A remote monitoring method and system of an intelligent mutual inductor and a storage medium

Abstract

The application relates to the technical field of power equipment monitoring, and discloses a remote monitoring method and system of an intelligent mutual inductor and a storage medium, the method comprising the following steps: collecting the environmental temperature and power grid load data of the mutual inductor through a sensor network; determining a sampling offset based on the data; if the offset exceeds a preset threshold, a pre-trained aging trend prediction model is called to generate a compensation parameter for compensation; the measurement error trend is predicted based on the offset after compensation and the load value, and if the error amplification degree exceeds a preset threshold, the mutual inductor sampling resolution is dynamically adjusted to obtain an optimization value; the mutual inductor state data is obtained based on the optimized resolution, and a final configuration is generated, and a stable measurement data is obtained by processing an output signal; if the data deviates from historical data by more than a preset threshold, the compensation parameter is adjusted and a debugging performance index is output. The application improves the measurement accuracy, stability and equipment life of the mutual inductor in a complex environment.

Description

Technical Field

[0001] This application relates to the field of power equipment monitoring technology, and in particular to a remote monitoring method, system and storage medium for an intelligent instrument transformer. Background Technology

[0002] In modern power systems, smart instrument transformers, as one of the key devices, are widely used in areas such as power grid load monitoring, power quality assessment, and equipment condition monitoring. With the continuous development of smart grids, traditional instrument transformer monitoring methods can no longer meet the demands for efficient, accurate, and real-time monitoring. Traditional monitoring systems rely heavily on periodic inspections and manual maintenance, failing to reflect changes in equipment operating status in real time. This often leads to the failure to detect equipment faults and aging in a timely manner, thus affecting the stability of the power grid and the reliability of power supply. To improve equipment reliability and the accuracy of aging monitoring, more and more technologies are adopting sensor-based real-time monitoring solutions. While some power equipment monitoring systems exist, they still suffer from problems such as untimely data acquisition, low monitoring accuracy, and a lack of dynamic adjustment mechanisms. Especially under complex conditions such as power grid load fluctuations, temperature changes, and equipment aging, existing systems fail to fully consider the impact of environmental factors on monitoring data, making it impossible to achieve long-term monitoring and real-time assessment of the aging trends of instrument transformers.

[0003] Therefore, how to achieve accurate monitoring of smart transformers, especially in complex power grid environments, dynamically and in real time adjust monitoring parameters, and ensure the high accuracy and stability of monitoring data, has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a remote monitoring method, system, and storage medium for intelligent instrument transformers, which improves the measurement accuracy and stability of instrument transformers in complex environments and extends the lifespan of instrument transformers through precise aging assessment and status monitoring.

[0005] In a first aspect, this application provides a remote monitoring method for an intelligent current transformer, the method comprising: Step S1: Remotely collect ambient temperature data and power grid load data of the current transformer through the environmental monitoring sensor network to obtain the current temperature value and the current power grid load value; Step S2: Based on the current temperature value and the current power grid load value, determine the sampling offset of the current transformer; Step S3: If the sampling offset exceeds the first preset threshold, the pre-trained aging trend prediction model is called to generate compensation parameters and the sampling offset is compensated to obtain the compensated offset. Step S4: Based on the compensated offset and the current power grid load value, predict the measurement error trend and determine the degree of error amplification. If the degree of error amplification exceeds the second preset threshold, dynamically adjust the acquisition resolution of the current transformer to obtain an optimized resolution value. Step S5: Based on the optimized resolution value, obtain the current transformer status data and generate the final resolution configuration. Based on the final resolution configuration, process the current transformer output signal to obtain stable measurement data. Step S6: If the deviation between the stable measurement data and the historical data exceeds the third preset threshold, adjust the compensation parameters, optimize the overall configuration, and obtain the transformer debugging performance index.

[0006] Secondly, this application provides a remote monitoring system for an intelligent current transformer, the system comprising: The data acquisition unit is used to remotely acquire ambient temperature data and power grid load data of the instrument transformer through an environmental monitoring sensor network, and obtain the current temperature value and the current power grid load value. The offset determination unit is used to determine the sampling offset of the instrument transformer based on the current temperature value and the current power grid load value; An offset compensation unit is used to call a pre-trained aging trend prediction model to generate compensation parameters and compensate for the sampling offset if the sampling offset exceeds a first preset threshold, so as to obtain the compensated offset. The resolution acquisition unit is used to predict the measurement error trend and determine the degree of error amplification based on the compensated offset and the current power grid load value. If the degree of error amplification exceeds the second preset threshold, the acquisition resolution of the current transformer is dynamically adjusted to obtain an optimized resolution value. The resolution configuration unit is used to acquire transformer status data based on the optimized resolution value, generate a final resolution configuration, process the transformer output signal based on the final resolution configuration, and obtain stable measurement data. The indicator debugging unit is used to adjust the compensation parameters and optimize the overall configuration if the deviation between the stable measurement data and the historical data exceeds a third preset threshold, so as to obtain the current transformer debugging performance index.

[0007] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned remote monitoring method for an intelligent instrument sensor.

[0008] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The technical solution provided in this application collects ambient temperature and grid load data of the instrument transformer through an environmental monitoring sensor network, and depicts the equipment operating conditions accordingly. Based on the collected data, the sampling offset is determined, and when the offset exceeds a threshold, a pre-trained aging trend prediction model is activated to generate compensation parameters for dynamic correction of the offset. Further, based on the compensated offset and the real-time load prediction measurement error trend, if the error amplification exceeds a preset range, the instrument transformer's acquisition resolution is adaptively adjusted to optimize the data acquisition quality. Based on the optimized resolution configuration, equipment status data is acquired, and the output signal is processed to obtain stable and reliable measurement results. Finally, by comparing the deviation between stable data and historical data, the compensation parameters are adjusted in reverse, and a debugging performance index that reflects the health status of the equipment is output, completing closed-loop optimization.

[0009] This application improves the measurement accuracy and data stability of instrument transformers in complex operating environments by integrating multi-dimensional data such as temperature, load, and vibration and introducing a dynamic compensation mechanism, effectively suppressing error drift caused by external disturbances. Furthermore, adaptive resolution adjustment based on aging trend prediction and error trend analysis not only extends the service life of the equipment but also enables early warning and intervention for performance degradation, thereby reducing the risk of unexpected downtime. Moreover, the entire method significantly enhances the intelligence level and autonomous decision-making capability of the monitoring process by constructing a closed-loop feedback system from data acquisition and status assessment to parameter adjustment, reducing reliance on manual intervention. This application comprehensively improves the reliability, operation and maintenance efficiency, and economy of remote monitoring of intelligent instrument transformers, providing solid technical support for the stable operation of power systems. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an embodiment of a remote monitoring method for an intelligent instrument transformer according to the present application. Figure 2 This is a schematic diagram of the aging trend prediction curve update in the embodiments of this application; Figure 3 This is a schematic diagram showing the comparison between the compensated stable measurement data and historical benchmark data in the same time period in the embodiments of this application; Figure 4 This is a schematic diagram of one embodiment of a remote monitoring system for an intelligent instrument transformer according to the present application. Detailed Implementation

[0012] This application provides a remote monitoring method, system, and storage medium for an intelligent instrument transformer. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0013] Example 1: Existing remote monitoring methods for instrument transformers cannot accurately assess equipment status, dynamically adjust equipment parameters, or respond in real time to the effects of factors such as temperature and load fluctuations. This results in untimely monitoring of equipment aging and an inability to effectively ensure stable operation and high-precision measurement. Therefore, this application provides a remote monitoring method for intelligent instrument transformers. By combining ambient temperature, power grid load, and mechanical vibration monitoring data, and through multi-dimensional data fusion and dynamic adjustment mechanisms, the method improves the measurement accuracy and stability of instrument transformers in complex environments. Furthermore, it extends equipment lifespan through precise aging assessment and status monitoring.

