Insulating oil dielectric loss test system based on temperature compensation

Abstract

The invention relates to the technical field of electrical equipment insulation performance detection, in particular to an insulating oil dielectric loss test system based on temperature compensation, which comprises a temperature acquisition module for acquiring insulating oil temperature data and environment temperature data; the dielectric loss measurement module measures a dielectric loss value and a dielectric loss angle through a self-adaptive anti-interference algorithm, and synchronously records voltage and frequency; the intelligent compensation module matches temperature characteristic parameters for different temperature intervals, and compensates a dielectric loss detection value through a segmented temperature compensation model; the state analysis module judges the aging degree and state of the insulating oil according to the compensated dielectric loss value and acid value, generates a multi-stage early warning signal and a diagnosis report by combining the breakdown voltage and the moisture content, and dynamically adjusts the test frequency according to the oil sample type and the operating age limit; and the cloud management platform stores historical data and compensation parameters, establishes a performance degradation model, analyzes a dielectric loss trend and generates maintenance suggestions. Therefore, the problems of insufficient test precision, poor environmental adaptability and the like in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment insulation performance testing technology, specifically to a temperature-compensated insulating oil dielectric loss testing system. Background Technology

[0002] Insulating oil, as the core insulating and cooling medium in power equipment, has dielectric loss characteristics that are key indicators for assessing the insulation status of equipment and directly affect the safe and stable operation of the power system. During long-term service, the dielectric loss value of insulating oil changes significantly due to factors such as ambient temperature fluctuations, changes in electric field strength, and impurity intrusion. Ignoring the influence of temperature during testing can easily lead to misjudgments of the insulation status, increasing the risk of equipment failure. With the development of power systems towards higher voltage, larger capacity, and greater intelligence, higher demands are placed on the accuracy, real-time performance, and environmental adaptability of insulating oil dielectric loss testing. Therefore, a dielectric loss testing system with temperature compensation capabilities is urgently needed to accurately reflect the true performance status of the insulating oil.

[0003] However, traditional insulating oil dielectric loss testing has significant limitations: the testing process lacks a systematic temperature compensation mechanism, measurement data at different temperatures lack a unified benchmark, making it difficult to achieve performance comparisons across temperature ranges; the measurement equipment has weak anti-interference capabilities and is easily affected by external interference in complex electromagnetic environments, leading to data deviations; temperature correction methods are simple and crude, often using a single coefficient correction, which cannot adapt to the nonlinear characteristics of insulating oil in different temperature ranges; and the correlation analysis between test data and equipment operating status is insufficient, making it difficult to form effective insulation status assessment and early warning. Overall, it faces technical challenges such as insufficient test accuracy, poor environmental adaptability, and low data utilization, failing to meet the stringent requirements of modern power systems for insulation status monitoring. Summary of the Invention

[0004] This application provides a temperature-compensated insulating oil dielectric loss testing system to address the problems of insufficient testing accuracy and poor environmental adaptability in existing technologies.

[0005] The first aspect of this application provides a temperature-compensated insulating oil dielectric loss testing system, comprising: a temperature acquisition module, a dielectric loss measurement module, an intelligent compensation module, a state analysis module, and a cloud management platform; wherein, the temperature acquisition module is used to acquire real-time temperature data of the insulating oil and ambient temperature data; the dielectric loss measurement module is used to measure the dielectric loss detection value and dielectric loss angle of the insulating oil through an adaptive anti-interference algorithm, and simultaneously record the voltage and frequency parameters during the test; the intelligent compensation module is used to match the temperature characteristic parameters of the insulating oil for different temperature ranges, and to perform temperature compensation on the dielectric loss detection value through a segmented temperature compensation model combined with the dielectric loss temperature coefficient, generating a compensated standard value; the state analysis module is used to determine the aging degree and state of the insulating oil based on the compensated dielectric loss value and acid value, and to generate multi-level early warning signals and diagnostic reports by combining the insulating oil breakdown voltage and moisture content correlation parameters, and to dynamically adjust the test frequency according to the oil sample type, operating years, and the diagnostic report; the cloud management platform is used to store historical test data and compensation parameters, establish an insulating oil performance degradation model based on the Weibull distribution, perform full life cycle dielectric loss trend analysis, and generate preventive maintenance suggestions.

[0006] Preferably, the temperature acquisition module includes: a platinum resistance temperature sensor, an infrared temperature sensor array, and a temperature and humidity sensor, wherein the platinum resistance sensor is used to monitor the oil sample temperature in real time, the infrared array is used to capture the temperature gradient distribution of the oil sample, and the temperature and humidity sensor is used to record environmental parameters and eliminate environmental interference.

[0007] Preferably, the dielectric loss detection module includes an anti-interference measurement unit, a loss measurement module, a parameter synchronization recording module, and a waveform storage unit. The anti-interference measurement unit is used to suppress power frequency interference and harmonic interference using an adaptive anti-interference algorithm. The loss detection module is used to measure the dielectric loss detection value and dielectric loss angle using a phase difference measurement algorithm. The parameter synchronization recording module is used to correlate and record the applied voltage, operating frequency, and dielectric loss measurement results in real time during the test. The waveform storage unit is used to save the voltage and current waveform data during the measurement process.

[0008] Preferably, the intelligent compensation module includes a temperature characteristic database and a compensation algorithm execution unit. The temperature characteristic database is used to store the dielectric loss temperature coefficient and viscosity characteristic parameters of different types of insulating oil in different temperature ranges. The compensation algorithm execution unit is used to perform temperature compensation on the dielectric loss detection value based on the segmented temperature compensation model and the difference between the measured temperature and the standard temperature, and generate a correction value at the standard temperature.

[0009] Preferably, the status analysis module includes an aging determination unit, an early warning generator, a diagnostic report compilation unit, and a test scheduling unit. The aging determination unit is used to classify the aging status level of the insulating oil based on the compensated dielectric loss value and capacitance change rate. The early warning generator is used to generate multi-level early warning prompts based on the degree of aging. The diagnostic report compilation unit is used to summarize test data and determination results to generate a report with accompanying fault cause analysis and insulation status diagnostics. The test scheduling unit is used to dynamically adjust the test time interval and test items based on the oil sample type, operating years, and diagnostic results.

[0010] Preferably, the cloud management platform includes a historical database, a performance degradation modeling unit, and a maintenance strategy generator. The historical database is used to store test data, compensation parameters, and diagnostic reports. The performance degradation modeling unit is used to construct an insulating oil performance degradation model based on the Weibull distribution, extrapolate future dielectric loss trends from historical data, and identify critical points for performance mutations. The maintenance strategy generator is used to generate preventative maintenance recommendations based on equipment type and operating conditions.

[0011] The second aspect of this application provides a temperature-compensated method for testing the dielectric loss of insulating oil, comprising: acquiring temperature data of the insulating oil and ambient temperature data; based on the temperature data of the insulating oil and ambient temperature data, measuring the dielectric loss detection value and dielectric loss angle of the insulating oil using an adaptive anti-interference algorithm, simultaneously recording voltage and frequency parameters during the test, matching the temperature characteristic parameters of the insulating oil for different temperature ranges, and performing temperature correction on the dielectric loss detection value using a segmented temperature compensation model combined with the dielectric loss temperature coefficient to generate a compensated dielectric loss detection value; determining the aging degree and state of the insulating oil based on the compensated dielectric loss detection value, acid value, and related parameters of the insulating oil breakdown voltage and moisture content, generating multi-level early warning signals and diagnostic reports, and dynamically adjusting the test frequency according to the oil sample type and operating years; uploading the diagnostic report, historical test data, and compensation parameters to a management platform, and performing a full life cycle dielectric loss trend analysis using an insulating oil performance degradation model to generate preventive maintenance recommendations.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a temperature-compensated insulating oil dielectric loss testing method as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a temperature-compensated insulating oil dielectric loss testing method as described in the above embodiments.

