Power utilization safety monitoring device based on magnetic suspension power distribution technology

Abstract

The invention relates to the technical field of power utilization monitoring, and discloses a power utilization safety monitoring device based on a magnetic suspension power distribution technology, and the device comprises a current detection system which obtains the working state information of a current transformer and a Hall effect sensor through a data collection module; the data analysis module performs normalization processing on the frequency, the current, the direct current component, the environmental magnetic field intensity, the power supply voltage and the aging drift amount; the reliability analysis module calculates the reliability of the two types of sensors based on the normalized index; the weight endowing module dynamically distributes sensor weights in combination with the temperature influence factors; and the current summary calculation module outputs the final detection current through weighted fusion. The problem that a single sensor is easily influenced by frequency deviation, direct current component, temperature drift and magnetic field interference under complex working conditions is solved, high-precision and high-reliability self-adaptive current monitoring is realized, and the safety monitoring capability of a magnetic levitation power distribution system is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of electricity monitoring technology, and in particular relates to an electricity safety monitoring device based on magnetic levitation power distribution technology. Background Technology

[0002] In modern intelligent power distribution systems, maglev power distribution technology, with its non-contact, high reliability, low loss, and intelligent control characteristics, has become an important development direction for new power distribution and management. This technology achieves non-mechanical contact transmission of electrical energy through the principle of magnetic levitation, significantly improving the system's safety and flexibility. Against this backdrop, high-precision and high-reliability real-time monitoring of current is not only the cornerstone of accurate power metering and load management, but also a crucial link in ensuring the stable operation of the maglev power distribution system and achieving rapid fault isolation and early warning.

[0003] Currently, in the field of current monitoring, the widely used technologies are mainly based on two types of sensors: current transformers and Hall effect sensors. Current transformers, based on the principle of electromagnetic induction, have advantages such as mature technology, high accuracy in measuring power frequency AC current, and relatively low cost, and dominate in traditional power distribution networks. Hall effect sensors, based on the principle of magnetoelectric conversion, have the ability to measure both AC and DC current, and do not exhibit magnetic saturation. They play a significant role in applications with DC components or complex waveforms, such as variable frequency speed control and new energy power generation.

[0004] However, in the complex current environment of maglev power distribution technology, characterized by high dynamics, non-sinusoidal currents, and potential DC bias, both types of sensors exhibit significant limitations when used alone. Current transformers cannot respond to DC components, and their cores are prone to saturation under overcurrent or deep bias conditions, leading to measurement distortion or even failure; their frequency response range is narrow, and they have low sensitivity to harmonic components. While Hall effect sensors can measure DC, their output characteristics are easily affected by changes in ambient temperature, external stray magnetic field interference, and fluctuations in supply voltage, exhibiting significant temperature and time drift. Their long-term stability and absolute accuracy often fail to meet the high standards required by maglev systems.

[0005] Furthermore, existing technologies lack an intelligent fusion mechanism for measurement results from two sensors, making it impossible to dynamically adjust the weights of different sensors based on actual operating conditions. In complex environments such as temperature variations and magnetic field interference, the measurement error of a single sensor may increase significantly, and existing systems cannot automatically identify and compensate for this error, leading to a decrease in overall measurement accuracy. Simultaneously, existing devices do not adequately monitor the sensor's own operating status, making it difficult to promptly detect potential problems such as sensor aging and power supply anomalies, thus affecting the long-term reliability of the system.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] The purpose of this invention is to provide an electricity safety monitoring device based on magnetic levitation power distribution technology, in order to solve the above-mentioned problems.

[0008] This invention is implemented as follows: an electrical safety monitoring device based on magnetic levitation power distribution technology includes a current detection system for processing the detection results of current transformers and Hall effect sensors and outputting the final detected current, comprising:

[0009] The data acquisition module is used to acquire the basic operating status information of the current transformer (first) and the basic operating status information of the Hall effect sensor (second).

[0010] The data analysis module is used to normalize the basic work status information 1 and basic work status information 2 to output basic work status index information 1 and basic work status index information 2.

[0011] The current transformer reliability analysis module is used to construct a current transformer reliability model based on the basic operating state index information and output the current transformer reliability.

[0012] The Hall effect sensor reliability analysis module is used to construct a Hall effect sensor reliability model based on the basic operating state index information and output the Hall effect sensor reliability.

[0013] The weighting module, at the current temperature, constructs a weighting model based on the reliability of the current transformer and the reliability of the Hall effect sensor, and outputs the respective weights of the current transformer detection results and the Hall effect sensor detection results.

[0014] The current aggregation calculation module imports the detection results from the current transformer and the Hall effect sensor, along with the weights assigned to the model output of the current transformer and Hall effect sensor detection results, into the current aggregation calculation model to output the final detected current.

[0015] A further technical solution is that the current aggregation calculation model is as follows: ,in, To ultimately detect the current, Calculate the weights for the current transformers. Calculate weights for Hall effect sensors. The results are from the current transformer test. The results are from the Hall effect sensor.

[0016] A further technical solution is that the weights are assigned to the model as follows:

[0017]

[0018] in, Calculate the weights for the current transformers. Calculate weights for Hall effect sensors. For the reliability of current transformers, For the reliability of Hall effect sensors, The temperature influence factor of the current transformer. This represents the temperature influence factor of the Hall effect sensor.

