CN108508318B - Method and system for judging operation state of unbalanced load of transformer - Google Patents
Method and system for judging operation state of unbalanced load of transformer Download PDFInfo
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Abstract
The invention discloses a method and a system for judging the unbalanced load operation state of a transformer, wherein the method comprises the following implementation steps: acquiring an acoustic signal of a transformer to be detected, extracting an independent characteristic parameter according to the acoustic signal of the transformer to be detected, inputting the independent characteristic parameter into a trained machine learning model to obtain the current unbalanced load operation state of the transformer to be detected, wherein the trained machine learning model comprises a mapping relation between the independent characteristic parameter and the unbalanced load operation state; the system includes a computer device programmed to perform the foregoing method. The method for effectively detecting the running state of the three-phase unbalanced load of the transformer has the advantages of no contact with live equipment, convenience in operation and high detection efficiency.
Description
Technical Field
The invention relates to the field of detection of running states of transformers, in particular to a method and a system for judging running states of unbalanced loads of transformers.
Background
Transformers are an important component of electrical power systems and are responsible for the transmission of electrical energy between power networks of different voltage classes. The transformer operates under the condition that the three-phase load unbalance degree is large, adverse effects can be caused to a power grid and the transformer, and the adverse effects are mainly expressed in that: firstly, when three-phase loads are unbalanced, three-phase currents are different, unbalanced current is generated on a neutral line of a power grid, on one hand, the line loss is increased, on the other hand, the neutral point of a transformer is drifted, and the voltage of each phase is changed, so that the condition that electric equipment is burnt or cannot be used is easily caused; secondly, the electric energy loss of the transformer is increased; hysteresis and eddy current loss are generated in the steel component, so that the transformer is locally overheated and is insulated and aged; and fourthly, the output capacity of the transformer cannot reach a rated value, the spare capacity is reduced, the overload capacity is reduced, and when the three-phase load is seriously unbalanced, the transformer can be burnt down due to overhigh phase current along with the increase of the running time.
Therefore, in order to improve the power supply reliability, the three-phase load unbalance of the transformer is generally strictly required in the power system. At present, the problem of three-phase load imbalance is mainly found by checking a transformer gateway meter, but in order to check whether the meter result is accurate, the three-phase load current and the neutral current of the transformer are often required to be manually detected on site. The method mainly has the problems of time and labor waste, need of contacting with charged equipment and the like, and particularly has more personnel and equipment required by detection for distribution transformers with large quantity and wide distribution. In addition, for distribution transformers with remote positions, three-phase load unbalance detection means are often lacked, the load unbalance phenomenon is often difficult to find, the test difficulty is also large, and the safe and reliable operation of the transformers is not facilitated.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problems in the prior art, the invention provides a method and a system for judging the running state of the unbalanced load of the transformer.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for judging the unbalanced load operation state of a transformer comprises the following implementation steps:
1) acquiring an acoustic signal of a transformer to be detected;
2) extracting independent characteristic parameters according to the acoustic signal of the transformer to be detected;
3) and inputting the independent characteristic parameters into a trained machine learning model to obtain the current unbalanced load running state of the transformer to be detected, wherein the trained machine learning model comprises the mapping relation between the independent characteristic parameters and the unbalanced load running state.
Preferably, the detailed steps of step 2) include:
2.1) preprocessing the acoustic signal of the transformer to be detected;
2.2) carrying out spectrum analysis and wavelet packet analysis on the preprocessed acoustic signals, and extracting characteristic parameters of unbalanced load operation;
and 2.3) carrying out principal component analysis on the characteristic parameters of the unbalanced load operation to obtain independent characteristic parameters.
Preferably, the detailed steps of step 2.2) include:
2.2.1) Fourier transforming the preprocessed acoustic signal of the transformer, intercepting the acoustic signal in the range of 1kHzA frequency spectrum; calculating the ratio R of the acoustic signal energy of 50Hz and odd harmonic frequency thereof to the acoustic signal energy of 50Hz and even harmonic frequency thereof in the range of 1kHz of the acoustic signaloe；
2.2.2) calculating the harmonic distortion rate R of the acoustic signal in the range of 1kHz with the frequency of 100Hz as the fundamental frequencyTHD；
2.2.3) carrying out 3-layer wavelet packet analysis on the preprocessed acoustic signals of the transformer, and calculating a wavelet packet energy characteristic E;
2.2.4) ratio RoeHarmonic distortion rate RTHDAnd the characteristic E of the wavelet packet energy jointly form an unbalanced load operation characteristic parameter.
