CN111273100B - Power transformer winding state evaluation method based on vibration phase - Google Patents

Power transformer winding state evaluation method based on vibration phase Download PDF

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CN111273100B
CN111273100B CN202010104092.3A CN202010104092A CN111273100B CN 111273100 B CN111273100 B CN 111273100B CN 202010104092 A CN202010104092 A CN 202010104092A CN 111273100 B CN111273100 B CN 111273100B
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CN111273100A (en
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郑婧
黄海
潘杰
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Zhejiang University ZJU
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Abstract

The invention discloses a power transformer winding state evaluation method based on vibration phases, which is based on a winding vibration generation mechanism, utilizes the characteristics that the vibration phases are more sensitive to mechanical structure change caused by faults and are not easily influenced by environmental factors (load change, voltage fluctuation, temperature change and the like), directly associates winding faults with vibration phase information, and provides a basis for the effectiveness and the scientificity of fault diagnosis. In addition, the invention has no electrical connection with the transformer, does not need to power off the transformer and has little influence on the operation of the whole power system.

Description

Power transformer winding state evaluation method based on vibration phase
Technical Field
The invention belongs to the technical field of power transformer safety fault detection, and particularly relates to a power transformer winding state evaluation method based on vibration phases.
Background
The large power transformer is used as an important ring in a power system, and the safe operation of the large power transformer is important to ensure the safe and reliable power supply of a power grid. In a power transformer fault, the fault rate of a winding is as high as 46.4%, which is the most important component of the transformer fault, and a serious fault caused by mechanical deformation of the winding under the action of an electrodynamic force accounts for 70% of the total fault of the winding, and even if the winding is slightly deformed, the problems of deterioration of mechanical performance of the winding, reduction of insulation strength and short-circuit resistance and the like are caused, so that great potential safety hazards are brought. Therefore, it is necessary and important to monitor and evaluate the condition of the winding of the main fault component in the live monitoring of the transformer.
The vibration analysis method is used for analyzing the collected oil tank wall vibration signals to obtain the state information of the internal parts of the transformer, and when the internal mechanical structure of the transformer is changed, the system response of a vibration system is inevitably changed. Therefore, a transformer fault diagnosis algorithm based on a vibration analysis method usually monitors the vibration of the surface of the transformer oil tank through a sensor, extracts state characteristics capable of effectively reflecting the state change of the internal structure of the transformer from vibration signals, and carries out state evaluation on the transformer based on the state characteristics.
At present, a lot of researchers have achieved a sufficiently successful result in this direction, and a transformer state monitoring technology based on a wavelet packet is proposed in the document 'application of wavelet packet analysis in monitoring the conditions of a transformer core and a winding by a vibration method [ J ]. Chinese Motor engineering reports, 2001(12): 25-28' by Chongchang et al. Chinese patent (CN201410317366.1) proposes a method for monitoring the winding state of a power transformer, which arranges at least two vibration sensors on the surface of a transformer oil tank, acquires vibration signals acquired by each vibration sensor, extracts the vibration amplitude of fundamental frequency in each group of vibration signals at regular time intervals, acquires the winding vibration variation between the vibration amplitudes at every two adjacent time intervals, generates vibration correlation characteristic quantity, and determines the winding state of the power transformer based on the characteristic quantity. Chinese patent (CN201611040125.2) proposes a transformer online monitoring method based on surface vibration signal analysis, the method trains a generalized recurrent neural network according to transformer operating voltage, load current, oil temperature historical data and transformer surface vibration historical data, calculates the amplitude of the surface vibration fundamental frequency of an oil tank through the network based on the transformer operating voltage, load current and oil temperature historical data, and judges the operating state of a transformer according to the difference between the calculated value and the measured value of the vibration amplitude of the vibration fundamental frequency, thereby realizing the online monitoring of the transformer vibration.
However, it is not difficult to find that most of vibration characteristics in transformer fault diagnosis at present are usually based on vibration amplitude, vibration phases are rarely found in research and application of transformer fault diagnosis, and phases can be directly related to the transmission characteristics of a mechanical structure and have higher sensitivity to structural changes; in addition, since the vibration amplitude is not only affected by the structural characteristics, but also affected by external factors such as load current and temperature, it is difficult to determine whether the change is caused by a mechanical structural change only by the amplitude change, which may cause an error in the state evaluation.