[0014] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of a remote monitoring method for an intelligent instrument transformer in this application includes: Step S1: Remotely collect ambient temperature data and power grid load data of the current transformer through the environmental monitoring sensor network to obtain the current temperature value and the current power grid load value.

[0015] Step S1 further includes: collecting ambient temperature data through a temperature sensor to obtain the current temperature value; collecting grid load data through a load sensor to obtain the current grid load value; integrating electrical performance indicators and mechanical vibration monitoring data to assess the initial state of the instrument transformer; integrating aging data acquisition frequency and equipment material degradation indicators to determine the initial aging level under the aging state classification standard; processing electrical performance indicators and mechanical vibration monitoring data through a data fusion algorithm to generate an initial state assessment result; determining the initial aging level based on the initial state assessment result and the aging data acquisition frequency; updating the aging state classification standard based on the initial aging level and the current temperature value; and generating an initial aging level assessment report based on the aging state classification standard.

[0016] Specifically, to address the issue of accurately assessing equipment status in remote monitoring methods for instrument transformers, the following steps are taken: First, environmental temperature data and grid load data of the instrument transformer are remotely collected via an environmental monitoring sensor network to obtain the current temperature and grid load values. Specifically, a temperature sensor is used to monitor the air temperature around the instrument transformer in real time, converting the collected analog signal into a digital signal as the current temperature value. The temperature sensor can be a DS18B20 digital temperature sensor, encapsulated in a moisture-proof, insulated housing. Thermally conductive silicone is used to tightly adhere it to the surface of the heat sink or the housing near the windings of the instrument transformer under monitoring, allowing for direct and accurate sensing of the operating temperature of the instrument transformer itself. Simultaneously, a grid load sensor, such as a Hall effect current sensor, measures the current intensity in the grid and calculates the corresponding power load value, obtaining grid load data. This data is directly applied to the output conductor on the secondary side of the instrument transformer to measure its output current. The acquisition frequency can be set to once per second or every few seconds. Then, combined with electrical performance indicators and mechanical vibration monitoring data, the initial state of the instrument transformer is assessed. Specifically, electrical performance indicators, such as insulation resistance and dielectric loss, are collected. Insulation resistance is measured using a Megger sensor. The MIT520 insulation tester measures at 500V DC. Dielectric loss is obtained using a Schenck loss factor bridge, and mechanical vibration monitoring data is also used. Mechanical vibration monitoring data is obtained using a piezoelectric accelerometer as the vibration sensing unit, with a measurement range covering ±50g and a frequency response range of not less than 0.The sensor operates at 5Hz to 5kHz to meet the accuracy requirements for monitoring the mechanical condition of the instrument transformer. It is fixed to the upper middle part of the transformer's insulating shell using a magnetic base or high-strength adhesive. This position effectively captures the main vibration signals caused by magnetostriction of the internal core, loose windings, or stress changes in external connectors. A weighted average method is used to fuse electrical performance indicators and mechanical vibration monitoring data to calculate a comprehensive condition score. If the comprehensive score is higher than a preset standard (e.g., 80 points), it can be assessed as a good initial condition; if it is lower than 80 points, it can be assessed as a moderate condition. This provides an accurate starting point for subsequent aging assessment and condition monitoring. The acquisition of electrical performance indicators and mechanical vibration monitoring data is described below. This is also achieved through an environmental monitoring sensor network. Mechanical vibration monitoring data is acquired in real time using corresponding vibration sensors and measuring instruments, which are typically installed in critical parts of the equipment. Electrical performance indicators are obtained through insulation resistance testers and dielectric loss measuring instruments. The insulation resistance tester performs high-voltage tests on the transformer, measuring the current-to-voltage ratio to determine insulation performance, while the dielectric loss meter measures the phase difference of the current to evaluate the electrical performance of the equipment. When the transformer is operating, this data can be monitored in real time and transmitted to the monitoring system. After the above electrical performance indicators and mechanical vibration monitoring data are fused, it can help determine whether the equipment has performance degradation or potential faults. Furthermore, the "equipment" mentioned above refers to the monitored "transformer" itself.

[0017] Furthermore, by integrating the collection frequency of aging data and equipment material degradation indicators, and combining this with historical operating data of the equipment, the aging status of the current transformer can be determined. In the specific implementation process, the collection frequency of aging data can be set to once every half hour or once every hour, and the equipment material degradation indicators can be quantified. For example, the crack density of the insulation material can be used to represent the degradation index. Based on different numerical ranges of the degradation index, such as a degradation index below 0.1 per square centimeter, it can be regarded as a low aging level. By accurately classifying the aging level of the equipment, the monitoring strategy can be optimized, the reliability of aging monitoring can be improved, and the risk of unexpected equipment downtime can be reduced.

[0018] During data fusion, the Kalman filter algorithm is used to fuse electrical performance indicators and mechanical vibration monitoring data. The state vector is initialized as [health index, rate of change]. The state transition equation, based on a first-order linear model and observation equations, maps the state to a comprehensive state score. The estimation error is minimized by initializing the error covariance matrix and using a real-time prediction-update loop, thereby generating accurate initial state assessment results. The Kalman filter effectively eliminates the interference of environmental noise and improves the accuracy of state assessment. For example, in high-noise environments such as wind farms, the filtering algorithm can effectively improve the health index of equipment, reduce the false alarm rate, and thus improve the accuracy of fault prediction.

[0019] Subsequently, based on the comprehensive evaluation results and acquisition frequency, the initial aging level of the instrument transformer is determined, and the aging status classification standard is updated according to this level. A mathematical model of the relationship between the degradation process and temperature is used to calculate the factor by which temperature accelerates equipment aging. This mathematical model employs the Arrhenius equation, widely used in the field of material aging. In this embodiment, key parameters are determined by conducting accelerated aging experiments on the instrument transformer insulation material at different temperatures and using linear regression to fit the relationship between aging data and the reciprocal of temperature. The acceleration factor calculated through this model quantifies the impact of the current operating temperature on the aging rate. For example, when the acceleration factor caused by temperature is greater than a certain value, it indicates that the current temperature will accelerate material degradation. The aging value is then dynamically adjusted accordingly to achieve self-adaptation of the aging assessment standard to the operating environment. Based on the impact of the current temperature on aging, the aging threshold is adjusted, for example, by increasing the aging threshold from 0.5 to 0.6, achieving dynamic adaptation of the aging standard. The setting of the aging threshold can reference industry standards such as IEC 60076. The dynamic update mechanism ensures that the classification standard can be adjusted in real time according to environmental changes, improving the accuracy of the aging assessment.

