Typical insulation defect discharge mode identification method under complex electric stress of converter transformer valve side

Abstract

The invention discloses a typical insulation defect discharge mode identification method under complex electric stress of a converter transformer valve side, which comprises the following steps of: 1, acquiring a partial discharge signal of the converter transformer valve side, recording the amplitude and the phase of each partial discharge by taking T as a period, and stopping acquisition when a certain discharge characteristic quantity exceeds a normal value required by field operation and maintenance; 2, dividing discharge amplitude intervals, and calculating the discharge times of each interval; 3, normalizing the discharge times of each interval; 4, extracting data features of normalized discharge times; and 5, identifying a discharge mode. According to the method disclosed by the invention, the discharge mode identification of the typical insulation defect is realized by extracting the PRPD spectrogram characteristics under the condition of considering the complex electric stress action of power frequency alternating current, direct current and high-proportion harmonic waves at the valve side of the converter transformer, so that targeted measures can be conveniently taken for different insulation defects in actual operation.

Description

Technical Field

[0001] The present invention belongs to the field of high voltage and insulation technology, and specifically relates to a method for identifying a typical insulation defect discharge mode under complex electrical stress on a converter transformer valve side. Background Art

[0002] The converter transformer is the core equipment of the UHV DC transmission project and an important component connecting the AC and DC systems. The primary side (grid side) winding of the converter transformer is connected to the AC transmission system, and the secondary side (valve side) winding is connected to the DC converter valve, which undertakes the important task of voltage conversion and isolation of the AC and DC systems. Therefore, the reliable operation of the converter transformer is crucial to the safety and stability of the entire transmission system. The State Grid Corporation of China conducted statistics on the faults of converter transformers of different voltage levels from 2006 to 2015 and found that valve side winding faults accounted for 76% of the total faults, among which partial discharge caused by insulation defects was an important factor leading to equipment failure. Domestic and foreign scholars have constructed a series of reliable pattern recognition methods using machine learning methods such as artificial neural networks and support vector machines, as well as cluster analysis (fuzzy clustering, Markov clustering, K-means clustering), fuzzy recognition and other methods.

[0003] However, existing research mainly faces the following difficulties: First, the existing pattern recognition methods are mainly aimed at partial discharge under DC, industrial frequency AC and AC / DC composite voltages, and do not consider the complex electrical stress under superimposed high-proportion harmonics, so it is difficult to apply to the insulation defect discharge scenario on the valve side of the converter transformer; second, in the actual engineering site, the longitudinal insulation voltage waveform on the valve side of the converter transformer cannot be directly obtained, and it is difficult to establish a phase reference for the discharge data, which increases the difficulty of pattern recognition. The typical insulation defects on the valve side of the oil-immersed converter transformer mainly include oil gap discharge without solid insulation, oil-paper insulation discharge with oil gap, and oil-paper insulation discharge without oil gap, so it is very important to effectively identify the specific discharge mode. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for identifying the discharge pattern of typical insulation defects under complex electrical stress on the valve side of a converter transformer. The method of the present invention realizes the identification of typical insulation defect discharge patterns by extracting PRPD spectrum features under the complex electrical stress of industrial frequency AC, DC, and high-proportion harmonics on the valve side of the converter transformer, which is convenient for taking targeted measures for different insulation defects in actual operation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for identifying a typical insulation defect discharge mode under complex electrical stress on the valve side of a converter transformer comprises the following steps:

[0007] Step 1: Collect partial discharge information

[0008] Collect the partial discharge signal on the valve side of the converter transformer, record the amplitude and phase of each partial discharge with a period of T, and stop collecting when a discharge characteristic exceeds the normal value required by on-site operation and maintenance;

[0009] Step 2: Divide the discharge amplitude interval and calculate the number of discharges in each interval

[0010] Extract the maximum discharge amplitude U among all partial discharges m , with U m / N a Divide the discharge amplitude interval into spans and calculate the number of discharges N in each interval i ,i=1,2,....N a , N a Maximum interval number;

[0011] Step 3: Normalize the number of discharges in each interval;

[0012] Extract the maximum value N of the number of discharges in each interval im , the normalized discharge number n i for:

[0013]

[0014] Obtain the normalized data n of the number of discharges in each discharge amplitude interval i ,i=1,2,....N a ;

[0015] Step 4: Extract data features of normalized discharge times

[0016] Taking the kth group as the boundary, the discharge number normalized data n from the 1st group to the kth group i， The maximum value in is n z , where i = 1, 2, ... k; the kth group to the Nth group a Normalized data of discharge times of group n i The maximum value in is n y , where i = k,...N a ; k∈[2,N a -1], and k is the group number with the smallest normalized discharge number except the first and last groups;

[0017] Step 5: Identify the discharge pattern

[0018] When n z ≠1, the discharge mode is pure oil gap discharge;

[0019] When n z =1, and F <n yWhen <1, the discharge mode is oil-paper insulation discharge with oil gap;

[0020] When n z =1, and 0 <n y When ≤F, the discharge mode is oil-paper insulation discharge without oil gap.

