Mutual inductor metering error online monitoring method and system

Abstract

The invention relates to the technical field of mutual inductor error monitoring, in particular to a mutual inductor metering error online monitoring method and system, and the method comprises the steps: obtaining a plurality of pieces of metering data of a mutual inductor, determining a target set of the metering data, taking the local similarity degree of each piece of data in the target set as a weight, and calculating the metering error of the mutual inductor; performing weighted average on the data in the target set to obtain density characteristics of the measurement data; the normalized value of the difference of the density characteristics of the adjacent data in the target set is used for weighting and summing the difference of the density characteristics of the two corresponding adjacent data, and the drift deviation of the measurement data is obtained; and calculating a correction value of the measurement data based on the drift deviation so as to monitor the measurement error of the mutual inductor according to the difference between the correction value of the measurement data acquired in real time and a preset reference value. According to the invention, the accuracy of determining the metering error of the mutual inductor can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mutual inductor error monitoring, and more specifically, to a method and system for online monitoring of mutual inductor measurement errors. Background Art

[0002] Instrument transformers are important measuring devices in power systems, used for current and voltage conversion, so as to facilitate the safe measurement and monitoring of high voltage and high current. With the increasing power load and the rapid development of smart grids, the accuracy and reliability of instrument transformers are particularly important. Metering errors will directly affect electricity billing, load analysis and power quality monitoring. Therefore, real-time monitoring of instrument transformer metering errors has become an important task in power systems.

[0003] In the related art, the patent application document with publication number CN103018709A discloses an error monitoring method and system for a gateway metering device. The method includes: collecting the current parameter and voltage parameter of the voltage transformer through a current sensor and a voltage sensor, and calculating the composite error of the voltage transformer according to the current parameter and voltage parameter of the voltage transformer; collecting the current parameter and voltage parameter of the current transformer through another current sensor and a voltage sensor, and calculating the composite error of the current transformer according to the current parameter and voltage parameter; collecting the secondary voltage drop value of the voltage transformer, and calculating the error of the gateway metering device through an error comprehensive formula according to the secondary voltage drop value, the composite error of the voltage transformer and the composite error of the current transformer; if the error of the gateway metering device exceeds a preset error threshold, an alarm message is sent.

[0004] However, in the above scheme, the influence of environmental factors is not taken into account when determining the metering error, and the error caused by the environment may be misjudged as the metering error of the transformer, resulting in low accuracy of the determined metering error of the transformer, and thus the metering error of the transformer cannot be accurately monitored. Summary of the invention

[0005] In order to solve the problem that the metering error of a mutual inductor cannot be accurately monitored, the present invention provides a method and system for online monitoring of the metering error of a mutual inductor.

[0006] According to a first aspect of the present invention, there is provided a method for online monitoring of mutual inductor metering error, comprising: Obtain some measurement data of the transformer; The set of multiple measurement data collected before the sampling time corresponding to any measurement data is taken as the target set of any measurement data, and the local similarity of each data in the target set is taken as the weight. The data in the target set is weighted averaged to obtain the density feature of any measurement data. The local similarity represents the similarity between the corresponding data and several previous data. Using the normalized value of the difference in density features of adjacent data in the target set, the difference in density features of two corresponding adjacent data is weighted and summed to obtain the drift deviation of any measurement data; The correction value of any metering data is calculated, and the correction value is negatively correlated with the drift deviation, so as to monitor the metering error of the transformer according to the difference between the correction value of the metering data collected in real time and the preset reference value.

[0007] When calculating the drift deviation of the metering data, the present invention takes into account environmental factors, such as the influence of temperature changes on data distribution, so that when the metering data is corrected by using the drift deviation, the influence of environmental factors can be eliminated, thereby avoiding the deviation caused by environmental factors from being misjudged as a deviation caused by the mutual inductor itself, thereby improving the accuracy of the metering error of the monitored mutual inductor.

[0008] Preferably, the drift deviation of any measurement data satisfies the following relationship: ; In the formula, For the Drift deviation of measurement data; , Respectively In the target set of measurement data Data and Density characteristics of individual data; is the absolute value symbol; is the total number of data in the target set; is the hyperbolic tangent function.

[0009] The present invention can limit the range of drift deviation of measurement data to within 0-2, and can reduce the difficulty of calculation and the amount of calculation.

[0010] Preferably, the method for obtaining the local similarity degree includes: Calculate the sum of the squares of the differences between any data in the target set and several measurement data before the corresponding sampling time of the data, take the average value, and use the opposite of the average value as the power of the natural exponential function to perform power operation to obtain the local similarity of the data.