[0019] A further technical solution involves incorporating ambient temperature into the formula. Obtain the temperature influence factor of the current transformer. ,in, For ambient temperature, The optimal operating temperature for current transformers This refers to the effective operating temperature range of the current transformer.

[0020] A further technical solution involves incorporating ambient temperature into the formula. Obtain the temperature influence factor of the Hall effect sensor ,in, For ambient temperature, The temperature during sensor calibration. This is the temperature drift coefficient.

[0021] A further technical solution is proposed: the reliability model for the current transformer is as follows: ,in, For the reliability of current transformers, For frequency index, The current index, This is the DC component index.

[0022] A further technical solution is proposed: the reliability model for the Hall effect sensor is as follows: ,in, For the reliability of Hall effect sensors, The environmental magnetic field strength index. This refers to the power supply voltage index. This is the aging drift index.

[0023] In a further technical solution, the first basic operating status information includes frequency, current and DC component, and the second basic operating status information includes ambient magnetic field strength, power supply voltage and aging drift amount.

[0024] A further technical solution, the specific process of the data analysis module is as follows:

[0025] Import frequency into the formula Obtain the frequency index ,in, This is the measured frequency. The rated frequency designed for current transformers The effective frequency range of the current transformer;

[0026] Introduce current into the formula Obtain the current index ,in, This represents the absolute value of the measured current from the current transformer. This is the rated current of the current transformer. This is the saturation current of the current transformer;

[0027] Import the DC component into the formula Obtain the DC component index ,in, To estimate the proportion of the DC component, The critical value of the DC component that leads to severe saturation of the CT.

[0028] Import the ambient magnetic field strength into the formula Obtain the environmental magnetic field strength index ,in, To measure the ambient magnetic field strength, The maximum permissible external magnetic field interference intensity;

[0029] Import the supply voltage into the formula Obtain the power supply voltage index ,in, For actual measured supply voltage, Rated operating voltage and To recommend the minimum and maximum operating voltage range, This is the absolute maximum rated voltage;

[0030] Import aging drift into the formula Obtain the aging drift index ,in, The sensor has been in use for [number] years. This refers to the design life of the sensor.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] By integrating measurement data from current transformers and Hall effect sensors, and dynamically allocating weights based on real-time operating status, environmental parameters, and device aging, the limitations of a single sensor under specific operating conditions are overcome. For example, in current environments containing DC components or harmonics, the weight of the current transformer is automatically reduced to avoid measurement distortion caused by core saturation. In high-temperature or strong magnetic field interference scenarios, the temperature drift and magnetic drift errors of the Hall effect sensor are suppressed to ensure the accuracy and stability of the final output current. Without significantly increasing hardware complexity, the performance potential of existing sensors is fully explored through algorithmic innovation, achieving a measurement effect of "1+1>2". This approach retains the high-precision advantage of current transformers in power frequency measurements while leveraging the applicability of Hall effect sensors in wide-bandwidth, AC / DC mixed scenarios, thus improving the overall cost-effectiveness of the system. Attached Figure Description

[0033] Figure 1 The flowchart of an electricity safety monitoring device based on magnetic levitation power distribution technology provided by the present invention. Detailed Implementation

[0034] 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.

[0035] In existing technologies, maglev power distribution technology enhances system safety and flexibility through contactless power transmission, but faces monitoring challenges under complex current environments. Traditional current transformers are prone to saturation distortion under DC component measurement and overcurrent conditions, while Hall effect sensors are significantly affected by temperature drift and magnetic field interference. During the operation of a maglev power distribution system in a substation, frequent harmonic superposition of DC components caused current transformer saturation, while the high-temperature environment triggered temperature drift in the Hall effect sensors. A single sensor could not provide reliable data, affecting fault diagnosis and isolation efficiency.

[0036] To address these issues, researchers discovered that dual-sensor data fusion is a promising direction, but the challenge of dynamic weight allocation remains. First, the failure mechanisms of the two types of sensors were analyzed: current transformers are constrained by frequency, current amplitude, and DC component, while Hall effect sensors are affected by temperature, supply voltage, and aging. By establishing a multi-parameter normalization model to quantify sensor reliability, a temperature compensation factor was introduced to dynamically adjust the weights, ultimately forming an adaptive weighted fusion algorithm.

[0037] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0038] like Figure 1As shown, an embodiment of the present invention provides an electrical safety monitoring device based on magnetic levitation power distribution technology, including a current detection system for processing the detection results of current transformers and Hall effect sensors and outputting the final detected current, comprising:

[0039] The data acquisition module is used to acquire the basic operating status information of the current transformer (first) and the basic operating status information of the Hall effect sensor (second).

[0040] The data analysis module is used to normalize the basic work status information 1 and basic work status information 2 to output basic work status index information 1 and basic work status index information 2.

[0041] The current transformer reliability analysis module is used to construct a current transformer reliability model based on the basic operating state index information and output the current transformer reliability.

[0042] The Hall effect sensor reliability analysis module is used to construct a Hall effect sensor reliability model based on the basic operating state index information and output the Hall effect sensor reliability.

[0043] The weighting module, at the current temperature, constructs a weighting model based on the reliability of the current transformer and the reliability of the Hall effect sensor, and outputs the respective weights of the current transformer detection results and the Hall effect sensor detection results.

[0044] The current aggregation calculation module imports the detection results from the current transformer and the Hall effect sensor, along with the weights assigned to the model output of the current transformer and Hall effect sensor detection results, into the current aggregation calculation model to output the final detected current.

