threshold methods. By integrating multi-dimensional parameters such as power-voltage matching deviation, impedance spectrum characteristics, and temperature field distribution, dynamic quantitative assessment of fault risks is achieved, thereby triggering early warnings in the early stages of abnormal output power or temperature field imbalance, reducing operation and maintenance costs and improving device reliability.

[0122] Preferably, the step of comparing the temperature field coefficient, impedance spectrum analysis coefficient, power-voltage matching coefficient, and risk quantification coefficient with corresponding thresholds to obtain judgment information is as follows:

[0123] The temperature field coefficient is compared with the preset temperature field coefficient threshold. If the temperature field coefficient is not within the temperature field coefficient threshold, a shutdown and maintenance judgment is generated.

[0124] The impedance spectrum analysis coefficients are compared with the preset impedance spectrum analysis coefficient thresholds. If the impedance spectrum analysis coefficients are not within the impedance spectrum analysis coefficient thresholds, a shutdown and maintenance judgment is generated.

[0125] The power-voltage matching coefficient is compared with the preset power-voltage matching coefficient threshold. If the power-voltage matching coefficient is not within the power-voltage matching coefficient threshold, a shutdown and maintenance judgment is generated.

[0126] The risk quantification coefficient is compared with the preset risk quantification coefficient threshold. If the risk quantification coefficient is not within the risk quantification coefficient threshold, a shutdown and maintenance judgment is generated.

[0127] Specifically, the temperature field coefficient threshold is set as the allowable temperature distribution fluctuation range. When the temperature field coefficient exceeds this range, it indicates an abnormal temperature gradient or local overheating risk within the device, triggering a shutdown and maintenance signal to prevent capacitance drift. The impedance spectrum analysis coefficient threshold is set as the safety boundary for impedance parameters. When this coefficient exceeds the threshold, it indicates that the resonant frequency or phase angle shift has affected the device's impedance characteristics, requiring immediate shutdown to prevent the latent fault from worsening. The power-voltage matching coefficient threshold is set as the critical value for power output matching. When this coefficient deviates from the threshold range, it indicates that voltage imbalance has led to abnormal electrical conditions, requiring shutdown and maintenance to restore the power-voltage matching relationship. The risk quantification coefficient threshold is set as the upper limit of the comprehensive risk level. When this coefficient exceeds the threshold, it indicates that the synergistic effect of multiple parameters has placed the device in a high-risk state, requiring mandatory shutdown for systematic investigation. The independent judgment of these four thresholds forms a hierarchical early warning mechanism. Any abnormal parameter can trigger a maintenance decision, avoiding the risk of missed detections due to single-dimensional judgment.

[0128] Compared to existing technologies, traditional methods rely solely on single-parameter threshold judgments, such as monitoring only output power or ambient temperature, failing to distinguish the coupled effects of abnormal temperature field distribution and impedance characteristic shifts. This solution, however, identifies early signs of abnormal temperature gradients and resonant frequency shifts by comparing independent thresholds for temperature field coefficients and impedance spectrum analysis coefficients. Furthermore, by combining power-voltage matching coefficient threshold judgments, it distinguishes the direct correlation between voltage imbalance and power anomalies. Finally, it achieves coordinated risk assessment of multiple parameters through risk quantification coefficient thresholds. The problem in existing technologies where the lack of coordinated analysis of temperature field, impedance spectrum, and electrical status leads to fault warnings lagging behind the actual risk accumulation process is addressed in this solution through a four-layer threshold judgment mechanism.

[0129] Through the above technical solutions, this application can identify the risk of capacitance drift caused by uneven temperature distribution in advance, accurately detect latent faults caused by gradual shifts in impedance spectrum parameters, promptly determine voltage imbalance caused by abnormal power-voltage matching, and achieve collaborative risk assessment of multi-dimensional parameters through a risk quantification model. When any dimension parameter exceeds the safety threshold, a shutdown and maintenance command is immediately triggered, avoiding the fault omissions or misjudgments caused by single-parameter judgment in traditional methods, and significantly improving the timeliness and accuracy of shutdown decisions.

[0130] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0131] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A capacitor voltage divider power extraction device, characterized in that, include: A fault risk prediction system is used to predict the fault risk of equipment, including: The temperature field analysis module constructs a temperature field model based on ambient temperature, high voltage arm capacitor temperature, and rectifier tube temperature, and outputs temperature field coefficients. The impedance spectrum analysis module constructs an impedance spectrum analysis model based on impedance amplitude, phase angle offset, and resonant frequency offset, and outputs impedance spectrum analysis coefficients. The electrical condition analysis model is based on the output power under impedance spectrum analysis coefficients, temperature field coefficients, and the low-voltage arm voltage before rectification to construct a power-voltage matching model with output power-voltage matching coefficients. The fault risk assessment module constructs a risk quantification model based on temperature field coefficient, impedance spectrum analysis coefficient, and power-voltage matching coefficient, and outputs risk quantification coefficient. The judgment module compares the temperature field coefficient, impedance spectrum analysis coefficient, power-voltage matching coefficient, and risk quantification coefficient with the corresponding thresholds to obtain judgment information. The risk quantification model is expressed as follows: in, This represents the risk quantification coefficient. Indicates the current power-voltage matching degree. Indicates the ideal power-voltage matching degree. Indicates the standard deviation of power-voltage matching. Represents the impedance spectroscopy analysis coefficients. Represents the temperature field coefficient. This indicates the response weight.