Preferably, the ratio R in step 2.2.1)oeThe formula (1) is shown in the formula;
in the formula (1), A2iFor 50Hz even harmonic amplitude, A2i-1Is the amplitude of the 50Hz odd harmonic of the signal, and N is the number of 50Hz harmonic frequencies of the signal in the range of 1 kHz.
Preferably, the harmonic distortion rate R in step 2.2.2)THDThe formula (2) is shown in the formula;
in the formula (2), A2For a harmonic amplitude of 100Hz of the signal, AiIs the ith harmonic amplitude of 50Hz in the signal, and N is the harmonic frequency quantity of 50Hz in the range of 1 kHz.
Preferably, the calculation function expression of the wavelet packet energy characteristic E in the step 2.2.3) is shown as the formula (3);
in the formula (3), EiAnd n is 3, namely the subband energy of the ith wavelet packet, and the number of wavelet packet decomposition layers.
Preferably, when the principal component analysis is performed on the unbalanced load operation characteristic parameters in step 2.3), the dimension of the independent characteristic parameters output by the principal component analysis is 2 dimensions, and the principal component condition is that the independent characteristic contribution rate exceeds 85%, so that the independent characteristic parameters corresponding to the unbalanced load operation characteristic parameters are finally obtained.
Preferably, the machine learning model in step 3) is a least squares support vector machine classification model.
Preferably, the training step of the least squares support vector machine classification model comprises:
s1) respectively collecting the acoustic signals x when three-phase unbalanced load does not occur aiming at the sample transformer1iAnd an acoustic signal x when a three-phase unbalanced load occurs2i；
S2) for the acoustic signal x of the sample transformer when no three-phase unbalanced load occurs1iAnd an acoustic signal x when a three-phase unbalanced load occurs2iExtracting independent characteristic parameters;
s3) classifying the independent characteristic parameters of the sample transformer according to whether the sample transformer is in a three-phase unbalanced load running state when the vibration acceleration signal is collected, wherein the type of the characteristic parameter of the acoustic signal when the three-phase unbalanced load does not occur is '1', and the type of the characteristic parameter of the acoustic signal when the three-phase unbalanced load occurs is '-1';
s4) forming a training set by the classified independent characteristic parameters and the characteristic parameter classes thereof, and training the training set by a least square support vector machine method to obtain a least square support vector machine classification model comprising the mapping relation between the independent characteristic parameters and the unbalanced load running state.
The present invention also provides a system for determining an unbalanced load operation state of a transformer, comprising a computer device programmed to perform the steps of the aforementioned method for determining an unbalanced load operation state of a transformer according to the present invention.
The method for judging the unbalanced load operation state of the transformer has the following advantages that:
1. when the common current detection method cannot be carried out, the three-phase unbalanced load running state of the transformer can be effectively detected;
2. the invention has no electric contact with the charged equipment, does not need to be wired or installed with other measuring equipment in the testing process, and has higher testing safety and detection efficiency, convenient operation and lower detection cost.
The system for determining the operating state of the unbalanced load of the transformer of the present invention is a system corresponding to the method for determining the operating state of the unbalanced load of the transformer of the present invention, and has the aforementioned advantages of the method for determining the operating state of the unbalanced load of the transformer of the present invention, and therefore, the details are not repeated herein.
Drawings
Fig. 1 is a schematic flow chart of the implementation of the method according to the embodiment of the present invention.
Fig. 2 is a frequency spectrum of an acoustic signal of a transformer when no three-phase unbalanced load occurs in the embodiment of the present invention.
Fig. 3 is a spectrum of an acoustic signal of a transformer when a three-phase unbalanced load occurs in an embodiment of the present invention.
Fig. 4 is a classification determination result of the three-phase unbalanced load state of the transformer to be predicted in the embodiment of the present invention.