[0014] The fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing a temperature-compensated insulating oil dielectric loss testing method as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects: This application embodiment captures real-time changes in insulating oil and ambient temperature through a temperature acquisition module. Combined with a segmented temperature compensation model from an intelligent compensation module, it accurately matches oil quality characteristic parameters across different temperature ranges. The dielectric loss value is corrected using the dielectric loss temperature coefficient, eliminating interference from temperature fluctuations and improving data accuracy, thus avoiding misjudgments of insulation status due to temperature deviations. The dielectric loss measurement module incorporates an adaptive anti-interference algorithm to resist interference signals in complex electromagnetic environments, reducing voltage and frequency parameter recording errors and enhancing measurement stability under harsh conditions. The status analysis module accurately determines the aging degree and status of the insulating oil by integrating compensated dielectric loss value, acid value, breakdown voltage, and moisture content. It dynamically adjusts the test frequency based on oil sample type and operating years, ensuring timely monitoring while avoiding resource waste. The cloud management platform, relying on historical data storage and the Weibull distribution degradation model, conducts full lifecycle dielectric loss trend analysis, generating targeted preventative maintenance suggestions to proactively identify potential faults, reduce equipment downtime risks, and improve the intelligence and operational reliability of power insulation monitoring. This solves the problems of insufficient testing accuracy and poor environmental adaptability in existing technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a temperature-compensated insulating oil dielectric loss testing system according to an embodiment of this application; Figure 2 This is a schematic diagram of a temperature acquisition module according to an embodiment of this application; Figure 3 This is a schematic diagram of a dielectric loss measurement module according to an embodiment of this application; Figure 4 This is a schematic diagram of an intelligent compensation module provided according to an embodiment of this application; Figure 5 This is a schematic diagram of a state analysis module provided according to an embodiment of this application; Figure 6This is a schematic diagram of a cloud management platform provided according to an embodiment of this application; Figure 7 This is a schematic diagram of a temperature-compensated insulating oil dielectric loss testing system according to an embodiment of this application; Figure 8 The flowchart shows a temperature-compensated dielectric loss testing method for insulating oil according to an embodiment of this application. Figure 9 This is a schematic diagram of the insulating oil test of a main transformer in a 110kV substation according to an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The following describes an embodiment of the temperature-compensated dielectric loss testing system for insulating oil, with reference to the accompanying drawings. Addressing the issue of insufficient testing accuracy mentioned in the background section, this application provides a temperature-compensated dielectric loss testing system for insulating oil. In this system, a temperature acquisition module captures real-time changes in the temperature of the insulating oil and the ambient environment. Combined with a segmented temperature compensation model from an intelligent compensation module, it accurately matches the oil's characteristic parameters across different temperature ranges. The dielectric loss value is corrected using the dielectric loss temperature coefficient, eliminating the interference of temperature fluctuations on the test results, improving data accuracy, and avoiding misjudgments of insulation status due to temperature deviations. The dielectric loss measurement module incorporates an adaptive anti-interference algorithm to resist interference signals in complex electromagnetic environments, reduce voltage and frequency parameter recording errors, and enhance measurement stability under harsh operating conditions. The status analysis module accurately determines the aging degree and status of the insulating oil by fusing multi-dimensional parameters such as compensated dielectric loss value, acid value, breakdown voltage, and moisture content. It dynamically adjusts the testing frequency based on oil sample type and operating years, ensuring timely monitoring while avoiding resource waste. The cloud-based management platform, relying on historical data storage and the Weibull distribution degradation model, conducts full lifecycle dielectric loss trend analysis, generates targeted preventative maintenance recommendations, proactively identifies potential faults, reduces equipment downtime risks, and improves the intelligence level and operational reliability of power insulation monitoring. This solves the problems of insufficient testing accuracy and poor environmental adaptability in existing technologies.

[0020] Figure 1This is a schematic diagram of a temperature-compensated insulating oil dielectric loss testing system provided in an embodiment of this application.

[0021] This application provides a temperature-compensated insulating oil dielectric loss testing system, the system 10 comprising: Temperature acquisition module 100, dielectric loss measurement module 200, intelligent compensation module 300, status analysis module 400, cloud management platform 500.

[0022] The system includes the following modules: Temperature Acquisition Module 100, which collects real-time temperature data of the insulating oil and ambient temperature data; Dielectric Loss Measurement Module 200, which measures the dielectric loss detection value and dielectric loss angle of the insulating oil using an adaptive anti-interference algorithm, and simultaneously records the voltage and frequency parameters during testing; Intelligent Compensation Module 300, which matches the temperature characteristic parameters of the insulating oil to different temperature ranges, and performs temperature compensation on the dielectric loss detection value using a segmented temperature compensation model combined with the dielectric loss temperature coefficient to generate a compensated standard value; State Analysis Module 400, which determines the aging degree and state of the insulating oil based on the compensated dielectric loss value and acid value, and generates multi-level early warning signals and diagnostic reports by combining the insulating oil breakdown voltage and moisture content correlation parameters, and dynamically adjusts the test frequency according to the oil sample type, operating years, and diagnostic reports; and Cloud Management Platform 500, which stores historical test data and compensation parameters, establishes an insulating oil performance degradation model based on the Weibull distribution, performs full life cycle dielectric loss trend analysis, and generates preventive maintenance recommendations.

[0023] It is understood that in this embodiment, the temperature acquisition module captures real-time changes in insulating oil and ambient temperature, and combined with the segmented temperature compensation model of the intelligent compensation module, accurately matches the oil quality characteristic parameters in different temperature ranges. The dielectric loss value is corrected using the dielectric loss temperature coefficient, eliminating the interference of temperature fluctuations on the detection results, improving data accuracy, and avoiding misjudgments of insulation status due to temperature deviations. The adaptive anti-interference algorithm of the dielectric loss measurement module resists interference signals in complex electromagnetic environments, reduces voltage and frequency parameter recording errors, and enhances measurement stability under harsh operating conditions. The status analysis module accurately determines the aging degree and status of the insulating oil by integrating multi-dimensional parameters such as compensated dielectric loss value, acid value, breakdown voltage, and moisture content. It dynamically adjusts the test frequency based on oil sample type and operating years, ensuring timely monitoring while avoiding resource waste. The cloud management platform relies on historical data storage and the Weibull distribution degradation model to conduct full life-cycle dielectric loss trend analysis, generating targeted preventative maintenance suggestions, proactively identifying potential faults, reducing equipment downtime risks, and improving the intelligence level and operational reliability of power insulation monitoring. This solves the problems of insufficient testing accuracy and poor environmental adaptability in existing technologies.

[0024] In this embodiment of the application, the temperature acquisition module 100 includes: Figure 2 As shown, there is a platinum resistance temperature sensor, an infrared temperature sensor array, and a temperature and humidity sensor.

[0025] Among them, the platinum resistance sensor is used to monitor the oil sample temperature in real time, the infrared array is used to capture the temperature gradient distribution of the oil sample, and the temperature and humidity sensor is used to record environmental parameters and eliminate environmental interference.

[0026] It is understood that the embodiments of this application acquire basic oil sample temperature data in real time through a platinum resistance sensor, and capture the oil sample temperature gradient distribution by an infrared temperature sensor array, so as to achieve multi-dimensional and accurate perception of oil sample temperature; at the same time, environmental parameters are recorded by temperature and humidity sensors to eliminate environmental interference, providing comprehensive and reliable temperature reference data for the subsequent intelligent compensation module, avoiding the limitations of a single temperature acquisition method, and improving the integrity and accuracy of temperature data.

[0027] For example, in insulating oil dielectric loss testing, the Pt100 platinum resistance sensor is precisely deployed in the lower middle part of the oil sample container, directly contacting the insulating oil to capture core temperature data. Its ultra-wide measurement range of -200℃ to 850℃ perfectly matches the oil sample temperature range of 30℃ to 90℃ during power equipment operation. With a measurement accuracy of ±0.1℃ and a response speed of 0.1 seconds, it can keenly capture the rapid temperature rise process from 35℃ to 70℃ when the transformer load suddenly increases, and can even identify minute fluctuations at the level of 0.5℃. This sensor transmits data to the control system in real time via a dedicated cable with a metal shielding layer, effectively resisting interference in the strong electromagnetic environment of the substation. The temperature data it collects is complementary to the 5°C temperature difference distribution between the upper and lower layers of the oil sample captured by the infrared temperature sensor array. Combined with the ±3°C ambient temperature fluctuation data recorded by the temperature and humidity sensor, a multi-dimensional temperature correction matrix is ​​constructed. Ultimately, the temperature correction error of the dielectric loss detection value is strictly controlled within 0.1%, providing a stable input for the segmented model of the intelligent compensation module. This ensures that the testing system can always output accurate data reflecting the true state of the insulating oil in diverse scenarios such as converter stations and switchgear.

[0028] In this embodiment of the application, the dielectric loss detection module 200 includes: Figure 3 As shown, it includes an anti-interference measurement unit, a loss detection module, a parameter synchronization recording module, and a waveform storage unit.

[0029] The anti-interference measurement unit is used to suppress power frequency interference and harmonic interference through an adaptive anti-interference algorithm; the loss detection module uses a phase difference measurement algorithm to measure the dielectric loss detection value and dielectric loss angle; the parameter synchronization recording module is used to associate and record the applied voltage, operating frequency and dielectric loss measurement results in real time during the test; and the waveform storage unit is used to save the voltage and current waveform data during the measurement process.

[0030] It is understood that the anti-interference measurement unit in this application embodiment accurately suppresses power frequency and harmonic interference with an adaptive anti-interference algorithm, providing a stable environment for measuring dielectric loss detection values ​​and dielectric loss angles, and improving data accuracy; the loss detection module uses a phase difference measurement algorithm to accurately measure dielectric loss detection values ​​and dielectric loss angles, providing core detection data; the parameter synchronization recording module correlates the applied voltage, operating frequency, and dielectric loss measurement results in real time, ensuring that all parameters and detection results form a complete data chain, providing a comprehensive and relevant parameter background for subsequent analysis; the waveform storage unit retains voltage and current waveform data during the measurement process, providing original evidence for tracing abnormal data and troubleshooting fault causes, which not only enhances the anti-interference capability of the measurement process but also ensures the integrity and traceability of the data.