[0020] Using the aforementioned technical means, an initial aging level assessment report for the instrument transformer is generated. The report includes the aging level, performance status, and potential failure risks of the equipment, providing strong support for maintenance decisions and equipment optimization. Through precise data processing and dynamic adjustment, this application can achieve stable monitoring and early warning of intelligent instrument transformers in complex operating environments, improving the long-term reliability and operating efficiency of the equipment.

[0021] Step S2: Determine the sampling offset of the instrument transformer based on the current temperature value and the current power grid load value.

[0022] The determination of the sampling offset of the instrument transformer includes: using a temperature-induced offset calculation method to process the current temperature value and the current grid load value to obtain the sampling offset; integrating insulation resistance measurement accuracy information to determine the degree of influence of temperature on electrical performance indicators; processing equipment material degradation indicators through data fusion weight allocation to obtain a preliminary offset estimate for the aging trend prediction model; determining the offset correction coefficient based on the preliminary offset estimate and the sampling offset; adjusting the sampling offset using the offset correction coefficient to obtain the corrected offset; generating an offset analysis report based on the corrected offset and insulation resistance measurement accuracy information; updating the aging trend prediction model using the offset analysis report; and determining the dynamic range of the sampling offset based on the aging trend prediction model.

[0023] Specifically, in order to address the problem that existing intelligent instrument transformers are unable to accurately assess equipment status and aging in real time under dynamic environments, especially under complex grid load fluctuations and temperature changes, and to improve the measurement accuracy and monitoring reliability of instrument transformers, this application introduces a multi-dimensional data fusion algorithm and a dynamic adjustment mechanism to provide stable measurement data and predict equipment aging trends under different operating conditions, thereby improving the long-term operational stability of instrument transformers.

[0024] Specifically, the process begins with a temperature-induced offset calculation method, such as using a thermodynamic model. Through testing and calibration, the current temperature and grid load values ​​are used as inputs to estimate the initial sampling offset based on thermodynamic principles. The core of this calculation method is the interaction between temperature and load. By calculating the degree of influence of temperature changes on the sampled signal and combining this with the impact of load on the equipment's operating status, an initial offset value is obtained. Next, the insulation resistance measurement accuracy information of the instrument transformer is integrated to further determine the degree of influence of temperature on electrical performance indicators. For example, in high-temperature environments, temperature changes may cause fluctuations in electrical performance, thus affecting sampling accuracy. By comparing this value with a preset temperature threshold, the impact score of temperature on equipment performance is calculated, and the offset is then corrected.

[0025] Subsequently, using a data fusion weighting method, appropriate weights are assigned to each material degradation index based on aging indicators of the equipment materials, such as material fatigue and corrosion degree. This data is then input into the aging trend prediction model. The weight allocation rule can utilize the entropy weighting method to assign weights based on data uncertainty; for example, insulation resistance is weighted at 0.6, and vibration data at 0.4. The aging trend prediction model employs time series analysis tools such as autoregressive integral moving average. The training data source is a long-term dataset of 1000 sets of historical operating data, including historical temperature, load, vibration, and insulation resistance degradation indicators, to predict the aging trend of the equipment and obtain a preliminary offset estimate. This process accurately estimates the aging process of equipment based on historical aging data and real-time collected data, ensuring more accurate performance evaluation under different environments. Based on the preliminary offset estimate and sampling offset, the offset correction coefficient is further calculated. The correction coefficient is calculated by the ratio of the preliminary offset estimate to the sampling offset, such as correction coefficient = preliminary offset estimate / sampling offset. It is then smoothed by moving average filtering to ensure that the sampling offset can fully compensate for errors caused by temperature changes and equipment aging. By multiplying the sampling offset by the correction coefficient, the corrected offset is obtained. This correction process can dynamically adapt to environmental changes and effectively eliminate the influence of external disturbances.

[0026] Finally, based on the corrected offset and insulation resistance measurement accuracy information, a detailed offset analysis report is generated. This report comprehensively analyzes the electrical performance and mechanical vibration data of the equipment, further updates the aging trend prediction model, and provides a basis for dynamic adjustments. This analysis report enables real-time updates to the parameters in the prediction model, improving the predictability and accuracy of the equipment's future operating status. Ultimately, based on the updated aging trend prediction model, the dynamic range of the sampling offset is determined, providing reliable data support for long-term equipment monitoring. The training data for the aging trend prediction model includes historical temperature, load, vibration, and insulation resistance data as variables, as well as historical sampling offsets or assessed aging levels as tag data. This technical solution, through multi-dimensional data fusion and dynamic compensation mechanisms, can effectively address the complex impacts of power grid load fluctuations, ambient temperature changes, and equipment aging on transformer performance, thereby achieving more efficient and accurate remote monitoring and aging assessment.

[0027] Step S3: If the sampling offset exceeds the first preset threshold, the pre-trained aging trend prediction model is called to generate compensation parameters and the sampling offset is compensated to obtain the compensated offset.

[0028] The compensated offset includes: if the sampling offset exceeds a first preset threshold, generating initial values ​​for compensation parameters through an aging trend prediction model; acquiring real-time updated mechanical vibration monitoring data through remote data transmission; applying a deviation compensation algorithm to process insulation resistance measurement accuracy and determine the dynamic range of compensation parameters; adjusting compensation parameters based on the initial values ​​and updated mechanical vibration monitoring data; calculating the compensated offset using compensation parameters and sampling offset; updating the aging trend prediction model based on the compensated offset; generating a compensation parameter adjustment report using the aging trend prediction model; and determining the validity of the compensated offset based on the compensation parameter adjustment report.

[0029] Specifically, in order to address the problems of existing intelligent instrument transformers in dynamic monitoring and aging assessment, especially in the face of temperature changes, power grid load fluctuations, and measurement deviations caused by equipment aging, this application aims to improve the measurement accuracy of instrument transformers in complex environments and achieve intelligent aging assessment and preventive maintenance by introducing a multi-dimensional data fusion and dynamic compensation mechanism.

[0030] Specifically, the sampling offset is compensated in the following way: First, when the sampling offset exceeds a preset first threshold, a pre-trained aging trend prediction model is invoked to generate initial compensation parameters. When using the pre-trained aging trend prediction model, the current sampling offset, ambient temperature, power grid load, and mechanical vibration data are used as model inputs, directly outputting initial values ​​of the compensation parameters for real-time compensation. The aging trend prediction model uses linear regression, with training data covering a temperature range of -40°C to 85°C and a load range of 0-100%. Parameters are optimized using gradient descent. The generation of these compensation parameters is based on the analysis of historical aging data of the instrument transformers. Specifically, historical aging data of the instrument transformers is first collected, including the insulation material degradation rate and electrical performance decay curves, which are derived from the equipment's operating logs. Then, the historical data is fitted using linear regression, and the linear relationship between aging level and time is calculated using the least squares method to obtain the fitting slope. The initial compensation parameters are then calculated based on the current sampling offset and this slope. This process ensures that future offsets are predicted using historical data and can offset the effects of temperature-induced or accelerated aging.

