CN115236385A - Automatic identification method for waveform polarity of high-frequency pulse current - Google Patents
Automatic identification method for waveform polarity of high-frequency pulse current Download PDFInfo
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- 235000012771 pancakes Nutrition 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/14—Indicating direction of current; Indicating polarity of voltage
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- 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
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Abstract
The invention discloses an automatic identification method of waveform polarity of high-frequency pulse current, belonging to the technical field of insulation fault detection of power equipment, and the method is a head wave and polarity identification method which deeply utilizes waveform characteristics of pulse signals; firstly, under the condition of a laboratory, the propagation characteristics of head waves of different typical discharge positions and types are obtained by injecting a steep pulse mode, the response signal waveform is actually measured through each outgoing line coupling end, and a typical response waveform sample library of each injection mode and position is established; and then the waveform sequence is taken as an input vector, the method for training the first wave waveform and the polarity of the input waveform sequence through the artificial neural network identifies the waveform details by using the artificial neural network, thereby realizing the automatic, efficient and accurate identification of the polarity of the high-frequency current waveform of the transformer, providing key diagnostic information for the diagnosis of the key states such as the discharge type, the position and the like of the partial discharge, and improving the robustness and the automation level of the diagnosis.
Description
Technical Field
The invention belongs to the technical field of insulation fault detection of power equipment, and particularly relates to an automatic identification method for waveform polarity of high-frequency pulse current
Background
Partial discharge is an important sign of insulation defect in the power equipment and also an important basis for equipment insulation fault diagnosis and fault location; the high-frequency partial discharge detection has wide frequency band and rich information content, and is easy to install and implement through a device grounding wire. The polarity information of the high-frequency pulse current signal is an important basis for identifying the interference pulse, and judging the discharge position and the discharge type.
Under the environment condition of live-line operation of field equipment, the polarity of an actually measured pulse current waveform is influenced by background noise and interference signals, and the head wave of a discharge waveform of the actually measured pulse current waveform is polluted, so that the head wave is difficult to distinguish; in addition, since the high frequency signal is attenuated and distorted after propagating through the winding, the long wire, etc., the head wave identification is also difficult. At present, in the field partial discharge live detection, mainly testers manually confirm the waveform polarity based on experience, and perform time delay estimation according to the waveform polarity, so as to analyze and judge the discharge type and the discharge position. Since personal safety risks are inevitably generated during the testing process of the defective equipment by field personnel, machine identification through an on-line monitoring or intensive care system is necessary. The existing methods for judging the polarity of a pulse waveform mainly include the following methods:
(1) Firstly, filtering interference signals by various filtering means, reducing the background noise level, improving the signal to noise ratio of detection and improving the identifiability of a first wave signal; the threshold method is to set a threshold according to the background noise level, and determine the polarity of the pulse according to the level value of the pulse waveform when the head wave exceeds the threshold, as shown in fig. 1. Two parallel horizontal lines in the middle of fig. 1 are thresholds, and the head wave has positive polarity when passing through the thresholds. The method is simple and intuitive, but when the background noise level is high or interference signals exist, the judgment of the polarity of the head wave is very difficult because the signal-to-noise ratio of the detection signal is reduced.
(2) Judging the polarity of the first-time threshold value as the polarity of the first wave directly through waveform analysis, wherein the judging method is based on the method, but is influenced by threshold value setting, background noise and interference signals (as shown in figure 2) or adopts a correlation analysis method, a representative waveform X is selected as a reference, the polarity of the waveform is known, and a waveform u with the polarity to be determined is used as a reference 2 (j) Calculating the similarity coefficient with the template file Y by adopting a formula (1),
the similarity coefficient rho is larger than or equal to k, the k is a judgment threshold value, the rho value is between 0 and 1, and the closer to 1, the higher the similarity of the two waveforms is, the higher the consistency of the polarity of the waveform to be detected and the reference waveform is. The method is also a basic algorithm for waveform polarity judgment and time delay estimation. However, this method is also greatly affected by the background noise level, and the effectiveness of the correlation method is seriously reduced due to attenuation and distortion of the signal propagation process, especially the superposition effect of the catadioptric signals.
