CN110632477A - A Vibration and Acoustic Detection Method and System for Transformer Operating State Using Hilbert Space Factor - Google Patents

A Vibration and Acoustic Detection Method and System for Transformer Operating State Using Hilbert Space Factor Download PDF

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CN110632477A
CN110632477A CN201911062087.4A CN201911062087A CN110632477A CN 110632477 A CN110632477 A CN 110632477A CN 201911062087 A CN201911062087 A CN 201911062087A CN 110632477 A CN110632477 A CN 110632477A
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transformer
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space factor
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翟明岳
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Guangdong University of Petrochemical Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements

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Abstract

本发明的实施例公开一种利用Hilbert空间因子的变压器运行状态振声检测方法和系统，所述方法包括：步骤1，输入实测的信号序列S；步骤2，根据Hilbert空间因子性质判断变压器运行状态。具体为：如果第K个窗口Hilbert空间因子HK满足判断条件|HK|≥e0，则在所述信号序列S的第K点处，变压器处于非正常运行状态；否则，变压器处于正常运行状态。其中，e0为状态判断阈值。

The embodiment of the present invention discloses a method and system for detecting vibration and sound of a transformer operating state using the Hilbert space factor. The method includes: step 1, inputting the measured signal sequence S; step 2, judging the transformer operating state according to the property of the Hilbert space factor . Specifically: if the Hilbert space factor H K of the Kth window satisfies the judgment condition |H K |≥e 0 , then at the Kth point of the signal sequence S, the transformer is in abnormal operation; otherwise, the transformer is in normal operation state. Among them, e 0 is the state judgment threshold.

Description

一种利用Hilbert空间因子的变压器运行状态振声检测方法 和系统A Vibration and Acoustic Detection Method of Transformer Operating State Using Hilbert Space Factor and system

技术领域technical field

本发明涉及电力领域，特别是涉及一种变压器运行状态振声检测方法及系统。The invention relates to the field of electric power, in particular to a method and system for detecting vibration and sound in the operating state of a transformer.

背景技术Background technique

随着智能电网的高速发展，电力设备安全稳定运行显得尤其重要。目前，对超高压及以上电压等级的电力设备开展运行状态检测，尤其是对异常状态的检测显得愈加重要和迫切。电力变压器作为电力系统的重要组成部分，是变电站中最重要的电气设备之一，其可靠运行关系到电网的安全。With the rapid development of smart grid, the safe and stable operation of power equipment is particularly important. At present, it is more and more important and urgent to carry out the operation state detection of the power equipment with ultra-high voltage and above voltage level, especially the detection of abnormal state. As an important part of the power system, the power transformer is one of the most important electrical equipment in the substation, and its reliable operation is related to the safety of the power grid.

变压器运行状态检测的基本原理是提取变压器运行中的各个特征量，分析、辨识并跟踪特征量以此监测变压器的异常运行状态。当前变压器运行状态的常用检测方法中，包括检测局部放电的脉冲电流法和超声波检测法、检测绕组变形的频率响应法以及检测机械及电气故障的振动检测法等。这些检测方法主要检测变压器绝缘状况及机械结构状况，其中以变压器振动信号(振声)的检测最为全面，对于大部分变压器故障及异常状态均能有所反应。The basic principle of transformer operation state detection is to extract various characteristic quantities in the operation of the transformer, analyze, identify and track the characteristic quantities to monitor the abnormal operation state of the transformer. The current commonly used detection methods for transformer operation status include pulse current method and ultrasonic detection method for partial discharge detection, frequency response method for detection of winding deformation, and vibration detection method for detection of mechanical and electrical faults. These detection methods mainly detect transformer insulation status and mechanical structure status, among which the detection of transformer vibration signal (vibration sound) is the most comprehensive, and can respond to most transformer faults and abnormal states.

虽然变压器振声检测方法在变压器运行状态监测中有着广泛的应用，且技术相对成熟，但是由于振声检测方法利用了变压器发出的振动信号，很容易受到环境噪声的影响，所以此方法在实际工作环境中应用时常常得不到令人满意的结果。Although the transformer vibration and sound detection method is widely used in the monitoring of transformer operation status, and the technology is relatively mature, but because the vibration and sound detection method uses the vibration signal sent by the transformer, it is easily affected by environmental noise, so this method is not suitable for practical work. Often unsatisfactory results are obtained when applied in the environment.