[0031] It should be noted that the formula for the adaptive anti-interference algorithm is: ; ; in, This is the output signal at time n; The order of the filter; For summation index variables; Let n be the value of the i-th weight coefficient of the filter at the current time n; Let n be the value of the input signal at time n−i; Let be the value of the i-th weight coefficient of the filter at the next time step (n+1); Step size factor; This is the error signal at the current time n.

[0032] The formula for the phase difference measurement algorithm is: ; ; ; in, For the angle of loss; The current is the resistive component; This is the capacitive component current; The phase difference angle; Active power; This is the effective value of the voltage; This is the effective value of the current.

[0033] The loss detection module uses a phase difference measurement algorithm to first capture the voltage signal across the dielectric and the current signal flowing through it, and then analyzes the phase difference between the two to obtain key data. In an ideal lossless dielectric, the current leads the voltage by 90 degrees; however, when there are losses in the actual dielectric, the current leads by less than 90 degrees. This angle difference is the dielectric loss angle. The module calculates the dielectric loss angle based on the measured phase difference, and then obtains the dielectric loss detection value, which reflects the degree of energy loss in the dielectric, thereby achieving a quantitative assessment of the dielectric loss situation.

[0034] For example, in the periodic testing of insulating oil in substations, the loss detection module starts working: First, it connects to the oil sample detection sensor via a dedicated interface, automatically extracts the insulating oil sample to be tested, and stabilizes the test environment parameters. The module's built-in signal acquisition unit captures the dielectric loss signal of the oil sample under an AC electric field. After filtering to remove environmental electromagnetic interference, the analog signal is converted into a digital signal by the AD conversion module. Based on the dielectric loss detection principle, the core processing unit calculates the dielectric loss value at the current temperature (e.g., 0.008), while simultaneously recording the real-time test temperature of 38℃. Subsequently, the module compares the raw test data with the preset insulating oil quality qualification threshold (e.g., 0.005), generates a preliminary test report, marks cases where the measured value exceeds the threshold, and synchronizes the data to the compensation algorithm unit for temperature correction. This process realizes automated detection of insulating oil loss, providing raw data support for evaluating the oil's insulation performance and judging potential equipment risks, and is a key link in the condition monitoring of power equipment.

[0035] In this embodiment of the application, the intelligent compensation module 300 includes: Figure 4 As shown, the temperature characteristic database and the compensation algorithm execution unit are shown.

[0036] The temperature characteristic database stores the dielectric loss temperature coefficient and viscosity characteristic parameters of different types of insulating oil in different temperature ranges; the compensation algorithm execution unit is used to perform temperature compensation on the dielectric loss detection value based on the segmented temperature compensation model and the difference between the measured temperature and the standard temperature, and generate the correction value at the standard temperature.

[0037] It is understood that the embodiments of this application store the dielectric loss temperature coefficient and viscosity characteristic parameters of different types of insulating oil in various temperature ranges through a temperature characteristic database, providing data support for compensation. Then, the compensation algorithm execution unit performs temperature compensation on the dielectric loss detection value based on the segmented temperature compensation model and the difference between the measured and standard temperatures, generating a correction value at the standard temperature. This provides accurate temperature compensation for the dielectric loss detection value of the insulating oil, effectively eliminating the interference of temperature differences on the detection results, adapting to various types of insulating oil and temperature ranges, and improving the accuracy and reliability of the detection results.

[0038] It should be noted that, based on the segmented temperature compensation model, and combined with the difference between the measured temperature and the standard temperature, temperature compensation is performed on the dielectric loss detection value to generate a correction value at the standard temperature. First, the difference between the measured temperature and the preset standard temperature is obtained. Then, the dielectric loss temperature coefficient and viscosity characteristic parameters of the corresponding insulating oil type in the temperature range are called from the temperature characteristic database. The segmented temperature compensation model is used to specifically correct the currently detected dielectric loss value. According to the magnitude of the temperature difference and the range it is in, the detection value is adjusted according to the compensation rules of the corresponding segment, and finally, the correction value at the standard temperature is generated.

[0039] Segmented temperature compensation model formula: ; in, The loss tangent at the target temperature; This represents the total number of measurement data points; For summation index; represents the weighting coefficient for the i-th temperature range; Let be the representative temperature of the i-th temperature range; This is the temperature compensation function; These are temperature-related parameters; For temperature The measured loss tangent value is shown below; To compensate for the residual.

[0040] For example, during routine testing of dielectric loss in substation insulating oil, the ambient temperature was 38℃, significantly higher than the standard test temperature of 20℃. The compensation algorithm execution unit immediately started working: first, it automatically collected the measured temperature and calculated the difference from the standard temperature to be 18℃. Based on a preset threshold, this temperature difference was determined to be within the high-temperature compensation range. Since high temperatures accelerate the thermal motion of polar molecules in the insulating oil, the measured dielectric loss value at this time would be larger than the true value under the standard temperature, directly using it would affect the accurate assessment of oil quality. The execution unit then invoked the high-temperature segment algorithm of the segmented temperature compensation model, using the temperature coefficient of 0.025 fitted for 25# transformer oil in the DL / T industry standard, combined with the 18℃ temperature difference, to correct the measured value to 0.0042 under the standard temperature. This correction effectively eliminated the interference caused by the temperature difference. After comparison with historical data under the same conditions, it accurately reflected the current aging degree of the insulating oil, providing a reliable basis for maintenance personnel to determine whether the oil sample needs to be replaced and to prevent equipment insulation failures, effectively ensuring the safe and stable operation of power transformers.

[0041] In this embodiment of the application, the state analysis module 400 includes, as follows: Figure 5 As shown, there are aging determination unit, early warning generator, diagnostic report compilation unit and test scheduling unit.

[0042] The aging determination unit is used to classify the aging status level of insulating oil based on the compensated dielectric loss value and capacitance change rate; the early warning generator is used to generate multi-level early warning prompts based on the degree of aging; the diagnostic report compilation unit is used to summarize test data and determination results and generate a report with attached fault cause analysis and insulation status diagnosis; the test scheduling unit is used to dynamically adjust the test time interval and test items based on oil sample type, operating years and diagnostic results.

[0043] It is understood that the aging determination unit in this application accurately classifies the aging level of insulating oil based on the compensated dielectric loss value and capacitance change rate, providing a quantitative basis for condition assessment; the early warning generator issues multi-level early warnings based on the degree of aging, enabling early identification and intervention of risks; the diagnostic report compilation unit summarizes the data and determination results, and generates a diagnostic report with fault cause analysis, providing a comprehensive reference for operation and maintenance decisions; the test scheduling unit dynamically adjusts the test interval and items based on oil sample type, operating years and diagnostic results, avoiding over-testing or omission of key tests, improving the accuracy and timeliness of insulating oil condition determination, and optimizing testing resources through scientific scheduling, effectively ensuring the safe operation of power equipment and reducing operation and maintenance costs.

[0044] It should be noted that the aging status of insulating oil is classified according to the compensated dielectric loss value and capacitance change rate. The dielectric loss value and capacitance change rate after temperature compensation are the core inputs. By comparing with the preset aging status thresholds of insulating oil (e.g., no aging corresponds to dielectric loss value ≤0.003 and capacitance change rate ≤2%; light aging corresponds to dielectric loss value 0.003-0.005 and capacitance change rate ≤5%; moderate aging corresponds to dielectric loss value 0.005-0.008 and capacitance change rate 5%-10%; heavy aging corresponds to dielectric loss value >0.008 and capacitance change rate >10%), the oil quality is clearly classified into aging status levels of no aging, light aging, moderate aging, and heavy aging.

[0045] The early warning generator generates differentiated multi-level early warning prompts based on the aging level of the insulating oil (non-aged, light, moderate, and severe), enabling accurate risk transmission and graded response: When it is determined to be non-aged, no early warning is triggered, and only the normal state is recorded; when lightly aged, a blue warning is issued, reminding maintenance personnel to pay attention to the oil quality change trend through a system interface pop-up or log record, and suggesting regular periodic tracking and testing; when moderately aged, a yellow warning is activated, triggering a local audible and visual alarm on the equipment in addition to the interface prompt, along with the operation suggestion of "shortening the testing interval and strengthening oil quality monitoring"; when severely aged, a red emergency warning is immediately triggered, not only displayed at the top of the monitoring system, but also automatically pushing an SMS / APP notification to the maintenance manager's mobile phone, clearly indicating "there is a risk of insulation breakdown, and it is recommended to immediately stop the machine, take samples for re-inspection, and assess the necessity of oil replacement".

[0046] The diagnostic report compilation unit summarizes the original test data (such as measured dielectric loss value, initial and current capacitance values, and ambient temperature records), the correction results output by the compensation algorithm (standard temperature dielectric loss value), and the aging judgment unit's classification level (such as moderate aging), forming a data ledger. Subsequently, it combines the equipment's operating years, historical test data trends, oil sample type (such as 25# transformer oil), and on-site operating conditions (such as load rate and operating temperature) to analyze the aging causes through the built-in fault attribution model (such as "accelerated oxidation due to long-term high-temperature operation" and "poor sealing leading to moisture exacerbating deterioration"). The final diagnostic report not only includes a data summary table and aging level judgment conclusions, but also includes targeted fault cause analysis (such as "a 30% exceedance of dielectric loss value is directly related to the oil temperature consistently above 60℃ for the past 6 months"), a comprehensive insulation status assessment (such as "current insulation performance has decreased to 75% of new oil, posing a risk of partial discharge"), and maintenance recommendations (such as "vacuum filtration is recommended and the testing cycle should be shortened to 1 month").