(3) Compared with a method for directly judging through a threshold value method, the method for reading the head wave of the pulse signal can improve the identification consistency and stability, but the effect of the method is still limited by background noise and an interference signal to a great extent (as shown in FIG. 2), and the method is degraded significantly as the level of the interference signal increases; the energy accumulation method is essentially a second-order statistic method, and is a method for observing the initial position of the head wave of a signal after the signal is squared. Considering that the signal energy is proportional to the square of the voltage, the voltage waveform of the pulse signal can be converted into an energy-related value accumulation curve, and when the partial discharge signal is much larger than the background noise, an obvious inflection point is generated on the curve, and the inflection point can be regarded as the starting moment of the partial discharge. Un is the voltage value of the nth point on the signal waveform, h (h < N) is the number of points of signal accumulation calculation, and the accumulated energy is
In the formula: ti is the signal acquisition start time;
u (t ') is the UHF signal amplitude at time t';
r is the input impedance of the acquisition system.
This results in an energy accumulation curve, the inflection point of which is considered to be the starting point of the signal. The starting time of the original signal is converted into the inflection point of the energy accumulation curve, as shown in fig. 3. As can be seen from fig. 3, the transition region of the energy accumulation curve is very gentle, the arrival position of the first wave is not easy to be distinguished, and the polarity information of the waveform disappears due to the square transformation. It is clear that background noise and interfering signals can severely degrade the inflection information of the method.
(4) The FLOS is introduced into a high-resolution multipath time delay estimation algorithm, so that the capability of the algorithm for resisting impulse noise can be improved, the problem of performance degradation of a classical algorithm in a distributed noise environment is solved, and the prior knowledge of the characteristics of a noise signal has higher dependency.
In summary, the existing method for identifying the polarity of the head wave and the waveform has a better effect under the condition of high signal-to-noise ratio. However, as the background noise level and the interference signal intensity increase and the signal-to-noise ratio decreases, the accuracy of identifying the polarity of the head wave and the waveform decreases seriously, and the requirements of diagnosing and evaluating the discharge type, position and state in engineering cannot be met. The lack of discovery and utilization of waveform characteristics is a significant cause of poor performance of these methods at lower signal-to-noise ratios. Therefore, on the basis of observation and analysis of a large amount of measured data, an automatic pulse signal head wave polarity identification method based on a neural network is provided, and tests show that the method is high in judgment accuracy and adaptability for the head wave polarity of the high-frequency partial discharge signal, and is suitable for fault analysis and diagnosis without manual intervention in the processes of online monitoring and intensive care.
Disclosure of Invention
The invention aims to provide an automatic identification method of waveform polarity of high-frequency pulse current, which is characterized in that the method is a head wave and a polarity identification method thereof which deeply utilize the waveform characteristics of pulse signals; firstly, under the condition of a laboratory, the propagation characteristics of head waves of different typical discharge positions and types are obtained by injecting a steep pulse mode, the response signal waveform is actually measured through each outgoing line coupling end, and a typical response waveform sample library of each injection mode and position is established; the waveform sequence is used as an input vector, the artificial neural network is used for training the waveform and the polarity of the input waveform sequence, the artificial neural network is used for identifying the details of the waveform, and due to the nonlinear characteristic of the neural network, subsequent reflection superposition signals and attenuation oscillation signals which are irrelevant to the head wave in the pulse waveform can automatically reduce the weight, so that the accuracy of identifying the head wave and the polarity of the head wave is improved; the method specifically comprises the following steps:
(1) Building a transformer solid model platform, and simulating internal discharge of equipment in a mode of injecting signals into the solid transformer model through a steep pulse generator, wherein the injection mode comprises simulating winding group earth discharge, winding turn or inter-cake discharge, winding external discharge and winding inter-phase discharge;
(2) Installing high-frequency CT sensors at the positions of a transformer bushing end screen, a neutral point, an iron core, a clamp grounding wire, an oil tank grounding wire and the like, and synchronously sampling high-frequency pulse response waveforms through a collecting device; at the moment, because the equipment is not electrified under the condition of injecting the signal, no external interference signal enters a test loop basically, and the background noise level is very low;
(3) Establishing a waveform sample library through the injection of an obstructed position and an obstructed mode, and marking the polarity of a first wave of the waveform sample library;
(4) Intercepting a subsequence of a waveform in a sample library, taking a k-time of a root mean square value of the sequence as a threshold, taking a pulse subsequence of 1us from the first threshold-passing point in the sequence to the front and 2us from the back as an artificial neural network input, taking the polarity of a head wave as an output to train a network parameter matrix of the neural network, stopping the training until reaching set identification precision, setting the initial stage as 100%, and if the set iteration frequency limit value is reached, not converging, reducing the precision limit value by 0.1% and carrying out re-iteration training; wherein k is 1.3-1.5;
(5) Artificially adding noise signals into the waveform sequence in the sample library, and controlling the signal-to-noise ratio by adjusting the amplitude level of the noise signals at a specified signal-to-noise ratio SNR th In the above, the neural network is further subjected to the strengthening training to improve the adaptability, and the set identification precision is reached, namely, the network parameters can be output; SNR th The signal-to-noise ratio threshold is not less than 10dB for the set signal-to-noise ratio threshold.