[0021] Where F is the mode discrimination threshold, F∈(0,1).

[0022] Preferably, in the first step, a certain discharge characteristic quantity exceeding the normal value required by on-site operation and maintenance means that the cumulative number of discharges exceeds the cumulative limit value N of the discharge number, the cumulative discharge time exceeds the cumulative limit value t of the discharge time, or the acetylene content exceeds the acetylene content limit value P.

[0023] Preferably, in the first step, the method for collecting the local discharge signal on the valve side of the converter transformer adopts a pulse current method, an ultra-high frequency method or an ultrasonic method. The pulse current method has the advantages of high sensitivity and strong anti-interference ability; the ultra-high frequency method has good anti-corona performance and is suitable for online monitoring of local discharge; the ultrasonic method is simple to operate on site and convenient to use; the local discharge signal collection form adopts continuous collection or triggered collection. Continuous collection can obtain all subtle discharge signals and improve the sensitivity of pattern recognition; triggered collection can filter out part of the signal interference caused by environmental reasons and improve the reliability of pattern recognition.

[0024] Preferably, in the first step, T is an integer multiple of 20ms; N≥100. T is an integer multiple of 20ms, that is, an integer multiple of the power frequency period, which is convenient for dividing the amplitude interval of the discharge signal in one or more cycles; N≥100, that is, when the discharge points reach more than 100, judgment and identification are performed in order to reduce the impact of the randomness of partial discharge and improve the reliability of the identification result.

[0025] Preferably, the maximum interval number N a ∈[5,20], when the number of amplitude divisions is greater than or equal to 5, the characteristics of the overall discharge amplitude distribution can be better reflected, and N a The larger the amplitude distribution feature, the more obvious the recognition accuracy; i is the interval number, and the i-th group discharge amplitude interval is [U m / N a *(i-1),U m / N a *i].

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] Taking into account the complex electrical stress of power frequency AC, DC, and high-proportion harmonics on the valve side of the converter transformer, the typical insulation defect discharge mode recognition is realized by extracting the PRPD spectrum features, which is convenient for taking targeted measures for different insulation defects in actual operation. Compared with the prior art, the present invention does not require a large number of training samples, and only requires a small amount of data to determine the value of the field mode distinction threshold F. At the same time, the present invention takes into account the complex electrical stress of high-proportion harmonics superimposed on the valve side of the converter transformer, and is more suitable for the insulation defect discharge scenario on the valve side of the converter transformer than the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the method of the present invention.

[0029] Figure 2 It is the normalized data change curve of discharge times, where (a) is oil insulation, (b) is oil-paper insulation without oil gap, and (c) is oil-paper insulation with oil gap. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0031] The following introduces the implementation case by taking the YY connection longitudinal insulation discharge on the high end of the converter transformer valve side as an example.

[0032] like Figure 1 As shown, the present invention provides a typical insulation defect discharge mode identification method under complex electrical stress on the valve side of a converter transformer, comprising the following steps:

[0033] Step 1: Collect partial discharge information

[0034] Collect the partial discharge signal on the valve side of the converter transformer, record the amplitude and phase of each partial discharge with T as the period, and stop collecting when a certain discharge characteristic exceeds the normal value required by on-site operation and maintenance (including but not limited to the cumulative discharge times exceeding the cumulative discharge times limit N, the cumulative discharge time exceeding the cumulative discharge time limit t, and the acetylene content exceeding the acetylene content limit P), and enter the second step. Among them: the partial discharge signal collection method includes but is not limited to the pulse current method, the ultra-high frequency method, the ultrasonic method, etc., and the partial discharge signal collection form includes but is not limited to continuous collection and triggered collection; T is an integer multiple of 20ms; N≥100.

[0035] In this example, T=20ms, and the discharge characteristic value limit is selected as N=1000.

[0036] Step 2: Divide the discharge amplitude interval and calculate the number of discharges in each interval

[0037] Extract the maximum discharge amplitude U among all partial discharges m , with U m / N a Divide the discharge amplitude interval into spans and calculate the number of discharges N in each interval i ,(i=1,2,....N a ). Among them, N a ∈[5,20], i is the interval number, and the discharge amplitude interval of the i-th group is [U m / N a *(i-1),U m / N a *i].

[0038] In this example: N a =10.

[0039] Step 3: Normalize the number of discharges in each interval

[0040] Extract the maximum value N of the number of discharges in each interval im , the normalized discharge number n i for:

[0041]

[0042] Obtain the normalized data n of the number of discharges in each discharge amplitude interval i ,(i=1,2,....N a ).

