CN119001585B - A transformer operation status monitoring terminal based on artificial intelligence - Google Patents
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- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/02—Testing or calibrating of apparatus covered by the other groups of this subclass of auxiliary devices, e.g. of instrument transformers according to prescribed transformation ratio, phase angle, or wattage rating
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Abstract
The invention relates to the technical field of online monitoring of electric power metering, in particular to an artificial intelligence-based transformer running state monitoring terminal which comprises a data acquisition module, a time acquisition module, a state detection module and an error verification module, wherein the data acquisition module is used for acquiring error data of a transformer in running, the error data comprise temperature, current and voltage of the transformer in running and change values of current boundaries, the time acquisition module is used for acquiring an error time sequence corresponding to the error data, determining the change trend of the error time sequence in running of the transformer and the change quantity of a predicted value, the state detection module is used for detecting the state of the transformer and determining the change coefficient of corresponding inductance of the transformer and the change probability of electric stress of the transformer when the error data appear, the error verification module is used for verifying the influence coefficient of the change of the electric stress on the actual output power of the current transformer under different error conditions, the running reliability of the transformer is improved, and the risk of faults is reduced.
Description
Technical Field
The invention relates to the technical field of online monitoring of electric power metering, in particular to an artificial intelligence-based transformer running state monitoring terminal.
Background
As an important component of the electric energy metering device, the accuracy and reliability of the metering performance of the mutual inductor are directly related to fairness and fairness of electric energy trade settlement. The Capacitive Voltage Transformer (CVT) is divided by a series capacitor, then is reduced and isolated by an electromagnetic transformer, and can be used as an instrument for converting voltage, and the capacitive voltage transformer can also couple carrier frequency to a power transmission line for long-distance communication, selective line high-frequency protection, remote control and other functions. Compared with the conventional electromagnetic voltage transformer, the capacitive voltage transformer has the advantages of high impact insulation strength, simple manufacture, small volume, light weight and the like, and has a plurality of advantages in economy and safety.
In the actual operation process of the CVT, the transformer error is affected by the acquisition principle, the severe environment and the like, and measurement deviation out-of-limit can occur in the service life of the CVT, so that accurate and rapid diagnosis can be carried out when the measurement error is out of tolerance, further, timely prediction needs to be carried out on the degradation trend of the measurement error of the CVT, so that relevant operation maintenance personnel can arrange maintenance work, and if the state degradation of the transformer cannot be found timely, the operation of a power grid can be affected.
As disclosed in chinese patent publication No. CN113821938a, a short-term prediction method and apparatus for measuring error states of a transformer are disclosed, first, CVT error data is preprocessed by an error stripping method, a trend prediction model is constructed by using an ARIMA algorithm, and error prediction values of the transformer and additional error prediction values are added to predict error values in a period of time close to the period of time and obtain a transformer error state prediction curve.
In the prior art, when the error analysis is performed on the transformer, the transformer is monitored mainly aiming at the frequency, the predicted value and the like of the error, but under the condition, the trend of the error generated by the transformer and the specific problem corresponding to the trend are required to be clarified, and the related data corresponding to the current and the voltage are verified to determine the problem corresponding to the transformer, so that the effect of monitoring the state of the transformer is improved.
Disclosure of Invention
In order to solve the technical problems, the technical scheme adopted by the invention is that the transformer running state monitoring terminal based on artificial intelligence comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring error data existing in running of a transformer, and the error data comprise temperature, current and voltage and a change value of a current boundary in running of the transformer.
The time acquisition module is used for acquiring an error time sequence corresponding to the error data and determining the change trend of the error time sequence when the transformer operates and the change quantity of the predicted value.
The state detection module is used for detecting the state of the transformer and determining the change coefficient of the corresponding inductor of the transformer when error data appear and the electric stress change probability of the transformer.
And the error verification module is used for verifying the influence coefficient of the change of the electric stress on the actual output power of the current transformer under different error conditions of the transformer.
And the state evaluation module is used for evaluating the working state of the transformer according to the acquired influence coefficient to obtain the evaluation coefficient of the current transformer.