[0047] For example, in the insulation oil testing of a 110kV transformer in a substation, the aging judgment unit starts working: First, it receives the standard temperature dielectric loss value of 0.006 (with the interference of the measured temperature of 35℃ eliminated) output by the compensation algorithm execution unit, and simultaneously obtains the capacitance change rate of 7% returned by the capacitance detection module. The unit calls the built-in aging level threshold library and compares the two parameters with the preset standard—0.006 is in the medium aging dielectric loss value range (0.005~0.008), and 7% is in the medium aging capacitance change rate range (5%~10%). After logical verification confirms that the two parameters match, the insulation oil is judged to be in a "medium aging" state. A structured result containing the judgment level, parameter values, and threshold comparison is then generated and simultaneously pushed to the early warning generator and test scheduling unit, providing the core judgment basis for subsequent yellow warning triggering and test interval adjustment (from 1 year to 6 months). The entire process avoids misjudgment by a single indicator through dual-parameter collaborative verification, making the aging status assessment both quantitatively accurate and closely aligned with the actual equipment, laying a reliable foundation for tracking the degradation trend of insulation oil.

[0048] In this embodiment of the application, the cloud management platform 500 includes, for example: Figure 6 As shown, the system includes a historical database, a performance degradation modeling unit, and a maintenance strategy generator.

[0049] The historical database is used to store test data, compensation parameters, and diagnostic reports; the performance degradation modeling unit is used to build an insulating oil performance degradation model based on the Weibull distribution, extrapolate future dielectric loss trends from historical data, and identify critical points for performance mutations; the maintenance strategy generator is used to generate preventive maintenance recommendations by combining equipment type and operating conditions.

[0050] It is understood that the historical database in this application embodiment centrally stores test data, compensation parameters, and diagnostic reports, providing a complete data foundation for subsequent analysis; the performance degradation modeling unit constructs an insulating oil performance degradation model based on the Weibull distribution, uses historical data to extrapolate future dielectric loss trends and identify performance mutation critical points, enabling early prediction of degradation risks; the trend analysis module smooths the dielectric loss curve and accurately identifies performance mutation points, avoiding interference from occasional fluctuations in judgment; the maintenance strategy generator generates preventive suggestions based on equipment type and operating conditions, ensuring that maintenance measures meet actual needs, storing and tracing data, accurately grasping performance degradation patterns through modeling analysis, and generating targeted maintenance plans, effectively improving the foresight and scientific nature of insulating oil condition management, helping power equipment operation and maintenance shift from passive response to proactive prevention, and reducing fault risks and operation and maintenance costs.

[0051] It should be noted that the formula for the insulating oil performance degradation model is as follows: ; ; ; in, It is the probability density function; It is the cumulative distribution function; For risk functions; It is a time variable; For shape parameters; For scale parameters; It is a natural constant.

[0052] The performance degradation modeling unit is used to construct an insulating oil performance degradation model based on the Weibull distribution. It extrapolates future dielectric loss trends using historical data and identifies critical points of performance abrupt changes. By collecting historical performance data of the insulating oil, such as dielectric loss values ​​at different time points, this data is used to determine the shape and scale parameters of the Weibull distribution, thereby establishing an accurate performance degradation model. Using this model, future dielectric loss trends can be extrapolated; that is, based on the model's parameters and distribution characteristics, the possible values ​​and magnitudes of dielectric loss at different future time points can be predicted. Simultaneously, by analyzing the rate of change of the performance degradation curve in the model, when the rate of increase in dielectric loss value shows a significant abrupt change and exceeds a preset normal threshold range, this time point can be identified as a critical point of abrupt change in insulating oil performance. This critical point indicates that the insulating oil performance may be entering a rapid deterioration stage, requiring timely maintenance measures.

[0053] For example, in the condition monitoring of transformer insulating oil in high-altitude and cold regions, a performance degradation modeling unit with integrated temperature compensation function is used: First, the temperature of the insulating oil is monitored in real time (covering the range of -20℃ to 120℃) using a long-period fiber grating (LPFG) sensor, and dielectric loss data at different temperatures are collected simultaneously; then, a temperature conversion factor table specifically for high-altitude and cold regions (e.g., conversion factor k=0.00171 at -10℃) is used to correct the dielectric loss value at each temperature point to a reference temperature of 20℃; based on the Weibull proportional hazards model, temperature is used as a covariate (β=0.08 indicates that the risk increases by 8% for every 1℃ increase in oil temperature) to construct a degradation model. It is found that the dielectric loss value after temperature compensation suddenly increases from 0.3% to 0.5% in the 18th month, exceeding the equipment standard threshold of 0.45%; the system immediately triggers an early warning and recommends replacing the insulating oil, ultimately avoiding equipment failure caused by insulation deterioration.

[0054] This application proposes a temperature-compensated dielectric loss testing system for insulating oil. A temperature acquisition module captures real-time temperature changes in both the insulating oil and the environment. Combined with a segmented temperature compensation model from an intelligent compensation module, it accurately matches oil quality parameters across different temperature ranges. The dielectric loss value is corrected using the dielectric loss temperature coefficient, eliminating interference from temperature fluctuations, improving data accuracy, and preventing misjudgments of insulation status due to temperature deviations. The dielectric loss measurement module incorporates an adaptive anti-interference algorithm to resist interference signals in complex electromagnetic environments, reducing voltage and frequency parameter recording errors and enhancing measurement stability under harsh conditions. The status analysis module accurately determines the aging degree and status of the insulating oil by integrating multi-dimensional parameters such as compensated dielectric loss value, acid value, breakdown voltage, and moisture content. It dynamically adjusts the testing frequency based on oil sample type and operating years, ensuring timely monitoring while avoiding resource waste. The cloud management platform, relying on historical data storage and the Weibull distribution degradation model, conducts full lifecycle dielectric loss trend analysis, generating targeted preventative maintenance suggestions to proactively identify potential faults, reduce equipment downtime risks, and improve the intelligence and operational reliability of power insulation monitoring. This solves the problems of insufficient testing accuracy and poor environmental adaptability in existing technologies.

[0055] The following will illustrate a temperature-compensated insulating oil dielectric loss testing system through a specific embodiment, such as... Figure 7 As shown, it includes: For the dielectric loss test scenario of 110kV transformer insulating oil, the temperature acquisition module adopts a "contact + non-contact" composite temperature measurement scheme. The hardware configuration has undergone multiple rounds of selection and verification: the PT1000 platinum resistance sensor was selected because its linearity error in the range of -50~200℃ is <0.05%. The sealed probe is made of 316 stainless steel, which is resistant to long-term corrosion of insulating oil. Inserting it into the bottom half depth of the oil sample container can avoid the influence of liquid level fluctuations. The MLX90640 infrared array with a 32×24 pixel distribution can cover 80% of the oil sample surface. The 5Hz sampling frequency can capture the dynamic changes of temperature gradient. Its temperature measurement accuracy of ±1℃ is better than that of a single-point infrared sensor in the analysis of the temperature field of the oil sample surface. The SHT30 temperature and humidity sensor is deployed in a ventilated position 1m away from the test area to avoid the influence of heat dissipation of the test circuit. Its I²C interface supports 16-bit data output, and the humidity measurement resolution reaches 0.01%RH. In the signal processing circuit design, the AD8221 instrumentation amplifier has a common-mode rejection ratio (CMRR) >100dB, effectively suppressing ground potential difference interference. The 16-bit ADC (ADS1115) with a 2μV resolution ensures that minute temperature changes are captured. Combined with an RC filter network with a 1kHz cutoff frequency (R=10kΩ, C=16nF), temperature fluctuations caused by electromagnetic interference are controlled within ±0.02℃. The software is developed based on Keil MDK5. The FreeRTOS system prioritizes the temperature acquisition task to the highest level, ensuring stable execution at the 10Hz / 5Hz sampling frequency. The data preprocessing module uses a moving average filter with a window size of 5, combined with the 3σ criterion to remove outliers such as bubble disturbances, improving the smoothness of the temperature data by 40%. The calibration process used a FLUKE9170 constant temperature oil bath (±0.05℃) to perform calibration at six points: -20℃, 0℃, 20℃, 40℃, 60℃, and 80℃. The correction curve was fitted using the least squares method, ultimately achieving a full-range measurement error of ≤0.08℃ and an ambient humidity acquisition error of ≤1%RH, providing a reliable data foundation for subsequent temperature compensation.

