CN112201260A - Transformer running state online detection method based on voiceprint recognition - Google Patents
Transformer running state online detection method based on voiceprint recognition Download PDFInfo
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Abstract
The invention provides a transformer running state online detection method based on voiceprint recognition, and belongs to the technical field of transformer fault detection. The method comprises the following steps: s101, performing framing processing on the acquired voiceprint signals when the transformer normally operates, and solving a feature vector of each frame of voiceprint signals; s102, fusing all characteristic values in the characteristic vector to obtain a comprehensive evaluation index and the weight of each characteristic value; s103, if the comprehensive evaluation index follows normal distribution, an alarm line is obtained for the comprehensive evaluation index by using a3 sigma rule in statistics; and S104, calculating a corresponding comprehensive evaluation index of the transformer in the unknown operation state according to the weight of each obtained characteristic value, and if the calculated comprehensive evaluation index continuously exceeds an alarm line for multiple times, judging that the transformer in the unknown operation state is abnormal. By adopting the invention, the detection rate of the abnormally operated transformer can be improved, the false detection rate is reduced, and a large number of fault samples are not needed.
Description
Technical Field
The invention relates to the technical field of transformer fault detection, in particular to a transformer running state online detection method based on voiceprint recognition.
Background
In recent years, with the rapid development of extra-high voltage backbone network frames, the strong direct-current power grid structure brings huge pressure to the safe and stable operation of a power grid. The extra-high voltage part equipment is high in technical complexity and still in a quality unstable period, and once a fault occurs, the production and the life are greatly influenced. The traditional detection method mainly comprises the following steps: oil chromatography, ultrasonic detection, vibration detection, etc.; wherein,
the oil chromatographic detection method judges the fault type and the severity degree by detecting the content of the gas component in the oil, and the method has no timeliness because a certain time is provided from the fault occurrence to the change of the gas component in the oil;
the ultrasonic detection method is mainly used for detecting and positioning partial discharge faults at present, has good identification capability only for the partial discharge faults, and has limited detection capability for other fault modes;
the vibration detection method is used for extracting a transformer vibration signal through a sensor tightly attached to the surface of a transformer, and the method is used as a contact detection means, and the detection effect of the transformer on the running state is directly influenced by the installation position and the number of the sensors.
In summary, because the traditional detection method has the limitations of incomplete detection, inconvenient use and the like, a new detection means is required to realize the online detection of the running state of the transformer.
The transformer can generate voiceprint signals with different frequencies in the operation process and radiate outwards through an air medium, and the voiceprint signals mainly comprise voiceprint signals of a transformer body structure, voiceprint signals of a cooling device during working, voiceprint signals generated during working of an on-load tap-changer and the like. Of the voiceprint signals, the dominant transformer body voiceprint signal has a very obvious spectrum characteristic, namely, the 100Hz waveform is taken as a fundamental frequency in normal operation and is mixed with a higher harmonic component. The frequency of the voiceprint signal caused by the operation of the cooling system is typically concentrated in a low frequency region within 100 Hz. And the voiceprint signal generated when the on-load tap changer works is generally a high-frequency component. Therefore, the transformer voiceprint signal contains important information capable of reflecting the operation state of the transformer, and the transformer fault can be effectively detected by analyzing the voiceprint characteristics of the transformer in different operation states. The voiceprint signal can be collected through the acoustic sensor, the acoustic sensor is a non-contact sensor, and compared with a vibration acceleration sensor, the voiceprint signal collection device is more flexible, and the voiceprint signal collection device can be completed without contacting a transformer body.