2. The capacitor voltage divider power extraction device according to claim 1, characterized in that, Also includes: The data acquisition module is used to acquire ambient temperature, high-voltage arm capacitor temperature, rectifier tube temperature, impedance amplitude, phase angle offset, resonant frequency offset, low-voltage arm voltage before rectification, and output power.

3. The capacitor voltage divider power extraction device according to claim 1, characterized in that, The steps for comparing the temperature field coefficient, impedance spectral analysis coefficient, power-voltage matching coefficient, and risk quantification coefficient with their respective thresholds to obtain judgment information are as follows: The temperature field coefficient is compared with the preset temperature field coefficient threshold. If the temperature field coefficient is not within the temperature field coefficient threshold, a shutdown and maintenance judgment is generated. The impedance spectrum analysis coefficients are compared with the preset impedance spectrum analysis coefficient thresholds. If the impedance spectrum analysis coefficients are not within the impedance spectrum analysis coefficient thresholds, a shutdown and maintenance judgment is generated. The power-voltage matching coefficient is compared with the preset power-voltage matching coefficient threshold. If the power-voltage matching coefficient is not within the power-voltage matching coefficient threshold, a shutdown and maintenance judgment is generated. The risk quantification coefficient is compared with the preset risk quantification coefficient threshold. If the risk quantification coefficient is not within the risk quantification coefficient threshold, a shutdown and maintenance judgment is generated.

4. The capacitor voltage divider power extraction device according to claim 1, characterized in that, The steps for constructing a power-voltage matching model based on the output power under impedance spectral analysis coefficients, temperature field coefficients, and low-voltage arm voltage are as follows: Based on the impedance spectral analysis coefficients and temperature field coefficients, an efficiency correction model is constructed, and an efficiency correction factor is output. The efficiency correction model is expressed as follows: in, This represents the efficiency correction factor. Represents the temperature field coefficient. Represents the impedance spectroscopy analysis coefficients. Represents the weight coefficient and ; The theoretical power reference model is constructed based on the low-voltage arm voltage before rectification and the equivalent load resistance to output the theoretical power. The theoretical power reference model is expressed as follows: in, Indicates theoretical power. Indicates the low-voltage arm voltage. Indicates the equivalent resistance. This indicates the rectifier bridge efficiency. This represents the efficiency correction factor; Based on the theoretical power and output power, a power-voltage matching model is constructed, and the output power-voltage matching coefficients are determined. The power-voltage matching model is expressed as follows: in, Indicates power-voltage matching degree. Indicates output power. Indicates the low-voltage arm voltage. Indicates the equivalent resistance. This indicates the rectifier bridge efficiency. This represents the efficiency correction factor.

5. The capacitor voltage divider power extraction device according to claim 1 or 4, characterized in that, The steps for constructing a temperature field model and outputting the temperature field coefficients based on ambient temperature, high-voltage arm capacitor temperature, and rectifier tube temperature are as follows: Import the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature into the temperature gradient model to obtain the temperature gradient value; Import the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature into the temperature distribution standard deviation model to obtain the temperature distribution standard deviation. The temperature deviation value is obtained by subtracting the reference temperature from the maximum value of the current ambient temperature, the current high voltage arm capacitor temperature, and the current rectifier tube temperature. The temperature gradient value, the standard deviation of the temperature distribution, and the temperature deviation value are respectively subjected to maximum-min normalization to obtain the current temperature gradient index, the current temperature distribution standard deviation index, and the temperature deviation index. A temperature field model is constructed based on the temperature gradient index, the standard deviation index of temperature distribution, and the temperature deviation index, and the output temperature field coefficients are then defined. The temperature field model is expressed as follows: in, Represents the temperature field coefficient. Indicates the temperature gradient exponent. The standard deviation index of temperature distribution is represented by the index. Indicates the temperature deviation index. Represents the weight coefficient and .

6. The capacitor voltage divider power extraction device according to claim 1 or 4, characterized in that, The steps for constructing an impedance spectrum analysis model and outputting impedance spectrum analysis coefficients based on impedance magnitude, phase angle offset, and resonant frequency offset are as follows: The current impedance amplitude, current phase angle offset, and current resonant frequency offset are subjected to maximum-minimum normalization to obtain the impedance amplitude index, phase angle offset index, and resonant frequency offset index. An impedance spectrum analysis model is constructed based on the impedance magnitude exponent, phase angle offset exponent, and resonant frequency offset exponent, and the impedance spectrum analysis coefficients are output. The impedance spectrum analysis model is expressed as follows: in, Represents the impedance spectroscopy analysis coefficients. Indicates the impedance magnitude index. Indicates the phase angle offset index. Indicates the resonant frequency offset index. Represents the weight coefficient and .

7. The capacitor voltage divider power extraction device according to claim 5, characterized in that, The temperature gradient model is expressed as follows: in, Indicates the temperature gradient value. This indicates the surface temperature of the high-voltage arm capacitor. Indicates ambient temperature. This indicates the temperature of the rectifier tube.

8. The capacitor voltage divider power extraction device according to claim 5, characterized in that, The temperature distribution standard deviation model is expressed as follows: in, Indicates the standard deviation of temperature distribution. Indicates the number of terms. Indicates the temperature at each point. This represents the arithmetic mean of the temperatures at each point.

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