GIS defect type diagnosis method

Abstract

The invention relates to the technical field of GIS defect type diagnosis, and discloses a GIS defect type diagnosis method. In actual use, n groups of original waveform data of a GIS are obtained through two measurement modes, and a corresponding partial discharge signal pulse sequence is obtained through the n groups of original waveform data; then, correlation coefficients between the characteristic values of the two partial discharge signal pulse sequences corresponding to the two measurement modes are calculated respectively, and finally, the correlation coefficients of different characteristic values are analyzed, so that the type of the GIS fault can be accurately detected on the basis of accurately detecting the GIS fault.

Description

Technical Field

[0001] The present invention relates to the technical field of GIS defect type diagnosis, and in particular to a GIS defect type diagnosis method. Background Art

[0002] In recent years, with the rapid development of urbanization and the large-scale construction of high-voltage power grids, power accidents caused by gas insulated switchgear (GIS) failures have also increased, directly affecting the safety of energy transmission channels and the stability of the entire power grid system. GIS failures are mainly caused by insulation defects that occur during production, transportation, assembly and operation.

[0003] At present, most GIS faults are detected by only one single method. However, the problems are that first, the detection accuracy is low and the GIS fault information cannot be accurately detected. Second, the GIS fault type cannot be accurately determined. The GIS fault types include suspended discharge, tip discharge, surface discharge, air gap discharge and particle discharge. Summary of the invention

[0004] In view of the shortcomings of the background technology, the present invention provides a method for diagnosing defect types of GIS. The technical problem to be solved is that the current GIS fault detection is inaccurate and the specific fault type cannot be detected.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a GIS defect type diagnosis method, comprising the following steps:

[0006] S1: GIS is measured by two measurement methods to obtain n groups of original waveform data respectively;

[0007] S2: for each set of original waveform data, generate a corresponding partial discharge signal pulse sequence;

[0008] S3: extracting characteristic values ​​of the local discharge signal pulse sequence corresponding to each set of original waveform data, wherein the characteristic values ​​include a maximum discharge amplitude, an average discharge value, a discharge number, and a discharge repetition rate;

[0009] S4: respectively calculating the correlation coefficients between the maximum discharge amplitudes, the correlation coefficients between the discharge average values, the correlation coefficients between the discharge times and the correlation coefficients between the discharge repetition rates of the partial discharge signal pulse sequences corresponding to the two measurement methods;

[0010] S5: Based on the correlation coefficient between the maximum discharge amplitudes of two partial discharge signal pulse sequences corresponding to the two measurement methods, the correlation coefficient between the discharge average values, the correlation coefficient between the discharge times and the correlation coefficient between the discharge repetition rates, the fault type of the GIS is diagnosed and the fault type of the GIS is determined.

[0011] In a certain implementation manner, in step S1, a UHF sensor and a fluorescent fiber optic sensor are used to measure the GIS to obtain raw waveform data.

[0012] In some implementations, the formula for calculating the correlation coefficient r in step S4 is as follows:

[0013]

[0014] where x i and i is a characteristic value of the local discharge signal pulse sequence corresponding to the two i-th groups of original waveform data;

[0015] is the average value of a characteristic value of a partial discharge signal pulse sequence corresponding to n groups of original waveform data;

[0016] σ x and σ y is the standard deviation of a characteristic value of the partial discharge signal pulse sequence corresponding to n groups of original waveform data.

[0017] In some implementations, the original waveform data includes discharge time data and discharge voltage value data;

[0018] The process of step S2 is as follows: the time interval and amplitude difference between two adjacent discharge pulses are calculated according to each set of original waveform data, so as to generate a partial discharge signal pulse sequence.

[0019] In a certain embodiment, the method of extracting the characteristic value of the partial discharge signal pulse sequence corresponding to each set of original waveform data in step S3 is as follows:

[0020] First, m discharge cycles are selected from the local discharge signal pulse sequence corresponding to each set of original waveform data;

[0021] For the maximum amplitude amplification, the extraction method is as follows: first obtain the maximum discharge amplitude in each discharge cycle, then calculate the average value of the maximum discharge amplitudes of m discharge cycles, and the average value is the maximum discharge amplitude;

[0022] For the discharge average value, the extraction method is as follows: calculate the arithmetic average value of the discharge pulse amplitudes in m discharge cycles, and the arithmetic average value is the discharge average value;

[0023] For the number of discharges, the extraction method is as follows: count the number of all discharge pulses in m discharge cycles, and the number of all discharge pulses is the number of discharges;

[0024] For the discharge repetition rate, the extraction method is as follows: the number of discharges is divided by the total time to obtain the discharge repetition rate.