[0031] After obtaining the initial values ​​of the compensation parameters, real-time mechanical vibration monitoring update data is acquired via remote data transmission. Real-time vibration data is crucial for assessing the mechanical condition of the instrument transformer, as mechanical vibration may exacerbate the aging process of the equipment. Subsequently, deviation compensation algorithms such as PID control are applied to handle the current insulation resistance measurement accuracy. Data noise is processed by measuring the resistance value and combining it with the Kalman filter algorithm. The Kalman filter is a recursive estimation method that can minimize the estimation error by continuously predicting and updating the measurement data. In the aging monitoring of the instrument transformer, the Kalman filter algorithm can accurately process the electrical performance data under the influence of vibration, helping to determine the dynamic range of the compensation parameters. The range of the compensation parameters is set according to the 3σ principle of historical deviation, with an upper limit of +5% and a lower limit of -5%.

[0032] Based on the initial values ​​of the compensation parameters and real-time vibration data, the compensation parameters are adjusted. The adjustment is achieved by using a weighted summation method, with the vibration amplitude as a weighting factor, to obtain new compensation parameters. This method ensures that the compensation parameters can effectively cope with equipment offset under high load or high vibration environments. Subsequently, the offset after compensation is calculated using the compensation parameters and the sampled offset. The offset is then adjusted with the compensation parameters using addition operations to obtain the final compensation result.

[0033] The compensated offset was then validated, and the aging trend prediction model was updated based on the results. For example, the model was retrained with new data every 24 hours or when the offset change rate exceeded 10%. The compensated offset was collected as new data points, inserted into the historical dataset, and the linear regression fitting was rerun. This process can dynamically update the aging trend prediction model to make more accurate predictions of future offsets and optimize the accuracy of aging assessment. By validating the updated model and comparing the mean square error between the predicted value and the actual offset, the accuracy of the prediction and the stability of the model were ensured.

[0034] Finally, a compensation parameter adjustment report is generated based on the updated aging trend prediction model. The model parameters and compensation effect data are summarized to form a structured report. The effectiveness of the compensated offset is confirmed by comparing the predicted offset with the actual offset in the report. If the difference is less than the preset threshold, the compensation is deemed effective, thus ensuring the accuracy and reliability of the entire compensation process and providing support for the commissioning and maintenance of the transformer.

[0035] The above technical solutions effectively solve the problem of inaccurate instrument transformer status monitoring caused by multiple factors in existing technologies. Through dynamic adjustment and intelligent compensation mechanisms, the stability of equipment operation, measurement accuracy, and reliability of aging monitoring are improved, ultimately ensuring the stable operation of the power system and the long-term health of the instrument transformers.

[0036] Figure 2 The diagram illustrating the aging trend prediction curve update visually demonstrates the dynamic process of the above model update. Figure 2 In the model, the "early aging trend" curve is a prediction based on historical data, which deviates to some extent from the actual potential aging path of the equipment. At the "prediction update time", the compensated offset containing the current equipment state is used as new data input to the model, thereby generating the "updated aging trend" curve. It can be clearly observed that the updated prediction curve corrects the future aging trajectory, and its slope and direction are more in line with the actual aging law of the equipment. This confirms that the model of this application has good self-learning and self-adaptation capabilities, and can continuously optimize the prediction accuracy through real-time data, providing a more reliable basis for error trend analysis and equipment life assessment.

[0037] Step S4: Based on the compensated offset and the current grid load value, predict the measurement error trend and determine the degree of error amplification. If the degree of error amplification exceeds the second preset threshold, dynamically adjust the acquisition resolution of the current transformer to obtain an optimized resolution value.

[0038] Step S4 further includes: using a load fluctuation impact analysis method to process the compensated offset and the current grid load value, generating an error trend prediction result; determining the degree of error amplification based on the error trend prediction result; evaluating the sources of deviation under the dynamic range of compensation parameters by combining real-time performance feedback data; determining the distribution of error sources through a deviation source identification method; integrating aging threshold setting parameters, processing resistance evaluation environment correction, obtaining the preliminary adjustment value of the deviation compensation mechanism, and generating an error trend analysis report based on the preliminary adjustment value and the degree of error amplification; updating the deviation source identification method through the error trend analysis report; and determining the dynamic threshold of the degree of error amplification based on the deviation source identification method.

[0039] Specifically, during long-term operation, existing intelligent instrument transformers accumulate measurement errors due to factors such as power grid load fluctuations, temperature changes, and equipment aging, which in turn affects the accurate monitoring and condition assessment of the equipment. This application introduces load fluctuation impact analysis and error trend prediction methods to dynamically adjust the instrument transformer's acquisition resolution in real time under different environmental conditions, optimize the error compensation mechanism, and ensure the stability and monitoring accuracy of the equipment.

[0040] Specifically, load fluctuation impact analysis methods, such as the rolling standard deviation of load values, are used to generate error trend prediction results by calculating the compensated offset and load values. The ARIMA model is used to predict the error trend. Specifically, the fluctuation range of the load value is first calculated, and the degree of load change is quantified using the standard deviation calculation method. Then, the compensated offset is multiplied by the fluctuation range to obtain a preliminary error trend vector, which reflects the amplification effect of the offset under different load conditions. Next, the preliminary error trend vector is fitted using a linear regression method to generate the error trend prediction results, thereby obtaining the predicted error value sequence.

[0041] Based on the error trend prediction results, the degree of error amplification is further determined. Specifically, the difference between the maximum and minimum values ​​in the predicted error value sequence is calculated. If the difference exceeds the preset static threshold, it is determined to be a high degree of amplification; otherwise, it is a low degree of amplification. This judgment process helps to quickly classify the error trend, thereby providing a basis for subsequent dynamic adjustments.

[0042] By combining real-time performance feedback data, the sources of deviation under the dynamic range of compensation parameters are further evaluated. In this process, electrical performance data of the transformer, such as insulation resistance value, are collected as real-time feedback data. By defining the dynamic range of compensation parameters and mapping the performance feedback data to this range, normalization processing is applied to obtain standardized deviation values. Then, K-means clustering analysis is used to group the standardized deviation values. The Bayesian prior is set to a Gaussian distribution based on the historical error distribution to identify potential sources of deviation, such as temperature-induced or mechanical vibration-induced errors. This process can reveal the root cause distribution of errors.

[0043] Based on this, the distribution of error sources is further determined through deviation source identification methods. Specifically, histogram statistics are used to visualize the distribution of error sources, calculate the weighted average to obtain the overall error source distribution vector, and update the distribution vector using Bayes' theorem. New feedback data is then integrated to optimize the accuracy of error source identification. Through the above steps, the main error sources can be accurately identified, providing a targeted basis for adjusting the compensation mechanism.

[0044] Finally, by integrating the aging threshold setting parameters and applying an environmental correction to the resistance value using a temperature correction formula, a preliminary adjustment value for the compensation mechanism is obtained. Using a weighted summation method, the aging level is integrated with the corrected resistance value to calculate the preliminary adjustment value for the deviation compensation mechanism. This adjustment value provides a starting point for subsequent optimization of the compensation parameters. During this process, the compensation parameters are dynamically adjusted to address the impact of load fluctuations and environmental changes on measurement accuracy, thereby optimizing the transformer's commissioning performance.

[0045] By generating an error trend analysis report, the method for identifying deviation sources and the compensation mechanism are further optimized. Based on this report, the method for identifying deviation sources is updated, and the threshold for error amplification is dynamically adjusted. For example, the dynamic threshold is twice the historical average error value and is updated every 100 sampling points to adapt to the long-term operating requirements of the equipment in complex environments. Therefore, the above technical solution can automatically adjust the performance parameters of the equipment based on real-time monitoring data, improve the accuracy of error compensation, and ensure the stability and high-precision measurement of the current transformer under different load and temperature conditions.