The method has the advantages that the time sequence analysis method can be used for identifying and predicting the change trend of error data more accurately, the state of the transformer can be detected more comprehensively by comprehensively considering the inductance change coefficient and the electric stress change probability, reliable basis is provided for fault diagnosis by verifying the influence of electric stress on actual output power under different error conditions, a comprehensive evaluation coefficient can be obtained by carrying out weighted summation on a plurality of indexes, the working state of the transformer can be comprehensively reflected, detailed evaluation can be carried out aiming at different variables by a hierarchical evaluation method, and the granularity and accuracy of the evaluation are improved.
Drawings
The invention will be further described with reference to the drawings and examples.
Fig. 1 is a system frame diagram of an artificial intelligence based transformer operating state monitoring terminal.
Fig. 2 is a system flow diagram of an artificial intelligence based transformer operating condition monitoring terminal.
Fig. 3 is a schematic diagram of an apparatus for monitoring the operational status of a transformer based on artificial intelligence.
500 Parts of the figure, electronic equipment 510 parts of the figure, memory 511 parts of the figure, computer programs 520 parts of the figure, and processors.
Detailed Description
Embodiments of the present invention are described in detail below. The following examples are illustrative only and are not to be construed as limiting the invention. The examples are not to be construed as limiting the specific techniques or conditions described in the literature in this field or as per the specifications of the product.
Referring to fig. 1 and 2, the transformer operation state monitoring terminal based on artificial intelligence comprises a data acquisition module, a time acquisition module, a state detection module, an error verification module and a state evaluation module, wherein the data acquisition module inputs data into the time acquisition module, the time acquisition module inputs processing data of an error time sequence into the state detection module, the state detection module inputs corresponding coefficients into the error verification module, and the error verification module evaluates the whole through the state evaluation module after verification.
The data acquisition module is used for acquiring error data of the transformer in operation, wherein the error data comprises temperature, current and voltage of the transformer in operation and a change value of a current boundary.
The time acquisition module is used for acquiring an error time sequence corresponding to the error data and determining the change trend of the error time sequence when the transformer operates and the change quantity of the predicted value.
The state detection module is used for detecting the state of the transformer and determining the change coefficient of the corresponding inductor of the transformer when error data appear and the electric stress change probability of the transformer.
And the error verification module is used for verifying the influence coefficient of the change of the electric stress on the actual output power of the current transformer under different error conditions of the transformer.
And the state evaluation module is used for evaluating the working state of the transformer according to the acquired influence coefficient to obtain the evaluation coefficient of the current transformer.
When the maximum allowable current and voltage change, the current transformer is aged or has other problems, and the current transformer processing effect is reduced.
In one embodiment of the invention, the error data in the data acquisition module determines the temperature and the error of the current when the transformer runs by arranging the current sensor, the voltage sensor and the temperature sensor on the transformer, the voltage sensor is used for determining the stability of the current, the change value of the current boundary at the moment is determined in an auxiliary manner, the change value of the current boundary refers to the upper limit value and the lower limit value of the three-phase current triggering protection action when the three-phase current normally works, and the change of the boundary value occurs under the using time of the transformer, so that whether the transformer has aging fault or not can be detected, and whether the state of the current transformer needs to be adjusted or not.
At this time, an ammeter is used for measuring current, a voltmeter is used for measuring voltage, an overcurrent protection relay is used for acquiring whether the current exceeds a boundary value or not, the current boundary value recognized at this time is changed, and a temperature sensor is used for detecting the temperature, so that temperature data are acquired under the condition that errors exist.
In one embodiment of the present invention, the time acquisition module processes the error time series as follows.
And for the obtained error time sequence, adopting a time sequence analysis mode, recording the change trend of the error time sequence according to the time of occurrence of error data of the transformer, recording the change condition of corresponding values of the error data when errors occur, for example, determining the change values of temperature, current, voltage and current boundaries, sequentially calculating the change trend quantity of each temperature, current, voltage and current boundary in the error time sequence, and taking the comprehensive value of the change trend quantity of the temperature, current, voltage and current boundaries as the change trend identified at the moment.
When the variation trend is verified, the variation trend of each error data under the condition that the variation trend occurs is verified, the residual error of the recognized variation trend quantity is used as the variation quantity of the output predicted value, the residual value of the error caused by each error condition when the predicted value is changed and the occurrence condition of the error which can be influenced finally under different conditions are verified, and the calculated residual error is the difference value between the variation trend quantity and the expected value to determine the corresponding residual error.
The change trend quantity of the temperature is expressed as that the change trend of the temperature in the error time sequence is extracted, the change trend quantity of the temperature is calculated according to the change trend of the temperature, and the change trend of the temperature within 7 days is calculated to obtain the change trend of the temperature required currently.