[0045] The first set of basic operating status information includes frequency, current, and DC component. The second set includes ambient magnetic field strength, supply voltage, and aging drift. Frequency refers to the deviation between the actual operating frequency of the current transformer and its rated frequency. This can be achieved by measuring the periodic changes of the power frequency signal using a frequency sensor. Frequency deviation from the rated value will lead to a decrease in electromagnetic coupling efficiency, directly affecting the measurement accuracy of the current transformer. Current refers to the instantaneous amplitude of the measured current. This can be achieved by using a current sampling circuit to obtain real-time waveform data. When the current exceeds the rated range, the magnetic saturation of the iron core will cause nonlinear errors. The DC component refers to the proportion of DC bias superimposed on the current waveform. This can be achieved by separating the AC and DC components using digital signal processing algorithms. Excessive DC component will exacerbate the accumulation of residual magnetism in the iron core, causing a deterioration in the dynamic characteristics of the current transformer. Ambient magnetic field strength refers to the level of external stray magnetic field interference in the space where the Hall effect sensor is located. This can be achieved by using a three-dimensional fluxgate sensor for spatial magnetic field vector detection. External magnetic fields will change the magnetization state of the sensor's sensitive element, causing deviations in the output signal. The supply voltage refers to the voltage fluctuation range of the power supply for the Hall effect sensor. Specifically, the stability of the power supply can be monitored through a voltage sampling circuit. Voltage deviation from the rated value will cause drift in the sensor's internal reference voltage, affecting measurement linearity. Aging drift refers to the degree of performance degradation caused by long-term use of the sensor. This can be estimated by combining cumulative operating time statistics with a temperature-accelerated aging model. Device aging will cause irreversible decay of the magnetoelectric conversion coefficient. The reliability assessment of the current transformer selects frequency, current, and DC component as core parameters. These three parameters correspond to key failure mechanisms such as electromagnetic coupling efficiency, core saturation risk, and hysteresis intensity, respectively. When the system detects a current frequency deviation from the rated value, a frequency index is generated through normalization to reflect the weight of the frequency deviation on measurement accuracy. When the detected current exceeds the rated range, a current index is calculated based on the saturation current threshold to quantify the core magnetic saturation degree. The DC component index is calculated by separating the DC component in the current waveform in real time to assess the risk of residual magnetism accumulation. The reliability assessment of the Hall effect sensor focuses on the ambient magnetic field strength, supply voltage, and aging drift. These three correspond to external interference suppression capability, power supply stability, and long-term reliability indicators, respectively. The environmental magnetic field strength index is generated by comparing the measured magnetic field with the allowable threshold; the power supply voltage index is calculated segmented according to the voltage fluctuation range; and the aging drift index is determined by the ratio of cumulative usage time to design life. The three-dimensional parameter systems selected for both types of sensors construct complete reliability assessment models from three dimensions: dynamic operating state, environmental interference, and device degradation. Traditional current monitoring devices typically assess sensor status based on only a single parameter, such as current amplitude or temperature change, lacking systematic modeling of multi-dimensional failure factors.This solution, by defining a three-dimensional evaluation system of frequency, current, and DC components, is the first to incorporate the dynamic electromagnetic characteristics and core magnetization state of current transformers into reliability calculations. Simultaneously, for the magnetic field-voltage-aging parameter combination proposed for Hall effect sensors, it innovatively models the correlation between external interference, power supply quality, and device lifespan, forming a full lifecycle evaluation framework covering instantaneous operating conditions, environmental interference, and long-term degradation. This application effectively solves the problem of inaccurate selection of sensor reliability evaluation parameters under complex current environments. By constructing a multi-dimensional parameter system strongly correlated with the failure mechanisms of the two types of sensors, it achieves accurate quantification of the electromagnetic dynamic characteristics of current transformers and the environmental adaptability of Hall effect sensors. This solution provides physically meaningful reliability data for subsequent weight allocation models, enabling the final fused detection current to adaptively adjust the contribution weights of the two types of sensors, significantly improving the current monitoring accuracy of maglev power distribution systems under wide-bandwidth, strong interference, and long-cycle operation conditions.

[0046] The data acquisition module synchronously acquires the frequency, current amplitude, and DC component parameters of the current transformer, as well as the environmental magnetic field strength, supply voltage, and aging drift parameters of the Hall effect sensor, through a multi-channel signal conditioning circuit, providing raw data for subsequent analysis. The data analysis module uses piecewise linear functions to convert parameters of different dimensions into exponential values ​​in the 0-1 range; for example, it calculates the frequency exponent using the frequency deviation ratio to eliminate the influence of parameter unit differences on model calculations. The current transformer reliability analysis module performs a geometric average calculation of the frequency exponent, current exponent, and DC component exponent to reflect the degree of comprehensive performance degradation under the coupling effect of multiple factors. The Hall effect sensor reliability analysis module performs a geometric average calculation of the magnetic field interference index, voltage stability index, and aging index to quantify the impact of environmental and device aging on measurement accuracy. The weighting module dynamically adjusts the weight allocation ratio based on the temperature influence factor; for example, it reduces the weight ratio of low-temperature drift sensitive sensors in high-temperature environments. The current aggregation calculation module performs a weighted summation operation, fusing the dual sensor measurements according to their reliability ratio to obtain the final output current value.