Disclosure of Invention
In view of the above, the present invention provides a method for evaluating the winding state of a power transformer based on vibration phases, which can detect the mechanical structure state of the winding of the power transformer on line.
A power transformer winding state evaluation method based on vibration phases comprises the following steps:
(1) the method comprises the following steps that a plurality of vibration sensors are dispersedly arranged on the surface of an oil tank of the power transformer corresponding to the positions of windings, vibration signals of the vibration sensors of the power transformer under the condition of normal live operation are collected, and current signals of the power transformer are synchronously collected;
(2) extracting fundamental frequency vibration phases corrected by current, namely taking current signals synchronously acquired as correction reference signals, and calculating the fundamental frequency vibration phases in each group of vibration signals acquired by each vibration sensor;
(3) and for each vibration sensor, calculating the variation of the fundamental frequency vibration phase of the vibration sensor in one day as the state characteristic quantity of the power transformer, and judging the mechanical structure state of the power transformer winding according to the characteristic quantity.
Further, the number of the vibration sensors arranged in the step (1) is N, the vibration signals of the vibration sensors and the current signals of the power transformer are collected once every Δ T time in one day, each sampling time is T seconds, the sampling frequency is f, N is a natural number greater than 1, Δ T is greater than 1 minute, and T is greater than 0.02 second.
Further, the current signal is a primary side current or a secondary side current of the power transformer.
Further, the specific implementation manner of the step (2) is as follows:
A1. taking a data volume of one day, dividing the data volume into a plurality of samples, wherein each sample comprises a vibration signal of each vibration sensor and a current signal of a power transformer in the same sampling time;
A2. constructing a sample matrix corresponding to each group of samples as follows:
Figure BDA0002387902210000031
wherein: smA sample matrix corresponding to the mth group of samples, im(t) is the signal value of the current signal in the mth set of samples corresponding to time t, vm,1(x) For the vibration signal of the 1 st vibration sensor in the m-th group of samples, the signal value at the x-time, vm,N(x) The vibration signal of the Nth vibration sensor in the mth group of samples corresponds to the signal value at the x moment, and t equals to tm,tm+ΔT,tm+2ΔT,...,tm+T，tmThe sampling start time of the mth group of samples is delta T1/f, M is a natural number, M is more than or equal to 1 and less than or equal to M, and M is the number of samples;
A3. delaying each group of samples for a certain time and ensuring that the initial phases of current signals in all the samples are equal, wherein the sample matrix corresponding to each group of samples after delay is as follows:
Figure BDA0002387902210000032
wherein: sm,TmFor the sample matrix corresponding to the delayed mth group of samples, TmIs tmThe time length to the set ideal starting position;
A4. for each set of samples, S is extractedm,TmFundamental frequency components of vibration signals of the 1 st to the N th vibration sensors and corresponding phase values are calculated
Figure BDA0002387902210000034
As the corrected fundamental vibration phase.
Further, the ideal starting position is an arbitrary and preferably time position corresponding to the maximum value of the current signal in the sample.
Further, the specific implementation manner of the step (2) is as follows:
B1. taking a data volume of one day, dividing the data volume into a plurality of samples, wherein each sample comprises a vibration signal of each vibration sensor and a current signal of a power transformer in the same sampling time;
B2. for each set of samples, the current signal i in the sample is measuredm(t) squaring to obtain
Figure BDA0002387902210000033
And extracting fundamental frequency components thereof
Figure BDA0002387902210000041
Simultaneously extracting fundamental frequency component v of vibration signal in samplem,n,100(t); wherein M is a natural number, M is more than or equal to 1 and less than or equal to M, M is the number of samples, im(t) is the current signal in the mth group of samples, N is a natural number and N is more than or equal to 1 and less than or equal to N, vm,n,100(t) is the fundamental frequency component of the vibration signal of the nth vibration sensor in the mth group of samples;
B3. calculating fundamental frequency component
Figure BDA0002387902210000042
And vm,n,100(t) corresponding phase value
Figure BDA0002387902210000043
And
Figure BDA0002387902210000044
thereby obtaining the corrected base frequency vibration phase of the vibration signal of the nth vibration sensor in the mth group of samples
Figure BDA0002387902210000045
Further, in the step (3), for any vibration sensor, the vibration signals of all samples of the vibration sensor are corrected to obtain the vibration phase of the fundamental frequency
Figure BDA0002387902210000046
Forming a sequence, and taking the variance, standard deviation or peak-to-peak value of the sequence as the variation of the fundamental frequency vibration phase of the vibration sensor in one day.
