Insulation resistance online monitoring and early warning method

Abstract

The invention relates to the technical field of electrical safety monitoring, and discloses an insulation resistance online monitoring and early warning method, which comprises the following steps: collecting insulation resistance, environment temperature and humidity and equipment operation condition data in real time; extracting aging trend characteristics of the insulation resistor through a sliding time window; constructing a four-dimensional aging state characterization vector fusing the commissioning duration, the load current, the temperature and the humidity; dynamically generating an early warning threshold value based on a piecewise exponential attenuation function; and when the measured value is lower than a threshold value, triggering graded early warning and calculating a safety margin coefficient. According to the method, a traditional fixed threshold value early warning mode is abandoned, and a dynamic early warning mechanism with the aging track of the equipment individual as the core is established. By fusing multi-dimensional information such as insulation resistance time sequence trend, equipment operation load, environmental stress and service duration, an aging state representation vector capable of truly reflecting an equipment insulation degradation process is constructed, and a dynamic early warning threshold evolved along with the actual state of equipment is generated accordingly.

Description

Technical Field

[0001] This invention belongs to the field of electrical safety monitoring technology, specifically relating to a method for online monitoring and early warning of insulation resistance. Background Technology

[0002] With the increasing demands for operational safety and reliability in power systems, new energy equipment, and industrial automation equipment, insulation resistance, as a core indicator for measuring the insulation performance of electrical equipment, has become a crucial link in ensuring stable system operation through its online monitoring and fault early warning capabilities. Traditional insulation monitoring methods generally use fixed thresholds for anomaly detection, i.e., a uniform lower limit value for insulation resistance is preset, and an early warning is triggered once the measured value falls below this threshold.

[0003] However, such methods ignore the dynamic evolution of the insulation performance of equipment throughout its entire life cycle: newly commissioned equipment has excellent insulation condition, and the insulation resistance value is generally much higher than the fixed threshold, which leads to slight fluctuations being misjudged as faults, resulting in a high false alarm rate; while aging equipment that has been running for a long time has continuously deteriorated insulation performance, and even if the insulation resistance is on the edge of danger, it may still fail to alarm in time because it has not fallen below the fixed threshold, resulting in a significant increase in the risk of missed alarms.

[0004] Online insulation resistance monitoring technology aims to assess the insulation health status by real-time acquisition of the insulation resistance values ​​to ground or between phases, combined with environmental and operational parameters, and to issue early warnings before potential faults occur. The basic principle of this technology is to obtain the equivalent resistance value of the insulation circuit using methods such as DC injection, AC bridge circuits, or high-frequency signal excitation, and to determine the status using a data processing unit. However, existing technologies have fundamental flaws in their early warning mechanism design, making it difficult to consider the differentiated needs of equipment at different stages of service.

[0005] Existing insulation monitoring systems typically simplify their early warning logic to static comparison operations, lacking comprehensive consideration of multi-dimensional factors such as equipment aging trends, operating load, and temperature and humidity changes. Their fixed thresholds neither adaptively adjust to the equipment's health status nor dynamically optimize the judgment boundaries based on real-time operating conditions, leading to an irreconcilable contradiction between early warning sensitivity and reliability. Especially in critical scenarios with high reliability requirements (such as rail transit traction systems, energy storage power stations, and medical electrical equipment), false alarms can trigger unnecessary downtime for maintenance, increasing operating costs, while missed alarms may directly induce insulation breakdown, short circuits, or even fires. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention is proposed. Embodiments of this invention provide an online monitoring and early warning method for insulation resistance. This method constructs a dynamic benchmark model based on the individual aging trajectory of the equipment by real-time acquisition of insulation resistance time-series data, environmental temperature and humidity parameters, and equipment operating condition information. A sliding time window is used to extract trend features from historical insulation resistance data, and a multi-dimensional aging state characterization vector is established by combining equipment commissioning time, cumulative load current, and environmental stress factors. Based on this, an adaptive threshold generation mechanism is employed to dynamically adjust the lower limit of the insulation resistance early warning based on the aging state characterization vector, ensuring that the early warning threshold evolves synchronously with the actual aging degree of the equipment. When the real-time insulation resistance value is lower than the current dynamic early warning threshold, a graded early warning signal is triggered, and the remaining safety margin assessment result of the equipment is output, thereby achieving accurate perception and early risk warning of the insulation degradation state of electrical equipment at different service stages.