[0047] Specifically, after the data acquisition module obtains the sensor's operating parameters in real time, the data analysis module converts parameters such as frequency deviation, current over-limit values, and DC component proportion into standardized indices. The reliability analysis module calculates the comprehensive reliability score for both the current transformer and the Hall sensor; a lower score indicates a higher risk of measurement error under the current operating conditions. The weighting module calculates dynamic weights based on the reliability score and ambient temperature. For example, when the reliability of the current transformer decreases due to excessive DC component, the weight of the Hall sensor is automatically increased. Finally, a weighted fusion algorithm outputs the comprehensive current value, maintaining measurement accuracy even when some sensors fail.

[0048] Compared to existing technologies, traditional solutions rely on single-sensor measurements, which are prone to systematic errors under complex operating conditions. This solution, through multi-parameter reliability assessment and dynamic weight allocation, automatically switches to Hall sensor-dominated measurement when the current transformer is saturated, and prioritizes current transformer data in low-temperature environments, achieving environmentally adaptive optimization of measurement accuracy.

[0049] Through the above technical solutions, this application can accurately identify sensor failure risks in current environments containing harmonics and DC components, dynamically adjust data fusion strategies, and avoid monitoring distortion caused by single sensor errors. In high-temperature or strong magnetic field interference scenarios, temperature compensation and weight redistribution are used to suppress the influence of sensor drift, ensuring the reliability of current monitoring in maglev power distribution systems under complex operating conditions.

[0050] The current aggregation calculation model is as follows: ,in, To ultimately detect the current, Calculate the weights for the current transformers. Calculate weights for Hall effect sensors. The results are from the current transformer test. The results are from the Hall effect sensor.

[0051] The final detected current refers to the comprehensive current value obtained by weighted fusion of the detection results from the two sensors. This can be achieved using a linear weighted summation method to balance the measurement errors of different sensors under complex operating conditions. The current transformer calculation weight reflects the reliability ratio of the current transformer in the current environment. This weight can be calculated using a reliability model and temperature influence factor, and is used to dynamically adjust the contribution of the current transformer's detection result to the final result. The Hall effect sensor calculation weight reflects the reliability ratio of the Hall effect sensor in the current environment. This weight can be determined through a complementary relationship with the current transformer weight, ensuring that the sum of the weights of the two sensors is a fixed value and avoiding weight imbalance. The current transformer detection result refers to the current value directly measured by the current transformer, achieved through the principle of electromagnetic induction, suitable for high-precision measurement of power frequency AC current. The Hall effect sensor detection result refers to the current value directly measured by the Hall effect sensor, achieved through the principle of magnetoelectric conversion, suitable for current measurement containing DC components or high-frequency components.

[0052] Specifically, this technical solution fuses the detection data from two sensors by dynamically allocating weighting coefficients. In scenarios dominated by AC components, the weight of the current transformer can be set to a higher value to utilize its high accuracy; while in scenarios with DC components or high-frequency interference, the weight of the Hall effect sensor can be increased to compensate for the limitations of the current transformer. The weight allocation is based on sensor reliability and the influence of ambient temperature. For example, in high-temperature environments, the temperature drift of the Hall effect sensor may lead to a decrease in its weight. Normalization ensures that the sum of the two weights is constant at 1, so that the fused current value is always within a reasonable range.

[0053] Compared to existing technologies, traditional current monitoring devices typically rely on a single sensor, such as a current transformer in power frequency scenarios and a Hall effect sensor in scenarios containing DC components. Such methods cannot adapt to complex operating conditions involving simultaneous AC harmonics, DC bias, and temperature variations. This solution achieves adaptive fusion of data from both sensors through real-time dynamic weight calculation. It retains the accuracy advantage of the current transformer in the power frequency band while utilizing the stability of the Hall effect sensor in wideband measurements, thus overcoming the inherent limitations of a single sensor.

[0054] Through the above technical solution, this application can effectively suppress the measurement error of a single sensor in a mixed AC and DC current environment. For example, when DC bias occurs in a maglev power distribution system, the current transformer may fail to measure due to core saturation. In this case, the weight of the Hall effect sensor automatically increases to ensure the accuracy of the final detected current. At the same time, under steady-state conditions at power frequency, the high-precision characteristics of the current transformer are preserved through a higher weight, thereby improving the reliability of the overall measurement results.

[0055] The weights assigned to the model are:

[0056]

[0057] in, Calculate the weights for the current transformers. Calculate weights for Hall effect sensors. For the reliability of current transformers, For the reliability of Hall effect sensors, The temperature influence factor of the current transformer. This represents the temperature influence factor of the Hall effect sensor.

[0058] Among them, the temperature influence factor of current transformer This refers to a parameter reflecting the performance stability of a current transformer under different ambient temperatures. Specifically, it can be implemented using a piecewise linear function. For example, when the ambient temperature deviates from the optimal operating temperature, its value decreases linearly with the increase of the temperature deviation until it drops to zero when it exceeds the effective operating range, thereby suppressing measurement errors caused by temperature exceeding limits. (Hall effect sensor temperature influence factor) This refers to the parameter characterizing the effect of temperature drift on a Hall effect sensor. Specifically, it can be implemented using an inverse proportional function. For example, when the ambient temperature deviates from the calibration temperature, its value decreases at a rate adjusted by the temperature drift coefficient as the temperature difference increases, thereby quantifying the degree of influence of temperature change on the sensor output.