[0025] In a certain implementation manner, the correlation coefficient of the maximum discharge amplitudes corresponding to the two measurement methods is recorded as r1, the correlation coefficient of the average discharge values ​​corresponding to the two measurement methods is recorded as r2, the correlation coefficient of the number of discharges corresponding to the two measurement methods is recorded as r3, and the correlation coefficient of the discharge repetition rates corresponding to the two measurement methods is recorded as r4. Then, the process of diagnosis in step S5 is as follows:

[0026] If r1 is greater than 0.9, r2 is greater than 0.8 and less than 0.9, r3 is greater than 0.7 and less than 0.8, and r4 is equal to 1, it is considered that the GIS has a suspended discharge fault;

[0027] If r1 is greater than 0.6 and less than 0.85, r2 is greater than 0.65 and less than 0.75, r3 is greater than 0.6 and less than 0.75, and r4 is greater than 0.55 and less than 0.65, the GIS is considered to have a tip discharge fault;

[0028] If r1 is greater than 0.8 and less than 0.9, r2 is greater than 0.75 and less than 0.8, r3 is greater than 0.85 and less than 0.95, and r4 is greater than 0.6 and less than 0.65, it is considered that the GIS has a surface discharge fault;

[0029] If r1, r2, r3 and r4 are all 0, it is considered that there is an air gap discharge fault in the GIS;

[0030] If r1, r2, r3 and r4 are all greater than 0.8, it is considered that there is a particle discharge fault in the GIS.

[0031] Compared with the prior art, the present invention has the following beneficial effects: in actual use, the present invention obtains n groups of original waveform data of GIS through two measurement methods, and obtains corresponding local discharge signal pulse sequences through the n groups of original waveform data, then respectively calculates the correlation coefficients between the characteristic values ​​of two local discharge signal pulse sequences corresponding to the two measurement methods, and finally analyzes the correlation coefficients of different characteristic values, so that the fault type of GIS can be accurately detected on the basis of accurately detecting the GIS fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the present invention in an embodiment. DETAILED DESCRIPTION

[0033] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0034] like Figure 1 As shown, a GIS defect type diagnosis method includes the following steps:

[0035] S1: Measure GIS using two measurement methods to obtain n groups of original waveform data respectively.

[0036] Specifically, in this embodiment, in step S1, a UHF sensor and a fluorescent fiber optic sensor are used to measure the GIS to obtain original waveform data.

[0037] In actual use, the two methods of measurement can ensure the accuracy of GIS fault detection through the probability of GIS fault being detected. On the other hand, the data of the two measurement methods can be processed through the following steps to obtain the specific GIS fault type.

[0038] In actual use, the value of n is set according to actual needs and is not shown here. Exemplary n can be 5, 10, 15 or other values. When the value of n is larger, the reliability of the correlation coefficient obtained in the following steps is higher.

[0039] Specifically, in this embodiment, the corresponding relationship between the n groups of original waveform data respectively obtained by the two measurement methods is as follows: the n groups of original waveform data have a one-to-one corresponding installation and acquisition sequence.

[0040] S2: For each set of original waveform data, a corresponding partial discharge signal pulse sequence is generated.

[0041] Wherein, for the original waveform data in step S1, the original waveform data includes discharge time data and discharge voltage value data;

[0042] The process of step S2 is as follows: the time interval and amplitude difference between two adjacent discharge pulses are calculated according to each set of original waveform data, so as to generate a pulse partial discharge signal pulse sequence, wherein a partial discharge signal pulse sequence is generated for each measurement mode.

[0043] S3: extracting characteristic values ​​of the local discharge signal pulse sequence corresponding to each set of original waveform data, wherein the characteristic values ​​include a maximum discharge amplitude, an average discharge value, a discharge number of times and a discharge repetition rate.

[0044] Specifically, in this embodiment, the method of extracting the characteristic value of the partial discharge signal pulse sequence corresponding to each group of original waveform data in step S3 is as follows:

[0045] First, m discharge cycles are selected from the partial discharge signal pulse sequence corresponding to each set of original waveform data, where m is a positive integer, and an exemplary m is 50;

[0046] For the maximum amplitude amplification, the extraction method is as follows: first obtain the maximum discharge amplitude in each discharge cycle, then calculate the average value of the maximum discharge amplitudes of m discharge cycles, and the average value is the maximum discharge amplitude;

[0047] For the discharge average value, the extraction method is as follows: calculate the arithmetic average value of the discharge pulse amplitudes in m discharge cycles, and the arithmetic average value is the discharge average value;

[0048] For the number of discharges, the extraction method is as follows: count the number of all discharge pulses in m discharge cycles, and the number of all discharge pulses is the number of discharges;

[0049] For the discharge repetition rate, the extraction method is as follows: the number of discharges is divided by the total time to obtain the discharge repetition rate.