[0011] Preferably, a method for obtaining density features of any measurement data includes: Calculate the density feature of the target set of any measurement data, and use the density feature of the target set as the density feature of the measurement data; The density characteristics of the target set satisfy the following relationship: ; In the formula, For the The density characteristics of the target set of measurement data; For the In the target set of measurement data The local similarity of data; For the In the target set of measurement data The value of the data; The total number of data in the target collection.

[0012] The present invention uses the density features of the target set as the density features of the corresponding measurement data, which can avoid the influence of the accidental errors of a single measurement data on the calculation results and ensure the accuracy of the density features.

[0013] Preferably, a method for obtaining a correction value of any measurement data includes: The ratio of any measurement data to the drift deviation of the measurement data is calculated to obtain the correction value of any measurement data.

[0014] The correction value of the measurement data determined by the present invention can eliminate the influence of environmental factors, thereby avoiding misjudging the deviation caused by the environmental factors as the deviation caused by the mutual inductor itself.

[0015] Preferably, monitoring the metering error of the transformer according to the difference between the correction value of the metering data collected in real time and the preset reference value includes: Obtain a preset reference value, calculate the difference between the correction value of the current metering data and the preset reference value, obtain the error of the current metering data, and use the error as the metering error of the mutual inductor at the current moment to monitor the metering error of the mutual inductor in real time.

[0016] The present invention can improve the accuracy of the determined measurement error of the mutual inductor.

[0017] Preferably, the metering data is voltage data or current data.

[0018] According to a second aspect of the present invention, there is provided an online monitoring system for mutual inductor metering errors. The system comprises a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps of the first aspect of the present invention.

[0019] The present invention has the following effects: 1. The corrected metering data determined by the present invention can eliminate the influence of environmental factors, so that when the metering error of the transformer is monitored by using the difference between the corrected value of the metering data collected in real time and the preset reference value, it is possible to avoid misjudging the deviation caused by environmental factors as the deviation caused by the transformer itself, thereby accurately measuring the metering error of the transformer and improving the accuracy of the metering error of the transformer.

[0020] 2. The density feature of the present invention characterizes the data distribution and can reflect environmental factors, such as the influence of temperature changes, so that the determined drift deviation can measure the deviation caused by environmental factors, and then when correcting the measurement data based on the drift deviation, the influence of environmental factors can be eliminated.

[0021] 3. The present invention utilizes the data distribution in the target set to map the density characteristics of the corresponding measurement data, which can avoid accidental errors in single measurement data and ensure the accuracy of the density characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a schematic flow chart of the steps of the online monitoring method for mutual inductor metering error according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 The online monitoring method of mutual inductor measurement error includes steps S1 to S5, which are as follows: S1: Obtain some measurement data of the transformer.

[0026] In an exemplary embodiment of the present invention, the acquired metering data of the transformer is voltage data or current data.

[0027] It should be noted that the use of transformers to obtain metering data can monitor the load changes of the power system in real time, realize real-time monitoring and data analysis of the entire power system, and improve the efficiency of power management. The transformer mainly focuses on parameters such as current and voltage. Therefore, the present invention uses the current or voltage collected by the transformer as the data basis to analyze the metering error of the transformer.

[0028] Specifically, the acquisition frequency can be set to 50 Hz, and voltage data or current data of the transformer can be collected during the sampling period to obtain some measurement data of the transformer. This embodiment does not specifically limit the data acquisition frequency.

[0029] Next, taking voltage data as an example, the specific content of the present invention is described in detail.

[0030] S2: taking a set of multiple measurement data collected before the sampling time corresponding to any measurement data as the target set of any measurement data, and determining the local similarity of each data in the target set, where the local similarity represents the similarity between the corresponding data and several previous data.

[0031] It should be noted that when using a mutual inductor to measure voltage, as the number of electrical devices increases, the voltage in the line will gradually increase, causing the line temperature to increase, and the increase in line temperature will affect the resistance of the line, and then affect the voltage. That is, as the electrical devices continue to operate, the voltage data in the line will gradually change. Therefore, the present invention utilizes this feature and can effectively identify abnormal data fluctuations that occur during the detection process by analyzing the similarities of the collected voltage data in a local range.

[0032] The target set refers to a set of multiple consecutive measurement data. The total number of data in the target set It can be set according to specific circumstances. =50.

[0033] In an exemplary embodiment of the present invention, the determination of the local similarity of any data in the target set can be achieved by the following steps: Calculate the sum of the squares of the differences between any data in the target set and several measurement data before the corresponding sampling time of the data, take the average value, and use the opposite of the average value as the power of the natural exponential function to perform power operation to obtain the local similarity of the data.

[0034] The natural exponential function refers to the natural constant The calculation process of the local similarity of any data in the target set of any measurement data is as follows: first, the measurement data collected in step S1 can be used as target data in sequence, and the measurement data before the sampling time corresponding to each target data can be used as the target set of target data. It should be noted that the total number of data in the target set of all target data is They are all the same, both are 50.