[0059] Specifically, this model calculates the weighting ratios by multiplying the reliability of the current transformer by a temperature influence factor, and the reliability of the Hall effect sensor by a temperature influence factor, then normalizing the products. When the ambient temperature is within the optimal operating range of the current transformer... When the current approaches 1, the reliability advantage of the current transformer is fully preserved; when the temperature deviates, The decrease leads to a reduction in the weight of the current transformer, while the weight of the Hall effect sensor increases accordingly. For Hall effect sensors, The temperature sensitivity is dynamically adjusted by a temperature drift coefficient. The coefficient is higher when the temperature is close to the calibration value and decreases inversely proportionally when the temperature is far from the calibration value, thus compensating for temperature errors. A normalized denominator structure ensures that the weight allocation always matches the overall temperature performance of the dual sensors, avoiding weight imbalances caused by sudden temperature changes in a single sensor.

[0060] Compared to existing technologies, traditional dual-sensor fusion methods typically allocate weights based solely on static reliability, neglecting the dynamic impact of temperature changes on sensor performance. For example, existing technologies use fixed ratios or simple threshold switching to allocate weights, which can easily lead to abrupt changes in measured values ​​or distortion of the fusion results when temperatures fluctuate drastically. This proposed solution, however, introduces the product of temperature influence factor and reliability as the basis for weight calculation, enabling real-time reflection of the coupling effect of temperature on sensor performance, thus achieving more stable weight allocation in complex temperature environments.

[0061] Through the above technical solution, this application solves the problems of sensor measurement error fluctuations and weight allocation inaccuracies caused by temperature changes in maglev power distribution systems, and achieves temperature-adaptive fusion of dual-sensor measurements. When the reliability of the current transformer decreases due to temperature deviation from its optimal operating range, its weight automatically decreases, while the weight of the Hall effect sensor increases to compensate for the measurement error. When the Hall effect sensor's output becomes unstable due to temperature drift, its weight decreases accordingly, and the system relies on the measurement results of the current transformer. This maintains the accuracy and stability of the final detected current over a wide temperature range, improving the environmental adaptability and safety monitoring reliability of the maglev power distribution system.

[0062] The reliability model for current transformers is as follows: ,in, For the reliability of current transformers, , For frequency index, The current index, This is the DC component index.

[0063] The frequency index is a parameter that quantifies the deviation between the measured frequency and the rated frequency of a current transformer. Specifically, it can be calculated as the proportion of the frequency deviation to the effective frequency range, reflecting the hysteresis loss characteristics of the magnetic core in different frequency bands. The current index characterizes the linear response of the current transformer within the rated current range and the degree of saturation after exceeding the limit. It can be implemented as a piecewise function, increasing linearly within the rated current and decreasing linearly according to the saturation current threshold after exceeding the rated current, used to dynamically capture the nonlinear distortion of the core magnetization curve. The DC component index assesses the impact of the DC component ratio on core saturation, calculated as the ratio of the DC component to the threshold value, used to quantify the flux deviation caused by DC bias.

[0064] Specifically, the parameters of frequency, current, and DC component are mapped to the [0,1] interval through normalization, and the influence factors of different dimensions are coupled using a geometric average method. When the frequency deviates from the rated value, the frequency index decreases as the deviation increases, reflecting the performance degradation of the current transformer in the non-design frequency band. When the current exceeds the rated value but does not reach the saturation point, the current index maintains a linear response, while the index drops rapidly after exceeding the saturation point, accurately characterizing the measurement distortion caused by core saturation. The DC component index dynamically reflects the cumulative effect of DC bias on core magnetization through the proportion of the critical value. The product of the three is normalized by cubic root operation to ensure that the reliability changes smoothly within the [0,1] interval, avoiding excessive influence of a single parameter mutation on the evaluation results.

[0065] Compared with existing technologies, traditional methods typically only consider a single factor such as current amplitude or use a fixed threshold to determine saturation, without systematically quantifying the coupling effects of frequency offset and DC component. This solution constructs a multi-dimensional dynamic evaluation model that incorporates frequency response characteristics, nonlinear saturation effects, and DC bias effects into a unified calculation framework, achieving refined modeling of the current transformer's operating state.

[0066] Through the above technical solution, this application solves the measurement distortion problem caused by multi-factor coupling of current transformers under complex operating conditions. By dynamically evaluating the synergistic effects of frequency deviation, current over-limit and DC component on core saturation, its reliability is accurately quantified, providing standardized input for subsequent weighted data fusion, thereby improving the anti-interference and environmental adaptability of the final current detection results.

[0067] The reliability model for Hall effect sensors is as follows: ,in, For the reliability of Hall effect sensors, , The environmental magnetic field strength index. This refers to the power supply voltage index. This is the aging drift index.

