Partial discharge original data filtering method and system

Abstract

The invention relates to a partial discharge original data filtering method, and aims to effectively eliminate inherent bias of a partial discharge acquisition board and improve the accuracy and reliability of partial discharge signal monitoring. The method comprises three main steps that firstly, collected original data are converted from code values to millivolt values, data of a plurality of periods are extracted according to a specific format, and a to-be-filtered data matrix is constructed; secondly, determining a baseline threshold value and a fluctuation threshold value by using a baseline calculation function and a fluctuation coefficient, and providing necessary parameter support for data filtering; and finally, according to the to-be-filtered data and the calculated threshold value, filtering processing is carried out, and partial discharge data with inherent bias removed is obtained. The method has extremely high adaptability and universality, can be applied to various kinds of power equipment, improves the precision of partial discharge monitoring, reduces the misjudgment risk, provides a scientific basis for health management and maintenance of the power equipment, and has remarkable economic benefits and safety guarantee.

Description

TECHNICAL FIELD

[0001] The application relates to a partial discharge original data filtering method, system, storage medium and computing device, and specifically belongs to the technical field of power equipment fault evaluation. BACKGROUND

[0002] Partial discharge is an important indicator of the deterioration of the insulation material of electrical equipment, and its monitoring and analysis are crucial to ensuring the safe and stable operation of power equipment. Partial discharge usually occurs inside or on the surface of the insulation material of electrical equipment, especially in high-voltage equipment, which can reflect the health status of the insulation system and its potential failure risk. Therefore, accurately capturing and analyzing partial discharge signals is of great significance to the maintenance and management of the power industry.

[0003] However, when detecting partial discharge, the inherent bias of the partial discharge acquisition board becomes a technical problem that needs to be solved. These inherent biases may be caused by many factors, including but not limited to the electrical characteristics of the equipment itself, environmental factors such as temperature and humidity, noise in the measurement circuit, and interference during signal transmission. These factors often cause errors in the original data that cannot be ignored, which affects subsequent data analysis and judgment, and may lead to misjudgment or omission of partial discharge activities, thereby posing potential risks to the safe operation of the equipment.

[0004] Therefore, there is an urgent need for an effective filtering method to systematically identify and remove the inherent bias of the partial discharge acquisition board. SUMMARY

[0005] The application provides a partial discharge original data filtering method, system, storage medium and electronic device, which can improve the accuracy of partial discharge evaluation of power equipment and identify and remove the inherent bias of the partial discharge acquisition board.

[0006] TECHNICAL SCHEME

[0007] The application provides a partial discharge original data filtering method, which comprises the following steps:

[0008] Collecting partial discharge original data in code value format, converting the original data into calculable data with a unit of millivolt, setting a calculation period, and extracting a number of periods of calculable data in a fixed format to construct to-be-filtered data;

[0009] Sending a preset deviation coefficient and the to-be-filtered data into a baseline calculation function, ignoring elements in the to-be-filtered data with a fluctuation rate greater than the deviation coefficient, and outputting the average value of the elements that have not been ignored as a baseline threshold; the fluctuation rate is the ratio of the absolute value of the difference between the current element and the average value of all elements in the to-be-filtered data to the standard deviation of all elements in the to-be-filtered data;

[0010] The baseline threshold is multiplied by a preset fluctuation coefficient to obtain a fluctuation threshold; a deviation rate of each element in the to-be-filtered data is calculated, the deviation rate is compared with the fluctuation threshold, elements in the to-be-filtered data with a fluctuation rate less than or equal to the fluctuation threshold are removed, and values of all retained elements are updated as corresponding deviation rates as filtered partial discharge data; the deviation rate is an absolute value of a difference between a current element and the fluctuation threshold.

[0011] Further, the original data is converted into computable data with a unit of millivolt, and a calculation formula is:

[0012]

[0013] wherein, is the computable data, is a code value corresponding to the original data, is a reference code value, is a reference millivolt value.

[0014] Further, the to-be-filtered data is constructed, including:

[0015] The to-be-filtered data is stored in a to-be-filtered data matrix, and is represented as:

[0016]

[0017] wherein, is the to-be-filtered data matrix, is a corresponding element, is an element number, is an element number, , is a total number of phases in one cycle, is a total number of cycles.

[0018] Further, the baseline calculation function includes:

[0019] calculating the to-be-filtered data matrix a mean value of all elements , a standard deviation .