[0046] This technical solution effectively improves the monitoring accuracy and equipment lifespan of instrument transformers through multi-dimensional data analysis and dynamic compensation mechanisms. It is especially suitable for power grid environments with large load fluctuations or harsh environments such as high temperature and high vibration, thereby solving the problem of inaccurate state assessment caused by error accumulation during long-term operation of instrument transformers in existing technologies.

[0047] The optimized resolution value includes: if the error amplification exceeds a second preset threshold, generating dynamic resolution adjustment parameters based on the predicted trend; determining the optimized resolution value using the dynamic resolution adjustment parameters; integrating monitoring parameter optimization information to process the remote monitoring stability of electrical performance indicators; determining compensation effect verification indicators by integrating deviation source identification methods through an evaluation cycle adjustment strategy; generating a resolution adjustment report based on the compensation effect verification indicators and the optimized resolution value; updating the remote monitoring stability parameters using the resolution adjustment report; adjusting the evaluation cycle based on the remote monitoring stability parameters; and determining the effective range of the optimized resolution value using the evaluation cycle.

[0048] Specifically, to address the problem of inaccurate equipment status assessment caused by the impact of error amplification, load fluctuations, and environmental factors on measurement accuracy in dynamic monitoring of existing intelligent instrument transformers, traditional monitoring methods often fail to fully consider the error changes and error amplification effects of the equipment under different load conditions. Therefore, how to dynamically adjust the resolution and monitoring parameters of the instrument transformer has become a key issue that this invention needs to solve. This application, through load fluctuation impact analysis, dynamic resolution adjustment, and real-time feedback mechanisms, can optimize the monitoring accuracy and stability of the instrument transformer in complex environments.

[0049] Specifically, if the error amplification exceeds a second preset threshold, a dynamic resolution adjustment parameter is generated based on the predicted trend. First, the predicted trend is analyzed to extract key features such as the trend slope and peak points. These key features are used to calculate the adjustment parameter, which, combined with the current monitoring conditions, determines the optimized resolution value. Then, by applying this dynamic resolution adjustment parameter to the current resolution formula, a new optimized resolution value is calculated. The optimized resolution value can improve measurement accuracy in subsequent transformer monitoring, ensuring that the equipment can provide stable and reliable monitoring data under different operating conditions.

[0050] During real-time monitoring, integrating and optimizing monitoring parameters and addressing the remote monitoring stability of electrical performance indicators are crucial steps. Real-time mechanical vibration monitoring updates and insulation resistance measurement accuracy data acquired through the remote data transmission system are weighted and averaged to calculate a stability score, further evaluating the consistency and reliability of electrical performance indicators during remote transmission. When the monitoring stability score falls below a set threshold, further optimization is triggered. This optimization can reduce data misjudgments caused by monitoring instability by responding to load changes and improving the accuracy of equipment lifespan prediction.

[0051] Furthermore, the application of evaluation cycle adjustment strategies is a core element for further optimization. Based on historical deviation backtracking information, the monitoring cycle length is dynamically adjusted to achieve timely verification. For example, when a large deviation is detected, the monitoring cycle is automatically shortened to correct the equipment's measurement errors in real time. During the deviation source identification process, data from different sources, such as temperature, load, and vibration, are integrated to identify the root cause of the error, and the compensation effect verification indicators are calculated based on the identification results. Through continuous evaluation and optimization of these indicators, the effectiveness of the compensation mechanism can be ensured, and the stability of equipment commissioning and operation can be further improved.

[0052] The generated resolution adjustment report provides a structured summary and recommendations for the optimization adjustments, and updates remote monitoring stability parameters through the report. Based on these stability parameters, the evaluation cycle is further adjusted to ensure the accuracy of monitoring data and the stability of equipment operation. In the effectiveness range test of the optimized resolution value, after multiple tests and verifications, it was determined that the range can remain stable under different load and temperature conditions, and provides accurate parameter support for subsequent monitoring and data processing.

[0053] This technical solution solves the problem of the inability to dynamically optimize the performance of current transformers in existing technologies by integrating dynamic adjustment and real-time feedback mechanisms. Especially in complex environments such as large load fluctuations and high temperature and high vibration, intelligent adjustment of resolution and compensation parameters not only improves monitoring accuracy but also enhances the adaptability and stability of the equipment in dynamic environments.

[0054] Step S5: Based on the optimized resolution value, acquire the current transformer status data and generate the final resolution configuration. Based on the final resolution configuration, process the current transformer output signal to obtain stable measurement data.

[0055] The process of generating the final resolution configuration includes: acquiring transformer status data by optimizing the resolution value; integrating the transformer status data into the resolution adjustment module to generate the final resolution configuration; enhancing the insulation resistance measurement accuracy under the compensation effect verification index by fusing mechanical vibration monitoring data through signal filtering processing; generating a resolution configuration report based on the final resolution configuration and mechanical vibration monitoring data; updating the signal filtering processing parameters based on the resolution configuration report; adjusting the compensation effect verification index based on the signal filtering processing parameters; determining the stability of the final resolution configuration based on the compensation effect verification index; and generating a transformer status data analysis report based on the stability.

[0056] Specifically, to address the problem that existing intelligent instrument transformers cannot adjust resolution accurately and in real time in dynamic environments, and to handle error fluctuations, especially under the influence of complex factors such as temperature changes, load fluctuations, and vibrations, how to improve the monitoring accuracy and reliability of the equipment? Existing monitoring methods fail to fully integrate real-time data such as vibration and temperature changes during equipment operation, leading to amplified errors and inaccurate status assessments. This application optimizes the resolution value and signal filtering processing mechanism, enabling automatic adjustment of the acquisition resolution and compensation parameters according to the current status of the equipment, thereby maintaining high-precision monitoring performance under different operating conditions.

[0057] Specifically, in this embodiment, transformer status data is acquired through optimized resolution values, and a final resolution configuration is generated. First, transformer status data is collected from sensors using the optimized resolution values. This data includes real-time indicators such as temperature, load, and vibration. The collected transformer status data is integrated into a resolution adjustment module. This module uses a lookup table method to map the transformer status data (temperature, load, vibration) to predefined resolution values, and then aggregates them to generate the final resolution configuration. To enhance measurement accuracy, especially for errors caused by mechanical vibration, signal filtering technology is employed. Mechanical vibration monitoring data is fused with insulation resistance satellite measurement data, and the interference weight of vibration on insulation resistance measurement is calculated. The signal is then processed using a Kalman filter algorithm to minimize the impact of environmental noise on the measurement and improve the monitoring stability of electrical performance. The state vector of the Kalman filter is [signal value, derivative], the process noise covariance Q = 0.1, and the observation noise covariance R = 1.0.