[0007] This invention provides a method for online monitoring and early warning of insulation resistance, comprising: The insulation resistance monitoring unit acquires the insulation resistance measurement value sequence of the device under test in real time. The insulation resistance monitoring unit uses the DC injection method or the AC bridge method to periodically sample the insulation impedance between the main circuit of the device under test and the ground. The sampling period is no more than 10 seconds. Simultaneously acquire temperature and relative humidity data of the environment where the device under test is located, as well as operating current data and cumulative operation time data of the device under test; The insulation resistance measurement value sequence is processed by a sliding time window. The window length of the sliding time window is 7 days and the step size is 1 day. The mean, standard deviation and first-order difference slope of the insulation resistance value in each window are calculated to form an insulation resistance aging trend feature set. Based on the cumulative commissioning time data, the integral value of the operating current data, the weighted average value of the ambient temperature data, and the peak frequency of the relative humidity data, a 4-dimensional aging state characterization vector is constructed. Based on the 4-dimensional aging state characterization vector, a dynamic early warning threshold for the current moment is generated through a preset nonlinear mapping function. The nonlinear mapping function is a piecewise exponential decay function, which has a relatively slow decay rate in the early stage of equipment commissioning, and a significantly increased decay rate after more than five years of operation. The current insulation resistance measurement value is compared with the dynamic warning threshold. If the insulation resistance measurement value is less than the dynamic warning threshold, a first-level warning or a second-level warning signal is triggered according to the degree of deviation, and the equipment insulation safety margin coefficient is calculated. The safety margin coefficient is defined as the ratio of the current insulation resistance measurement value to the dynamic warning threshold. The warning signal and safety margin coefficient are uploaded to the monitoring center and displayed visually on the local human-machine interface.

[0008] This invention provides an online insulation resistance monitoring and early warning system, comprising: The insulation resistance real-time acquisition module is used to acquire the insulation resistance measurement value sequence of the device under test in real time through the insulation resistance monitoring unit. The insulation resistance monitoring unit uses the DC injection method or the AC bridge method to periodically sample the insulation impedance between the main circuit of the device under test and the ground. The sampling period is no more than 10 seconds. The multi-source environment and operating condition data acquisition module is used to simultaneously acquire temperature and relative humidity data of the environment in which the device under test is located, as well as operating current data and cumulative operation time data of the device under test. The insulation resistance trend feature extraction module is used to perform sliding time window processing on the insulation resistance measurement value sequence. The window length of the sliding time window is 7 days and the step size is 1 day. The mean, standard deviation and first-order difference slope of the insulation resistance value in each window are calculated to form an insulation resistance aging trend feature set. The aging state characterization vector construction module is used to construct a 4-dimensional aging state characterization vector based on the cumulative commissioning time data, the integral value of the operating current data, the weighted average value of the ambient temperature data, and the peak frequency of the relative humidity data. The dynamic early warning threshold generation module is used to generate the dynamic early warning threshold at the current moment based on the 4-dimensional aging state characterization vector and through a preset nonlinear mapping function. The nonlinear mapping function is a piecewise exponential decay function, which has a relatively slow decay rate in the early stage of equipment commissioning and a significantly increased decay rate after more than five years of commissioning. The early warning judgment and safety margin calculation module is used to compare the current insulation resistance measurement value with the dynamic early warning threshold. If the insulation resistance measurement value is less than the dynamic early warning threshold, a first-level or second-level early warning signal is triggered according to the degree of deviation, and the equipment insulation safety margin coefficient is calculated. The safety margin coefficient is defined as the ratio of the current insulation resistance measurement value to the dynamic early warning threshold. The early warning information output module is used to upload the early warning signal and safety margin coefficient to the monitoring center and display them visually on the local human-machine interface.