[0020] defining a number statistic count and a sum statistic sum, and setting initial values of the two as 0; traversing the to-be-filtered data matrix , a control variable of the traversal is i, and an initial value is 1; judging whether a fluctuation rate of the i-th element is greater than a deviation coefficient; if yes, skipping the element, and increasing the count by 1; if not, updating a sum value as a sum of the original sum value and , and increasing the count by 1; ending the traversal when the count is greater than .

[0021] calculating a baseline threshold , the baseline threshold being a ratio of a sum statistic sum and a count statistic count;

[0022] the fluctuation rate of the i-th element is expressed as:

[0023] .

[0024] Further, the removing the element with a fluctuation rate less than or equal to the fluctuation threshold from the to-be-filtered data comprises:

[0025] calculating a fluctuation threshold according to the baseline threshold and a preset fluctuation coefficient , the formula being:

[0026]

[0027] traversing the to-be-filtered data, calculating a deviation rate of a corresponding element i from the baseline threshold , the formula being:

[0028]

[0029] judging whether the deviation rate is greater than the fluctuation threshold ; if yes, subtracting the baseline threshold from a value of a corresponding to-be-filtered element; if no, replacing the corresponding to-be-filtered data with 0; being expressed as:

[0030]

[0031] traversing the to-be-filtered data to obtain is the filtered partial discharge data.

[0032] The application further provides a partial discharge original data filtering system, comprising:

[0033] a data construction module, used for collecting partial discharge original data in a code value format, converting the original data into computable data with a unit of millivolt, setting a calculation period, and extracting computable data of a plurality of periods in a fixed format to construct to-be-filtered data;

[0034] ​​​​The volatility threshold module is used to calculate the volatility of each element in the data to be filtered, compare the volatility with a preset deviation coefficient, ignore elements in the data to be filtered whose volatility is greater than the deviation coefficient, and use the average value of all the elements that are not ignored as the volatility threshold; the volatility is the ratio of the absolute value of the difference between the current element and the mean of all elements in the data to be filtered to the standard deviation of all elements in the data to be filtered.

[0035] The filtering module is used to calculate the deviation rate of each element in the data to be filtered, compare the deviation rate with the fluctuation threshold, remove elements in the data to be filtered whose fluctuation rate is less than or equal to the fluctuation threshold, and update the values ​​of all retained elements with the corresponding deviation rate as the filtered partial discharge data; the deviation rate is the absolute value of the difference between the current element and the fluctuation threshold.

[0036] Furthermore, the conversion of raw data into computable data involves converting code values ​​into millivolt values, calculated using the following formula:

[0037]

[0038] in, For computable data, The code value corresponding to the original data. As the base code value, This is the base millivolt value.

[0039] Furthermore, the construction of the data to be filtered includes:

[0040] The data to be filtered is stored in a data matrix to be filtered, represented as follows:

[0041]

[0042] in, The data matrix to be filtered. For the corresponding element, Number the elements. The number of elements , The total number of phases within one period. This represents the total number of cycles.

[0043] Furthermore, the baseline calculation function includes:

[0044] Calculate the data matrix to be filtered mean Standard deviation ;

[0045] Define a count (count) and a sum (sum), and set their initial values ​​to 0; iterate through the matrix of data to be filtered. , the control variable of the traversal is i, and the initial value is 1; it is judged whether the fluctuation of the i-th element is greater than the deviation coefficient; if yes, the element is skipped, and count is increased by 1; if not, the sum value is updated as the sum of the original sum value and , and count is increased by 1; the traversal is ended when count is greater than ;

[0046] The baseline threshold is calculated as the ratio of the sum statistics sum and the number statistics count.

[0047] The fluctuation of the i-th element is represented as:

[0048] .

[0049] Further, the filtering function comprises:

[0050] The fluctuation threshold is calculated according to the baseline threshold and a preset fluctuation coefficient , and the formula is:

[0051]

[0052] The deviation rate of the corresponding element i and the baseline threshold is calculated according to the to-be-filtered data, and the formula is:

[0053] It is judged whether the deviation rate is greater than the fluctuation threshold

[0054] ; if greater, the corresponding to-be-filtered data is subtracted by the baseline threshold; if less than or equal to, the corresponding to-be-filtered data is replaced by 0; and it is represented as:

[0055] The to-be-filtered data obtained after the traversal is completed is the filtered partial discharge data.