The existing feature extraction methods mainly include time-Frequency domain features for extracting signals such as feature Frequency, kurtosis and mean values, energy features extraction by adopting wavelet decomposition or wavelet packet decomposition, Mel-Frequency Cepstral Coefficients (MFCCs) feature vectors extraction and the like, and can effectively extract effective information of the transformer vibration and sound signals, but the quantity and selection of feature values are a difficulty. If too many extracted characteristic values exist, the sensitivity of the system is reduced, and the fault detection rate is low; if the extracted characteristic value is too small, the signal characteristics of the vibration sound signal cannot be comprehensively mastered, so that the stability of the system is poor and the false alarm rate is too high. On the other hand, in the feature recognition, the methods adopted by the existing research and patents mainly include Vector Quantization (VQ), Hidden Markov Model (HMM), Support Vector Machine (SVM), expert analysis system, correlation analysis, and the like, and the biggest problems of these methods are: a reasonable classification model can be constructed only on the premise that a large number of fault samples are needed, but the number of fault samples of the transformer is often small, and how to effectively identify faults under the condition of an unbalanced data set is a core key problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides a transformer running state online detection method based on voiceprint recognition, through weight analysis, the detection rate of an abnormally running transformer can be improved, the false detection rate is reduced, a large number of fault samples are not needed, and only voiceprint signal samples when the transformer normally runs need to be trained, so that the method is effective for all abnormal states (or fault types), and the application range is greatly improved. The technical scheme is as follows:
in one aspect, a transformer operation state online detection method based on voiceprint recognition is provided, and the method comprises the following steps:
s101, performing framing processing on the acquired voiceprint signals when the transformer normally operates, and solving a feature vector of each frame of voiceprint signals;
s102, fusing all characteristic values in the characteristic vector to obtain a comprehensive evaluation index and the weight of each characteristic value;
s103, judging whether the obtained comprehensive evaluation index is in accordance with normal distribution or not, and if so, solving an alarm line for the comprehensive evaluation index by using a3 sigma rule in statistics;
and S104, calculating a corresponding comprehensive evaluation index of the transformer in the unknown operation state according to the weight of each obtained characteristic value, and if the calculated comprehensive evaluation index continuously exceeds an alarm line for multiple times, judging that the transformer in the unknown operation state is abnormal.
Further, the framing the acquired voiceprint signals of the transformer during normal operation, and solving the feature vector of each frame of voiceprint signals includes:
and performing framing processing on the acquired voiceprint signals of the transformer in normal operation by taking a preset time interval as a frame, and solving 13 characteristic values of the mean value, the variance, the peak value, the peak-to-peak value, the root mean square value, the skewness index, the kurtosis index, the margin index, the pulse index, the peak index, the pass frequency value, the frequency spectrum energy and the main frequency of each frame of voiceprint signals to form a characteristic vector.
Further, the fusing the feature values in the feature vector to obtain the comprehensive evaluation index and the weight of each feature value includes:
a1, carrying out normalization processing on an m multiplied by n dimensional data matrix formed by m frames of voiceprint signals obtained after the framing processing, wherein n represents the number of characteristic values in a characteristic vector;
a2, determining the information entropy e of the jth eigenvalue in the normalized data matrixjAnd according to the obtained information entropy ejDetermining the information utility value d of the jth characteristic valuej；
A3, according to the obtained information utility value djDetermining the weight W of the jth feature valuej；
A4, determining a comprehensive evaluation index based on the weights of all the obtained feature values.
Further, the expression of the normalization process is:
wherein x isijFor the j characteristic value, y, of the i-th frame of voiceprint signal before normalization processingijIs the j characteristic value, x, of the normalized ith frame voiceprint signalj min、xj maxRespectively representing the minimum value and the maximum value of the jth characteristic value in the data matrix.
Further, the information entropy ejExpressed as:
wherein,
denotes yijThe proportion of the sum of jth characteristic value of m frames of voiceprint signals after normalization processing, yijIs the j characteristic value of the normalized ith frame voiceprint signal.
Further, the information utility value djExpressed as:
dj＝1-ej。
further, the weight WjExpressed as:
further, if the normal distribution is obeyed, finding an alarm line for the comprehensive evaluation index by using a3 σ criterion in statistics includes:
if the normal distribution is obeyed, 2 sigma and 3 sigma values are obtained for the comprehensive evaluation index by using a3 sigma rule in statistics, and the 2 sigma and 3 sigma values are respectively used as a primary alarm line and a secondary alarm line.