[0056] The hardware architecture of the dielectric loss measurement module is designed around "anti-interference + high precision": The anti-interference measurement unit adopts a combination of AD630 phase-sensitive detector and MAX293 programmable notch filter. The 1MHz bandwidth of AD630 can cover the signal frequency required for dielectric loss measurement. With the notch filter circuit with a center frequency of 50Hz and Q value of 10, the suppression ratio of power frequency interference is more than 60dB, and the attenuation of the 3rd and 5th harmonics is ≥45dB and 40dB, respectively. The high-voltage signal source of the loss measurement module adopts an autotransformer + high-voltage transformer structure. The output voltage is continuously adjustable from 0 to 10kV, the ripple coefficient is <0.5%, and the frequency stability is ±0.1Hz, which meets the requirements of DL / T421-2019 standard for test power supply. The 1000:1 precision voltage divider adopts a series RC structure, with a voltage division error of <0.05% and a phase difference of <0.01°. The 0.1% linearity of the LEMLA28-NP current sensor ensures that small loss currents are accurately captured. The data acquisition unit uses an AD7606 18-bit synchronous ADC, with a 100kHz sampling rate capable of fully recording the detailed features of voltage and current waveforms. The FPGA (XC7K325T) accelerates waveform preprocessing to 1 million points per second through hardware acceleration, including baseline correction, harmonic separation, and RMS calculation. Preprocessed data is stored in 2GB DDR3 memory, ensuring no data loss during continuous 8-hour testing. In terms of software algorithms, the FFT-based dielectric loss angle calculation method employs a Hanning window to suppress spectral leakage, achieving a fundamental component extraction accuracy of 0.001°. The phase difference Δφ calculated using the arctangent function, after approximation with tanδ=sin(Δφ), has a measurement error of <0.002%. The calibration process strictly followed the JJG1027-2007 standard, using a 100pF±0.01% standard capacitor for phase calibration. After a 30-minute warm-up, the phase difference measurement error stabilized at ≤0.001°. Under a 100V / m power frequency electric field interference on site, a standard oil sample with tanδ=1.0% was continuously measured 10 times. The difference between the maximum and minimum values ​​was only 0.06%, which is far better than the 0.3% fluctuation range of the traditional method, verifying the effectiveness of the anti-interference design. The intelligent compensation module is based on an ARM Cortex-A9 processor (1GHz), equipped with 2GB DDR3 memory and 32GB eMMC storage, and the hardware configuration meets the performance requirement of 50 compensation calculations per second. The temperature characteristic database was constructed through systematic experiments: four typical oil samples were selected: mineral oil 25#, 45#, silicone oil, and ester oil. For each oil sample, a test point was set every 5℃ within the range of -20~80℃, and each point was measured repeatedly 10 times and the average value was taken to eliminate random errors. The experimental environment was controlled with temperature fluctuation <±0.1℃ and humidity <60%RH to ensure data comparability. The database includes fields such as oil sample type, temperature, dielectric loss value, dielectric loss temperature coefficient, and viscosity. For example, the dielectric loss temperature coefficient of mineral oil 25# is 0.012 / ℃ at 20℃, increasing to 0.015 / ℃ at 40℃, reflecting the characteristic of increased oil polarization with increasing temperature. The software is developed based on QtCreator 5.15. The compensation algorithm consists of four steps: First, the average temperature t of the oil sample is calculated (70% weight for platinum resistance data and 30% weight for infrared array data, balancing the advantages and disadvantages of contact and non-contact measurements); then, the corresponding parameters are called according to the oil sample type; next, the range of t is determined (-20~0℃, 0~40℃, 40~80℃), and the matching piecewise formula is selected; finally, the measured dielectric loss value tanδt is substituted to calculate the standard value tanδ20 at 20℃. Regarding the influence of humidity, a correction coefficient table is established through comparative experiments: when the ambient humidity is >85%RH, a humidity correction factor of 1.02~1.05 is introduced, and the correction value increases linearly with increasing humidity. Performance testing was conducted on three oil samples at three temperature points: 10℃, 30℃, and 50℃. The measured tanδt of mineral oil 25# at 30℃ was 1.2%, and after compensation, tanδ20 was 1.05%, with an error of only 1.9% compared to the measured value of 1.03% at 20℃. The compensation error for silicone oil at 50℃ was 2.3%, and for ester oil at 10℃ it was 2.8%, both <3%, which is a significant improvement over the traditional fixed coefficient method (error 15%). This demonstrates that the segmented compensation model can effectively adapt to the temperature characteristics of different oil types.

[0057] The status analysis module hardware utilizes an Advantech ARK-2120L industrial computer, featuring an Intel Core i5 processor supporting multi-threaded parallel processing. Multiple USB and Ethernet interfaces enable high-speed communication with the front-end module. The signal isolation circuit employs a TLP290 optocoupler, whose 2500Vrms isolation voltage meets the safety requirements for power equipment testing. The relay output interface can drive a DC24V audible and visual alarm for physical warning. The software is developed using Python 3.8, and the GUI interface built with PyQt5 includes functional areas for data monitoring, status determination, and report generation. The aging assessment unit combines the compensated tanδ20 with the capacitance change rate ΔC / C0 to classify the condition into 5 levels: Level 1 (Excellent): tanδ20 < 0.5% and ΔC / C0 < 5%, indicating good equipment insulation; Level 2 (Acceptable): tanδ20 0.5%~1.0% and ΔC / C0 5%~10%, requiring routine monitoring; Level 3 (Caution): tanδ20 1.0%~2.0% and ΔC / C0 10%~15%, indicating that the oil quality is beginning to deteriorate; Level 4 (Warning): tanδ20 2.0%~3.0% and ΔC / C0 15%~20%, requiring a shorter testing cycle; Level 5 (Severe): tanδ20 > 3.0% and ΔC / C0 > 20%, indicating a potential risk of insulation breakdown. The warning generator triggers different signals based on the warning level: Level 1-2, green light is constantly on; Level 3, yellow light flashes (frequency 1Hz); Level 4, orange light flashes (frequency 2Hz) accompanied by intermittent beeping; Level 5, red light is constantly on and beeping continuously. The diagnostic report includes test data, status level, fault analysis (e.g., "high tanδ value may be caused by oil quality deterioration or excessive moisture"), and handling suggestions, and supports PDF export. The test scheduling unit has a preset basic cycle (3 months / test for new oil samples, 1 month / test for oil samples that have been running for more than 5 years), which is dynamically adjusted based on the judgment results: the cycle for Level 3 status is shortened by 50%, and Level 4 and above immediately trigger a retest. Blind test results on 100 groups of oil samples with known statuses show an overall judgment accuracy of 92%, with a 100% accuracy rate for Level 5 severe status and an 88% accuracy rate for Level 3 attention status, verifying the reliability of the multi-parameter judgment logic.

[0058] The cloud management platform utilizes two Dell PowerEdge R750 application servers in a dual-machine hot standby cluster. Each server is equipped with an Intel Xeon Gold 6330 processor (32 cores), 256GB of memory, and a 4TB SSD, supporting 1000 concurrent requests per second. The storage server employs a RAID 5 disk array, with a 100TB capacity capable of storing 100,000 sets of test data. Data recovery time in the event of a single disk failure is less than 30 minutes. The network layer uses a Huawei S5720 gigabit switch for inter-module communication. The USG6000 firewall is configured with a whitelist policy, allowing only authorized IPs to access the network, ensuring data security. The software adopts a B / S architecture, with the backend developed based on the Spring Boot framework, supporting RESTful API interfaces. The frontend uses Vue.js to build a responsive web interface, adaptable to PCs and mobile terminals. The database design uses MySQL 8.0, storing test records (number, oil sample ID, time, temperature, dielectric loss value, etc.), compensation parameters, and diagnostic reports by time (monthly) partitions. Index optimization ensures that the historical data query response time is less than 1 second. The performance degradation modeling module is based on the Weibull distribution and fits historical data using the maximum likelihood estimation method to obtain the shape parameter m and the scale parameter η. The model expression is... In this model, F(t) represents the probability of performance degradation at time t. When m > 1, the degradation rate accelerates over time, and the trend of tanδ20 can be predicted up to 6 months in advance. The maintenance strategy generator combines equipment type (transformer, reactor, etc.), operating years, and degradation trend to generate differentiated recommendations: for example, if a 10-year-old transformer is predicted to reach level 3 in 6 months, it is recommended to "perform oil purification treatment"; if newly commissioned equipment reaches level 4, it prompts "check for installation contamination". In a 220kV substation application, the system analyzed the main transformer oil that had been in operation for 15 years and predicted that tanδ20 would increase from 1.51% to 1.9% in 6 months. The retested value of 1.75% after 3 months was consistent with the trend, verifying the model's predictive ability and providing data support for preventive maintenance.

[0059] In summary, the embodiments of this application achieve high-precision temperature acquisition through a composite temperature measurement module, providing a reliable data foundation for subsequent compensation; the dielectric loss measurement module, with its strong anti-interference design and high-precision algorithm, ensures measurement stability even in complex field environments; the intelligent compensation module, through segmented models and humidity correction, adapts to different oil types and temperature ranges, significantly improving compensation accuracy; the status analysis module, combining multiple parameters, achieves graded early warning and accurate assessment, ensuring reliable insulation status determination; and the cloud management platform, through data storage, trend prediction, and maintenance suggestion generation, supports preventive operation and maintenance, improves testing accuracy, anti-interference capability, and status assessment accuracy, ensuring the safe and stable operation of the transformer and reducing the risk of failure.