[0050] S4: respectively calculating the correlation coefficients between the maximum discharge amplitudes, the correlation coefficients between the average discharge values, the correlation coefficients between the discharge times and the correlation coefficients between the discharge repetition rates of the partial discharge signal pulse sequences corresponding to the two measurement methods.

[0051] Specifically, in this embodiment, the formula for calculating the correlation coefficient r in step S4 is as follows:

[0052]

[0053] where x i and i is a characteristic value of the local discharge signal pulse sequence corresponding to the two i-th groups of original waveform data;

[0054] is the average value of a characteristic value of a partial discharge signal pulse sequence corresponding to n groups of original waveform data;

[0055] σ x and σ y is the standard deviation of a characteristic value of the partial discharge signal pulse sequence corresponding to n groups of original waveform data.

[0056] Taking the calculation of the maximum discharge amplitude as an example, let n be 5 and m be 50. For the first measurement method, for each set of original waveform data, first obtain the maximum discharge amplitude in 50 discharge cycles respectively, and then calculate the average value of the maximum discharge amplitude in 50 discharge cycles. The average value is the maximum discharge amplitude of the local discharge signal pulse sequence corresponding to each set of original waveforms. The maximum discharge amplitudes of the local discharge signal pulse sequences corresponding to the five sets of original waveform data are m11, m12, m13, m14 and m15; similarly, obtain the maximum discharge amplitudes of the local discharge signal pulse sequences corresponding to the five sets of original waveform data for the second measurement method, which are recorded as m21, m22, m23, m24 and m25 respectively;

[0057] At this time, based on m11, m12, m13, m14 and m15, the average value and standard deviation of the maximum discharge amplitude corresponding to the first measurement method can be obtained, and based on m21, m22, m23, m24 and m25, the average value and standard deviation of the maximum discharge amplitude corresponding to the second measurement method can be obtained. Then, the above parameters are substituted into the above formula to obtain the correlation coefficient of the maximum discharge amplitudes corresponding to the two measurement methods.

[0058] S5: Based on the correlation coefficient between the maximum discharge amplitudes, the correlation coefficient between the average discharge values, the correlation coefficient between the discharge times and the correlation coefficient between the discharge repetition rates corresponding to the two measurement methods, the fault type of the GIS is diagnosed and the fault type of the GIS is determined.

[0059] Specifically, in this embodiment, the correlation coefficient of the discharge amplitudes corresponding to the two measurement methods is recorded as r1, the correlation coefficient of the discharge average values ​​corresponding to the two measurement methods is recorded as r2, the correlation coefficient of the discharge times corresponding to the two measurement methods is recorded as r3, and the correlation coefficient of the discharge repetition rate corresponding to the two measurement methods is recorded as r4. The process of diagnosis in step S5 is as follows:

[0060] If r1 is greater than 0.9, r2 is greater than 0.8 and less than 0.9, r3 is greater than 0.7 and less than 0.8, and r4 is equal to 1, it is considered that the GIS has a suspended discharge fault;

[0061] If r1 is greater than 0.6 and less than 0.85, r2 is greater than 0.65 and less than 0.75, r3 is greater than 0.6 and less than 0.75, and r4 is greater than 0.55 and less than 0.65, the GIS is considered to have a tip discharge fault;

[0062] If r1 is greater than 0.8 and less than 0.9, r2 is greater than 0.75 and less than 0.8, r3 is greater than 0.85 and less than 0.95, and r4 is greater than 0.6 and less than 0.65, it is considered that the GIS has a surface discharge fault;

[0063] If r1, r2, r3 and r4 are all 0, it is considered that there is an air gap discharge fault in the GIS;

[0064] If r1, r2, r3 and r4 are all greater than 0.8, it is considered that there is a particle discharge fault in the GIS.

[0065] In summary, in actual use, the present invention obtains n groups of original waveform data of GIS through two measurement methods, and obtains the corresponding local discharge signal pulse sequence through the n groups of original waveform data, and then calculates the correlation coefficients between the characteristic values ​​of the two local discharge signal pulse sequences corresponding to the two measurement methods respectively, and finally analyzes the correlation coefficients of different characteristic values, so as to accurately detect the fault type of GIS on the basis of accurately detecting the GIS fault.