When the change trend amount of the temperature is calculated, the value corresponding to the change trend of the temperature is identified, the moving average value of the temperature at each time point is calculated according to the sampled time points, the slope value of the temperature at each time point is determined based on the obtained moving average value, the slope value of the temperature is detected, and the value output after the detection is used as the change trend amount of the temperature at the moment.
The moving average of the temperature at each time point is expressed as:; A moving average of the temperature is indicated, A value indicating a trend of change in temperature at the i-th time point; A time window is indicated for adjusting the temperature value at the current time point to be the average value of the adjacent temperatures, for example, in the case of k=3, the second point is the average value from the first point to the third point, the third point is the average value from the second point to the fourth point, and so on; wherein k has a value smaller than n, Indicating the number of time points.
The slope value for the temperature at this time is expressed as: wherein, the method comprises the steps of, A slope value indicative of the temperature is provided,A time value representing the i-th time point,The average value of the time values is represented, the set time value at the moment is represented by how long the time phase at the current temperature changes, and the time value at the moment is represented by the difference value of the corresponding time point intervals; Represents the moving average of the temperature at the i-th time point, The standard value corresponding to the moving average of the temperature is shown. The slope value at this time can be known as the trend of the current temperature after the corresponding time point has elapsed.
Verifying the ratio of the calculated slope value to the standard error, and outputting the slope value with the ratio larger than the preset temperature ratio as the change trend quantity of the temperature.
The standard error is calculated by the following steps: wherein, the method comprises the steps of, Expressed as standard error for temperature.
At this time, through a plurality of processing modes, the change trend quantity of the temperature can be obtained to verify the corresponding data distribution of the temperature in the error time sequence and finally verify the result.
For the calculation of the trend of the current, data within 5 days can be selected to measure the change of the corresponding current, and for the calculation of the trend of the voltage, data within 3 days can be selected to calculate the change of the corresponding voltage.
At this time, the same time point as the temperature sampling is adopted to verify the amount of variation trend of the current and the voltage at this time.
The calculation mode of the change trend quantity of the current and the voltage is that the change trend of the current and the voltage is extracted from the error time sequence, the change rate of the current and the voltage is calculated, and the correlation coefficient of the change rate of the current and the voltage and the preset change rate is used as the output change trend quantity.
The rate of change of current and voltage at this time is expressed as: wherein, the method comprises the steps of, The current and voltage change rates at the ith time point are shown,Values representing the current and voltage at the (i+1) th time point; the values of the current and the voltage at the ith time point are represented, and the change rate of the corresponding current and voltage in the n time points is determined when the value range of i is 1 to n-1.
The correlation coefficients of the rates of change of the current and the voltage with the preset rate of change are shown below.
Wherein, the method comprises the steps of,A correlation coefficient representing the change rate of the current and the voltage and a preset change rate,The rate of change of the current at the i-th time point is indicated,Indicating the rate of change of the voltage at the i-th time point,A standard value representing the rate of change of the current,A standard value representing the rate of change of the voltage.
The correlation coefficient obtained at this time can reflect the correlation of the current and the voltage under the corresponding change trend, and at this time, the data corresponding to the correlation is output to obtain the change trend quantity of the current and the voltage.
The change trend of the current boundary can be determined by selecting data within 7 days, and at the moment, the change trend of the current boundary is realized by sequentially calculating a first probability value corresponding to the upper limit value of the current boundary and a second probability value corresponding to the lower limit value of the current boundary, subtracting the preset boundary value from the current boundary value when the first probability value and the second probability value take the maximum value, taking the subtracted value as the change trend of the current boundary identified at the moment, subtracting the preset boundary value from the upper limit value corresponding to the maximum value of the first probability value at the moment, then calculating the subtraction between the lower limit value of the second probability value taking the maximum value and the preset boundary value lower limit value, and taking the average value of the sum of the two subtracted values as the change trend at the moment.
The first probability value is calculated by dividing the number of times of one value by the total number of times of the current boundary upper limit value, the second probability value is the same, the boundary value needing to be focused is obtained, and the difference between the boundary value needing to be focused and the normal boundary value is determined.
And finally, outputting the variation trend quantity corresponding to the temperature, the current, the voltage and the current boundary, and subtracting the variation trend quantity from the expected value to obtain the variation quantity of the predicted value.