[0009] In one embodiment of the present invention, the insulation resistance monitoring unit includes a high-voltage DC power supply, a current-limiting resistor, a sampling resistor, a signal conditioning circuit, and an analog-to-digital converter. The output terminal of the high-voltage DC power supply is connected to the high-voltage terminal of the device under test via the current-limiting resistor, and the grounding terminal of the device under test is grounded via the sampling resistor. The signal conditioning circuit collects the voltage signal across the sampling resistor, amplifies and filters it, and then sends it to the analog-to-digital converter. The sampling accuracy of the analog-to-digital converter is not less than 16 bits, and the sampling rate is not less than 100 times per second. The insulation resistance measurement value is calculated using Ohm's law, that is, the output voltage of the high-voltage DC power supply divided by the current flowing through the sampling resistor.

[0010] In one embodiment of the present invention, the integral value of the operating current data is obtained by continuously integrating the secondary current signal output by the current transformer of the main circuit of the device under test, with the integration time span being from the start time of equipment commissioning to the current time; the weighted average value of the ambient temperature data is calculated using an exponential weighted moving average algorithm, with a weighting factor of 0.95, to highlight the accelerated aging effect of recent high temperatures on insulation materials; the peak frequency of the relative humidity data is defined as the percentage of days in the past 30 days where the maximum daily relative humidity exceeds 80%.

[0011] As one embodiment of the present invention, the specific expression of the piecewise exponential decay function is as follows: When the cumulative duration of operation When the number of days is ≤1825, ; When the cumulative duration of operation >1825 days, ; in, As the initial baseline threshold, The first attenuation coefficient is 5 × 10⁻⁶. -6 , This is the second attenuation coefficient, with a value of 2 × 10. -5 The initial reference threshold is set according to the equipment type. It is 100 megohms for dry-type transformers, 500 megohms for oil-immersed transformers, and 50 megohms for switchgear.

[0012] In one embodiment of the present invention, the first-level early warning signal is triggered when the insulation resistance measurement value is less than the dynamic early warning threshold but greater than or equal to 80% of the dynamic early warning threshold; the second-level early warning signal is triggered when the insulation resistance measurement value is less than 80% of the dynamic early warning threshold; the early warning signal is output to the substation integrated automation system through a hard contact, and simultaneously uploaded to the remote monitoring platform via an Ethernet interface in IEC61850 protocol format.

[0013] As one embodiment of the present invention, the calculation result of the safety margin coefficient is divided into three levels: greater than or equal to 0.9 is the normal state, between 0.8 and 0.9 is the attention state, and less than 0.8 is the abnormal state; the visualization display adopts a combination of red, yellow and green indicator lights and digital dashboard, with green indicating the normal state, yellow indicating the attention state, and red indicating the abnormal state, and simultaneously displays the specific safety margin coefficient value.

[0014] Furthermore, during the sliding time window processing, outlier values ​​are first removed from the insulation resistance measurement value sequence. The removal criterion is: if the absolute value of the difference between a certain sampling point and the median of the five sampling points before and after it is greater than three times the absolute deviation of the median, it is determined to be an outlier and removed. The data sequence after removal is then subjected to sliding window statistical calculation.

[0015] Furthermore, the initial benchmark threshold in the nonlinear mapping function is not a fixed empirical value, but is determined based on the measured value of insulation resistance in the equipment's factory test report. 70% of the measured value is taken as the initial benchmark threshold to ensure that the warning start point of different individual equipment matches its initial insulation performance.