[0058] Subsequently, based on the optimized resolution configuration and mechanical vibration monitoring data, a resolution configuration report is generated. This report automatically generates a structured report containing accuracy indicators by integrating transformer status data and filtered signals. The report summarizes the equipment status and requires optimization and adjustment suggestions. This report is used to further update signal filtering parameters and adjust compensation effect verification indicators to ensure consistency between the verification indicators and the actual equipment status. The compensation effect verification indicator is calculated as (1 - |Stable Measurement Data - Reference Value| / Reference Value) × 100%. After generating the final report, the stability of the final resolution configuration is evaluated using the compensation effect verification indicators. A stability score is calculated, for example, by calculating the variance of 100 consecutive sampling points; a variance less than 0.01 is considered stable, ensuring the long-term reliability of the monitoring data. Based on this, a transformer status data analysis report is generated according to the stability results. This report summarizes the equipment's aging level, potential risks, and optimization and adjustment suggestions. For example, if the stability score is high, the equipment's aging level is assessed as low, and it is recommended to extend the maintenance cycle, thus providing a basis for remote monitoring decisions.

[0059] This technical solution, by combining optimized resolution values, signal filtering processing, mechanical vibration data fusion, and compensation effect verification mechanisms, can effectively cope with error amplification and fluctuations in equipment under different operating environments, ensuring high-precision monitoring of the current transformer during long-term operation. This solution not only improves the stability of the equipment but also optimizes its debugging performance and maintains high reliability of the monitoring system in complex environments.

[0060] The process of obtaining stable measurement data includes: processing the transformer output signal under the final resolution configuration using a filtering algorithm to generate stable measurement data; reviewing historical deviation backtracking information to determine the adjustment effect of the parameter adjustment feedback loop on the real-time performance feedback; integrating resistance assessment environmental correction, processing the status integration real-time update, determining the final aging level, and generating a measurement data analysis report based on the stable measurement data and historical deviation backtracking information; updating the parameter adjustment feedback loop through the measurement data analysis report; adjusting the resistance assessment environmental correction parameters according to the parameter adjustment feedback loop; determining the dynamic range of the final aging level through the resistance assessment environmental correction parameters, and generating an aging level assessment report based on the dynamic range.

[0061] Specifically, in order to improve the monitoring accuracy and aging assessment accuracy of intelligent instrument transformers in complex environments due to factors such as load fluctuations, temperature changes, and mechanical vibrations, existing technologies have failed to fully consider the impact of these environmental factors, thus affecting the long-term stable operation of the equipment. This application optimizes the resolution configuration, filtering algorithm, and environmental correction to dynamically adjust the performance parameters of the instrument transformer, thereby achieving more accurate remote monitoring and aging level assessment.

[0062] Specifically, in this embodiment, the current transformer output signal is first processed according to the optimized resolution configuration, and a Kalman filter algorithm is used for prediction and updating to remove noise and generate stable measurement data. The filtering algorithm ensures the reliability of the data under high resolution configuration by predicting and correcting the actual measurement values, which helps in subsequent deviation analysis and aging assessment.

[0063] Furthermore, by reviewing historical deviation information and comparing current stable measurement data with historical deviation data, adjustment effect indicators are calculated to verify the effectiveness of the parameter adjustment feedback loop. If the feedback loop is effective, it can optimize long-term performance monitoring and improve the stability of aging assessment. At the same time, resistance assessment environmental correction is used to correct the impact of environmental factors on resistance measurement. By fusing temperature compensation formulas and real-time status data, the resistance value is corrected and the aging level is calculated, ensuring that the impact of environmental changes on equipment aging assessment is minimized. This correction method improves the accuracy of aging level assessment and is applicable to various complex environmental conditions.

[0064] Based on stable measurement data and historical deviation backtracking information, a measurement data analysis report is generated, including trend charts and statistical summaries, to facilitate subsequent parameter updates. The feedback in the report will be used to update the parameter adjustment feedback loop, further improving the device's adaptability. At the same time, based on the adjusted feedback, the resistance evaluation environment correction parameters are recalculated to ensure that the measurement results are stable and reliable in each operating cycle.

[0065] Finally, based on the resistance assessment environmental correction parameters, the dynamic range of the final aging level is determined. This dynamic range takes into account the impact of environmental factors on equipment aging and provides an assessment basis for adapting to different operating conditions. Based on the dynamic range, the final aging level assessment report is generated to provide decision support for remote monitoring and equipment maintenance.

[0066] This technical solution improves the monitoring accuracy and aging assessment reliability of current transformers under different environmental conditions by combining filtering algorithms, environmental correction, and historical data backtracking, thereby ensuring the long-term stable operation of the equipment.

[0067] Through the processing of steps S1 to S5 above, the stable measurement data of the current transformer is finally obtained. In order to intuitively verify the compensation effect of this method, Figure 3 This diagram illustrates a comparison between compensated stable measurement data and historical baseline data over the same time period, such as... Figure 3 As shown, the fluctuation range of the compensated data curve is significantly narrowed compared to the data before compensation, and the deviation from the historical benchmark data is controlled within the third preset threshold, which fully demonstrates the effectiveness of this method in improving data stability and accuracy.

[0068] Step S6: If the deviation between the stable measurement data and the historical data exceeds the third preset threshold, adjust the compensation parameters, optimize the overall configuration, and obtain the current transformer commissioning performance index.

[0069] The output transformer commissioning performance indicators include: if the deviation between stable measurement data and historical data exceeds a third preset threshold, the compensation parameters are adjusted retrospectively to generate adjusted compensation parameters; based on the adjusted compensation parameters, the resource consumption level is determined; real-time feedback data is obtained through the resource consumption level to optimize the overall configuration; based on the overall configuration, transformer commissioning performance indicators are generated; the aging influencing factor analysis is updated through remote data transmission; based on the aging influencing factor analysis, a resource consumption analysis report is generated; based on the resource consumption analysis report, the acquisition frequency of real-time feedback data is adjusted; and based on the acquisition frequency, the stability range of the transformer commissioning performance indicators is determined.

[0070] Specifically, in order to address the problem that the performance of intelligent instrument transformers cannot be evaluated in a timely and accurate manner in complex power grid environments due to the impact of factors such as load fluctuations, temperature changes, and equipment aging on measurement accuracy, traditional methods have failed to fully consider the impact of these dynamic factors on the commissioning and monitoring accuracy of instrument transformers. Therefore, this application optimizes the performance evaluation and resource consumption management of instrument transformers through intelligent compensation parameter adjustment, real-time feedback mechanism, and remote monitoring updates, ensuring that the equipment maintains stable and efficient monitoring performance under different operating conditions.

[0071] Specifically, in this embodiment, if the deviation between the stable measurement data and the historical data exceeds a third preset threshold, the gradient descent method is first used to retrospectively adjust the compensation parameters and generate the adjusted compensation parameters. This process identifies the source of the deviation by retrospectively checking the historical deviation data, such as temperature-induced offset or load fluctuation, and then reversely corrects the compensation parameters. Retrospective adjustment can effectively reduce the accumulation of errors under high load conditions and ensure the stability and accuracy of the transformer output.

[0072] Based on the adjusted compensation parameters, the resource consumption level is determined. The resource consumption level assessment considers the impact of the adjustment of compensation parameters on system resource usage. If the consumption level is high, real-time feedback data is obtained to optimize the overall configuration, including sampling resolution, compensation parameters, and filtering parameters. Through configuration optimization, it can be ensured that the system maintains high response efficiency under high load and avoids resource waste. For example, the resource consumption index can be: resource consumption level = CPU utilization × 0.5 + memory utilization × 0.5.