[0056] Advantages:

[0057] The application effectively eliminates the inherent bias of the partial discharge collection plate through systematic data conversion and threshold calculation, accurately extracts the real characteristics of the partial discharge signal, ensures that the collected data is real and effective, and improves the judgment accuracy of the health state of the power equipment.

[0058]

[0059] ​​​​​The application can dynamically adjust the filtering strategy according to the partial discharge characteristics of different equipment and working conditions through the calculation of the baseline threshold and the fluctuation threshold, thereby retaining the effective signal to the maximum extent and eliminating the interference noise, providing important data support for the long-term health management of the equipment, thereby effectively prolonging the service life of the equipment and reducing the maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A partial discharge raw data filtering method flow chart;

[0061] Figure 2 A waveform data graph obtained by sampling a partial discharge sampling board;

[0062] Figure 3 A waveform data graph obtained by sampling the method of the application. DETAILED DESCRIPTION

[0063] The application will be further illustrated below in combination with the drawings and specific embodiments.

[0064] Embodiment 1

[0065] As shown in the drawings, Figure 1 a partial discharge raw data filtering method comprises the following steps:

[0066] Step 1, data conversion.

[0067] The partial discharge raw data (code value) collected by the partial discharge collection board is converted into calculable data (millivolt value), and a plurality of periods of calculable data are extracted according to a given format to construct filtering data to be stored in a filtering data matrix. The period is the change period of alternating current, and the period of 50Hz alternating current is 20 milliseconds. 360 points are taken for one period, that is, one partial discharge raw data is taken for 1 degree phase to form one period, and a plurality of periods are taken to form the required filtering data. The partial discharge raw data is converted into calculable data, and the conversion formula is:

[0068]

[0069] wherein, is the calculable data, is the code value corresponding to the calculable data, is the reference code value, is the reference millivolt value, in this embodiment, the sampling board AD bit number is 14 bits, the range is -2V~2V, the reference code value is 2 14 / 2=8192, and the reference millivolt value is 2000mV.

[0070] The filtering data matrix to be filtered can be represented as:

[0071]

[0072] in, The data matrix to be filtered. For the corresponding element, Number the elements. The number of elements , The total number of phases within one period. This represents the total number of cycles.

[0073] Step 2, threshold calculation.

[0074] Combine the deviation coefficient, the data matrix C to be filtered, and the length of the data to be filtered (i.e., the number of elements in the data matrix to be filtered in step 1). The data is fed into a baseline calculation function to obtain a baseline threshold. Based on the baseline threshold and a preset fluctuation coefficient, a fluctuation threshold is calculated. The deviation coefficient is a predefined threshold; points exceeding the deviation coefficient are filtered out, thus achieving the filtering purpose.

[0075] The deviation coefficient, the data to be filtered, and the length of the data to be filtered are fed into the baseline calculation function to obtain the baseline threshold. The calculation process of the baseline calculation function is as follows: first, the mean and standard deviation of the data to be filtered are calculated; then, the data to be filtered is traversed, points whose volatility exceeds the deviation coefficient are removed, and the mean is recalculated to obtain the baseline threshold.

[0076] The calculation method for the mean and standard deviation of the data to be filtered can be expressed as follows:

[0077]

[0078] in, The mean of the data to be filtered is... denoted as the standard deviation of the data to be filtered.

[0079] The process of iterating through the data to be filtered, ignoring points where the volatility exceeds the deviation coefficient, is generally a one-dimensional loop structure. First, a count (count=0) and a summation count (sum=0) are defined. Then the loop begins, with the control variable i, ranging from 1 to... The value increments by 1 each time. Then, a judgment process is initiated: it checks if the volatility corresponding to the element is greater than the deviation coefficient. If it is, no action is taken. If it is less than or equal to the deviation coefficient, then `sum+=` is executed. The count is incremented by 1. The judgment ends, and the loop ends. Finally, the baseline threshold is... The calculation formula is expressed as:

[0080]

[0081] The method for calculating the volatility of the corresponding element is expressed as follows:

[0082]

[0083] wherein, is the fluctuation rate of the corresponding element.

[0084] The fluctuation threshold is calculated according to the baseline threshold and the preset fluctuation coefficient. The calculation method of the fluctuation threshold is represented as: wherein, is the fluctuation threshold, is the fluctuation coefficient, and the value of the fluctuation coefficient in the embodiment is 0.05.