[0016] Furthermore, the dynamic early warning threshold generation module also receives externally input equipment maintenance record information. When it detects that the equipment has completed a major overhaul or replaced insulation components, it automatically resets the cumulative commissioning time to 0 and rereads the first insulation resistance test value after maintenance as a new initial benchmark threshold, thereby realizing the life cycle reset of the early warning model.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention abandons the traditional fixed threshold early warning mode and establishes a dynamic early warning mechanism based on the aging trajectory of individual equipment. By integrating multi-dimensional information such as insulation resistance time-series trend, equipment operating load, environmental stress, and service duration, an aging state characterization vector that can truly reflect the insulation degradation process of equipment is constructed, and a dynamic early warning threshold that evolves with the actual state of the equipment is generated accordingly.

[0018] 2. It effectively solves the problem of frequent false alarms in newly commissioned equipment due to its excellent insulation performance, while significantly improving the ability to detect latent insulation degradation in long-term service equipment, avoiding the risk of missed alarms due to rigid thresholds.

[0019] 3. By introducing safety margin coefficients and graded early warning strategies, quantitative and intuitive status assessment basis is provided for operation and maintenance personnel, supporting differentiated maintenance decisions, extending the maintenance-free cycle of healthy equipment, and improving the reliability and economy of power system operation. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical architecture of the online monitoring and early warning method for insulation resistance proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the dynamic early warning threshold generation mechanism based on the individual aging trajectory of equipment in this invention; Figure 3 This is a flowchart illustrating the logical flow of the sliding time window processing and aging trend feature extraction of insulation resistance time-series data in this invention. Figure 4This is a logical flowchart of the construction of the 4D aging state characterization vector and the fusion of multi-source environmental and working condition data in this invention. Figure 5 This is a flowchart illustrating the logical flow of the graded early warning triggering and safety margin coefficient calculation in this invention. Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the local human-machine interface and the remote monitoring center in this invention. Detailed Implementation

[0021] Please refer to Figures 1 to 6 This invention provides an online monitoring and early warning method for insulation resistance, aiming to address the dual shortcomings of traditional fixed threshold early warning mechanisms, which exhibit high false alarm and false negative rates during the aging process of equipment throughout its entire life cycle. This method constructs a dynamic benchmark model centered on the individual aging trajectory of the equipment, integrating multi-source operating and environmental parameters to achieve accurate perception of insulation degradation and early risk warning.

[0022] The method includes the following steps: S1, the insulation resistance measurement value sequence of the device under test is obtained in real time through the insulation resistance monitoring unit; S2, synchronously acquire temperature and relative humidity data of the environment where the device under test is located, as well as operating current data and cumulative operation time data of the device under test; S3, perform sliding time window processing on the insulation resistance measurement value sequence, calculate the mean, standard deviation and first-order difference slope of the insulation resistance value in each window, and form an insulation resistance aging trend feature set. S4. Based on the cumulative commissioning time data, the integral value of the operating current data, the weighted average value of the ambient temperature data, and the peak frequency of the relative humidity data, a 4-dimensional aging state characterization vector is constructed. S5. Based on the 4-dimensional aging state representation vector, a dynamic early warning threshold for the current moment is generated through a preset nonlinear mapping function. S6, compare the current insulation resistance measurement value with the dynamic early warning threshold. If the insulation resistance measurement value is less than the dynamic early warning threshold, trigger a first-level or second-level early warning signal according to the degree of deviation, and calculate the equipment insulation safety margin coefficient. S7. The warning signal and safety margin coefficient are uploaded to the monitoring center and displayed visually on the local human-machine interface.