Further, the specific implementation process of determining the mechanical structure state of the power transformer winding in the step (3) is as follows: firstly, selecting a plurality of power transformers in normal, aging and fault states, calculating the variation of the fundamental frequency vibration phase of each vibration sensor of the power transformers in one day as a characteristic quantity according to the steps (1) to (3), and using the states of the power transformers as labels of the characteristic quantity; training by using state characteristic quantities and labels of all power transformers by adopting a machine learning method with supervision (such as a support vector machine) or unsupervised (such as linear classification, cluster analysis and the like) to obtain a winding state evaluation model of the power transformer; and finally, calculating the variation of the fundamental frequency vibration phase of each vibration sensor of the power transformer to be evaluated in one day according to the steps (1) to (3) to serve as a characteristic quantity, and inputting the characteristic quantity into a winding state evaluation model, thereby outputting and obtaining the winding state evaluation result of the power transformer to be evaluated.
Based on the technical scheme, the invention has the following beneficial technical effects:
1. the invention has no electrical connection with the transformer, does not need to power off the transformer and has little influence on the operation of the whole power system.
2. The method of the invention is different from most existing state evaluation methods in that the transformer state is judged by using the transformer vibration phase information, and the vibration phase information is not easily influenced by environmental factors (load change, voltage fluctuation, temperature change and the like), so that a diagnosis basis with higher effectiveness and scientificity can be provided.
3. The method is simple and easy to realize, can sensitively and effectively judge the state of the mechanical structure of the transformer, and provides guarantee for the safe operation of power grid equipment.
Drawings
FIG. 1 is a schematic flow chart of the steps of the method of the present invention.
FIG. 2 is a layout diagram of measuring points on the surface of a fuel tank of a power transformer.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
The specific experimental objects of the embodiment are three-phase oil-immersed power transformers in different operation states (normal, aging and fault), and in order to verify the effectiveness of the method, the state evaluation results of the three transformers in different operation states are compared. The normal transformer CZ #1 is a 220kV three-phase transformer which is put into operation in 2007, the aging transformer PS #1 is a 110kV three-phase transformer which is put into operation in 1999, and the fault transformer is a 110kV three-phase transformer which is put into operation in 1990 and has winding integral tilt fault.
During diagnosis, in order to conveniently obtain a vibration signal without distortion, a vibration sensor with higher sensitivity is selected; in order to ensure the vibration response of the sensor within the sampling filtering frequency band, the vibration sensor is fixed on the side wall of the oil tank by adopting a magnetic seat adsorption or glue bonding mode. The vibration sampling device comprises main modules such as pre-amplification, anti-aliasing filtering, AD sampling and the like, wherein the number of AD sampling bits is at least 12, and the cut-off frequency of an anti-aliasing filter is 2000 Hz; when the vibration signal is sampled, the sampling frequency is at least 4000 Hz. In this embodiment, the sampling frequency for collecting the vibration signal is set to 10000Hz, the sampling number of the AD module is 16 bits, and the whole experiment process is recorded in a continuous sampling mode.
As shown in fig. 1, the method for estimating the winding state of the power transformer based on the system order estimation of the present invention includes the following steps:
(1) and arranging vibration measuring points.
6 measuring points are arranged on the surface of the oil tank of the power transformer, and as shown in figure 2, the 6 measuring points are all arranged on the corresponding oil tank wall close to the winding.
(2) The method comprises the steps of collecting vibration signals and current signals in one day, namely collecting vibration signals of vibration sensors of a power transformer under the condition of normal charged operation every delta T time (recommended to be more than 1 minute) in one day, and synchronously collecting primary side or secondary side current of the power transformer, wherein the sampling time is T seconds (which should be more than 0.02 second).