(6) And intercepting the acquired pulse waveform by applying the trained network parameters, and taking the intercepted subsequence as an input vector of the neural network to realize the automatic identification of the polarity of the head wave of the high-frequency partial discharge pulse.
The method has the advantages that the waveform details are identified by the artificial neural network, the accumulated experience in the early stage can be effectively accumulated, samples can be continuously expanded through counterstudy, the completely automatic first wave polarity identification is realized, the method is efficient and concise in application, and the method is suitable for the application of real-time algorithm in online monitoring.
Drawings
FIG. 1 is a diagram illustrating waveform polarity determination by a threshold method;
fig. 2 is a schematic diagram of a background noise and interference signal pair mailbox for determining the polarity of a waveform.
FIG. 3 is an energy accumulation curve of a pulse waveform;
FIG. 4 illustrates an injection signal in simulating external interference with different discharge patterns, wherein a) the external interference signal; b) Winding to ground; c) Winding turns/cakes;
FIG. 5 is a schematic diagram of the location of signal coupling points;
FIG. 6 is a typical waveform sample library a) multi-terminal detection waveforms during the injection of the winding head end, b) multi-terminal detection waveforms during the inter-turn injection of the pancake in the middle of the winding;
fig. 7 is a flow chart of automatic identification of the waveform polarity of the high-frequency pulse current.
Detailed Description
The invention provides an automatic identification method of waveform polarity of high-frequency pulse current; the method is a method for identifying the head wave and the polarity thereof by deeply utilizing the waveform characteristics of pulse signals, firstly, under the condition of a laboratory, the propagation characteristics of the head wave of different typical discharge positions and types are obtained by injecting steep pulses, the response signal waveform is actually measured by each outgoing line coupling end, a typical response waveform sample library of each injection mode and position is established, then, a waveform sequence is taken as an input vector, and the method for training the waveform and the polarity of the head wave by the input waveform sequence through an artificial neural network is further explained by combining the attached drawings.
The specific process steps of identifying the head wave and the polarity thereof by depth utilizing the waveform characteristics of the pulse signal are shown in a high-frequency pulse current waveform polarity automatic identification flow chart of fig. 7:
(1) Building a transformer solid model platform, and simulating internal discharge of equipment in a mode of injecting signals into a solid transformer model through a steep pulse generator, wherein the injection mode comprises simulating winding earth discharge, winding turn or intertillage discharge, winding external discharge, winding interphase discharge and the like, and the injection signals are respectively shown in figure 4 in the modes of simulating external interference and different discharge forms, wherein a) external interference signals; b) Winding to ground; c) Winding turns/cakes;
(2) High-frequency CT sensors are arranged at positions of a transformer bushing end screen, a neutral point, an iron core, a clamping piece grounding wire, an oil tank grounding wire and the like, and high-frequency impulse response waveforms are synchronously sampled through a collecting device, as shown in a schematic diagram of the position of a signal coupling point in fig. 5. Since the device is not charged under the condition of the injected signal, basically no external interference signal enters the test loop, and the background noise level is very low.