发明内容Contents of the invention

虽然变压器振声检测方法在变压器运行状态监测中有着广泛的应用，且技术相对成熟，但是由于振声检测方法利用了变压器发出的振动信号，很容易受到环境噪声的影响，所以此方法在实际工作环境中应用时常常得不到令人满意的结果。Although the transformer vibration and sound detection method is widely used in the monitoring of transformer operation status, and the technology is relatively mature, but because the vibration and sound detection method uses the vibration signal sent by the transformer, it is easily affected by environmental noise, so this method is not suitable for practical work. Often unsatisfactory results are obtained when applied in the environment.

本发明的目的是提供一种利用Hilbert空间因子的变压器运行状态振声检测方法和系统，所提出的方法利用了不同运行状态下变压器振声信号差值与背景噪声差值在Hilbert空间因子方面的差异，提高了状态监测的性能。所提出的方法具有较好的鲁棒性，计算也较为简单。The purpose of the present invention is to provide a transformer operating state vibration and sound detection method and system utilizing the Hilbert space factor. The proposed method utilizes the difference between the transformer vibration and sound signal difference and the background noise difference in the Hilbert space factor under different operating states. difference, improving condition monitoring performance. The proposed method has better robustness and simpler computation.

为实现上述目的，本发明提供了如下方案：To achieve the above object, the present invention provides the following scheme:

一种利用Hilbert空间因子的变压器运行状态振声检测方法，包括：A vibration-acoustic detection method for a transformer operating state using Hilbert space factors, comprising:

步骤001输入实测的信号序列S；Step 001 inputs the measured signal sequence S;

步骤002根据Hilbert空间因子性质判断变压器运行状态。具体为：如果第K个窗口Hilbert空间因子HK满足判断条件|HK|≥e0，则在所述信号序列S的第K点处，变压器处于非正常运行状态；否则，变压器处于正常运行状态。其中，e0为状态判断阈值。Step 002 judges the operating state of the transformer according to the properties of the Hilbert space factor. Specifically: if the Hilbert space factor H K of the Kth window satisfies the judgment condition |H K |≥e 0 , then at the Kth point of the signal sequence S, the transformer is in abnormal operation; otherwise, the transformer is in normal operation state. Among them, e 0 is the state judgment threshold.

一种利用Hilbert空间因子的变压器运行状态振声检测系统，包括：A transformer operating state vibration and sound detection system using Hilbert space factor, comprising:

获取模块 输入实测的信号序列S；The acquisition module inputs the measured signal sequence S;

判断模块 根据Hilbert空间因子性质判断变压器运行状态。具体为：如果第K个窗口Hilbert空间因子HK满足判断条件|HK|≥e0，则在所述信号序列S的第K点处，变压器处于非正常运行状态；否则，变压器处于正常运行状态。其中，e0为状态判断阈值。The judging module judges the running state of the transformer according to the properties of the Hilbert space factor. Specifically: if the Hilbert space factor H K of the Kth window satisfies the judgment condition |H K |≥e 0 , then at the Kth point of the signal sequence S, the transformer is in abnormal operation; otherwise, the transformer is in normal operation state. Among them, e 0 is the state judgment threshold.

根据本发明提供的具体实施例，本发明公开了以下技术效果：According to the specific embodiments provided by the invention, the invention discloses the following technical effects:

虽然变压器振声检测方法在变压器运行状态监测中有着广泛的应用，且技术相对成熟，但是由于振声检测方法利用了变压器发出的振动信号，很容易受到环境噪声的影响，所以此方法在实际工作环境中应用时常常得不到令人满意的结果。Although the transformer vibration and sound detection method is widely used in the monitoring of transformer operation status, and the technology is relatively mature, but because the vibration and sound detection method uses the vibration signal sent by the transformer, it is easily affected by environmental noise, so this method is not suitable for practical work. Often unsatisfactory results are obtained when applied in the environment.