[0060] Next, referring to the accompanying drawings, a method for testing the dielectric loss of insulating oil based on temperature compensation is described according to an embodiment of this application.

[0061] like Figure 8 As shown, this temperature-compensated insulating oil dielectric loss test method includes the following steps: In step S101, the temperature data of the insulating oil and the ambient temperature data are obtained.

[0062] It is understood that the embodiments of this application obtain the temperature data of the insulating oil, which is the core data for accurate temperature compensation. Since the dielectric loss value changes significantly with temperature, this data can support the conversion of the measured dielectric loss value into a unified reference value at the standard temperature, eliminating the measurement deviation caused by temperature differences. The ambient temperature data can help correct the interference of environmental factors on the test system, ensure the stability of the original data, avoid misjudging the insulation status of the insulating oil due to temperature influence, improve the scientific nature of subsequent analysis and evaluation, provide reliable support for equipment insulation status diagnosis and operation and maintenance decisions, and effectively reduce the risk of failure.

[0063] In step S102, based on the temperature data of the insulating oil and the ambient temperature data, an adaptive anti-interference algorithm is used to suppress power frequency interference and harmonic interference. A phase difference measurement algorithm is used to measure the dielectric loss detection value and dielectric loss angle of the insulating oil. Voltage and frequency parameters during the test are recorded simultaneously. The temperature characteristic parameters of the insulating oil are matched for different temperature ranges. The dielectric loss detection value is corrected by temperature using a segmented temperature compensation model combined with the dielectric loss temperature coefficient, and a compensated dielectric loss detection value is generated.

[0064] Among them, the segmented temperature compensation model refers to a model that divides the medium loss detection value into different temperature ranges, matches the corresponding temperature characteristic parameters, and performs targeted temperature correction to eliminate the influence of temperature and ensure measurement accuracy.

[0065] It is understood that the embodiments of this application use a segmented temperature compensation model to accurately correct the differential temperature characteristics of insulating oil in different temperature ranges by matching corresponding parameters. This effectively eliminates the interference of different temperature environments on the dielectric loss detection value, improves the accuracy of temperature correction, and unifies the detection values ​​at different temperatures to the standard temperature reference. This enhances the comparability and reliability of the data, truly reflects the insulation performance of the insulating oil, and reduces the risk of misjudgment caused by temperature influence.

[0066] It should be noted that the matching of temperature characteristic parameters of insulating oil for different temperature ranges requires consideration of the variation of key parameters with temperature: In the low-temperature range (e.g., below 0℃), the viscosity of insulating oil will increase significantly, requiring the matching of oils with low pour points and slow viscosity growth at low temperatures to avoid decreased fluidity affecting heat dissipation and insulation uniformity. Simultaneously, attention must be paid to breakdown voltage stability, as low temperatures may cause trace amounts of water in the oil to crystallize; therefore, it is necessary to ensure sufficient dehydration and purification of the oil to maintain insulation strength. In the normal temperature range (20-40℃, the normal operating temperature of equipment), the focus should be on matching oils with low dielectric loss factor (typically ≤0.005) and high volume resistivity (≥10¹). 4 The parameters (Ω·cm) are used to ensure uniform electric field distribution and reduce dielectric loss and heat generation. At the same time, the viscosity needs to be in a moderate range (e.g., kinematic viscosity of 20-40 mm² / s at 40℃) to balance heat dissipation efficiency and oil film stability. In the high-temperature range (e.g., above 60℃), oils with high flash point (usually ≥135℃) and strong oxidation resistance are required to suppress the aging rate of oil at high temperatures and control the increase in dielectric loss factor (e.g., not exceeding 0.01 at 100℃) to avoid equipment failure caused by insulation degradation.

[0067] For example, in insulating oil dielectric loss detection, when using a segmented temperature compensation model, the temperature can be divided into three ranges based on the physicochemical properties of the insulating oil: -20℃ to 0℃, 0℃ to 40℃, and 40℃ to 80℃. In the -20℃ to 0℃ range, the dielectric loss temperature coefficient fluctuates significantly due to the higher viscosity of the oil; 0℃ to 40℃ is the normal operating range with the best parameter stability; and the 40℃ to 80℃ range is affected by thermal aging and requires the introduction of a nonlinear correction factor. When the measured insulating oil temperature is 25℃, the system automatically determines that it falls within the 0℃ to 40℃ range, calls the preset dielectric loss temperature coefficient matrix for this range, and uses the least squares method to fit the deviation between the measured data and the standard curve. This performs a stepwise correction on the dielectric loss detection value, ultimately eliminating the ±3% measurement error caused by the 25℃ ambient temperature, and obtaining a compensated detection value that meets the standard.

[0068] In step S103, based on the compensated dielectric loss detection value, acid value, and related parameters of insulating oil breakdown voltage and moisture content, the aging degree and state of insulating oil are determined, and multi-level early warning signals and diagnostic reports are generated. At the same time, the test frequency is dynamically adjusted according to the oil sample type and years of operation.

[0069] It is understood that the embodiments of this application comprehensively and accurately determine the aging degree and condition of insulating oil by using the comprehensive compensated dielectric loss detection value, acid value, breakdown voltage and moisture content parameters, providing a scientific basis for generating multi-level early warning signals and facilitating the early identification of potential risks; the diagnostic report can intuitively present the oil quality condition, helping to formulate targeted maintenance plans; and the dynamic adjustment of the test frequency according to the oil sample type and operating years can not only avoid the waste of resources caused by over-testing, but also prevent the omission of hidden dangers caused by insufficient testing, improve the efficient monitoring capability of insulating oil condition, and ensure the safe and stable operation of power equipment.

[0070] It should be noted that the degree and state of aging of insulating oil are determined based on the compensated dielectric loss detection value, acid value, and related parameters such as breakdown voltage and moisture content. When comprehensively assessing the aging degree and state of insulating oil using these parameters, the compensated dielectric loss detection value accurately reflects the degree of degradation of the oil's insulation performance; a higher value indicates more severe insulation deterioration. The acid value directly reflects the degree of oil oxidation and aging; an increased acid value means an increase in acidic substances in the oil, leading to accelerated oxidation and degradation. The breakdown voltage reflects the oil's ability to withstand electric fields; its decrease is usually related to increased impurities and moisture produced during aging, indicating a decline in insulation strength. Moisture content accelerates oil aging and deteriorates insulation performance; excessive moisture is often accompanied by abnormalities in other aging parameters. By analyzing the numerical range, trends, and interrelationships of these parameters, we can determine whether the oil is in a state of mild, moderate, or severe aging by using a logistic regression algorithm. This clarifies whether the oil is currently operating normally, requires attention, or needs emergency treatment, providing a precise basis for subsequent maintenance.

[0071] Logistic Regression Algorithm Formula: ; in, For probability; Label the target category; For specific category indexes; The input feature vector; This is the weight vector corresponding to the j-th type of aging state; This is the bias term corresponding to the j-th type of aging state; , This is the transpose of the weight vector; Weight vector The dot product with the eigenvector X; Index for category traversal; Total number of categories; This is the weight vector corresponding to the kth type of aging state; This is the bias term corresponding to the k-th type of aging state.

[0072] In step S104, the diagnostic report, historical test data and compensation parameters are uploaded to the management platform. Through the insulating oil performance degradation model, the trend analysis of dielectric loss throughout the entire life cycle is carried out, and preventive maintenance suggestions are generated.

[0073] Among them, the insulating oil performance degradation model is a model that describes or predicts the deterioration process of the insulating, physical and chemical properties of insulating oil over time and environmental factors.

[0074] It is understood that the embodiments of this application, by using an insulating oil performance degradation model, can integrate diagnostic reports, historical test data and compensation parameters to accurately analyze the dielectric loss trend of insulating oil throughout its entire life cycle, scientifically predict performance degradation, and generate targeted preventive maintenance recommendations. This can not only provide early warning of potential faults and reduce the risk of sudden equipment downtime, but also extend the service life of insulating oil and related equipment, optimize maintenance costs and resource allocation, and improve the safety and economy of equipment operation.

[0075] For example, when a substation was monitoring the insulating oil of a transformer that had been in operation for 10 years, the maintenance team uploaded monitoring data from the past 6 years: the dielectric loss value increased year by year from 0.4% at commissioning, reaching 1.3% in the 6th year; the breakdown voltage decreased from 50kV to 36kV; the acid value increased from 0.03mgKOH / g to 0.16mgKOH / g, exceeding the 0.1mgKOH / g standard, and the moisture content reached 35ppm. Simultaneously, the team recorded traces of sludge precipitation found in a recent diagnostic report, as well as the temperature difference compensation parameters for the area (winter -5℃ to summer 42℃), and the load compensation coefficient for an average monthly load rate of 85%, all to the management platform. After integrating the data, the insulating oil performance degradation model, combined with the cumulative effect of load fluctuations on oil aging, constructed a three-dimensional degradation model of dielectric loss value, acid value, and operating time. After correcting for the temperature influence coefficient, the model ultimately predicted that the dielectric loss value would exceed the critical value of 2.0% after 20 months, and the breakdown voltage might drop to 28kV. The model then generated a plan to "perform dual-stage vacuum oil filtration, replace the sealing gaskets, and strengthen quarterly monitoring within two months." A follow-up inspection three months after implementation showed that the dielectric loss value had decreased to 0.7% and the acid value to 0.07 mgKOH / g. Subsequent monitoring data remained stable over the following year, not only avoiding a potential 5 million yuan emergency repair loss but also ensuring continuous power supply to the regional power grid with zero interruptions.