[0023] In step S1, the insulation resistance measurement sequence of the device under test is acquired in real time through the insulation resistance monitoring unit. The insulation resistance monitoring unit uses a DC injection method to periodically sample the insulation impedance between the main circuit of the device under test and ground, with a sampling period set to 10 seconds. The insulation resistance monitoring unit consists of a high-voltage DC power supply, a current-limiting resistor, a sampling resistor, a signal conditioning circuit, and an analog-to-digital converter. The output terminal of the high-voltage DC power supply is connected to the high-voltage terminal of the device under test via the current-limiting resistor, and the grounding terminal of the device under test is grounded via the sampling resistor.

[0024] The signal conditioning circuit acquires the voltage signal across the sampling resistor. This signal undergoes gain adjustment by a preamplifier, is then filtered by a low-pass filter to remove high-frequency noise interference, and finally sent to the analog-to-digital converter (ADC). The ADC has a sampling accuracy of 16 bits and a sampling rate of 100 times per second. The insulation resistance measurement is calculated based on Ohm's law, which is the output voltage of the high-voltage DC power supply divided by the current flowing through the sampling resistor. To ensure measurement stability, the output voltage of the high-voltage DC power supply is set to 500 volts, the current-limiting resistor has a resistance of 10,000 ohms, and the sampling resistor has a resistance of 100 ohms. After each sampling, the system executes a self-calibration program, using a built-in reference voltage source to perform zero-point and gain correction on the signal conditioning channel to eliminate system errors caused by temperature drift and component aging.

[0025] In step S2, temperature and relative humidity data of the environment where the device under test (DUT) is located, as well as the operating current and cumulative operation time data of the DUT, are acquired synchronously. Temperature and relative humidity data are collected by a temperature and humidity sensor installed near the casing of the DUT. The sampling period is synchronized with the insulation resistance measurement, both being 10 seconds. The temperature and humidity sensor uses a digital output integrated module, with a temperature measurement range of -40°C to +80°C and an accuracy of ±0.5°C; and a relative humidity measurement range of 0% to 100% and an accuracy of ±3%. The operating current data is acquired from the secondary output of the current transformer in the main circuit of the DUT, and after passing through an isolation amplifier, is sent to a dedicated current acquisition module. This module samples at a rate of 1000 Hz and calculates the charge per unit time using a digital integration algorithm, thereby deriving the instantaneous current value.

[0026] The cumulative operation time is recorded by the system's internal real-time clock module, starting from the first power-on operation of the equipment, with a daily error of no more than 1 second. All collected data has a unified timestamp and is stored in local non-volatile memory, forming a structured multi-source data stream.

[0027] In step S3, a sliding time window is applied to the insulation resistance measurement value sequence. The window length of the sliding time window is set to 7 days, with a step size of 1 day. Before performing sliding window statistics, outlier removal is first performed on the original insulation resistance measurement value sequence. The removal criteria are as follows: For any sampling point in the sequence, the median M of the 11-dimensional subsequence formed by the five sampling points before and after it is calculated. Then, the median of the absolute values ​​of the differences between each point in the subsequence and M is calculated and denoted as MAD (Median Absolute Deviation). If the absolute value of the difference between the sampling point and M is greater than 3 times MAD, it is determined to be an outlier and removed. The removed data sequence is used for subsequent sliding window processing.

[0028] For each valid data point within a 7-day window, calculate its arithmetic mean. Standard deviation and the slope of the first difference Among them, the slope of the first difference The insulation resistance is obtained by least-squares linear fitting of the time-resistance pairs of all data points within the window, reflecting the overall trend of insulation resistance change over that time period. These three statistics together constitute a three-dimensional aging trend feature vector. The feature vectors corresponding to all windows are arranged in chronological order, forming an insulation resistance aging trend feature set. This feature set serves as a quantitative representation of the historical evolution of equipment insulation performance, providing a basic input for subsequent aging state modeling.