Further, the method further comprises:
and if the normal distribution is not complied with, performing normal conversion on the comprehensive evaluation index, solving 2 sigma and 3 sigma values of the normally converted comprehensive evaluation index by using a3 sigma rule in statistics, and respectively taking the 2 sigma and 3 sigma values as a primary alarm line and a secondary alarm line.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
in the embodiment of the invention, through weight analysis, more important characteristic values can be highlighted, so that the characteristic values occupy higher proportion, thereby being beneficial to improving the detection rate of the abnormally operated transformer and reducing the false detection rate; and the alarm line is obtained from the comprehensive evaluation index by using the 3 sigma criterion in statistics, so that a large number of fault samples are not needed, and only the voiceprint signal samples in the normal operation of the transformer need to be trained, therefore, the method is effective to all abnormal states (or fault types), and the application range is greatly improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a transformer operation state online detection method based on voiceprint recognition according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a transformer operation state online detection system based on voiceprint recognition according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a training and testing process of the transformer operation state online detection method based on voiceprint recognition according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an alarm line of a 220kV outdoor transformer of a certain substation provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a detection result of a simulated loosening fault of an iron core according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a detection result of a deformation fault of a simulation winding according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides an online detection method for an operating state of a transformer based on voiceprint recognition, where the method includes:
s101, performing framing processing on the acquired voiceprint signals when the transformer normally operates, and solving a feature vector of each frame of voiceprint signals;
s102, fusing all characteristic values in the characteristic vector to obtain a comprehensive evaluation index and the weight of each characteristic value;
s103, judging whether the obtained comprehensive evaluation index is in accordance with normal distribution or not, and if so, solving an alarm line for the comprehensive evaluation index by using a3 sigma rule in statistics;
and S104, calculating a corresponding comprehensive evaluation index of the transformer in the unknown operation state according to the weight of each obtained characteristic value, and if the calculated comprehensive evaluation index continuously exceeds an alarm line for multiple times, judging that the transformer in the unknown operation state is abnormal.
According to the transformer operation state online detection method based on voiceprint recognition, more important characteristic values can be highlighted through weight analysis, so that the more important characteristic values can occupy higher proportion, the detection rate of the transformer in abnormal operation can be improved, and the false detection rate is reduced; and the alarm line is obtained from the comprehensive evaluation index by using the 3 sigma criterion in statistics, so that a large number of fault samples are not needed, and only the voiceprint signal samples in the normal operation of the transformer need to be trained, therefore, the method is effective to all abnormal states (or fault types), and the application range is greatly improved.
The online detection method for the running state of the transformer based on voiceprint recognition provided by the embodiment of the invention is realized by the online detection system for the running state of the transformer based on voiceprint recognition shown in fig. 2, wherein the system consists of a plurality of acoustic sensors, a multi-channel data acquisition card and a computer, and a data storage and analysis module programmed by labview is arranged in the computer and is used for executing S101-S104.
In the embodiment, the type of the acoustic sensor can be GRAS 40PP CCP Free-field QC Microphone and is used for collecting voiceprint signals of the transformer, so that the running state of the transformer can be effectively detected in a non-contact manner in real time; the sampling frequency of the data acquisition card is 51.2kHz, and the precision is 24 bits. The acoustic sensors are respectively arranged on the periphery of the transformer, connected to the input end of the data acquisition card and used for multi-channel synchronous acquisition. The output end of the data acquisition card is connected to the network port end of the computer for data display, storage and analysis.
In a specific implementation manner of the foregoing method for online detecting an operating state of a transformer based on voiceprint recognition, further, the framing the acquired voiceprint signals during normal operation of the transformer, and obtaining a feature vector of each frame of voiceprint signals includes:
the method comprises the steps of performing framing processing on collected voiceprint signals when a transformer normally runs by taking a preset time interval (for example, 1s) as one frame, and obtaining 13 feature values of a mean value, a variance, a peak value, a peak-peak value, a root mean square value, a skewness index, a kurtosis index, a margin index, a pulse index, a peak index, a pass frequency value, spectrum energy and a main frequency of each frame of voiceprint signals to form a feature vector.
In this embodiment, S101 to S103 execute the training process in fig. 3, and first acquire a large number of voiceprint signals during normal operation of the transformer as training samples, and then perform framing processing on training set data formed by the training samples by taking 1S as a frame to obtain a feature vector formed by the 13 feature values, so that the framing interval can be shortened, the data size can be increased, and richer voiceprint information can be acquired without losing spectral features. Assuming that m seconds of transformer voiceprint signals are acquired, m frames of voiceprint signals are shared, and the 13 eigenvalues are calculated for each frame, so that an m × n data matrix can be obtained, where n represents the number of eigenvalues in the eigenvector, and n is 13.