Detailed Description
Referring to fig. 1, the implementation steps of the method for determining the unbalanced load operation state of the transformer in the embodiment include:
1) acquiring an acoustic signal of a transformer to be detected;
2) extracting independent characteristic parameters according to the acoustic signal of the transformer to be detected;
3) and inputting the independent characteristic parameters into the trained machine learning model to obtain the current unbalanced load running state of the transformer to be detected, wherein the trained machine learning model comprises the mapping relation between the independent characteristic parameters and the unbalanced load running state.
The method can effectively detect the problem of three-phase unbalanced load of the transformer under the condition of not detecting current, and has the advantages of no need of contacting with an equipment electrified part, and high detection safety and efficiency.
In this embodiment, when the acoustic signal of the transformer to be detected is measured in step 1), the position of the acoustic signal measuring point is located at any position within a range of 1m from the height of the transformer 1/2 to the high-voltage side vertical surface, and the position of the measuring point is required to be the same every time, the sampling frequency is 2048Hz, and a wavelet packet analysis method is adopted to perform noise reduction preprocessing on the background noise signal, so as to eliminate the interference of environmental noise. Due to the fact that the sound signals of the transformers at different positions have certain difference, the positions of the same measuring points are beneficial to guaranteeing that the testing results have comparability. Since the self-cooled transformer noise signal is typically in the 1kHz range, the sampling frequency should not be lower than 2 kHz. The test of the sound signal of the transformer is easily interfered by external factors, and the frequency band of the sound signal can be subdivided by adopting a wavelet packet analysis method, so that the frequency band where the environmental noise is located is found out, the environmental noise interference is filtered, and the measurement accuracy is improved.
Referring to fig. 1, the detailed steps of step 2) in this embodiment include:
2.1) preprocessing the acoustic signal of the transformer to be detected (background noise reduction);
2.2) carrying out spectrum analysis and wavelet packet analysis on the preprocessed acoustic signals, and extracting characteristic parameters of unbalanced load operation;
and 2.3) carrying out principal component analysis on the characteristic parameters of the unbalanced load operation to obtain independent characteristic parameters.
In this embodiment, the detailed steps of step 2.2) include:
2.2.1) carrying out Fourier transform on the sound signal of the preprocessed transformer, and intercepting the sound signal frequency spectrum within the range of 1 kHz; calculating the ratio R of the acoustic signal energy of 50Hz and odd harmonic frequency thereof to the acoustic signal energy of 50Hz and even harmonic frequency thereof in the range of 1kHz of the acoustic signaloe；
2.2.2) calculating the harmonic distortion rate R of the acoustic signal in the range of 1kHz with the frequency of 100Hz as the fundamental frequencyTHD；
2.2.3) carrying out 3-layer wavelet packet analysis on the preprocessed acoustic signals of the transformer, and calculating a wavelet packet energy characteristic E;
2.2.4) ratio RoeHarmonic distortion rate RTHDThe energy of the three components and the energy characteristic E of the wavelet packet form an unbalanced loadThe vehicle carries characteristic parameters.
In this example, the ratio R in step 2.2.1)oeThe formula (1) is shown in the formula;
in the formula (1), A2iFor 50Hz even harmonic amplitude, A2i-1Is the amplitude of the 50Hz odd harmonic of the signal, and N is the number of 50Hz harmonic frequencies of the signal in the range of 1 kHz.
In this embodiment, the harmonic distortion rate R in step 2.2.2)THDThe formula (2) is shown in the formula;
in the formula (2), A2For a harmonic amplitude of 100Hz of the signal, AiIs the ith harmonic amplitude of 50Hz in the signal, and N is the harmonic frequency quantity of 50Hz in the range of 1 kHz.
In this embodiment, the calculation function expression of the wavelet packet energy characteristic E in step 2.2.3) is shown in formula (3);
in the formula (3), EiAnd n is 3, namely the subband energy of the ith wavelet packet, and the number of wavelet packet decomposition layers.