[0029] In step S4, a 4-dimensional aging state characterization vector is constructed based on the cumulative commissioning time data, the integral value of the operating current data, the weighted average value of the ambient temperature data, and the peak frequency of the relative humidity data. (Cumulative commissioning time) Data is taken directly from the system's real-time clock module, measured in days. This is the integral value of the operating current data. The current signal of the main circuit is obtained by continuous numerical integration from the start of equipment operation to the current moment. The integration adopts the trapezoidal rule, with a time step of 10 seconds, and the result is expressed in ampere-seconds. The weighted average value T_w of the ambient temperature data is calculated using an exponentially weighted moving average algorithm, and its recursive formula is as follows: ; in For the first Temperature values ​​at each sampling time, initial value The average daily temperature on the day the equipment was put into operation is used. This algorithm assigns higher weight to recent high temperatures to accurately reflect the accelerating effect of high temperatures on the thermal aging process of insulation materials. Peak frequency of relative humidity data. Defined as within the past 30 calendar days, The percentage of days with a maximum relative humidity exceeding 80% is defined as follows. The maximum daily relative humidity is calculated by taking the maximum value from humidity samples taken at 10-second intervals throughout the day. The above four parameters— —After normalization, they are combined into a 4-dimensional aging state representation vector. ,in , For reference, the service life is 1825 days. The reference charge is 1 billion ampere-seconds. and These represent the minimum and maximum ambient temperatures at which the equipment can operate, respectively, and are set to -10 degrees Celsius and 50 degrees Celsius. This 4-dimensional vector comprehensively depicts the cumulative aging effects of the equipment across four dimensions: time, electrical stress, thermal stress, and wet stress.

[0030] In step S5, based on the 4-dimensional aging state representation vector, a dynamic early warning threshold for the current moment is generated through a preset nonlinear mapping function. The nonlinear mapping function is a piecewise exponential decay function, and its expression is as follows: When the cumulative duration of operation When the number of days is ≤1825, ; When the cumulative duration of operation >1825 days, ; in, As the initial baseline threshold, The first attenuation coefficient is 5 × 10⁻⁶. -6 , This is the second attenuation coefficient, with a value of 2 × 10. -5 Initial baseline threshold It is not a fixed empirical value, but rather based on the measured insulation resistance value in the equipment's factory test report. Confirm, take For devices without factory data, The attenuation coefficient is set according to equipment type: 100 megohms for dry-type transformers, 500 megohms for oil-immersed transformers, and 50 megohms for switchgear. The design basis for this piecewise function is that the insulation material performance is stable and the degradation rate is slow during the initial operation period (within five years), therefore a smaller attenuation coefficient is used. After more than five years, the insulation material enters an accelerated aging stage, and the degradation rate increases significantly. Therefore, it is necessary to switch to a material with a larger attenuation coefficient. Dynamic early warning threshold The system dynamically evolves with the actual service status of the equipment to ensure that the sensitivity of the early warning system matches the true health level of the equipment.

[0031] In step S6, the current insulation resistance measurement value is... With the dynamic early warning threshold Compare. If ≥ If so, the equipment insulation status is determined to be normal, and no warning is triggered. < Then, further determine the degree of deviation: when ≥0.8× When, a Level 1 warning signal is triggered; when <0.8× At this time, a level-two warning signal is triggered. Simultaneously, the equipment insulation safety margin factor is calculated. Its definition is = / The safety margin factor η directly reflects the margin of the equipment's current insulation performance relative to the dynamic safety boundary. ≥0.9 corresponds to the normal state, 0.8≤ <0.9 corresponds to a state of attention. A value <0.8 corresponds to an abnormal state. The early warning judgment logic is embedded in the interrupt service routine of the system's main control microprocessor, ensuring that the threshold comparison and early warning decision are completed within 10 milliseconds after each insulation resistance sampling.