[0066] The above is based on the present invention as an inspiration. Through the above description, relevant staff can make various changes and modifications without departing from the technical idea of ​​this invention. The technical scope of this invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A GIS defect type diagnosis method, characterized in that: The steps include: S1: GIS is measured by two measurement methods to obtain n groups of original waveform data respectively; S2: for each set of original waveform data, generate a corresponding partial discharge signal pulse sequence; S3: extracting characteristic values ​​of the local discharge signal pulse sequence corresponding to each set of original waveform data, wherein the characteristic values ​​include a maximum discharge amplitude, an average discharge value, a discharge number, and a discharge repetition rate; S4: respectively calculating the correlation coefficient between the maximum discharge amplitudes, the correlation coefficient between the discharge average values, the correlation coefficient between the discharge times and the correlation coefficient between the discharge repetition rates corresponding to the two partial discharge signal pulse sequences; S5: diagnose the fault type of the GIS based on the correlation coefficient between the maximum discharge amplitudes of the two local discharge signal pulse sequences, the correlation coefficient between the discharge average values, the correlation coefficient between the discharge times and the correlation coefficient between the discharge repetition rates, and determine the fault type of the GIS.

2. A GIS defect type diagnosis method according to claim 1, characterized in that: In step S1, a UHF sensor and a fluorescent fiber optic sensor are used to measure the GIS to obtain n groups of original waveform data.

3. A GIS defect type diagnosis method according to claim 1, characterized in that: The formula for calculating the correlation coefficient r in step S4 is as follows: where x i and i is a characteristic value of the local discharge signal pulse sequence corresponding to the two i-th groups of original waveform data; is the average value of a characteristic value of a partial discharge signal pulse sequence corresponding to n groups of original waveform data; σ x and σ y is the standard deviation of a characteristic value of the partial discharge signal pulse sequence corresponding to n groups of original waveform data.

4. A GIS defect type diagnosis method according to claim 1, characterized in that: The original waveform data includes discharge time data and discharge voltage value data; The process of step S2 is as follows: the time interval and amplitude difference between two adjacent discharge pulses are calculated according to each set of original waveform data, so as to generate a partial discharge signal pulse sequence.

5. A GIS defect type diagnosis method according to claim 4, characterized in that: The method of extracting the characteristic value of the local discharge signal pulse sequence corresponding to each set of original waveform data in step S3 is as follows: m discharge cycles are selected from the local discharge signal pulse sequence corresponding to each set of original waveform data; The maximum amplitude is amplified as follows: first, the maximum discharge amplitude in each discharge cycle is obtained, and then the average value of the maximum discharge amplitudes of m discharge cycles is calculated, and the average value is the maximum discharge amplitude; For the discharge average value, the extraction method is as follows: calculate the arithmetic average value of the discharge pulse amplitudes in m discharge cycles, and the arithmetic average value is the discharge average value; For the number of discharges, the extraction method is as follows: count the number of all discharge pulses in m discharge cycles, and the number of all discharge pulses is the number of discharges; For the discharge repetition rate, the extraction method is as follows: the number of discharges is divided by the total time to obtain the discharge repetition rate.

6. A GIS defect type diagnosis method according to claim 1, characterized in that: The correlation coefficient of the maximum discharge amplitude corresponding to the two measurement methods is recorded as r1, the correlation coefficient of the average discharge value corresponding to the two measurement methods is recorded as r2, the correlation coefficient of the number of discharges corresponding to the two measurement methods is recorded as r3, and the correlation coefficient of the discharge repetition rate corresponding to the two measurement methods is recorded as r4. The diagnosis process of step S5 is as follows: If r1 is greater than 0.9, r2 is greater than 0.8 and less than 0.9, r3 is greater than 0.7 and less than 0.8, and r4 is equal to 1, it is considered that the GIS has a suspended discharge fault; If r1 is greater than 0.6 and less than 0.85, r2 is greater than 0.65 and less than 0.75, r3 is greater than 0.6 and less than 0.75, and r4 is greater than 0.55 and less than 0.65, the GIS is considered to have a tip discharge fault; If r1 is greater than 0.8 and less than 0.9, r2 is greater than 0.75 and less than 0.8, r3 is greater than 0.85 and less than 0.95, and r4 is greater than 0.6 and less than 0.65, it is considered that the GIS has a surface discharge fault; If r1, r2, r3 and r4 are all 0, it is considered that there is an air gap discharge fault in the GIS; If r1, r2, r3 and r4 are all greater than 0.8, it is considered that there is a particle discharge fault in the GIS.

Images