As shown in fig. 2, when the three phases are all 50% load, and no three-phase unbalanced load occurs, the frequency spectrum of the transformer acoustic signal is mainly concentrated in the range of 1kHz, and the energy of the transformer acoustic signal is mainly concentrated on even-numbered multiples of 50Hz, such as 100Hz, 200Hz, 300Hz, 400Hz, 500Hz, 600Hz, and 700 Hz. Ratio RoeSmaller, 0.0885; rTHD0.4449; wavelet packet energy characteristic E ═ [14.3379,56.4032,3.4128,20.7894,0.0143,0.4541,2.4675,2.1208]. As shown in fig. 3, when the A, C-phase is 75% loaded and the B-phase is unloaded, and a three-phase unbalanced load occurs, the frequency spectrum distribution of the acoustic signal is more balanced than that of the three-phase balanced loadThe condition is changed remarkably, the proportion of the main frequency is more prominent at 200Hz, and the amplitudes of frequency components at 150Hz and 250Hz are increased. The amplitudes of the frequency components of 100Hz, 300Hz and 600Hz are reduced, wherein the amplitude change of the frequency component of 600Hz is most obvious. Ratio RoeIncrease to 0.1353; rTHD0.6532; wavelet packet energy characteristic E ═ 4.7488,60.4115,7.6629,18.3438,0.0490,0.7152,4.1015,3.9673]. This example compares the ratio RoeHarmonic distortion rate RTHDAnd the wavelet packet energy characteristic E are mutually complemented to form a three-phase unbalanced load characteristic parameter together.
In this embodiment, when the principal component analysis is performed on the unbalanced load operation characteristic parameter in step 2.3), the dimension of the independent characteristic parameter output by the principal component analysis is 2 dimensions, and the principal component condition is that the independent characteristic contribution rate exceeds 85%, so as to finally obtain the independent characteristic parameter corresponding to the unbalanced load operation characteristic parameter. Due to the ratio RoeHarmonic distortion rate RTHDAnd the wavelet packet energy characteristic E may be correlated with each other, so that in step 3) of this embodiment, a principal component analysis method is adopted to perform decorrelation processing on the wavelet packet energy characteristic E, so as to further reduce the characteristic quantity, and the final transformer three-phase unbalanced load characteristic parameters are only two, namely "characteristic parameter 1" and "characteristic parameter 2". It should be noted that the dimensionality reduction by principal component analysis is a basic application of the principal component analysis method, and therefore, the specific steps for performing the principal component analysis are not described in detail herein.
In this embodiment, the machine learning model in step 3) is a least squares support vector machine classification model, and other machine learning models may be adopted as needed.
In this embodiment, the training step of the least squares support vector machine classification model includes:
s1) respectively collecting the acoustic signals x when three-phase unbalanced load does not occur aiming at the sample transformer1iAnd an acoustic signal x when a three-phase unbalanced load occurs2i；
S2) for the acoustic signal x of the sample transformer when no three-phase unbalanced load occurs1iAnd the occurrence of triphasicAcoustic signal x when balancing load2iExtracting independent characteristic parameters;
s3) classifying the independent characteristic parameters of the sample transformer according to whether the sample transformer is in a three-phase unbalanced load running state when the vibration acceleration signal is collected, wherein the type of the characteristic parameter of the acoustic signal when the three-phase unbalanced load does not occur is '1', and the type of the characteristic parameter of the acoustic signal when the three-phase unbalanced load occurs is '-1';
s4) forming a training set by the classified independent characteristic parameters and the characteristic parameter classes thereof, and training the training set by a least square support vector machine method to obtain a least square support vector machine classification model comprising the mapping relation between the independent characteristic parameters and the unbalanced load running state.
As shown in fig. 4, in this embodiment, 80 sets of transformer acoustic signal samples to be predicted are adopted to train a least squares support vector machine classification model, where 40 sets are normal state samples, and 40 sets are three-phase unbalanced load operation state samples. Assuming that 100 groups of transformer acoustic signal samples to be predicted are unknown in classification, classifying and judging the 100 groups of acoustic signal characteristic parameters by adopting a trained least square support vector machine classification model, wherein 42 groups are judged to be in an unbalanced load operation state, 58 groups are judged to be in a three-phase balanced load operation state, the judgment accuracy is about 98%, and the practical application requirements are met.