本发明的目的是提供一种利用Hilbert空间因子的变压器运行状态振声检测方法和系统，所提出的方法利用了不同运行状态下变压器振声信号差值与背景噪声差值在Hilbert空间因子方面的差异，提高了状态监测的性能。所提出的方法具有较好的鲁棒性，计算也较为简单。The purpose of the present invention is to provide a transformer operating state vibration and sound detection method and system utilizing the Hilbert space factor. The proposed method utilizes the difference between the transformer vibration and sound signal difference and the background noise difference in the Hilbert space factor under different operating states. difference, improving condition monitoring performance. The proposed method has better robustness and simpler computation.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍。显而易见，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to these drawings without creative efforts.

图1为本发明的方法流程示意图；Fig. 1 is a schematic flow chart of the method of the present invention;

图2为本发明的系统流程示意图；Fig. 2 is a schematic flow chart of the system of the present invention;

图3为本发明的具体实施案例流程示意图。Fig. 3 is a schematic flow chart of a specific implementation case of the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述。显然，所描述的实施例仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

图1一种利用Hilbert空间因子的变压器运行状态振声检测方法的流程示意图Fig. 1 Schematic flow chart of a vibration and sound detection method for transformer operating status using Hilbert space factor

图1为本发明一种利用Hilbert空间因子的变压器运行状态振声检测方法的流程示意图。如图1所示，所述的一种利用Hilbert空间因子的变压器运行状态振声检测方法具体包括以下步骤：FIG. 1 is a schematic flow chart of a method for detecting vibration and sound in the operating state of a transformer using Hilbert space factors according to the present invention. As shown in Figure 1, the described method for detecting vibration and sound of a transformer operating state using the Hilbert space factor specifically includes the following steps:

步骤001输入实测的信号序列S；Step 001 inputs the measured signal sequence S;

步骤002根据Hilbert空间因子性质判断变压器运行状态。具体为：如果第K个窗口Hilbert空间因子HK满足判断条件|HK|≥e0，则在所述信号序列S的第K点处，变压器处于非正常运行状态；否则，变压器处于正常运行状态。其中，e0为状态判断阈值。Step 002 judges the operating state of the transformer according to the properties of the Hilbert space factor. Specifically: if the Hilbert space factor H K of the Kth window satisfies the judgment condition |H K |≥e 0 , then at the Kth point of the signal sequence S, the transformer is in abnormal operation; otherwise, the transformer is in normal operation state. Among them, e 0 is the state judgment threshold.

所述步骤002之前，所述方法还包括：Before the step 002, the method also includes:

步骤003求取所述Hilbert空间因子HK和所述状态判断阈值e0。Step 003 calculates the Hilbert space factor H K and the state judgment threshold e 0 .

所述步骤003还包括：The step 003 also includes:

步骤301生成第n个信号一阶差分序列，具体为：Step 301 generates the nth signal first-order difference sequence, specifically:

Figure BDA0002258203410000031
Figure BDA0002258203410000031

其中：in:

Figure BDA0002258203410000032
所述第n个信号一阶差分序列
Figure BDA0002258203410000032
The nth signal first-order difference sequence

sn：所述信号序列S的第n个元素，n＝1,2,···,Ns n : the nth element of the signal sequence S, n=1,2,...,N

N：所述信号序列S的长度N: length of the signal sequence S

n：元素下标，如果n>N，所对应的元素sn＝0n: element subscript, if n>N, the corresponding element s n =0

步骤302生成第n个信号二阶差分序列，具体为：Step 302 generates the second order difference sequence of the nth signal, specifically:

Figure BDA0002258203410000033
Figure BDA0002258203410000033

其中：in:

Figure BDA0002258203410000034
所述第n个信号二阶差分序列
Figure BDA0002258203410000034
The nth signal second-order difference sequence

n：元素下标，如果n>N，所对应的元素sn＝0n: element subscript, if n>N, the corresponding element s n =0

步骤303求取第n个期望差值序列

Figure BDA0002258203410000035
具体为：Step 303 Calculate the nth expected difference sequence
Figure BDA0002258203410000035
Specifically:

Figure BDA0002258203410000036
Figure BDA0002258203410000036

其中：in:

Wn：第n个期望权重矩阵W n : the nth expected weight matrix

λ：相关矩阵

Figure BDA0002258203410000038
的最大特征值λ: correlation matrix
Figure BDA0002258203410000038
The largest eigenvalue of