[0073] Furthermore, based on the optimized configuration, commissioning performance indicators for the instrument transformers are generated. These indicators reflect the accuracy and stability of the instrument transformers under different operating conditions, providing data support for equipment commissioning and optimization. Simultaneously, remote data transmission is used to update aging influencing factor analysis in real time. By analyzing current influencing factors such as mechanical vibration and temperature changes, a resource consumption analysis report is generated. This report helps assess the resource consumption trend under the current operating state of the equipment and provides a basis for adjusting the acquisition frequency. According to the generated resource consumption analysis report, the acquisition frequency of real-time feedback data will be adjusted. The acquisition frequency adjustment rule can be: acquisition frequency = base frequency × (1 + resource consumption level / 100), where the base frequency is 1Hz. By dynamically adjusting the acquisition frequency, the system load and data acquisition accuracy are balanced, further optimizing the monitoring effect of the instrument transformers under different environmental conditions. Finally, based on the adjusted acquisition frequency, the stability range of the instrument transformer commissioning performance indicators is determined. This range ensures that the equipment can maintain accuracy during long-term operation, avoiding resource waste caused by frequent acquisition.

[0074] The above-mentioned technical solution, through precise compensation adjustment, resource consumption management and feedback mechanism, can effectively cope with various challenges in complex power grid environments, improve the commissioning performance of instrument transformers, ensure that the equipment can still operate stably under high load and harsh environment, and keep data acquisition efficient and accurate, thereby providing support for remote monitoring and preventive maintenance.

[0075] Furthermore, step S1 also includes: collecting ambient temperature data and power grid load data through an environmental monitoring sensor network, fusing electrical performance indicators, mechanical vibration monitoring data, and aging data collection frequencies to generate comprehensive condition assessment data; determining the initial aging level based on the comprehensive condition assessment data; updating the collection parameters of ambient temperature data and power grid load data based on the initial aging level; generating a comprehensive condition assessment report based on the collection parameters; adjusting the collection frequency of the sensor network based on the comprehensive condition assessment report; determining the accuracy range of the current temperature value and the current power grid load value based on the collection frequency; and generating an initial condition monitoring report based on the accuracy range.

[0076] Specifically, in order to address the problems of unstable performance monitoring accuracy and inaccurate aging assessment faced by existing intelligent instrument transformers in complex environments, this application integrates environmental monitoring, equipment status assessment, data fusion, and dynamic adjustment mechanisms to ensure that the instrument transformer can reflect the health status of the equipment in real time and accurately, thereby improving the long-term operational stability and monitoring performance of the equipment.

[0077] In this embodiment, environmental temperature data and power grid load data are first collected through an environmental monitoring sensor network. At the same time, electrical performance indicators, mechanical vibration monitoring data, and aging data collection frequency are combined to generate comprehensive condition assessment data. By analyzing these data, the initial aging level is determined, and the collection parameters of environmental temperature data and power grid load data are adjusted according to this level. For example, when the equipment aging level is high, the temperature collection frequency will be shortened to facilitate more accurate monitoring; while when the aging level is low, the collection frequency can be appropriately extended to reduce resource consumption.

[0078] Based on the adjusted acquisition parameters, a comprehensive status assessment report is generated. This report integrates environmental data, electrical performance data, mechanical vibration data, and other information to assess the current status and aging level of the equipment. By analyzing the data in the report, the acquisition frequency of the sensor network is further adjusted. If the analysis report indicates a high accuracy requirement, such as when the equipment is operating under high load or high temperature, the acquisition frequency will be increased in a timely manner to ensure the accuracy and stability of the data.

[0079] After generating the preliminary monitoring report, the stable operating range of the equipment is further determined by analyzing the accuracy range of the current temperature value and the power grid load value. This accuracy range is determined by adjusting the acquisition frequency to ensure that the accuracy requirements of the equipment under different working conditions can be met and to avoid errors caused by environmental fluctuations or equipment aging.

[0080] This technical solution effectively enhances the remote monitoring capabilities of instrument transformers by adjusting the acquisition frequency in real time, optimizing environmental correction, and assessing aging levels. In practical applications, this solution is suitable for monitoring intelligent instrument transformers under various environmental conditions, especially in power grid environments with significant temperature variations and load fluctuations. It can provide more accurate and stable monitoring data, offering data support for instrument transformer performance optimization and fault prevention.

[0081] Through the coordination of the above steps, this application improves the measurement accuracy and stability of the current transformer in complex environments, and also extends the lifespan of the current transformer through precise aging assessment and condition monitoring.

[0082] Example 2: The above describes a remote monitoring method for an intelligent instrument transformer according to an embodiment of this application. The following describes a remote monitoring system for an intelligent instrument transformer according to an embodiment of this application. Please refer to [link / reference]. Figure 4 One embodiment of a remote monitoring system for an intelligent instrument transformer in this application includes: The data acquisition unit is used to remotely acquire ambient temperature data and power grid load data of the instrument transformer through an environmental monitoring sensor network, and obtain the current temperature value and the current power grid load value. The offset determination unit is used to determine the sampling offset of the instrument transformer based on the current temperature value and the current power grid load value. The offset compensation unit is used to call the pre-trained aging trend prediction model to generate compensation parameters and compensate the sampling offset if the sampling offset exceeds the first preset threshold, so as to obtain the compensated offset. The resolution acquisition unit is used to predict the measurement error trend and determine the degree of error amplification based on the compensated offset and the current power grid load value. If the degree of error amplification exceeds the second preset threshold, the acquisition resolution of the current transformer is dynamically adjusted to obtain an optimized resolution value. The resolution configuration unit is used to acquire transformer status data based on the optimized resolution value, generate the final resolution configuration, process the transformer output signal based on the final resolution configuration, and obtain stable measurement data. The index adjustment unit is used to adjust the compensation parameters and optimize the overall configuration if the deviation between the stable measurement data and the historical data exceeds the third preset threshold, so as to obtain the current transformer adjustment performance index.

[0083] Through the synergistic cooperation of the above components, the measurement accuracy and stability of the instrument transformer in complex environments are further improved, and the service life of the instrument transformer is extended.

[0084] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the remote monitoring method for an intelligent instrument sensor.

[0085] In summary, this application effectively solves the long-standing problem of decreased measurement accuracy and inaccurate status assessment caused by temperature fluctuations, load changes, and equipment aging in traditional instrument transformers under complex operating environments by constructing an intelligent monitoring system that integrates environmental perception, dynamic compensation, and closed-loop optimization. It achieves real-time and accurate perception and autonomous optimization control of the instrument transformer's operating status, which not only improves the accuracy and stability of measurements but also enables early warning of equipment aging trends and proactive extension of lifespan, thus providing a solid technical guarantee for the high-reliability operation of smart grids.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0087] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A remote monitoring method for an intelligent current transformer, characterized in that, The method includes: Step S1: Remotely collect ambient temperature data and power grid load data of the current transformer through the environmental monitoring sensor network to obtain the current temperature value and the current power grid load value; Step S2: Based on the current temperature value and the current power grid load value, determine the sampling offset of the current transformer; Step S3: If the sampling offset exceeds the first preset threshold, the pre-trained aging trend prediction model is called to generate compensation parameters and the sampling offset is compensated to obtain the compensated offset. Step S4: Based on the compensated offset and the current power grid load value, predict the measurement error trend and determine the degree of error amplification. If the degree of error amplification exceeds the second preset threshold, dynamically adjust the acquisition resolution of the current transformer to obtain an optimized resolution value. Step S5: Based on the optimized resolution value, obtain the current transformer status data and generate the final resolution configuration. Based on the final resolution configuration, process the current transformer output signal to obtain stable measurement data. Step S6: If the deviation between the stable measurement data and the historical data exceeds the third preset threshold, adjust the compensation parameters, optimize the overall configuration, and obtain the transformer debugging performance index.