In one embodiment of the present invention, the processing manner of the state detection module is as follows.
At this time, after the correlation analysis of the error time sequence is completed, whether the current inductance changes is determined, for example, whether the current identified inductance value changes or not and the corresponding insulation performance change of the corresponding inductance value can be calculated, so as to quantify the change coefficient of the inductance, for example, the change of the insulation performance of the current transformer can be determined when the current transformer is used, the change coefficient of the current inductance is verified, and the change coefficient of the current transformer corresponding to the inductance is determined by calculating the dielectric loss rate of the transformer and the change trend amounts of current and voltage under different insulation conditions.
And the dielectric loss rate, the current and voltage change trend amounts of the transformer are weighted and summed at the moment to obtain the change coefficient of the inductance.
The dielectric loss at this time can be expressed as: wherein, the method comprises the steps of, The dielectric loss rate is indicated by the ratio,Indicating that the power is lost,And representing the total power, and calculating the ratio of the lost power to the total power to determine the dielectric loss rate corresponding to the transformer.
The current transformer corresponds to the inductance of the inductor by a factor of change,Wherein, the method comprises the steps of,Representing the coefficient of variation of the inductance,A weight representing the dielectric loss rate is used,And a weight indicating the amount of change in the current and voltage.
The maximum current value and the maximum voltage value are expressed for the identified electric stress, and the electric stress change probability is verified by extracting corresponding values from the error time sequence to record whether the maximum current value and the maximum voltage value change and what the generated change value is.
The maximum current value at this time is different from the corresponding meaning contained in the current boundary set above, the maximum current value at this time refers to the current limit value of the transformer in design and refers to the maximum current value which can be borne by equipment or a system, the current boundary refers to the value which can trigger the transformer to perform self protection, the current boundary has more significance in distinguishing the current range corresponding to the normal operation and the fault state of the transformer, the positions of the two values and the corresponding structure are different after the two values are tested, and the verification range can be reduced when the current or the voltage is abnormal, so that the effect of monitoring the state of the transformer is improved.
The implementation mode of the electric stress change probability comprises the steps of taking a time interval corresponding to the maximum value of a first probability value and a second probability value in the current boundary as an initial interval according to the obtained change trend quantity of the current boundary, calculating the electric stress change probability in the initial interval, taking the electric stress change probability in the initial interval as the output electric stress change probability when the electric stress change probability in the initial interval reaches a preset threshold value, and obtaining adjacent time intervals of the initial interval when the electric stress change probability in the initial interval is smaller than the preset threshold value, wherein the sizes of the adjacent time intervals are consistent with the sizes of the initial interval, and taking the electric stress change probability calculated in the adjacent time intervals as the output electric stress change probability.
At this time, the initial interval is selected from the time point when the first probability value has the maximum value to the time point when the second probability value has the maximum value, so as to obtain a corresponding initial interval, and the maximum current value and the maximum voltage value in the interval are verified, so that whether the current transformer has a corresponding fault is determined.
The mode of calculating the change probability of the electric stress at this time is to calculate the corresponding value change rate of the electric stress at the extreme point existing in the corresponding time interval to obtain the change probability of the electric stress.
And simultaneously, for evaluating the situation in the corresponding time zone, calculating the average value of the ratio of the value of the electric stress at the extreme point to the preset standard value as the output value of the electric stress variation probability of the initial zone for the electric stress variation probability of the initial zone.
And the electric stress change probability in the adjacent time interval is obtained as the output electric stress change probability by acquiring a group of data with the largest difference value between the value of the electric stress at the extreme point and the preset standard value at the adjacent time point.
In one embodiment of the invention, the implementation of the error verification module is as follows.
This is used in the case where the current transformer is able to normally output power and processed values when the transformer is worn out due to some fault conditions or long-term use, so that it is possible to verify whether the performance of the transformer is degraded.
The obtained influence coefficient is a ratio of actual output power to theoretical output power of the transformer under error data to quantify the running state of the transformer under the fault condition.
In one embodiment of the present invention, the method for obtaining the evaluation coefficients of the state evaluation module is as follows.
In the final evaluation, the current transformer is comprehensively evaluated according to the obtained multiple values to identify corresponding values.