[0043] In this example: The normalized data of discharge times for each insulation defect n i (i=1,2,...,10) For example Figure 2 As shown in (a), (b) and (c).

[0044] Step 4: Extract data features of normalized discharge times

[0045] Taking the kth group as the boundary, the discharge number normalized data n from the 1st group to the kth group i The maximum value among (i=1,2,...k) is n z , group k to group N a Normalized data of discharge times of group n i (i=k,...N a ) is the maximum value n y . Where: k∈[2,N a -1], and k is the group number with the smallest normalized discharge number except the first and last groups.

[0046] In this example: for oil insulation, k = 2, and n z =0.02308, n y=1;

[0047] For oil-free paper insulation, k = 9, and n z =1,n y =0.00357;

[0048] For oil-paper insulation with oil gap, k = 7, and n is obtained z =1,n y =0.12367.

[0049] Step 5: Identify the discharge pattern

[0050] When n z ≠1, the discharge mode is pure oil gap discharge;

[0051] When n z =1, and F <n y When <1, the discharge mode is oil-paper insulation discharge with oil gap;

[0052] When n z =1, and 0 <n y When ≤F, the discharge mode is oil-paper insulation discharge without oil gap.

[0053] Where F is the mode discrimination threshold, F∈(0,1). In this example: F=0.12, for oil insulation, n z =0.02308≠1, so the identification result is pure oil gap discharge;

[0054] For oil-free paper insulation, n z =1, and 0 <n y =0.00357≤0.12, so the identification result is oil-paper insulation discharge without oil gap;

[0055] For oil-paper insulation with oil gap, n z =1, and 0.12 <n y =0.12367<1, so the identification result is oil-paper insulation discharge with oil gap.

Claims

1. A method for identifying typical insulation defect discharge patterns under complex electrical stress on the valve side of a converter transformer, characterized in that: The steps include: Step 1: Collect partial discharge information Collect the partial discharge signal on the valve side of the converter transformer, record the amplitude and phase of each partial discharge with a period of T, and stop collecting when a discharge characteristic exceeds the normal value required by on-site operation and maintenance; Step 2: Divide the discharge amplitude interval and calculate the number of discharges in each interval Extract the maximum discharge amplitude U among all partial discharges m , with U m / N a Divide the discharge amplitude interval into spans and calculate the number of discharges N in each interval i ,i=1,2,....N a , N a Maximum interval number; Step 3: Normalize the number of discharges in each interval; Extract the maximum value N of the number of discharges in each interval im , the normalized discharge number n i for: Obtain the normalized data n of the number of discharges in each discharge amplitude interval i ,i=1,2,....N a ; Step 4: Extract the data features of normalized discharge times; Taking the kth group as the boundary, the discharge number normalized data n from the 1st group to the kth group i， The maximum value in is n z , where i = 1, 2, ... k; the kth group to the Nth group a Normalized data of discharge times of group n i The maximum value in is n y , where i = k,...N a ; k∈[2,N a -1], and k is the group number with the smallest normalized discharge number except the first and last groups; Step 5: Identify the discharge pattern; When n z ≠1, the discharge mode is pure oil gap discharge; When n z =1, and F <n y When <1, the discharge mode is oil-paper insulation discharge with oil gap; When n z =1, and 0 <n y When ≤F, the discharge mode is oil-paper insulation discharge without oil gap. Where F is the mode discrimination threshold, F∈(0,1).

2. According to claim 1, a typical insulation defect discharge pattern identification method under complex electrical stress on the valve side of a converter transformer is characterized in that: In the first step, a certain discharge characteristic quantity exceeds the normal value required by on-site operation and maintenance, which means that the cumulative number of discharges exceeds the cumulative limit value N of the discharge number, the cumulative discharge time exceeds the cumulative limit value t of the discharge time, or the acetylene content exceeds the acetylene content limit value P.

3. The method for identifying typical insulation defect discharge patterns under complex electrical stress on the valve side of a converter transformer according to claim 1 is characterized in that: In the first step, the method for collecting the partial discharge signal on the valve side of the converter transformer adopts the pulse current method, the ultra-high frequency method or the ultrasonic method; the partial discharge signal collection form adopts continuous collection or triggered collection.

4. The method for identifying typical insulation defect discharge patterns under complex electrical stress on the valve side of a converter transformer according to claim 1 is characterized in that: In the first step, T is an integer multiple of 20ms; N≥100.

5. The method for identifying typical insulation defect discharge patterns under complex electrical stress on the valve side of a converter transformer according to claim 1 is characterized in that: Maximum interval number N a ∈[5,20], i is the interval number, and the discharge amplitude interval of the i-th group is [U m / N a *(i-1),U m / N a *i].

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