In this embodiment, the importance of different characteristic values is different, which means that the contribution values of different characteristic values to the evaluation of the transformer operating state are different, so that the importance of different characteristic values needs to be evaluated, and the evaluation criterion may be measured by a weight, where a larger weight indicates that the contribution value of the characteristic value to the evaluation of the transformer operating state is larger, and vice versa. In the present application, the entropy weight method may be used to analyze each eigenvalue (i.e., 13 eigenvalues) in the eigenvector to obtain weights of the 13 eigenvalues, and finally, according to the obtained weights of each eigenvalue, all eigenvalues are fused into a comprehensive evaluation index (MVF), which specifically includes the following steps:
a1, carrying out normalization processing on an m multiplied by n dimensional data matrix formed by m frames of voiceprint signals obtained after the framing processing, wherein n represents the number of characteristic values in a characteristic vector;
in this embodiment, the expression of the normalization process is:
wherein x isijFor the j characteristic value, y, of the i-th frame of voiceprint signal before normalization processingijIs the j characteristic value, x, of the normalized ith frame voiceprint signalj min、xj maxRespectively representing the minimum value and the maximum value of the j-th characteristic value in the data matrix, wherein the maximum value of i is m, and the maximum value of j is 13.
A2, determining the information entropy e of the jth eigenvalue in the normalized data matrixjAnd according to the obtained information entropy ejDetermining the information utility value d of the jth characteristic valuej；
In this embodiment, the information entropy value ejExpressed as:
wherein,
denotes yijThe proportion of the sum of jth characteristic value of m frames of voiceprint signals after normalization processing, yijIs the j characteristic value of the normalized ith frame voiceprint signal.
In this embodiment, the information utility value djExpressed as:
dj＝1-ej。
a3, according to the obtained information utility value djDetermining the weight W of the jth feature valuej；
In this embodiment, the weight WjExpressed as:
from weight WjAs can be seen from the expression of (c), the weight WjThe cumulative sum of (c) is 1.
A4, determining a comprehensive evaluation index (MVF) according to the weight of all the obtained characteristic values, wherein the MVF is expressed as:
in a specific embodiment of the above online detection method for the operating state of the transformer based on voiceprint recognition, further, if the normal distribution is obeyed, finding the alarm line for the comprehensive evaluation index by using a3 σ criterion in statistics includes:
if the normal distribution is obeyed, 2 sigma and 3 sigma values are obtained for the comprehensive evaluation index by using a3 sigma rule in statistics, and the 2 sigma and 3 sigma values are respectively used as a primary alarm line and a secondary alarm line.
In this embodiment, a K-S inspection method may be used to determine whether the obtained comprehensive evaluation index (MVF) complies with normal distribution, and if the obtained comprehensive evaluation index (MVF) complies with normal distribution, a3 σ criterion in statistics is used to obtain 2 σ and 3 σ values for the comprehensive evaluation index, and the 2 σ and 3 σ values are respectively used as a primary alarm line and a secondary alarm line;
and if the normal distribution is not complied with, performing normal conversion on the comprehensive evaluation index, solving 2 sigma and 3 sigma values of the normally converted comprehensive evaluation index by using a3 sigma rule in statistics, and respectively taking the 2 sigma and 3 sigma values as a primary alarm line and a secondary alarm line.
The alarm line of the 220kV outdoor transformer of a certain transformer substation obtained by the method is shown in fig. 4, wherein high load and low load can be judged according to a load diagram of the transformer.
In this embodiment, through S101 to S103, an alarm line has been established for the normal operation state of the transformer. For a transformer of unknown operating state, a test procedure as in fig. 3 is performed: acquiring a voiceprint signal of a transformer in an unknown running state as a test sample, performing frame division processing on test set data formed by the test sample by taking 1s as a frame, obtaining 13 characteristic values of the test set data, fusing the 13 characteristic values into 1 comprehensive evaluation index according to the weight of a training set, entering an observation state if the calculated comprehensive evaluation index exceeds a primary alarm line for multiple times, and judging that the transformer in the unknown running state is abnormal if the calculated comprehensive evaluation index exceeds a secondary alarm line for multiple times continuously.
In order to verify the effectiveness of the transformer running state online detection method and system based on voiceprint recognition provided by the embodiment of the invention, the method and system are applied to iron core loosening fault detection and winding deformation fault detection, the simulated iron core loosening fault detection result is shown in fig. 5, and the simulated winding deformation fault detection result is shown in fig. 6.