The evaluation coefficient is expressed by normalizing the change coefficient of the inductor, the electric stress change probability, the change amount of the predicted value and the influence coefficient of the actual output power, and calculating the normalized change coefficient of the inductor, the electric stress change probability, the change amount of the predicted value and the influence coefficient of the actual output power to obtain the evaluation coefficient.
The evaluation coefficients are expressed as: wherein, the method comprises the steps of, Represented as an evaluation coefficient,Expressed as a coefficient of variation of the inductance,Expressed as the probability of change in the electrical stress,Represented as the amount of change in the predicted value,An influence coefficient expressed as actual output power; Expressed as a weight of the coefficient of variation of the inductance, The weight expressed as the probability of change of the electrical stress,Weights expressed as the amount of change in the predicted value,The weight expressed as the influence coefficient of the actual output power.
When calculating the evaluation coefficients, since the variation amounts of the predicted values respectively represent the variation trend amounts of the temperature, the variation trend amounts of the current and the voltage, and the difference between the variation trend amounts of the current boundary and the expected value, the calculated evaluation coefficients need to be calculated respectively and divided into three evaluation coefficients for the variation trend amounts of the temperature, the evaluation coefficients for the variation trend amounts of the current and the voltage, the evaluation coefficients for the variation trend amounts of the current boundary, and the final evaluation coefficients are obtained by combining the three evaluation coefficients, and the combination mode of the three evaluation coefficients is as follows.
Wherein, the method comprises the steps of,The evaluation coefficients representing the final output are presented,An evaluation coefficient indicating the amount of change trend with respect to temperature,An evaluation coefficient indicating the amount of variation trend with respect to the current and the voltage,An evaluation coefficient indicating the amount of variation trend with respect to the current boundary,A weight indicating an evaluation coefficient for the amount of change trend of the temperature,Weights indicating evaluation coefficients for the amounts of variation trend of the current and the voltage,Weights indicating evaluation coefficients for the amount of variation trend of the current boundary,Represents the average value of the evaluation coefficients, which is the average value of the three evaluation coefficients,Indicating an exponential constant.
The final output evaluation coefficient can express the condition of the current transformer under the condition of corresponding temperature, current, voltage and current boundary, so as to quantify the final evaluation coefficient, and the final evaluation coefficient is taken as the output evaluation coefficient.
Referring to fig. 3, an electronic device 500 includes a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520, wherein the processor 520 implements the following steps when executing the computer program 511.
S1, collecting error data of the transformer in operation.
S2, determining a time sequence change trend of error data and predicting future change quantity, calculating the change trend quantity of each variable, and calculating the change quantity of the predicted value according to the difference between the change trend quantity and the expected value.
S3, extracting temperature time series data, calculating a moving average value at each time point, calculating a slope value of the temperature at each time point based on the moving average value, and detecting the slope value as a change trend amount of the temperature.
S4, extracting current and voltage time sequence data, calculating the change rate of the current and the voltage, calculating the correlation coefficient of the change rate and the preset change rate, and taking the correlation coefficient as the output change trend quantity.
S5, extracting time sequence data of an upper limit value and a lower limit value of a current boundary, calculating a first probability value corresponding to the upper limit value and a second probability value corresponding to the lower limit value of the current boundary, taking the maximum value of the first probability value and the second probability value, and subtracting the current boundary value corresponding to the maximum value from a preset boundary value to obtain the change trend quantity of the current boundary.
And S6, obtaining the dielectric loss rate, the current and voltage change trend quantity, and carrying out weighted summation on the dielectric loss rate, the current and voltage change trend quantity to obtain the inductance change coefficient.
S7, determining an initial interval according to the change trend quantity of the current boundary, calculating the electric stress change probability in the initial interval, taking the electric stress change probability as the output probability if the electric stress change probability reaches a preset threshold value, otherwise, acquiring an adjacent time interval, and repeating calculation until the condition is met.
S8, calculating actual output power, calculating theoretical output power and calculating an influence coefficient, namely the ratio of the actual output power to the theoretical output power.
And S9, normalizing the inductance change coefficient, the electric stress change probability, the change amount of the predicted value and the influence coefficient of the actual output power, and calculating an evaluation coefficient through weighted summation.
While embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention, which is also intended to be covered by the present invention.
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| CN119595979B (en) * | 2025-02-08 | 2025-05-09 | 江苏伟岸纵横科技股份有限公司 | Intelligent monitoring method, device and system for distribution box |
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