[0068] Among them, the environmental magnetic field strength index This refers to a parameter defined by quantifying the degree of interference from external stray magnetic fields on the sensor output. Specifically, it can be achieved by using the magnetic field detection unit built into the Hall sensor to measure the ambient magnetic field strength, combined with a preset maximum allowable magnetic field strength threshold. Through formula Calculated. This index reflects the direct impact of magnetic field interference on sensor reliability. When the measured magnetic field exceeds a threshold, the index returns to zero, directly reducing reliability. Power supply voltage index. This parameter characterizes the impact of sensor power supply voltage fluctuations on measurement stability. Specifically, it can be calculated using a voltage sampling circuit to monitor the power supply voltage in real time, combined with the rated voltage range and absolute maximum voltage, through piecewise function modeling. This index can capture sensor failure behavior under undervoltage, overvoltage, and extreme voltage conditions, ensuring rapid reliability degradation during voltage anomalies. Aging Drift Index This refers to a parameter defined based on the ratio of the sensor's service life to its design life. Specifically, it can be achieved by using a storage unit to record the sensor's cumulative operating time, and then using a formula... The index is calculated to dynamically characterize the performance degradation trend caused by device aging, providing a quantitative basis for reliability degradation during long-term operation.

[0069] Specifically, by collecting real-time data on ambient magnetic field strength, power supply voltage, and sensor operating time, the ambient magnetic field strength index, power supply voltage index, and aging drift index are calculated respectively. The reliability of the Hall effect sensor is then obtained by multiplying these three indices and taking the cube root. When any one of these indices approaches zero, the overall reliability decreases significantly, enhancing the sensitivity to key failure factors. For example, if the sensor's power supply voltage is lower than the minimum recommended operating voltage range, the power supply voltage index... When the power supply is zeroed out, the reliability of the Hall effect sensor is simultaneously zeroed out, directly reflecting the unreliable state of the sensor caused by abnormal power supply. At the same time, the geometric mean calculation method avoids misjudgment caused by a single parameter anomaly, ensuring that the three influencing factors have equal importance in the reliability assessment.

[0070] Compared to existing technologies, traditional methods typically assess sensor reliability solely through temperature compensation or single parameter thresholds, failing to comprehensively consider the coupled effects of magnetic field interference, voltage fluctuations, and aging drift. This solution, however, employs a multi-dimensional parameter fusion model that integrates environmental magnetic field strength, power supply voltage stability, and device aging levels into a unified evaluation framework. It utilizes a geometric averaging algorithm to dynamically calculate reliability under the synergistic effects of multiple factors, significantly improving the accuracy of assessments under complex operating conditions.

[0071] Through the above technical solution, this application effectively solves the problem of decreased measurement reliability of Hall effect sensors caused by environmental magnetic field interference, power supply voltage fluctuations, and device aging. By dynamically quantifying three key influencing factors and establishing a comprehensive reliability model, it is possible to identify the performance degradation of sensors in real time under scenarios such as strong magnetic fields, abnormal voltage, or end of life. This provides an accurate reliability basis for the adaptive weighted fusion of current monitoring data in maglev power distribution systems, improving the long-term stability and anti-interference capability of the sensors.

[0072] Import ambient temperature into the formula Obtain the temperature influence factor of the current transformer. , ,in, For ambient temperature, The optimal operating temperature for current transformers This refers to the effective operating temperature range of the current transformer.

[0073] Ambient temperature refers to the real-time temperature parameter of the space where the current transformer is located. This can be achieved using a digital temperature sensor or a thermistor, reflecting the temperature state of the sensor's operating environment. Optimal operating temperature refers to the temperature point at which the current transformer achieves its optimal performance during design, such as 25°C, which can be obtained from the sensor's technical manual. The effective operating temperature range refers to the temperature range within which the current transformer can maintain measurement accuracy without significant performance degradation, such as -10°C to 60°C. This parameter is determined by material properties and structural design. The temperature influence factor quantifies the impact of temperature deviation on sensor reliability through mathematical modeling. Its value is normalized to between 0 and 1 for easy subsequent weighting calculations.

[0074] Specifically, when the ambient temperature is within the effective operating temperature range, the temperature influence factor decreases linearly with the degree of temperature deviation from the optimal operating temperature. For example, when the ambient temperature is 30℃, the optimal operating temperature is 25℃, and the effective temperature range is 20℃, the temperature deviation is 5℃, and the temperature influence factor is calculated as follows: This linear relationship objectively reflects the gradual impact of temperature changes on sensor performance. When the ambient temperature exceeds the effective range, the temperature influence factor directly drops to zero, indicating that the measurement data from the current transformer is unreliable, and the system will rely entirely on the detection results of other sensors. By setting a threshold for the effective temperature range, the optimal weight allocation of the sensors under nominal operating conditions is preserved, while the measurement errors caused by extreme temperatures are avoided from interfering with the final calculation results.

[0075] Compared to existing technologies, traditional current transformer temperature compensation methods typically employ fixed coefficient corrections or simple threshold judgments, such as directly triggering an alarm and disabling the transformer when the temperature exceeds the specified range. These methods cannot dynamically quantify the impact of temperature deviations on reliability and are prone to causing abrupt changes in measurement data near the critical temperature. In contrast, this solution utilizes a piecewise function model to achieve continuous and smooth weight adjustments within the effective temperature range, while completely shielding unreliable data outside the critical temperature range. This ensures temperature adaptability while avoiding abrupt changes in weight allocation.

[0076] Through the above technical solution, this application can dynamically adjust the weighting of the current transformer detection results according to the ambient temperature, maintain the reliability of the measurement data within the effective temperature range, and automatically eliminate abnormal data interference when the temperature exceeds the design threshold. For example, when the maglev power distribution system encounters abnormal heat dissipation due to high temperature, this solution can reduce the weight of the current transformer in real time and rely on the measurement results of the Hall effect sensor instead, thereby ensuring the accuracy of the final detected current.