[0032] In step S7, the warning signal and safety margin coefficient are uploaded to the monitoring center and displayed visually on the local HMI. The warning signal is output through a hard-contact relay with a contact capacity of 250 volts AC and 5 amps, which can be directly connected to the alarm input circuit of the substation integrated automation system. Simultaneously, the warning information is encapsulated in IEC61850 protocol format via an Ethernet interface, including the device identifier, warning level, safety margin coefficient, dynamic warning threshold, current insulation resistance value, and timestamp, and uploaded to the remote monitoring platform. The local HMI consists of a 7-inch color LCD screen and three-color status indicator lights. A green indicator light indicates a normal state, a yellow indicator light indicates a warning state, and a red indicator light indicates an abnormal state. The LCD screen synchronously displays the safety margin coefficient value, the current insulation resistance value, the dynamic warning threshold, and the insulation resistance trend curve for the past 30 days. All displayed content is refreshed every second to ensure that maintenance personnel can monitor the equipment insulation status in real time.

[0033] Furthermore, the dynamic early warning threshold generation module also receives externally input equipment maintenance record information. When the system receives a "major overhaul completed" or "insulation component replaced" command input by maintenance personnel through the human-machine interface, it automatically performs a model reset operation: resetting the cumulative commissioning time t to 0, and reading the insulation resistance measurement value under the first stable operation state within 24 hours after maintenance completion as the new value. Recalculate Subsequently, the generation of dynamic early warning thresholds is based on the new service starting point, realizing the full lifecycle management of the early warning model.

[0034] The system also includes a data storage and backtracking module for storing at least three years of historical data, including original sampled values, aging trend feature sets, 4D aging state representation vectors, dynamic early warning threshold sequences, and records of all early warning events. Data is stored using a cyclic overwrite method, prioritizing the retention of high-density data from the 30 days before and after each early warning. The system supports exporting data packets for a specified time period via USB or network interface for offline analysis and fault diagnosis.

[0035] In the implementation of this method, all data processing is completed within an embedded real-time operating system environment. Task scheduling employs a priority preemption mechanism to ensure that insulation resistance sampling and early warning judgment tasks have the highest priority. The system is equipped with a watchdog timer and power monitoring circuitry, which can automatically reset and resume operation in the event of abnormal power failure or program crashes. The communication module supports dual network port redundancy configuration to ensure the reliability of data uploads.

[0036] In summary, this embodiment constructs a closed-loop, adaptive online monitoring and early warning system for insulation resistance through the coordinated execution of steps S1 to S7. This system, centered on the individual aging trajectory of equipment, integrates multi-physics stress information and dynamically adjusts the early warning boundaries, achieving a paradigm shift from "static threshold" to "dynamic benchmark," effectively improving the accuracy and timeliness of insulation condition assessment for power equipment.

[0037] The online insulation resistance monitoring and early warning system includes a real-time insulation resistance acquisition module, a multi-source environmental and operating condition data acquisition module, an insulation resistance trend feature extraction module, an aging state characterization vector construction module, a dynamic early warning threshold generation module, an early warning judgment and safety margin calculation module, and an early warning information output module. All modules are deployed on the same embedded hardware platform and interact with each other via shared memory and message queues.

[0038] The real-time insulation resistance acquisition module includes the aforementioned high-voltage DC power supply, current-limiting resistor, sampling resistor, signal conditioning circuit, and analog-to-digital converter. Its output is a calibrated sequence of insulation resistance measurements. The multi-source environmental and operating condition data acquisition module integrates a temperature and humidity sensor interface, a current transformer signal conditioning circuit, and a real-time clock chip, responsible for synchronously acquiring environmental and operating parameters. The insulation resistance trend feature extraction module implements outlier removal algorithms and sliding time window statistical calculations, outputting an aging trend feature set. The aging state characterization vector construction module performs 4D parameter normalization and vector assembly.

[0039] The dynamic early warning threshold generation module stores the parameters of the piecewise exponential decay function and calculates them in real time based on the aging state vector. The early warning judgment and safety margin calculation module executes comparison logic and... The calculation and early warning information output module drives relays, LCD screens, and network communication controllers to complete local and remote alarm outputs.