Figure BDA00022582034100000312
所述相关矩阵
Figure BDA0002258203410000039
的第j个特征矢量
Figure BDA00022582034100000312
The correlation matrix
Figure BDA0002258203410000039
The jth eigenvector of

j：下标，j＝1,2,···,Nj: subscript, j=1,2,...,N

ρ：所述相关矩阵

Figure BDA00022582034100000310
的迹ρ: the correlation matrix
Figure BDA00022582034100000310
trace

步骤304求取所述第K个窗口Hilbert空间因子，具体为：Step 304 obtains the Hilbert space factor of the Kth window, specifically:

Figure BDA00022582034100000311
Figure BDA00022582034100000311

其中：in:

σ：所述信号序列S的均方差σ: the mean square error of the signal sequence S

步骤305求取所述状态判断阈值e0，具体为：Step 305 calculates the state judgment threshold e 0 , specifically:

其中：in:

κj：矩阵

Figure BDA0002258203410000042
的第j个特征值κ j : matrix
Figure BDA0002258203410000042
The jth eigenvalue of

j：下标，j＝1,2,···,Nj: subscript, j=1,2,...,N

图2一种利用Hilbert空间因子的变压器运行状态振声检测系统的结构意图Fig. 2 Structural diagram of a vibration and sound detection system for transformer operating status using Hilbert space factor

图2为本发明一种利用Hilbert空间因子的变压器运行状态振声检测系统的结构示意图。如图2所示，所述一种利用Hilbert空间因子的变压器运行状态振声检测系统包括以下结构：FIG. 2 is a structural schematic diagram of a vibration-acoustic detection system for a transformer operating state using Hilbert space factors according to the present invention. As shown in Fig. 2, described a kind of vibration and sound detection system utilizing Hilbert space factor for transformer running state includes the following structure:

获取模块401输入实测的信号序列S；The acquisition module 401 inputs the measured signal sequence S;

判断模块402根据Hilbert空间因子性质判断变压器运行状态。具体为：如果第K个窗口Hilbert空间因子HK满足判断条件|HK|≥e0，则在所述信号序列S的第K点处，变压器处于非正常运行状态；否则，变压器处于正常运行状态。其中，e0为状态判断阈值。The judging module 402 judges the running state of the transformer according to the properties of the Hilbert space factor. Specifically: if the Hilbert space factor H K of the Kth window satisfies the judgment condition |H K |≥e 0 , then at the Kth point of the signal sequence S, the transformer is in abnormal operation; otherwise, the transformer is in normal operation state. Among them, e 0 is the state judgment threshold.

所述的系统，还包括：The system also includes:

计算模块403求取所述Hilbert空间因子HK和所述状态判断阈值e0。The calculation module 403 obtains the Hilbert space factor H K and the state judgment threshold e 0 .

所述计算模块403还包括下列单元，具体包括：The computing module 403 also includes the following units, specifically:

计算单元4031生成第n个信号一阶差分序列，具体为：The calculation unit 4031 generates the nth signal first-order difference sequence, specifically:

Figure BDA0002258203410000043
Figure BDA0002258203410000043

其中：in:

Figure BDA0002258203410000044
所述第n个信号一阶差分序列
Figure BDA0002258203410000044
The nth signal first-order difference sequence

sn：所述信号序列S的第n个元素，n＝1,2,···,Ns n : the nth element of the signal sequence S, n=1,2,...,N

N：所述信号序列S的长度N: length of the signal sequence S

n：元素下标，如果n>N，所对应的元素sn＝0n: element subscript, if n>N, the corresponding element s n =0

计算单元4032生成第n个信号二阶差分序列，具体为：The calculation unit 4032 generates the nth signal second-order difference sequence, specifically:

其中：in:

所述第n个信号二阶差分序列 The nth signal second-order difference sequence

n：元素下标，如果n>N，所对应的元素sn＝0n: element subscript, if n>N, the corresponding element s n =0

计算单元4033求取第n个期望差值序列

Figure BDA0002258203410000051
具体为：Calculation unit 4033 calculates the nth expected difference sequence
Figure BDA0002258203410000051
Specifically:

其中：in:

Wn：第n个期望权重矩阵W n : the nth expected weight matrix

Figure BDA0002258203410000053
Figure BDA0002258203410000053

λ：相关矩阵

Figure BDA0002258203410000054
的最大特征值λ: correlation matrix
Figure BDA0002258203410000054
The largest eigenvalue of