[0077] Import ambient temperature into the formula Obtain the temperature influence factor of the Hall effect sensor , ,in, For ambient temperature, The temperature during sensor calibration. This is the temperature drift coefficient.

[0078] Ambient temperature refers to the real-time temperature of the sensor's operating environment. This can be achieved by using a temperature sensor to collect data in real-time and convert it into an electrical signal, used to quantify the impact of temperature changes on the sensor's output characteristics. Calibration temperature... This refers to the reference temperature point used for parameter calibration of the sensor during factory manufacturing or field testing. Specifically, it can be achieved by recording the temperature data during calibration using a storage chip, serving as a reference for temperature compensation. Among these, the temperature drift coefficient... This refers to a parameter that reflects the sensitivity of the sensor output to temperature changes. Specifically, it can be determined by calibrating the output deviation curves at different temperatures in the laboratory and fitting the slope. It is used to adjust the rate of reliability decay when the temperature deviates from the calibration point.

[0079] Specifically, the temperature influence factor of Hall effect sensors The calculation achieves dynamic compensation through an exponential function. When the ambient temperature... Equal to calibrated temperature When the denominator is 1, then A maximum value of 1 indicates that the sensor is in optimal working condition. As the temperature deviates from the calibration point, the absolute value term... The linear increase leads to the denominator term The value increased. The value decreases non-linearly. Temperature drift coefficient. Control the steepness of the descent curve, for example when When the value is 0.05, for every 20°C deviation of the temperature from the calibration point, The value will decrease to 0.5; while When the value is 0.1, the deviation at the same temperature is... The value drops to 0.33. This model uses mathematical relationships to transform the temperature deviation into a confidence decay factor, which in turn affects the weighting of sensor detection results in the final current calculation.

[0080] Compared to existing technologies, traditional methods typically employ fixed temperature compensation coefficients or hardware temperature compensation circuits, which cannot adapt to dynamic changes under different operating conditions. For example, some solutions achieve linear compensation by integrating a thermistor inside the sensor, but this is limited by a narrow compensation range and cannot eliminate nonlinear temperature drift errors. This solution, however, establishes a nonlinear function model related to the calibration temperature deviation, which not only accurately reflects the actual impact of temperature on sensor accuracy but also allows for adjustments to... The parameters are adapted to the temperature characteristics of different sensor models.

[0081] Through the above technical solution, this application effectively suppresses the measurement error of Hall effect sensors at non-calibrated temperatures and solves the output drift problem caused by ambient temperature fluctuations. For example, when the maglev power distribution system encounters diurnal temperature differences or local temperature rises in equipment, this solution can automatically reduce the weight ratio of sensor data within abnormal temperature ranges, thereby avoiding the impact of single sensor data distortion on the final current calculation result and significantly improving the overall measurement accuracy under complex temperature environments.

[0082] The specific process of the data analysis module is as follows:

[0083] Import frequency into the formula Obtain the frequency index , ,in, This is the measured frequency. The rated frequency designed for current transformers The effective frequency range of the current transformer;

[0084] Introduce current into the formula Obtain the current index , It increases linearly within the rated current range, and then decreases linearly to 0 after exceeding the rated current. This represents the absolute value of the measured current from the current transformer. This is the rated current of the current transformer. This is the saturation current of the current transformer;

[0085] Import the DC component into the formula Obtain the DC component index , The larger the DC component, the lower the exponent. To estimate the proportion of the DC component, The critical value of the DC component that leads to severe saturation of the CT.

[0086] Import the ambient magnetic field strength into the formula Obtain the environmental magnetic field strength index , The greater the interference, the lower the index. To measure the ambient magnetic field strength, The maximum permissible external magnetic field interference intensity;

[0087] Import the supply voltage into the formula Obtain the power supply voltage index , ,in, For actual measured supply voltage, Rated operating voltage and To recommend the minimum and maximum operating voltage range, This is the absolute maximum rated voltage;

[0088] Import aging drift into the formula Obtain the aging drift index , The value is 1 for brand new and 0 for reaching the design life. The sensor has been in use for [number] years. This refers to the design life of the sensor.

[0089] The multi-dimensional parameters of the current transformer and the Hall effect sensor are normalized. By constructing the frequency index, current index, DC component index, ambient magnetic field strength index, power supply voltage index and aging drift index, each parameter is mapped to the [0,1] interval to form a standardized index.

[0090] The frequency index quantifies the deviation of the actual operating frequency of a current transformer from its rated frequency. It can be calculated as the ratio of the measured frequency to the rated frequency, reflecting the performance degradation of the sensor outside its effective frequency band. The current index characterizes the load state of the current transformer, calculated piecewise using the linear relationship between the rated current and the saturation current, automatically reducing measurement weight under overload conditions. The DC component index assesses the influence of the DC component in the current, calculated as the ratio of the DC component percentage to the saturation threshold, suppressing measurement distortion caused by core saturation. The ambient magnetic field strength index measures the intensity of external magnetic field interference, calculated as the ratio of the measured magnetic field strength to the maximum allowable value, eliminating stray magnetic field interference to the Hall sensor. The supply voltage index reflects the stability of the Hall sensor's power supply, calculated as a piecewise linear function to determine the degree of voltage deviation from the rated value, identifying abnormal power supply conditions. The aging drift index quantifies the degradation of the sensor's lifespan, calculated as the ratio of the years used to the design life, characterizing the impact of device aging on measurement accuracy.