The present embodiment also provides a system for interrupting an unbalanced load operation state of a transformer, which includes a computer device programmed to execute the steps of the method for judging an unbalanced load operation state of a transformer according to the present embodiment.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (6)
1. A method for judging the unbalanced load operation state of a transformer is characterized by comprising the following implementation steps:
1) acquiring an acoustic signal of a transformer to be detected;
2) extracting independent characteristic parameters according to the acoustic signal of the transformer to be detected;
3) inputting the independent characteristic parameters into a trained machine learning model to obtain the current unbalanced load running state of the transformer to be detected, wherein the trained machine learning model comprises a mapping relation between the independent characteristic parameters and the unbalanced load running state;
the detailed steps of the step 2) comprise:
2.1) preprocessing the acoustic signal of the transformer to be detected;
2.2) carrying out spectrum analysis and wavelet packet analysis on the preprocessed acoustic signals, and extracting characteristic parameters of unbalanced load operation;
2.3) carrying out principal component analysis on the characteristic parameters of the unbalanced load operation to obtain independent characteristic parameters;
the detailed steps of step 2.2) include:
2.2.1) carrying out Fourier transform on the sound signal of the preprocessed transformer, and intercepting the sound signal frequency spectrum within the range of 1 kHz; calculating the ratio R of the acoustic signal energy of 50Hz and odd harmonic frequency thereof to the acoustic signal energy of 50Hz and even harmonic frequency thereof within the range of 1kHzoe；
2.2.2) calculating the harmonic distortion rate R of the acoustic signal in the range of 1kHz with the frequency of 100Hz as the fundamental frequencyTHD(ii) a Wherein the harmonic distortion rate RTHDThe formula (2) is shown in the formula;
in the formula (2), A2For a harmonic amplitude of 100Hz of the signal, AiThe amplitude of the ith harmonic wave of 50Hz in the signal, and N is the harmonic frequency quantity of the 50Hz signal in the range of 1 kHz;
2.2.3) carrying out 3-layer wavelet packet analysis on the preprocessed acoustic signals of the transformer, and calculating a wavelet packet energy characteristic E; wherein, the calculation function expression of the wavelet packet energy characteristic E is shown as the formula (3);
in the formula (3), EiThe energy of the ith wavelet packet sub-band is n-3, and the number of wavelet packet decomposition layers is n;
2.2.4) ratio RoeHarmonic distortion rate RTHDAnd the characteristic E of the wavelet packet energy jointly form an unbalanced load operation characteristic parameter.
2. The method for determining the unbalanced load operation state of the transformer according to claim 1, wherein the ratio R in step 2.2.1)oeThe formula (1) is shown in the formula;
in the formula (1), A2iFor 50Hz even harmonic amplitude, A2i-1Is the amplitude of the 50Hz odd harmonic of the signal, and N is the number of 50Hz harmonic frequencies of the signal in the range of 1 kHz.
3. The method for judging the operating state of the unbalanced load of the transformer according to claim 1, wherein in the step 2.3), when principal component analysis is performed on the characteristic parameters of the unbalanced load operation, the dimension of the independent characteristic parameters output by the principal component analysis is 2 dimensions, and the principal component condition is that the contribution rate of the independent characteristic exceeds 85%, and finally the independent characteristic parameters corresponding to the characteristic parameters of the unbalanced load operation are obtained.
4. The method for determining the unbalanced load operation state of the transformer according to claim 1, wherein the machine learning model in step 3) is a least squares support vector machine classification model.
5. The method for judging the unbalanced load operation state of the transformer according to claim 4, wherein the training step of the least squares support vector machine classification model comprises:
s1) respectively collecting the acoustic signals x when three-phase unbalanced load does not occur aiming at the sample transformer1iAnd an acoustic signal x when a three-phase unbalanced load occurs2i；
S2) for the acoustic signal x of the sample transformer when no three-phase unbalanced load occurs1iAnd an acoustic signal x when a three-phase unbalanced load occurs2iExtracting independent characteristic parameters;
s3) classifying the independent characteristic parameters of the sample transformer according to whether the sample transformer is in a three-phase unbalanced load running state when the vibration acceleration signal is collected, wherein the type of the characteristic parameter of the acoustic signal when the three-phase unbalanced load does not occur is '1', and the type of the characteristic parameter of the acoustic signal when the three-phase unbalanced load occurs is '-1';
s4) forming a training set by the classified independent characteristic parameters and the characteristic parameter classes thereof, and training the training set by a least square support vector machine method to obtain a least square support vector machine classification model comprising the mapping relation between the independent characteristic parameters and the unbalanced load running state.
6. A system for determining an unbalanced load operation state of a transformer, comprising a computer device, wherein the computer device is programmed to perform the steps of the method for determining an unbalanced load operation state of a transformer according to any one of claims 1 to 5.
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