[0035] Then, the local similarity of any target data can be calculated; specifically, the local similarity of any target data satisfies the following relationship: ; In the formula, For the The local similarity of target data; For the The value of the target data; For the The target set of target data The value of the data; for the summation symbol; is the total number of data in the target set; The natural constant An exponential function with base .

[0036] in, Representative The difference in value between a target data and the data in the target set of the target data. The larger the value, the greater the difference between the target data and the data in the target set, and the smaller the local similarity of the corresponding target data.

[0037] Finally, the local similarity calculation formula can be used to calculate the local similarity of each data in the target set of any target data. When calculating the local similarity of any data in the target set of any target data, several measurement data before the sampling time corresponding to the any data can be used as the target set of the any data, so as to calculate the local similarity of the any data using the local similarity calculation formula, and then obtain the local similarity of each data in the target set.

[0038] In another embodiment, the similarity between any data in the target set and several previous data may also be determined by other calculation formulas, such as by using the absolute value of the difference instead of the sum of squares of the difference.

[0039] S3: Taking the local similarity of each data in the target set as the weight, weighted averaging the data in the target set is performed to obtain the density characteristics of any measurement data.

[0040] It should be noted that the interference of environmental factors on the transformer measurement data is usually manifested as non-uniform distribution, thereby affecting the overall distribution characteristics of the data. Therefore, in order to calculate the deviation caused by the external environment, it is necessary to obtain the distribution of all metering data in the time series, and since a single metering data may have accidental errors, the present invention uses the data distribution in the target set to map the density characteristics of the corresponding metering data.

[0041] Optionally, taking the local similarity of each data in the target set as a weight and performing weighted averaging on the data in the target set can more accurately reflect the true state of the target set, and data with higher local similarity contributes relatively more to the density feature, thus ensuring the accuracy of the density feature.

[0042] In an exemplary embodiment of the present invention, the determination of the density feature of the metrology data may be achieved by the following steps: Calculate the density feature of the target set of any measurement data, and use the density feature of the target set as the density feature of the measurement data; the density feature of the target set satisfies the following relationship: ; In the formula, For the The density characteristics of the target set of measurement data; For the In the target set of measurement data The local similarity of data; For the In the target set of measurement data The value of the data; The total number of data in the target collection.

[0043] It should be noted that since the single metering data and the corresponding target set are data of adjacent time periods, and the density feature of the target set reflects the influence of current environmental factors, such as temperature changes, it can better adapt to environmental changes and reflect the state in the same environment as the corresponding metering data. Therefore, this embodiment uses the density feature of the target set as the density feature of the corresponding metering data.

[0044] Furthermore, after the density feature of the target set of any metering data is determined, the density feature can be used as the density feature of the any metering data, and then the density features of all metering data can be calculated.

[0045] S4: Using the normalized value of the difference in density features of adjacent data in the target set, weight the difference in density features of two corresponding adjacent data, and sum them up to obtain the drift deviation of any measurement data.

[0046] Optionally, the difference in density features of adjacent data in the target set can be determined using a calculation formula that takes the absolute value after subtraction, or can be determined using a calculation formula for a ratio. This embodiment does not specifically limit the method for determining the difference in density features of adjacent data.

[0047] Specifically, the drift deviation of any measurement data satisfies the following relationship: ; In the formula, For the Drift deviation of measurement data; , Respectively In the target set of measurement data Data and Density characteristics of the data; is the absolute value symbol; is the total number of data in the target set; is the hyperbolic tangent function.

[0048] in, Reflects the The difference in density characteristics of adjacent data in the target set of measurement data; It reflects the normalized value of the difference in density features of adjacent data in the target set. The larger the value, the greater the weight of the difference in density features of the corresponding adjacent data. Conversely, the smaller the value, the smaller the weight of the difference in density features of the corresponding adjacent data.

[0049] It reflects the difference in density characteristics of adjacent data in the target set. When the difference is negative, it means that with the change of time, the measurement data measured by the mutual inductor is gradually deviating in a smaller direction; when the difference is positive, it means that with the change of time, the measurement data measured by the mutual inductor is gradually deviating in a larger direction.

[0050] You can The value range of is mapped between (0,2), which can reduce the amount of calculation and reduce the difficulty of calculation.

[0051] S5: Calculate a correction value of any metering data, where the correction value is negatively correlated with the drift deviation, so as to monitor the metering error of the transformer according to the difference between the correction value of the metering data collected in real time and the preset reference value.

[0052] In an exemplary embodiment of the present invention, the determination of the correction value of the metrology data can be achieved by the following steps: The ratio of any measurement data to the drift deviation of the measurement data is calculated to obtain the correction value of any measurement data.