(3) And processing the signals, and extracting the fundamental frequency vibration phase corrected by the current in one day.
First, a current signal and vibration signal sample matrix for each set of samples is constructed:
Figure BDA0002387902210000061
wherein: i.e. imIs the current signal of the mth group of samples in a day, t is the time, vm，nThe m group of sample vibration signals of the n vibration sensor;
then, each set of samples is delayed by TmAll samples SmThe initial phase values of the current signals in (1) are equal, if the current maximum value appearing position in all samples is taken as the initial position, the current phases are equal to 90 degrees, and the delayed samples become:
Figure BDA0002387902210000062
finally, extracting
Figure BDA0002387902210000063
The fundamental frequency component in the delayed vibration signal collected by the middle sensor is calculated, and the phase value is calculated
Figure BDA0002387902210000064
As the corrected fundamental frequency vibration phase.
(4) A state evaluation feature quantity is extracted based on a change in vibration phase within one day.
I.e. by calculating the phase sequence of the fundamental vibration of all samples of the nth vibration sensor during a day
Figure BDA0002387902210000065
The variance of (c) gives the results shown in table 1:
TABLE 1
Measurement Point #1 Measurement Point #2 Measurement Point #3 Measurement Point #4 Measurement Point #5 Measurement Point #6
Normal transformer 27.34° 22.74° 25.88° 24.9° 21° 25.9°
Aging transformer 27.83° 61.03° 28.32° 28.81° 49.8° 39.06°
Fault transformer 245.6° 124.51° 254.88° 113.28° 116.70° 233.40°
(5) Establishing a transformer diagnosis model: selecting 55 power transformers containing normal, aging and fault types, and respectively extracting variation of 100Hz fundamental frequency vibration phase obtained by actual measurement under the condition of different vibration sensor data
Figure BDA0002387902210000066
Establishing a vibration phase database of the power transformer, and taking the running state (normal, aging and fault) of the vibration phase database as a label value of all data in the database; then, training and learning are carried out on the database data by using a linear classification method, and the change degree of the fundamental frequency vibration phase of the transformer is established
Figure BDA0002387902210000067
And the transformer operating conditions (normal, aging, fault):
Figure BDA0002387902210000071
(6) the evaluation transformer winding states are shown in table 2:
TABLE 2
Figure BDA0002387902210000072
In the embodiment, when the number of aging and fault measuring points is less than 25%, the system is judged to be normal; the number of fault measuring points is more than 80 percent, and system faults are judged; in addition, the system is determined to be aging.
The embodiments described above are presented to enable a person having ordinary skill in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described embodiments may be made, and the generic principles defined herein may be applied to other embodiments without the use of inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.

Claims (7)

1. A power transformer winding state evaluation method based on vibration phases comprises the following steps:
(1) the method comprises the following steps that a plurality of vibration sensors are dispersedly arranged on the surface of an oil tank of the power transformer corresponding to the positions of windings, vibration signals of the vibration sensors of the power transformer under the condition of normal live operation are collected, and current signals of the power transformer are synchronously collected;
(2) extracting fundamental frequency vibration phases corrected by current, namely taking current signals acquired synchronously as correction reference signals, and calculating the fundamental frequency vibration phases in each group of vibration signals acquired by each vibration sensor, wherein the specific implementation process is as follows:
A1. taking a data volume of one day, dividing the data volume into a plurality of samples, wherein each sample comprises a vibration signal of each vibration sensor and a current signal of a power transformer in the same sampling time;
A2. constructing a sample matrix corresponding to each group of samples as follows:
Figure FDA0002878850160000011
wherein: smA sample matrix corresponding to the mth group of samples, im(t) is the signal value of the current signal in the mth set of samples corresponding to time t, vm,1(x) For the vibration signal of the 1 st vibration sensor in the m-th group of samples, the signal value at the x-time, vm,N(x) The vibration signal of the Nth vibration sensor in the mth group of samples corresponds to the signal value at the x moment, and t equals to tm,tm+ΔT,tm+2ΔT,...,tm+T，tmThe sampling start time of the mth group of samples is delta T1/f, M is a natural number, M is more than or equal to 1 and less than or equal to M, and M is the number of samples;
A3. delaying each group of samples for a certain time and ensuring that the initial phases of current signals in all the samples are equal, wherein the sample matrix corresponding to each group of samples after delay is as follows:
Figure FDA0002878850160000012
wherein: sm,TmFor the sample matrix corresponding to the delayed mth group of samples, TmIs tmThe time length to the set ideal starting position;
A4. for each set of samples, S is extractedm,TmFundamental frequency components of vibration signals of the 1 st to the N th vibration sensors and corresponding phase values are calculated
Figure FDA0002878850160000021
As corrected fundamental frequency vibration phase;
(3) and for each vibration sensor, calculating the variation of the fundamental frequency vibration phase of the vibration sensor in one day as the state characteristic quantity of the power transformer, and judging the mechanical structure state of the power transformer winding according to the characteristic quantity.