2. The method according to claim 1, characterized in that, In step S2, determining the sampling offset of the current transformer includes: A temperature-induced offset calculation method is used to process the current temperature value and the current power grid load value to obtain the sampling offset. Insulation resistance measurement accuracy information is integrated to determine the degree of temperature influence on electrical performance indicators. Through data fusion weight allocation, equipment material degradation indicators are processed to obtain a preliminary offset estimate for the aging trend prediction model. Based on the preliminary offset estimate and the sampling offset, an offset correction coefficient is determined. The sampling offset is adjusted using the offset correction coefficient to obtain the corrected offset. An offset analysis report is generated based on the corrected offset and the insulation resistance measurement accuracy information. The aging trend prediction model is updated using the offset analysis report. Finally, the dynamic range of the sampling offset is determined based on the aging trend prediction model.

3. The method according to claim 2, characterized in that, In step S3, the compensated offset is obtained, including: If the sampling offset exceeds a first preset threshold, an initial value for the compensation parameter is generated using the aging trend prediction model; real-time mechanical vibration monitoring update data is acquired via remote data transmission; a deviation compensation algorithm is applied to process the insulation resistance measurement accuracy and determine the dynamic range of the compensation parameter; the compensation parameter is adjusted based on the initial value of the compensation parameter and the updated mechanical vibration monitoring data; the offset after compensation is calculated using the compensation parameter and the sampling offset; the aging trend prediction model is updated based on the offset after compensation; a compensation parameter adjustment report is generated using the aging trend prediction model; and the validity of the offset after compensation is determined based on the compensation parameter adjustment report.

4. The method according to claim 1, characterized in that, Step S4 further includes: A load fluctuation impact analysis method is used to process the offset after compensation and the current grid load value to generate an error trend prediction result. Based on the error trend prediction result, the degree of error amplification is determined. Combined with real-time performance feedback data, the sources of deviation under the dynamic range of the compensation parameters are evaluated. The distribution of error sources is determined through a deviation source identification method. The aging threshold setting parameters are integrated, and the resistance evaluation environment correction is processed to obtain the preliminary adjustment value of the deviation compensation mechanism. Based on the preliminary adjustment value and the degree of error amplification, an error trend analysis report is generated. The deviation source identification method is updated through the error trend analysis report. The dynamic threshold of the degree of error amplification is determined based on the deviation source identification method.

5. The method according to claim 4, characterized in that, In step S4, obtaining the optimized resolution value includes: If the error amplification exceeds a second preset threshold, dynamic resolution adjustment parameters are generated based on the predicted trend; an optimized resolution value is determined using these dynamic resolution adjustment parameters; monitoring parameter optimization information is integrated to process the remote monitoring stability of electrical performance indicators; a compensation effect verification index is determined by integrating the deviation source identification method with the evaluation cycle adjustment strategy; a resolution adjustment report is generated based on the compensation effect verification index and the optimized resolution value; the remote monitoring stability parameters are updated using the resolution adjustment report; the evaluation cycle is adjusted based on the remote monitoring stability parameters; and the effective range of the optimized resolution value is determined using the evaluation cycle.

6. The method according to claim 5, characterized in that, In step S5, generating the final resolution configuration includes: The transformer status data is obtained by optimizing the resolution value; the transformer status data is integrated into the resolution adjustment module to generate the final resolution configuration; the insulation resistance measurement accuracy under the compensation effect verification index is enhanced by signal filtering and fusion of mechanical vibration monitoring data; a resolution configuration report is generated based on the final resolution configuration and the mechanical vibration monitoring data; the signal filtering parameters are updated based on the resolution configuration report; the compensation effect verification index is adjusted based on the signal filtering parameters; the stability of the final resolution configuration is determined based on the compensation effect verification index; and a transformer status data analysis report is generated based on the stability.

7. The method according to claim 6, characterized in that, In step S5, stable measurement data is obtained, including: A filtering algorithm is used to process the transformer output signal under the final resolution configuration to generate stable measurement data. Historical deviation backtracking information is reviewed to determine the effect of the parameter adjustment feedback loop on the real-time performance feedback. Resistance assessment environmental correction is integrated, and the status integration is updated in real time to determine the final aging level. Based on the stable measurement data and the historical deviation backtracking information, a measurement data analysis report is generated. The parameter adjustment feedback loop is updated using the measurement data analysis report. The resistance assessment environmental correction parameters are adjusted according to the parameter adjustment feedback loop. The dynamic range of the final aging level is determined using the resistance assessment environmental correction parameters, and an aging level assessment report is generated based on the dynamic range.

8. The method according to claim 1, characterized in that, In step S6, the output transformer commissioning performance indicators include: If the deviation between the stable measurement data and historical data exceeds a third preset threshold, the compensation parameters are adjusted retrospectively to generate adjusted compensation parameters. Based on the adjusted compensation parameters, the resource consumption level is determined. Real-time feedback data is obtained based on the resource consumption level to optimize the overall configuration. Based on the overall configuration, transformer commissioning performance indicators are generated. The aging influencing factor analysis is updated via remote data transmission. Based on the aging influencing factor analysis, a resource consumption analysis report is generated. Based on the resource consumption analysis report, the acquisition frequency of real-time feedback data is adjusted. Based on the acquisition frequency, the stability range of the transformer commissioning performance indicators is determined.

9. A remote monitoring system for an intelligent instrument transformer, used to implement the remote monitoring method for an intelligent instrument transformer as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition unit is used to remotely acquire ambient temperature data and power grid load data of the instrument transformer through an environmental monitoring sensor network, and obtain the current temperature value and the current power grid load value. The offset determination unit is used to determine the sampling offset of the instrument transformer based on the current temperature value and the current power grid load value; An offset compensation unit is used to call a pre-trained aging trend prediction model to generate compensation parameters and compensate for the sampling offset if the sampling offset exceeds a first preset threshold, so as to obtain the compensated offset. The resolution acquisition unit is used to predict the measurement error trend and determine the degree of error amplification based on the compensated offset and the current power grid load value. If the degree of error amplification exceeds the second preset threshold, the acquisition resolution of the current transformer is dynamically adjusted to obtain an optimized resolution value. The resolution configuration unit is used to acquire transformer status data based on the optimized resolution value, generate a final resolution configuration, process the transformer output signal based on the final resolution configuration, and obtain stable measurement data. The indicator debugging unit is used to adjust the compensation parameters and optimize the overall configuration if the deviation between the stable measurement data and the historical data exceeds a third preset threshold, so as to obtain the current transformer debugging performance index.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements a remote monitoring method for an intelligent instrument transformer as described in any one of claims 1-8.

Images