Claims

1. A method for online monitoring and early warning of insulation resistance, characterized in that, include: The insulation resistance measurement value sequence of the device under test is obtained in real time through the insulation resistance monitoring unit. Simultaneously acquire temperature and relative humidity data of the environment where the device under test is located, as well as operating current data and cumulative operation time data of the device under test; The insulation resistance measurement value sequence is processed by sliding time window, and the mean, standard deviation and first-order difference slope of the insulation resistance value in each window are calculated to form an insulation resistance aging trend feature set. Based on the cumulative commissioning time data, the integral value of the operating current data, the weighted average value of the ambient temperature data, and the peak frequency of the relative humidity data, a 4-dimensional aging state characterization vector is constructed. Based on the 4-dimensional aging state representation vector, a dynamic early warning threshold for the current moment is generated through a preset nonlinear mapping function. The current insulation resistance measurement value is compared with the dynamic warning threshold. If the insulation resistance measurement value is less than the dynamic warning threshold, a first-level or second-level warning signal is triggered according to the degree of deviation, and the equipment insulation safety margin coefficient is calculated. The safety margin coefficient is defined as the ratio of the current insulation resistance measurement value to the dynamic warning threshold. The warning signal and safety margin coefficient are uploaded to the monitoring center and displayed visually on the local human-machine interface.

2. The online monitoring and early warning method for insulation resistance according to claim 1, characterized in that, The insulation resistance monitoring unit uses either the DC injection method or the AC bridge method to periodically sample the insulation impedance between the main circuit of the device under test and ground.

3. The online monitoring and early warning method for insulation resistance according to claim 2, characterized in that, Before performing sliding time window processing on the insulation resistance measurement value sequence, the method further includes performing outlier removal operation on the insulation resistance measurement value sequence; the outlier removal operation includes: for any sampling point, calculating the median M and the absolute deviation of the median MAD of the subsequence formed by the five sampling points before and after it; if the absolute value of the difference between the sampling point and M is greater than 3 times MAD, it is determined to be an outlier and removed.

4. The online monitoring and early warning method for insulation resistance according to claim 3, characterized in that, The first-order difference slope is obtained by using time-resistance pairs of all data points within the least-squares linear fitting window.

5. The online monitoring and early warning method for insulation resistance according to claim 4, characterized in that, The integral value of the operating current data is obtained by continuously integrating the secondary current signal output by the current transformer of the main circuit of the tested equipment from the start time of equipment commissioning to the current time. The integration adopts the trapezoidal rule and the time step is 10 seconds.

6. The online monitoring and early warning method for insulation resistance according to claim 5, characterized in that, The weighted average of the ambient temperature data was calculated using an exponentially weighted moving average algorithm, with the following recursive formula: ;in For the first Temperature values ​​at each sampling time.

7. The online monitoring and early warning method for insulation resistance according to claim 6, characterized in that, The peak frequency of the relative humidity data is defined as the proportion of days with a maximum daily relative humidity exceeding 80% in the past 30 natural days; the maximum daily relative humidity is obtained by taking the maximum value of the humidity samples taken at 10-second intervals on that day.

8. The online monitoring and early warning method for insulation resistance according to claim 7, characterized in that, The 4-dimensional aging state characterization vector, after normalization, is expressed as follows: ,in: , This refers to the cumulative operating time. , This is the integral value of the operating current data; , This is a weighted average of the ambient temperature data; , This represents the peak frequency of the relative humidity data.

9. The online monitoring and early warning method for insulation resistance according to claim 8, characterized in that, The specific expression for the nonlinear mapping function is: When the cumulative duration of operation Dynamic early warning threshold when ≤1825 days ; when >1825 days, ;in This is the initial baseline threshold.

10. The online monitoring and early warning method for insulation resistance according to claim 9, characterized in that, The initial benchmark threshold According to the measured insulation resistance value in the equipment's factory test report Confirm, take ; For devices without factory data Configure according to device type.

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