所述相关矩阵的第j个特征矢量 The correlation matrix The jth eigenvector of

j：下标，j＝1,2,···,Nj: subscript, j=1,2,...,N

ρ：所述相关矩阵

Figure BDA0002258203410000057
的迹ρ: the correlation matrix
Figure BDA0002258203410000057
trace

计算单元4034求取所述第K个窗口Hilbert空间因子，具体为：Calculation unit 4034 obtains the Hilbert space factor of the Kth window, specifically:

Figure BDA0002258203410000058
Figure BDA0002258203410000058

其中：in:

σ：所述信号序列S的均方差σ: the mean square error of the signal sequence S

计算单元4035求取所述状态判断阈值e0，具体为：Calculation unit 4035 obtains the state judgment threshold e0, specifically:

Figure BDA0002258203410000059
Figure BDA0002258203410000059

其中：in:

Figure BDA00022582034100000510
的第j个特征值
Figure BDA00022582034100000510
The jth eigenvalue of

j：下标，j＝1,2,···,Nj: subscript, j=1,2,...,N

下面提供一个具体实施案例，进一步说明本发明的方案A specific implementation case is provided below to further illustrate the solution of the present invention

图3为本发明具体实施案例的流程示意图。如图3所示，具体包括以下步骤：Fig. 3 is a schematic flow chart of a specific implementation case of the present invention. As shown in Figure 3, it specifically includes the following steps:

0开始：输入实测的信号数据序列0 start: input the measured signal data sequence

S＝[s1,s2,···,sN-1,sN]S＝[s 1 ,s 2 ,...,s N-1 ,s N ]

其中：in:

S：实测信号序列，长度为NS: Measured signal sequence, the length is N

sn：所述信号序列S中的第n个元素s n : the nth element in the signal sequence S

n：下标，n＝1,2,···,Nn: subscript, n=1,2,...,N

1生成第n个信号一阶差分序列，具体为：1 Generate the first order difference sequence of the nth signal, specifically:

Figure BDA00022582034100000511
Figure BDA00022582034100000511

其中：in:

Figure BDA0002258203410000061
所述第n个信号一阶差分序列
Figure BDA0002258203410000061
The nth signal first-order difference sequence

sn：所述信号序列S的第n个元素，n＝1,2,···,Ns n : the nth element of the signal sequence S, n=1,2,...,N

N：所述信号序列S的长度N: length of the signal sequence S

n：元素下标，如果n>N，所对应的元素sn＝0n: element subscript, if n>N, the corresponding element s n =0

2生成第n个信号二阶差分序列，具体为：2 Generate the second order difference sequence of the nth signal, specifically:

Figure BDA0002258203410000062
Figure BDA0002258203410000062

其中：in:

Figure BDA0002258203410000063
所述第n个信号二阶差分序列
Figure BDA0002258203410000063
The nth signal second-order difference sequence

n：元素下标，如果n>N，所对应的元素sn＝0n: element subscript, if n>N, the corresponding element s n =0

3求取第n个期望差值序列具体为：3 Find the nth expected difference sequence Specifically:

Figure BDA0002258203410000065
Figure BDA0002258203410000065

其中：in:

Wn：第n个期望权重矩阵W n : the nth expected weight matrix

Figure BDA0002258203410000066
Figure BDA0002258203410000066

λ：相关矩阵

Figure BDA0002258203410000067
的最大特征值λ: correlation matrix
Figure BDA0002258203410000067
The largest eigenvalue of

Figure BDA0002258203410000068
所述相关矩阵
Figure BDA0002258203410000069
的第j个特征矢量
Figure BDA0002258203410000068
The correlation matrix
Figure BDA0002258203410000069
The jth eigenvector of

j：下标，j＝1,2,···,Nj: subscript, j=1,2,...,N

ρ：所述相关矩阵

Figure BDA00022582034100000610
的迹ρ: the correlation matrix
Figure BDA00022582034100000610
trace

4求取所述第K个窗口Hilbert空间因子，具体为：4 Find the Kth window Hilbert space factor, specifically:

Figure BDA00022582034100000611
Figure BDA00022582034100000611

其中：in:

σ：所述信号序列S的均方差σ: the mean square error of the signal sequence S

5求取所述状态判断阈值e0，具体为：5. Calculate the state judgment threshold e 0 , specifically:

Figure BDA00022582034100000612
Figure BDA00022582034100000612

其中：in:

κj：矩阵

Figure BDA00022582034100000613
的第j个特征值κ j : matrix
Figure BDA00022582034100000613
The jth eigenvalue of

j：下标，j＝1,2,···,Nj: subscript, j=1,2,...,N

6结束：判断事件6 End: Judgment Event

根据Hilbert空间因子性质判断变压器运行状态。具体为：如果第K个窗口Hilbert空间因子HK满足判断条件|HK|≥e0，则在所述信号序列S的第K点处，变压器处于非正常运行状态；否则，变压器处于正常运行状态。其中，e0为状态判断阈值。According to the property of Hilbert space factor, the running state of the transformer is judged. Specifically: if the Hilbert space factor H K of the Kth window satisfies the judgment condition |H K |≥e 0 , then at the Kth point of the signal sequence S, the transformer is in abnormal operation; otherwise, the transformer is in normal operation state. Among them, e 0 is the state judgment threshold.

本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的系统而言，由于其与实施例公开的方法相对应，所以描述较为简单，相关之处参见方法部分说明即可。Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other. As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for relevant details, please refer to the description of the method part.

本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处。综上所述，本说明书内容不应理解为对本发明的限制。In this paper, specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; meanwhile, for those of ordinary skill in the art, according to the present invention Thoughts, there will be changes in specific implementation methods and application ranges. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims (5)

1. A transformer operation state vibration and sound detection method utilizing Hilbert space factors is characterized by comprising the following steps:
step 001 inputting an actually measured signal sequence S;
and step 002, judging the running state of the transformer according to the properties of the Hilbert space factor. The method specifically comprises the following steps: if the Kth window Hilbert space factor HKSatisfies the judgment condition | HK|≥e0If so, at the Kth point of the signal sequence S, the transformer is in an abnormal operation state; otherwise, the transformer is in a normal operation state. Wherein e is0A threshold is determined for the state.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 003 of solving the Hilbert space factor HKAnd the state judgment threshold e0。
3. The method of claim 2, wherein step 3 comprises:
step 301 generates an nth signal first-order difference sequence, specifically:
Figure FDA0002258203400000011
wherein:
Figure FDA0002258203400000012
the nth signal first order difference sequence
sn: the nth element, N ═ 1,2, …, N, of the signal sequence S
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 302 generates an nth signal second order difference sequence, specifically:
Figure FDA0002258203400000013
wherein:
Figure FDA0002258203400000014
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 303 finds the nth expected difference sequence
Figure FDA0002258203400000015
The method specifically comprises the following steps:
Figure FDA0002258203400000016
wherein:
Wn: nth desired weight matrix
Figure FDA0002258203400000017
λ: correlation matrix
Figure FDA0002258203400000018
Maximum eigenvalue of
Figure FDA00022582034000000111
The correlation matrixThe jth feature vector of
j: subscript, j ═ 1,2, …, N
ρ: the correlation matrixTrace of
Step 304, calculating the Hilbert space factor of the kth window, specifically:
Figure FDA0002258203400000021
wherein:
σ: mean square error of the signal sequence S
Step 305 of obtaining the state determination threshold e0The method specifically comprises the following steps:
Figure FDA0002258203400000022
wherein:
κj: matrix array
Figure FDA0002258203400000023
The jth characteristic value of
j: subscript, j ═ 1,2, …, N.
4. A transformer running state vibration and sound detection system utilizing Hilbert space factors is characterized by comprising the following components:
an acquisition module inputs an actually measured signal sequence S;
and the judging module judges the running state of the transformer according to the properties of the Hilbert space factors. The method specifically comprises the following steps: if the Kth window Hilbert space factor HKSatisfies the judgment condition | HK|≥e0If so, at the Kth point of the signal sequence S, the transformer is in an abnormal operation state; otherwise, the transformer is in a normal operation state. Wherein e is0A threshold is determined for the state.
5. The system of claim 4, further comprising:
calculating the Hilbert space factor H by a calculation moduleKAnd the state judgment threshold e0。
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