[0085] Step 3, filtering processing.

[0086] The data matrix C to be filtered, the length of the data to be filtered, the baseline threshold B and the fluctuation threshold T in step 1 are sent into the filtering function, and finally the filtered partial discharge data is obtained. The calculation process of this step is generally a one-dimensional loop structure. First, the data to be filtered is traversed, the deviation rate of the corresponding element from the baseline threshold is calculated, then it is judged whether the deviation rate is greater than the fluctuation threshold, if yes, the corresponding data to be filtered is subtracted from the baseline threshold, if no, the corresponding data to be filtered is replaced by 0. The new data obtained is the filtered partial discharge data.

[0087] The calculation formula of the deviation rate of the corresponding element is represented as:

[0088]

[0089]

[0090] wherein, is the deviation rate of the corresponding element.

[0091] The construction method of the filtered partial discharge data element is represented as:

[0092]

[0093] wherein, is the filtered partial discharge data.

[0094] In order to verify the improvement of the method of the present application compared with the prior art, in this embodiment, the original data of the same group of partial discharge signals is processed based on the same experimental conditions, and the results are shown in Figure 2 , Figure 3 respectively. Among them, Figure 2 ​The waveform data graph collected by the partial discharge sampling plate used in the prior art, each point in the graph is a set of partial discharge data, representing a waveform position. It can be observed from the graph that there is a horizontal line of interference in the collected waveform data graph, which is the inherent bias of the sampling plate, which will have a negative impact on the diagnosis of the type of partial discharge. Figure 3 The waveform data graph obtained after the method of the present application processes the partial discharge raw data. It can be seen from the graph that, compared with the prior art, the method of the present application filters out interference and eliminates inherent bias, obtaining more accurate waveform data.

[0095] Example 2

[0096] Based on the technical solution of Example 1, the present application also discloses a computer software system of the above method, i.e. a partial discharge raw data filtering system, which comprises:

[0097] The data conversion module converts the partial discharge raw data (code value) collected by the partial discharge collection plate into calculable data (millivolt value), and extracts a number of periods of calculable data according to a given format to construct the data to be filtered.

[0098] The threshold calculation module inputs the deviation coefficient, the data to be filtered and the length of the data to be filtered into a baseline calculation function to obtain a baseline threshold, and calculates a fluctuation threshold according to the baseline threshold and a preset fluctuation coefficient.

[0099] The filtering processing module calculates the fluctuation threshold, eliminates elements in the data to be filtered whose fluctuation rate is less than or equal to the fluctuation threshold, and updates the values of all the remaining elements to the corresponding deviation rate as the filtered partial discharge data.

Claims

1. A method for filtering raw partial discharge data, characterized in that, include: The raw partial discharge data in code value format is collected, converted into calculable data in millivolts, the calculation period is set, and calculable data of several periods are extracted in a fixed format to construct the data to be filtered. The preset deviation coefficient and the data to be filtered are fed into the baseline calculation function. Elements in the data to be filtered whose volatility is greater than the deviation coefficient are ignored. The average value of the elements that are not ignored is output as the baseline threshold. The volatility is the ratio of the absolute value of the difference between the current element and the mean of all elements in the data to be filtered to the standard deviation of all elements in the data to be filtered. The baseline threshold is multiplied by a preset fluctuation coefficient to obtain the fluctuation threshold; The deviation rate of each element in the data to be filtered is calculated, the deviation rate is compared with the fluctuation threshold, elements in the data to be filtered whose fluctuation rate is less than or equal to the fluctuation threshold are removed, and the values ​​of all the remaining elements are updated with the corresponding deviation rates as the filtered partial discharge data; the deviation rate is the absolute value of the difference between the current element and the fluctuation threshold.

2. The partial discharge raw data filtering method according to claim 1, characterized in that, The process of converting the raw data into computable data in millivolts is described by the following formula: in, For computable data, The code value corresponding to the original data. As the base code value, This is the base millivolt value.

3. The partial discharge raw data filtering method according to claim 2, characterized in that, The construction of the data to be filtered includes: The data to be filtered is stored in a data matrix to be filtered, represented as follows: in, The data matrix to be filtered. For the corresponding element, Number the elements. The number of elements , The total number of phases within one period. This represents the total number of cycles.