In summary, the transformer operation state online detection method and system based on voiceprint recognition provided by the embodiment of the invention have the following effective effects:
1) the detection method provided by the invention has real-time performance, and can feed back abnormal information in time when the transformer is abnormal, thereby avoiding extra loss and serious disaster accidents;
2) the detection method provided by the invention can effectively detect the running state of the transformer in real time under the condition of not contacting with the transformer body, can replace manual inspection to a certain extent, and reduces human errors while improving the working efficiency;
3) the detection method provided by the invention can highlight more important characteristic values through weight analysis, so that the characteristic values occupy higher proportion, thereby improving the detection rate of the abnormally operated transformer, reducing the false detection rate and improving the stability of the system;
4) the detection method based on the 3 sigma criterion does not need a large number of fault samples, and only needs to train the voiceprint signal samples when the transformer normally operates, so that the method is effective to all abnormal states (or fault types), and the application range of the method is greatly improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A transformer running state online detection method based on voiceprint recognition is characterized by comprising the following steps:
s101, performing framing processing on the acquired voiceprint signals when the transformer normally operates, and solving a feature vector of each frame of voiceprint signals;
s102, fusing all characteristic values in the characteristic vector to obtain a comprehensive evaluation index and the weight of each characteristic value;
s103, judging whether the obtained comprehensive evaluation index is in accordance with normal distribution or not, and if so, solving an alarm line for the comprehensive evaluation index by using a3 sigma rule in statistics;
and S104, calculating a corresponding comprehensive evaluation index of the transformer in the unknown operation state according to the weight of each obtained characteristic value, and if the calculated comprehensive evaluation index continuously exceeds an alarm line for multiple times, judging that the transformer in the unknown operation state is abnormal.
2. The on-line detection method for the operation state of the transformer based on the voiceprint recognition according to claim 1, wherein the step of performing framing processing on the collected voiceprint signals during the normal operation of the transformer and solving the feature vector of each frame of voiceprint signals comprises the steps of:
and performing framing processing on the acquired voiceprint signals of the transformer in normal operation by taking a preset time interval as a frame, and solving 13 characteristic values of the mean value, the variance, the peak value, the peak-to-peak value, the root mean square value, the skewness index, the kurtosis index, the margin index, the pulse index, the peak index, the pass frequency value, the frequency spectrum energy and the main frequency of each frame of voiceprint signals to form a characteristic vector.
3. The online detection method for the operation state of the transformer based on the voiceprint recognition according to claim 2, wherein the step of fusing all the feature values in the feature vector to obtain the comprehensive evaluation index and the weight of each feature value comprises the steps of:
a1, carrying out normalization processing on an m multiplied by n dimensional data matrix formed by m frames of voiceprint signals obtained after the framing processing, wherein n represents the number of characteristic values in a characteristic vector;
a2, determining the information entropy e of the jth eigenvalue in the normalized data matrixjAnd according to the obtained information entropy ejDetermining the information utility value d of the jth characteristic valuej；
A3, according to the obtained information utility value djDetermining the weight W of the jth feature valuej；
A4, determining a comprehensive evaluation index based on the weights of all the obtained feature values.
4. The online detection method for the running state of the transformer based on the voiceprint recognition is characterized in that the expression of normalization processing is as follows:
wherein x isijFor the j characteristic value, y, of the i-th frame of voiceprint signal before normalization processingijIs the j characteristic value, x, of the normalized ith frame voiceprint signaljmin、xjmaxRespectively representing the minimum value and the maximum value of the jth characteristic value in the data matrix.
5. The online detection method for the operation state of the transformer based on the voiceprint recognition according to claim 3, wherein the information entropy e isjExpressed as:
wherein,
denotes yijThe proportion of the sum of jth characteristic value of m frames of voiceprint signals after normalization processing, yijIs the j characteristic value of the normalized ith frame voiceprint signal.
6. The on-line detection method for the operation state of the transformer based on the voiceprint recognition according to claim 5, wherein the information utility value djExpressed as:
dj＝1-ej。
7. the on-line detection method for the running state of the transformer based on the voiceprint recognition according to claim 6, wherein the weight W isjExpressed as:
8. the on-line detection method for the running state of the transformer based on the voiceprint recognition, according to claim 1, wherein if the normal distribution is obeyed, the step of solving an alarm line for the comprehensive evaluation index by using a3 sigma rule in statistics comprises the following steps:
if the normal distribution is obeyed, 2 sigma and 3 sigma values are obtained for the comprehensive evaluation index by using a3 sigma rule in statistics, and the 2 sigma and 3 sigma values are respectively used as a primary alarm line and a secondary alarm line.
9. The online detection method for the operation state of the transformer based on the voiceprint recognition according to claim 8, wherein the method further comprises the following steps:
and if the normal distribution is not complied with, performing normal conversion on the comprehensive evaluation index, solving 2 sigma and 3 sigma values of the normally converted comprehensive evaluation index by using a3 sigma rule in statistics, and respectively taking the 2 sigma and 3 sigma values as a primary alarm line and a secondary alarm line.
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