(3) And establishing a waveform sample library through the injection of an obstructed position and an obstructed mode, and labeling the polarity of the head wave of the waveform sample library. As shown in the exemplary waveform sample library of fig. 6. Wherein, a) multi-terminal detection waveform when the winding head end is injected, b) multi-terminal detection waveform injected between the pancake turns in the middle of the winding;
(4) For a waveform interception subsequence in a sample library, the method is that k times (k is 1.3-1.5) of the root mean square value of the sequence is used as a threshold value, a pulse subsequence of 1us is taken forward and 2us is taken backward from a first threshold value passing point in the sequence as an artificial neural network input, the head wave polarity is used as an output to train a network parameter matrix of the neural network, the training is stopped until the set identification precision is reached, the initial stage is set to be 100%, and if the set iteration frequency limit value is reached, the convergence is still not realized, the precision limit value can be reduced by 0.1% and the iterative training is carried out again;
(5) Artificially adding noise signals into the waveform sequence in the sample library, and controlling the signal-to-noise ratio by adjusting the amplitude level of the noise signals at a specified signal-to-noise ratio SNR th In the above, the neural network is further subjected to the strengthening training to improve the adaptability, and the set identification precision is reached, namely, the network parameters can be output;
(6) And intercepting the acquired pulse waveform by applying the trained network parameters, and taking the intercepted subsequence as an input vector of the neural network to realize the automatic identification of the polarity of the head wave of the high-frequency partial discharge pulse.
In conclusion, the invention organically integrates the prior knowledge of the propagation characteristic of the high-frequency pulse current and the like into the detection of the high-frequency partial discharge of the transformer, utilizes the artificial neural network to identify the waveform details, and automatically reduces the weight of subsequent reflection superposition signals and attenuation oscillation signals which are irrelevant to the first wave in the pulse waveform due to the nonlinear characteristic of the neural network, thereby realizing the automatic, efficient and accurate identification of the polarity identification of the high-frequency current waveform of the transformer, providing key diagnostic information for the diagnosis of the key states of the discharge type, the position and the like of the partial discharge, and improving the accuracy of the identification of the first wave and the polarity thereof. The robustness and automation level of the diagnosis are improved.
Claims (1)
1. An automatic identification method of waveform polarity of high-frequency pulse current is characterized in that the method is a head wave and polarity identification method which deeply utilizes waveform characteristics of pulse signals; firstly, under the condition of a laboratory, the propagation characteristics of head waves of different typical discharge positions and types are obtained by injecting a steep pulse mode, the response signal waveform is actually measured through each outgoing line coupling end, and a typical response waveform sample library of each injection mode and position is established; then, the waveform sequence is used as an input vector, the waveform and the polarity of the input waveform sequence are trained through an artificial neural network, the waveform details are identified through the artificial neural network, and due to the nonlinear characteristic of the neural network, subsequent reflection superposition signals and attenuation oscillation signals which are irrelevant to the head wave in the pulse waveform can automatically reduce the weight, so that the accuracy of identification of the head wave and the polarity of the head wave is improved; the method specifically comprises the following steps:
(1) Building a transformer solid model platform, and simulating internal discharge of equipment in a mode of injecting signals into the solid transformer model through a steep pulse generator, wherein the injection mode comprises simulating winding group earth discharge, winding turn or inter-cake discharge, winding external discharge and winding inter-phase discharge;
(2) Installing high-frequency CT sensors at the positions of a transformer bushing end screen, a neutral point, an iron core, a clamp grounding wire, an oil tank grounding wire and the like, and synchronously sampling high-frequency pulse response waveforms through a collecting device; at the moment, because the equipment is not electrified under the condition of injecting the signal, basically no external interference signal enters a test loop, and the background noise level is very low;
(3) Establishing a waveform sample library through injection at different positions and in different modes, and marking the head wave polarity of the waveform sample library;
(4) Intercepting a subsequence of a waveform in a sample library, taking a k-time of a root mean square value of the sequence as a threshold, taking a pulse subsequence of 1us from the first threshold-passing point in the sequence to the front and 2us from the back as an artificial neural network input, taking the polarity of a head wave as an output to train a network parameter matrix of the neural network, stopping the training until reaching set identification precision, setting the initial stage as 100%, and if the set iteration frequency limit value is reached, not converging, reducing the precision limit value by 0.1% and carrying out re-iteration training; wherein k is 1.3-1.5;
(5) Artificially adding noise signals into the waveform sequence in the sample library, and controlling the signal-to-noise ratio by adjusting the amplitude level of the noise signals at a specified signal-to-noise ratio SNR th In the above, the neural network is further subjected to the strengthening training to improve the adaptability, and the set identification precision is reached, namely, the network parameters can be output; SNR th The signal-to-noise ratio threshold value is set, and the signal-to-noise ratio threshold value is not less than 10dB;
(6) And intercepting the acquired pulse waveform by applying the trained network parameters, and taking the intercepted subsequence as an input vector of the neural network to realize the automatic identification of the polarity of the head wave of the high-frequency partial discharge pulse.
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