[0053] In another embodiment, the correction value of the measurement data may also be determined by other calculation formulas, such as by a difference calculation formula instead of a ratio calculation formula.

[0054] In an exemplary embodiment of the present invention, the measurement error of the transformer can be monitored by the following steps: Obtain a preset reference value, calculate the difference between the correction value of the current metering data and the preset reference value, obtain the error of the current metering data, and use the error as the metering error of the mutual inductor at the current moment to monitor the metering error of the mutual inductor in real time.

[0055] Optionally, the difference between the corrected value of the current metering data and the preset reference value may be the absolute value of the difference between the corrected value and the reference value, or the ratio between the corrected value and the reference value. This embodiment does not specifically limit the method for determining the difference before and after the correction of the metering data collected in real time. The preset reference value is the ideal value set according to the requirements in the actual application scenario. This embodiment does not specifically limit the value of the preset reference value.

[0056] It should be noted that the present invention uses drift deviation to correct the measurement data, taking into account the influence of environmental factors, and can avoid detecting the differences caused by environmental factors as the measurement error of the mutual inductor, thereby improving the accuracy of the determined measurement error of the mutual inductor.

[0057] Furthermore, after the measurement error of the transformer is determined, the data measured by the transformer can be corrected by temperature compensation, thereby reducing or eliminating the measurement error, so as to adjust the output of the transformer in real time and keep the measurement value of the transformer consistent with the preset reference value.

[0058] The present invention also provides an online monitoring system for transformer metering errors. The system includes a memory and a processor, and a computer program is stored in the memory. The computer program integrates the functions of an online monitoring method for transformer metering errors. When the computer program is executed, the accuracy of the determined transformer metering error can be improved through the online monitoring method for transformer metering errors.

[0059] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0060] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for online monitoring of mutual inductor measurement error, characterized in that: include: Obtain some measurement data of the transformer; A set of multiple measurement data collected before the sampling time corresponding to any measurement data is used as the target set of the measurement data, and the local similarity of each data in the target set is used as a weight to perform weighted averaging on the data in the target set to obtain the density feature of the measurement data, wherein the local similarity represents the similarity between the corresponding data and several previous data; Using the normalized value of the difference in density features of adjacent data in the target set, weighting the difference in density features of two corresponding adjacent data, and summing them up to obtain the drift deviation of any measurement data; A correction value of any of the metering data is calculated, wherein the correction value is negatively correlated with the drift deviation, so as to monitor the metering error of the mutual inductor according to the difference between the correction value of the metering data collected in real time and a preset reference value.

2. The method for online monitoring of mutual inductor measurement error according to claim 1, characterized in that: The drift deviation of any measurement data satisfies the following relationship: ； In the formula, For the Drift deviation of measurement data; , Respectively In the target set of measurement data Data and Density characteristics of the data; is the absolute value symbol; is the total number of data in the target set; is the hyperbolic tangent function.

3. The method for online monitoring of mutual inductor measurement error according to claim 1, characterized in that: The method for obtaining the local similarity degree comprises: The sum of squares of the differences between any data in the target set and several measurement data before the corresponding sampling time of the data is calculated, and the average value is taken. The opposite of the average value is used as the power of the natural exponential function to perform power operation to obtain the local similarity of the data.

4. The method for online monitoring of mutual inductor measurement error according to claim 3, characterized in that: The method for obtaining the density feature of any measurement data comprises: Calculate the density feature of a target set of any measurement data, and use the density feature of the target set as the density feature of the measurement data; The density characteristics of the target set satisfy the following relationship: ； In the formula, For the The density characteristics of the target set of measurement data; For the In the target set of measurement data The local similarity of data; For the In the target set of measurement data The value of each data; The total number of data in the target collection.

5. The method for online monitoring of mutual inductor measurement error according to claim 1, characterized in that: The method for obtaining the correction value of any measurement data comprises: The ratio of any measurement data to the drift deviation of the measurement data is calculated to obtain a correction value of the any measurement data.

6. The method for online monitoring of mutual inductor measurement error according to claim 5, characterized in that: The method of monitoring the metering error of the transformer according to the difference between the correction value of the metering data collected in real time and the preset reference value includes: Obtain a preset reference value, calculate the difference between the correction value of the current metering data and the preset reference value, obtain the error of the current metering data, and use the error as the metering error of the mutual inductor at the current moment to monitor the metering error of the mutual inductor in real time.

7. The method for online monitoring of mutual inductor measurement error according to claim 1, characterized in that: The metering data is voltage data or current data.

8. The online monitoring system for mutual inductor measurement error is characterized by: The transformer metering error online monitoring system comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

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