[0076] According to the embodiments of this application, a temperature-compensated dielectric loss testing method for insulating oil is proposed. This method uses a temperature acquisition module to capture real-time temperature changes in the insulating oil and the environment. Combined with a segmented temperature compensation model from an intelligent compensation module, it accurately matches oil quality characteristic parameters across different temperature ranges. The dielectric loss value is corrected using the dielectric loss temperature coefficient, eliminating interference from temperature fluctuations and improving data accuracy, thus avoiding misjudgments of insulation status due to temperature deviations. The dielectric loss measurement module incorporates an adaptive anti-interference algorithm to resist interference signals in complex electromagnetic environments, reducing voltage and frequency parameter recording errors and enhancing measurement stability under harsh operating conditions. The status analysis module accurately determines the aging degree and status of the insulating oil by integrating multi-dimensional parameters such as compensated dielectric loss value, acid value, breakdown voltage, and moisture content. It dynamically adjusts the testing frequency based on oil sample type and operating years, ensuring timely monitoring while avoiding resource waste. The cloud management platform, relying on historical data storage and the Weibull distribution degradation model, conducts full lifecycle dielectric loss trend analysis, generating targeted preventative maintenance suggestions to proactively identify potential faults, reduce equipment downtime risks, and improve the intelligence and operational reliability of power insulation monitoring. This solves the problems of insufficient testing accuracy and poor environmental adaptability in existing technologies.

[0077] The following will illustrate a temperature-compensated method for testing the dielectric loss of insulating oil through a specific embodiment. Figure 9 As shown, it includes: Taking a main transformer in a 110kV substation that has been in operation for 7 years as the test object, and the insulating oil type is No. 45 transformer oil, a temperature-compensated insulating oil dielectric loss test method is introduced to determine its aging degree and condition. The specific implementation process is as follows: Acquire temperature data To accurately acquire data on insulating oil and ambient temperature, four PT100 platinum resistance temperature sensors with an accuracy of ±0.1℃ were selected. Two of them were used to measure the insulating oil temperature. During installation, the mounting holes in the upper part of the insulating oil layer at the bottom of the transformer were cleaned to ensure that the sensor probes were completely immersed in the oil and not in contact with the transformer wall. They were then fixed with special seals to prevent oil leakage. The other two were used to measure the ambient temperature. They were installed on a well-ventilated metal bracket 1m away from the transformer (1.5m above the ground) and on an insulating bracket in a cool corner of the equipment, respectively, avoiding direct sunlight and direct impact from equipment heat dissipation. After installation, they were connected to a temperature acquisition module (model TC-8000, sampling rate up to 10Hz), and the data acquisition frequency was set to 1 time / second for 10 minutes. During the acquisition, the data stability was monitored in real time. If a momentary jump occurred (deviation exceeding 0.5℃), the data set was discarded and a new set was acquired. Finally, the average value of the valid data was taken to obtain an initial average insulating oil temperature of 45℃ and an average ambient temperature of 30℃. The data storage format was CSV, including timestamps, sensor numbers, and corresponding temperature values. Suppressing interference and measuring dielectric loss detection parameters A YD2810 dielectric loss measuring instrument (measuring range 0.001%~10%, accuracy ±0.005%) was selected and connected to the transformer's insulating oil test interface via a dedicated high-voltage cable (withstand voltage 20kV). The interface was sealed with a silicone rubber sealing ring to ensure a secure connection and insulation resistance ≥1000MΩ. The instrument's adaptive anti-interference function was activated. First, the built-in second-order Butterworth bandpass filter was activated, with the center frequency locked at 50Hz and the bandwidth at 2Hz. By setting the cutoff frequencies to 49Hz and 51Hz, harmonic interference from 45~48Hz and 52~55Hz was effectively filtered out. Then, an adaptive noise canceller based on the LMS algorithm was activated. Its reference input was taken from the voltage signal on the grid side. By acquiring interference signal characteristics in real time, the filter coefficients were dynamically adjusted at a frequency of 50 times / second to stabilize the attenuation of power frequency and harmonic interference signals above 40dB, ensuring a signal-to-noise ratio ≥60dB. After interference suppression, a 12-bit high-speed AD converter (sampling rate 10kHz) is used to acquire the dielectric loss signal of the insulating oil using a phase difference measurement algorithm. The dielectric loss angle is obtained by calculating the phase difference between the voltage and current signals, and then converted into the dielectric loss detection value. During the test, the instrument synchronously records the applied voltage (10kV, error ±0.5%) and the operating frequency (50Hz, error ±0.01Hz). After three consecutive measurements, the average value is taken to obtain the initial dielectric loss detection value of 1.8% and the dielectric loss angle of 0.103°. All data are automatically stored to the instrument's built-in SD card in XML format, including measurement time, operating parameters, and raw waveform data. Insulating oil temperature correction Based on the nonlinear relationship between the dielectric loss characteristics of insulating oil and temperature, and combined with the experimental data of GB / T5654-2007 standard and No. 25 transformer oil, the temperature range is divided into five intervals: -20℃~0℃, 0℃~20℃, 20℃~40℃, 40℃~60℃, and above 60℃. This division is based on the following criteria: In the -20℃~0℃ interval, the insulating oil viscosity is relatively high, molecular motion is slow, and dielectric loss is sensitive to temperature changes; in the 0℃~20℃ interval, the oil viscosity gradually decreases, and the rate of change in dielectric loss slows down; 20℃~40℃ is the normal temperature range, and the dielectric loss characteristics are relatively stable; in the 40℃~60℃ interval, the solubility of water in the oil increases, and the degree to which dielectric loss is affected by temperature changes again; above 60℃, the oil is prone to oxidation, and the temperature characteristics of dielectric loss exhibit new patterns. The dielectric loss temperature coefficients for each temperature range were obtained by fitting experimental data from 20 different temperatures. The coefficients were: -20℃ to 0℃: 0.02% / ℃; 0℃ to 20℃: 0.015% / ℃; 20℃ to 40℃: 0.01% / ℃; 40℃ to 60℃: 0.008% / ℃; and above 60℃: 0.005% / ℃. The initial insulating oil temperature was 45℃, falling within the 40℃ to 60℃ range; therefore, a coefficient of 0.008% / ℃ was used. Set 20℃ as the standard temperature (industry-standard benchmark). Based on the segmented temperature compensation model, the derivation process of the calculation formula for the compensated dielectric loss detection value is as follows: Based on the linear approximation relationship between dielectric loss and temperature, the compensation value = initial value × [1 + coefficient × (measured temperature - standard temperature)]. When substituting the data for calculation, the temperature difference (45-20=25℃) is first verified. After confirming that it is within the range, the formula is used to calculate 1.8% × [1 + 0.008 × 25] = 2.16%. The calculation process retains 4 significant figures, and the final result is rounded to two decimal places. At the same time, the temperature coefficient, standard temperature, and calculation process are recorded as compensation parameters for archiving. Determine the degree of aging and generate early warnings and reports. Key performance parameters of the insulating oil were tested simultaneously. Acid value was determined using the GB / T264-1983 titration method. A 50mL conical flask was used for sampling, with an ethanol-ether mixture as the solvent and phenolphthalein as the indicator. The solution was titrated to the endpoint with 0.05mol / L potassium hydroxide ethanol solution. Three parallel determinations were performed, and the average value was taken, yielding an acid value of 0.12mgKOH / g. Breakdown voltage was tested according to the GB / T507-2002 standard, using a 25mm diameter copper electrode oil cup at a voltage ramp rate of 3kV / s. Five consecutive breakdowns were performed, and the average value was taken, yielding a breakdown voltage of 40kV. Moisture content was determined using the Karl Fischer coulometric method with a KF-1 moisture analyzer. A 5mL sample was taken for testing, yielding a moisture content of 25ppm. Based on the established parameters and criteria: Good condition corresponds to a compensated dielectric loss ≤1.0% and an acid value ≤0.03 mgKOH / g; slight aging (Level 1 warning) corresponds to a dielectric loss of 1.0%~2.0% and an acid value of 0.03~0.1 mgKOH / g; moderate aging (Level 2 warning) corresponds to a dielectric loss of 2.0%~3.0% and an acid value of 0.1~0.2 mgKOH / g; severe aging (Level 3 warning) corresponds to a dielectric loss ≥3.0% and an acid value ≥0.2 mgKOH / g. Based on the test data, the compensated dielectric loss is 2.16%, the acid value is 0.12 mgKOH / g, the breakdown voltage is 40 kV, and the moisture content is 25 ppm, which meets the characteristics of moderate aging, triggering a Level 2 warning. Considering the oil sample type is No. 25 transformer oil and the operating age is 8 years, the testing frequency is dynamically adjusted according to preset rules: the original semi-annual routine test is adjusted to once every 3 months; if the indicators do not improve after two consecutive tests, it will be further shortened to once a month. The diagnostic report uses a standardized template and includes the test time (accurate to the minute), the model and serial number of the equipment used, the original data and processing results of each parameter, the basis for determining the degree of aging, the description of the warning level, and the adjusted test plan. The report becomes effective after being signed by both the tester and the reviewer. Upload data and generate maintenance suggestions The diagnostic report, raw test data (temperature, dielectric loss, voltage, etc.), historical test data (quarterly test records for the past 5 years), and temperature compensation parameters (dielectric loss temperature coefficient, standard temperature, calculation process) are uploaded to the power equipment status management platform via the industrial Ethernet within the substation using the MQTT protocol. Data transmission is encrypted using AES-256 before transmission to ensure security. The platform automatically verifies data integrity upon receipt, triggering a retransmission mechanism if any data is missing. The management platform utilizes an LSTM neural network-based insulating oil performance degradation model. This model uses historical data from the past 5 years as its training set, extracts features using a sliding window method, sets a time step of 3 months, and a prediction step of 6 months. After 10-fold cross-validation, the prediction error is controlled within 5%. After analyzing the dielectric loss trend throughout the entire lifecycle, the model predicts that within the next 6 months, without maintenance, the compensated dielectric loss will rise to 2.8%, the acid value will reach 0.18 mgKOH / g, the breakdown voltage will drop to 35 kV, and the moisture content will increase to 40 ppm, gradually approaching the severe aging threshold. Based on the predicted results, preventative maintenance recommendations are generated as follows: Within one month, use a ZLY-50 dual-stage vacuum oil filtration machine (processing capacity 50L / min, ultimate vacuum degree ≤5Pa) for oil filtration. During filtration, control the oil temperature at 40~50℃ to ensure a moisture removal rate ≥90% and an impurity filtration accuracy of 5μm. Simultaneously check the transformer's sealing rings (replace with nitrile rubber seals) and breather valve to prevent moisture intrusion. Retest all parameters within 72 hours after treatment. If the compensated dielectric loss ≤1.5% and acid value ≤0.08mgKOH / g meet the standards, resume the semi-annual testing frequency. If the standards are not met, perform a partial oil change (replace 30% of the total oil volume) and conduct additional chromatographic analysis to identify potential faults. The recommendations are simultaneously pushed to the substation operation and maintenance terminal and the superior management system to form a closed-loop management system.