[0091] Specifically, this technical solution establishes a parameter normalization model to convert sensor state parameters of different dimensions into standardized indices. For current transformers, when the measured frequency exceeds the effective range, the frequency index is set to zero to shield unreliable data; when the current exceeds the rated value, the current index decreases according to the slope of the saturation interval to avoid measurement errors caused by overload; the DC component index reflects the core magnetization state in real time, suppressing saturation distortion caused by DC bias. For Hall sensors, the ambient magnetic field strength index dynamically compensates for external magnetic interference, the power supply voltage index identifies undervoltage or overvoltage states, and the aging drift index automatically adjusts its reliability based on the device's lifespan. After normalization, all indices provide input parameters of a unified dimension for subsequent reliability calculations, enabling quantitative evaluation of sensor states under different operating conditions.

[0092] Compared to existing technologies, traditional methods rely solely on raw measurements from a single sensor, neglecting the impact of factors such as frequency shift, DC component, and environmental interference on detection accuracy. This proposed solution, through multi-dimensional parameter normalization, constructs a comprehensive evaluation system encompassing sensor operating status, environmental conditions, and device aging, overcoming the problem of measurement error accumulation caused by dynamic parameter changes in existing technologies.

[0093] Through the above technical solution, this application realizes dynamic quantitative evaluation of key state parameters of the sensor, solves the problem of decreased detection accuracy caused by factors such as frequency offset, current overload, DC bias, magnetic field interference, voltage fluctuation and device aging under complex working conditions, provides accurate standardized input for the subsequent weight allocation of the reliability model, and effectively improves the environmental adaptability and long-term stability of the current detection system.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power safety monitoring device based on magnetic levitation power distribution technology, characterized in that, Includes a current detection system for processing the detection results from current transformers and Hall effect sensors and outputting the final detected current, including: The data acquisition module is used to acquire the basic operating status information of the current transformer (first) and the basic operating status information of the Hall effect sensor (second). The data analysis module is used to normalize the basic work status information 1 and basic work status information 2 to output basic work status index information 1 and basic work status index information 2. The current transformer reliability analysis module is used to construct a current transformer reliability model based on the basic operating state index information and output the current transformer reliability. The Hall effect sensor reliability analysis module is used to construct a Hall effect sensor reliability model based on the basic operating state index information and output the Hall effect sensor reliability. The weighting module, at the current temperature, constructs a weighting model based on the reliability of the current transformer and the reliability of the Hall effect sensor, and outputs the respective weights of the current transformer detection results and the Hall effect sensor detection results. The current aggregation calculation module imports the detection results from the current transformer and the Hall effect sensor, along with the weights assigned to the model output of the current transformer and Hall effect sensor detection results, into the current aggregation calculation model to output the final detected current.

2. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 1, characterized in that, The current aggregation calculation model is as follows: ,in, For the final detection of current, Calculate the weights for the current transformers. Calculate weights for Hall effect sensors. The results are from the current transformer test. The results are from the Hall effect sensor.

3. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 2, characterized in that, The weights assigned to the model are: in, Calculate the weights for the current transformers. Calculate weights for Hall effect sensors. For the reliability of current transformers, For the reliability of Hall effect sensors, The temperature influence factor of the current transformer. This represents the temperature influence factor of the Hall effect sensor.

4. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 3, characterized in that, Import ambient temperature into the formula Obtain the temperature influence factor of the current transformer. ,in, For ambient temperature, The optimal operating temperature for current transformers This refers to the effective operating temperature range of the current transformer.

5. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 3, characterized in that, Import ambient temperature into the formula Obtain the temperature influence factor of the Hall effect sensor ,in, For ambient temperature, The temperature during sensor calibration. This is the temperature drift coefficient.

6. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 3, characterized in that, The reliability model for current transformers is as follows: ,in, For the reliability of current transformers, For frequency index, The current index, This is the DC component index.

7. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 3, characterized in that, The reliability model for Hall effect sensors is as follows: ,in, For the reliability of Hall effect sensors, The environmental magnetic field strength index. This refers to the power supply voltage index. This is the aging drift index.

8. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 6 or 7, characterized in that, The first basic operating status information includes frequency, current and DC component, and the second basic operating status information includes ambient magnetic field strength, power supply voltage and aging drift.

9. The power safety monitoring device based on magnetic levitation power distribution technology according to claim 8, characterized in that, The specific workflow of the data analysis module is as follows: Import frequency into the formula Obtain the frequency index ,in, This is the measured frequency. The rated frequency designed for current transformers The effective frequency range of the current transformer; Introduce current into the formula Obtain the current index ,in, This represents the absolute value of the measured current from the current transformer. This is the rated current of the current transformer. This is the saturation current of the current transformer; Import the DC component into the formula Obtain the DC component index ,in, To estimate the proportion of the DC component, The critical value of the DC component that leads to severe saturation of the CT. Import the ambient magnetic field strength into the formula Obtain the environmental magnetic field strength index ,in, To measure the ambient magnetic field strength, The maximum permissible external magnetic field interference intensity; Import the supply voltage into the formula Obtain the power supply voltage index ,in, This is the actual measured supply voltage. Rated operating voltage and To recommend the minimum and maximum operating voltage range, This is the absolute maximum rated voltage; Import aging drift into the formula Obtain the aging drift index ,in, The sensor has been in use for many years. This refers to the design life of the sensor.

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