2. A power transformer winding state evaluation method according to claim 1, characterized by: the number of the vibration sensors arranged in the step (1) is N, the vibration signals of the vibration sensors and the current signals of the power transformer are collected once every delta T time in one day, each sampling time is T seconds, the sampling frequency is f, N is a natural number larger than 1, delta T is larger than 1 minute, and T is larger than 0.02 second.
3. A power transformer winding state evaluation method according to claim 1, characterized by: the current signal is a primary side current or a secondary side current of the power transformer.
4. A power transformer winding state evaluation method according to claim 1, characterized by: the ideal starting position is a time position corresponding to the maximum value of the current signal in the sample.
5. A power transformer winding state evaluation method according to claim 2, characterized by: the specific implementation manner of the step (2) is as follows:
B1. taking a data volume of one day, dividing the data volume into a plurality of samples, wherein each sample comprises a vibration signal of each vibration sensor and a current signal of a power transformer in the same sampling time;
B2. for each set of samples, the current signal i in the sample is measuredm(t) squaring to obtain
Figure FDA0002878850160000022
And extracting fundamental frequency components thereof
Figure FDA0002878850160000023
Simultaneously extracting fundamental frequency component v of vibration signal in samplem,n,100(t); wherein M is a natural number, M is more than or equal to 1 and less than or equal to M, M is the number of samples, im(t) is the current signal in the mth group of samples, N is a natural number and N is more than or equal to 1 and less than or equal to N, vm,n,100(t) is the fundamental frequency component of the vibration signal of the nth vibration sensor in the mth group of samples;
B3. calculating fundamental frequency component
Figure FDA0002878850160000024
And vm,n,100(t) corresponding phase value
Figure FDA0002878850160000025
And
Figure FDA0002878850160000026
thereby obtaining the corrected base frequency vibration phase of the vibration signal of the nth vibration sensor in the mth group of samples
Figure FDA0002878850160000027
6. A power transformer winding state evaluation method according to claim 1 or 5, characterized by: in the step (3), for any vibration sensor, the vibration signals of all samples of the vibration sensor are corrected to obtain the vibration phase of the fundamental frequency
Figure FDA0002878850160000028
Forming a sequence, and taking the variance, standard deviation or peak-to-peak value of the sequence as the variation of the fundamental frequency vibration phase of the vibration sensor in one day.
7. A power transformer winding state evaluation method according to claim 1, characterized by: the concrete implementation process of judging the mechanical structure state of the power transformer winding in the step (3) is as follows: firstly, selecting a plurality of power transformers in normal, aging and fault states, calculating the variation of the fundamental frequency vibration phase of each vibration sensor of the power transformers in one day as a characteristic quantity according to the steps (1) to (3), and using the states of the power transformers as labels of the characteristic quantity; training by using state characteristic quantities and labels of all power transformers by adopting a supervised or unsupervised machine learning method to obtain a winding state evaluation model of the power transformer; and finally, calculating the variation of the fundamental frequency vibration phase of each vibration sensor of the power transformer to be evaluated in one day according to the steps (1) to (3) to serve as a characteristic quantity, and inputting the characteristic quantity into a winding state evaluation model, thereby outputting and obtaining the winding state evaluation result of the power transformer to be evaluated.
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