4. The partial discharge raw data filtering method according to claim 3, characterized in that, The baseline calculation function includes: Calculate the data matrix to be filtered Mean of all elements Standard deviation ; Define a count (count) and a sum (sum), and set their initial values ​​to 0; iterate through the matrix of data to be filtered. The control variable for iterating is i, initialized to 1; check the i-th element. Check if the volatility is greater than the deviation coefficient; if so, skip the element and increment the count by 1; if not, update the sum value to the sum of the original sum value and the sum of the deviation coefficient. The sum of the numbers increments the count by 1; when the count is greater than 1, the count increments by 1. End the traversal at this time; Calculate baseline threshold The baseline threshold is the ratio of the summation statistic (sum) to the count statistic (count); The i-th element volatility Represented as: 。 5. The partial discharge raw data filtering method according to claim 4, characterized in that, The process of removing elements from the data to be filtered whose volatility is less than or equal to the volatility threshold includes: Based on baseline threshold and preset volatility coefficient Calculate the fluctuation threshold The formula is: Iterate through the data to be filtered and calculate the deviation rate of the corresponding element i from the baseline threshold. The formula is: Determine the deviation rate Is it greater than the fluctuation threshold? ;like If the value of the corresponding element to be filtered is subtracted from the baseline threshold; if If the value is 0, then the corresponding data to be filtered will be replaced with 0; this is represented as: The result of the traversal This is the filtered partial discharge data.

6. A partial discharge raw data filtering system, characterized in that, include: The data construction module is used to collect partial discharge raw data in code value format, convert the raw data into calculable data in millivolts, set the calculation period, and extract calculable data of several periods in a fixed format to construct the data to be filtered. The volatility threshold module is used to calculate the volatility of each element in the data to be filtered, compare the volatility with a preset deviation coefficient, ignore elements in the data to be filtered whose volatility is greater than the deviation coefficient, and use the average value of all the elements that are not ignored as the volatility threshold; the volatility is the ratio of the absolute value of the difference between the current element and the mean of all elements in the data to be filtered to the standard deviation of all elements in the data to be filtered. The filtering module is used to calculate the deviation rate of each element in the data to be filtered, compare the deviation rate with the fluctuation threshold, remove elements in the data to be filtered whose fluctuation rate is less than or equal to the fluctuation threshold, and update the values ​​of all retained elements with the corresponding deviation rate as the filtered partial discharge data; the deviation rate is the absolute value of the difference between the current element and the fluctuation threshold.

7. The partial discharge raw data filtering system according to claim 6, characterized in that, The process of converting raw data into computable data involves converting code values ​​into millivolt values, calculated using the following formula: in, For computable data, The code value corresponding to the original data. As the base code value, This is the base millivolt value.

8. The partial discharge raw data filtering system according to claim 7, characterized in that, The construction of the data to be filtered includes: The data to be filtered is stored in a data matrix to be filtered, represented as follows: in, The data matrix to be filtered. For the corresponding element, Number the elements. The number of elements , The total number of phases within one period. This represents the total number of cycles.

9. The partial discharge raw data filtering system according to claim 8, characterized in that, The baseline calculation function includes: Calculate the data matrix to be filtered mean Standard deviation ; Define a count (count) and a sum (sum), and set their initial values ​​to 0; iterate through the matrix of data to be filtered. The control variable for iterating is i, initialized to 1; check the i-th element. Check if the volatility is greater than the deviation coefficient; if so, skip the element and increment the count by 1; if not, update the sum value to the sum of the original sum value and the sum of the deviation coefficient. The sum of the numbers increments the count by 1; when the count is greater than 1, the count increments by 1. End the traversal at this time; Calculate baseline threshold The baseline threshold is the ratio of the summation statistic (sum) to the count statistic (count); The i-th element volatility Represented as: 。 10. The partial discharge raw data filtering system according to claim 9, characterized in that, The filtering function includes: Based on baseline threshold and preset volatility coefficient Calculate the fluctuation threshold The formula is: Iterate through the data to be filtered and calculate the deviation rate of the corresponding element i from the baseline threshold. The formula is: Determine if the deviation rate is greater than the fluctuation threshold. If the value is greater than the baseline threshold, the corresponding data to be filtered is subtracted from the baseline threshold; if the value is less than or equal to the baseline threshold, the corresponding data to be filtered is replaced with 0; this is represented as: The result of the traversal This is the filtered partial discharge data.

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