[0078] In summary, the embodiments of this application effectively eliminate the influence of temperature, power frequency, and harmonic interference on dielectric loss detection by using high-precision temperature acquisition and adaptive anti-interference, combined with a segmented temperature compensation model, significantly improving detection accuracy. By determining the degree of aging through multi-parameter correlation and dynamically adjusting the test frequency, accurate assessment and graded early warning of insulating oil condition are achieved, providing timely warnings of potential equipment risks. With the help of data uploading and performance degradation model analysis, targeted preventive maintenance suggestions are generated, which can avoid equipment failures in advance, extend the service life of insulating oil and transformers, optimize maintenance costs and resource allocation, ensure the safe and stable operation of substation main equipment, and improve the reliability of power grid supply.

[0079] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0080] When the processor 1002 executes the program, it implements the temperature-compensated insulating oil dielectric loss test method provided in the above embodiments.

[0081] Furthermore, electronic devices also include: Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0082] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0083] The memory 1001 may include high-speed RAM (Random Access Memory) and may also include non-volatile memory, such as at least one disk storage.

[0084] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0085] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0086] The processor 1002 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0087] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for testing the dielectric loss of insulating oil based on temperature compensation.

[0088] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described method for testing the dielectric loss of insulating oil based on temperature compensation.

[0089] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0090] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0091] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0092] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0094] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A temperature-compensated insulating oil dielectric loss testing system, characterized in that, include: The system includes a temperature acquisition module, a dielectric loss measurement module, an intelligent compensation module, a status analysis module, and a cloud management platform. The temperature acquisition module is used to collect real-time temperature data of the insulating oil and ambient temperature data. The dielectric loss measurement module is used to measure the dielectric loss detection value and dielectric loss angle of insulating oil through an adaptive anti-interference algorithm, and simultaneously record the voltage and frequency parameters during the test. The intelligent compensation module is used to match the temperature characteristic parameters of the insulating oil for different temperature ranges. Through a segmented temperature compensation model, combined with the dielectric loss temperature coefficient, the dielectric loss detection value is temperature compensated to generate a compensated standard value. The state analysis module is used to determine the degree of aging and state of insulating oil based on the compensated dielectric loss value and acid value. It combines the insulating oil breakdown voltage and moisture content-related parameters to generate multi-level early warning signals and diagnostic reports. The test frequency is dynamically adjusted according to the oil sample type, years of operation, and the diagnostic reports. The cloud management platform is used to store historical test data and compensation parameters, establish an insulating oil performance degradation model based on the Weibull distribution, conduct full life cycle dielectric loss trend analysis, and generate preventive maintenance recommendations.

2. The temperature-compensated insulating oil dielectric loss testing system according to claim 1, characterized in that, The temperature acquisition module includes a platinum resistance temperature sensor, an infrared temperature sensor array, and a temperature and humidity sensor. The platinum resistance sensor is used to monitor the oil sample temperature in real time, the infrared array is used to capture the temperature gradient distribution of the oil sample, and the temperature and humidity sensor is used to record environmental parameters and eliminate environmental interference.

3. The insulating oil dielectric loss testing system based on temperature compensation according to claim 1, characterized in that, The dielectric loss detection module includes an anti-interference measurement unit, a loss measurement module, a parameter synchronization recording module, and a waveform storage unit. The anti-interference measurement unit is used to suppress power frequency interference and harmonic interference using an adaptive anti-interference algorithm. The loss detection module is used to measure the dielectric loss detection value and dielectric loss angle using a phase difference measurement algorithm. The parameter synchronization recording module is used to correlate and record the applied voltage, operating frequency, and dielectric loss measurement results in real time during the test process. The waveform storage unit is used to save the voltage and current waveform data during the measurement process.

4. The insulating oil dielectric loss testing system based on temperature compensation according to claim 1, characterized in that, The intelligent compensation module includes a temperature characteristic database and a compensation algorithm execution unit. The temperature characteristic database is used to store the dielectric loss temperature coefficient and viscosity characteristic parameters of different types of insulating oil in different temperature ranges. The compensation algorithm execution unit is used to perform temperature compensation on the dielectric loss detection value based on the segmented temperature compensation model and the difference between the measured temperature and the standard temperature, and generate a correction value at the standard temperature.

5. The insulating oil dielectric loss testing system based on temperature compensation according to claim 1, characterized in that, The status analysis module includes an aging determination unit, an early warning generator, a diagnostic report compilation unit, and a test scheduling unit. The aging determination unit classifies the aging status of the insulating oil based on the compensated dielectric loss value and capacitance change rate. The early warning generator generates multi-level early warning prompts based on the degree of aging. The diagnostic report compilation unit summarizes test data and determination results to generate a report including fault cause analysis and insulation status diagnostics. The test scheduling unit dynamically adjusts the test time interval and test items based on the oil sample type, operating years, and diagnostic results.

6. The insulating oil dielectric loss testing system based on temperature compensation according to claim 1, characterized in that, The cloud management platform includes a historical database, a performance degradation modeling unit, and a maintenance strategy generator. The historical database is used to store test data, compensation parameters, and diagnostic reports. The performance degradation modeling unit is used to construct an insulating oil performance degradation model based on the Weibull distribution, extrapolate future dielectric loss trends from historical data, and identify critical points for performance mutations. The maintenance strategy generator is used to generate preventative maintenance recommendations based on equipment type and operating conditions.

7. A method for testing the dielectric loss of insulating oil based on temperature compensation according to any one of claims 1-6, characterized in that, The method includes: Obtain temperature data for insulating oil and ambient temperature data; Based on the temperature data of the insulating oil and the ambient temperature data, an adaptive anti-interference algorithm is used to suppress power frequency interference and harmonic interference. A phase difference measurement algorithm is used to measure the dielectric loss detection value and dielectric loss angle of the insulating oil. Voltage and frequency parameters are recorded simultaneously during the test. The temperature characteristic parameters of the insulating oil are matched for different temperature ranges. The dielectric loss detection value is corrected by a segmented temperature compensation model combined with the dielectric loss temperature coefficient to generate a compensated dielectric loss detection value. Based on the compensated dielectric loss detection value, acid value, and related parameters of insulating oil breakdown voltage and moisture content, the aging degree and state of insulating oil are determined, and multi-level early warning signals and diagnostic reports are generated. At the same time, the test frequency is dynamically adjusted according to the oil sample type and years of operation. The diagnostic report, historical test data, and compensation parameters are uploaded to the management platform. Through the insulating oil performance degradation model, the trend analysis of dielectric loss throughout the entire life cycle is carried out, and preventive maintenance recommendations are generated.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the temperature-compensated insulating oil dielectric loss test method of claim 7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When a computer program or instruction is executed, it implements the temperature-compensated insulating oil dielectric loss test method of claim 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When a computer program or instruction is executed, it implements the temperature-compensated insulating oil dielectric loss test method of claim 7.

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