JP2008185463A - Insulation abnormality diagnosis system of electrical facility - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To surely judge presence or absence of a portion with insulation abnormality even when discharging is weak or a distance between an acoustic wave sensor and a discharging point is large. <P>SOLUTION: The acoustic wave sensor 2 detects an acoustic wave signal generated by a transformer 10. A control circuit 8 extracts a specific frequency component from the acoustic wave signal to detect an envelope curve signal from the signal of the specific frequency component. The control circuit 8 computes a frequency spectrum from the envelope curve signal to compute a Mahalanobis distance D<SP>2</SP>using the frequency spectrum intensity. The control circuit 8 judges the presence or absence of a portion with insulation abnormality based on the comparison result between the Mahalanobis distance D<SP>2</SP>and a prescribed threshold. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an insulation abnormality diagnosis system for electrical equipment that determines the presence or absence of an insulation abnormality location by detecting creeping discharge that occurs on the surface of an operating electrical equipment or partial discharge that occurs inside.

  In electrical equipment such as transformers, the performance of the insulation may deteriorate due to factors such as the surrounding environment and the heat generated by the equipment itself, leading to failure and accidents. It is known that when the insulation performance deteriorates, creeping discharge occurs on the equipment surface and partial discharge occurs inside the equipment, and discharge current flows and electromagnetic waves, ultrasonic waves, ultraviolet rays, etc. are radiated simultaneously. Therefore, conventionally, it has been performed to detect the presence or absence of an abnormal insulation portion by detecting these physical quantities. However, since the physical quantity generated along with the discharge is very weak, it has become a problem how to accurately extract only the physical quantity associated with the discharge from the measurement signal including noise.

  FIG. 10A shows an example of a waveform obtained by extracting a 40 kHz component from a sound wave signal detected at the time of occurrence of discharge, and FIG. 10B shows an example of an AC voltage waveform applied to the electrical equipment at that time. Is shown. Discharge occurs when the voltage applied to the location where the discharge occurs exceeds a certain threshold value, and stops when the voltage falls below the threshold value. For this reason, it often has a specific pattern that occurs twice during one cycle of the applied AC voltage. In addition, once discharge starts, it often continues for a certain period of time. The presence or absence of discharge is determined based on the characteristics of such a discharge phenomenon.

Patent Document 1 as a conventional technique discloses a method for detecting and judging an ultrasonic wave accompanying discharge. This method extracts the frequency component peculiar to the discharge from the signal captured by the ultrasonic sensor and detects the envelope, and then extracts the frequency component twice the applied AC voltage from the signal, The presence / absence of discharge is judged by strength.
Japanese Patent Laid-Open No. 09-127181

  However, in the conventional method of determining the presence or absence of discharge based on the strength of the frequency component twice the applied AC voltage, if the discharge is weak or the distance between the discharge location and the acoustic wave sensor is long, the applied AC voltage There is a possibility that the intensity of the double frequency component becomes weak and it may be erroneously determined that the discharge is not generated although the discharge is generated.

  The present invention has been made in view of the above circumstances, and an object of the present invention is to reliably detect the occurrence of discharge even when the discharge is weak or when the distance between the acoustic wave sensor and the discharge location is long. An object of the present invention is to provide an insulation abnormality diagnosis system for electrical equipment that can determine the presence or absence of a location.

In order to achieve the above object, an insulation abnormality diagnosis system for electrical equipment according to claim 1,
An insulation abnormality diagnosis system for electrical equipment that determines the presence or absence of an insulation abnormality by detecting creeping discharge generated on the surface of the electrical equipment in operation or partial discharge generated inside,
A sound wave sensor for detecting sound waves generated during the discharge;
A sound wave signal filter means for extracting a specific frequency component from the sound wave signal detected by the sound wave sensor;
An envelope signal detection means for detecting an envelope signal from the signal output by the sound wave signal filter means;
An envelope signal frequency spectrum calculating means for calculating the frequency spectrum from the envelope signal detected by the envelope signal detecting means;
Mahalanobis distance calculating means for calculating the Mahalanobis distance using the frequency spectrum calculated by the envelope signal frequency spectrum calculating means as a variable,
The present invention is characterized by comprising an insulation abnormality determining means for determining the presence or absence of an insulation abnormality location based on a comparison between the Mahalanobis distance calculated by the Mahalanobis distance calculating means and a predetermined threshold value.

  According to this configuration, when a specific frequency component is extracted from the sound wave signal and the frequency spectrum of the envelope signal of the specific frequency component signal is calculated, the pattern of the spectrum distribution varies depending on the presence or absence of discharge. The presence / absence of discharge can be determined by quantifying the spectral distribution using the Mahalanobis distance and comparing it with a predetermined threshold.

  According to the present invention, the distance of Mahalanobis at the time of occurrence of discharge is limited to a certain range regardless of the strength of the discharge. Therefore, it is possible to reliably detect the occurrence of discharge and correctly determine the presence or absence of an insulation abnormality. Can do.

(First embodiment)
Hereinafter, a first embodiment of the present invention will be described with reference to FIGS.
FIG. 1 is a block diagram showing the configuration of an insulation abnormality diagnosis system. The insulation abnormality diagnosis system 1 according to the present embodiment determines (diagnosis) the presence or absence of an insulation abnormality by detecting creeping discharge generated on the surface of an operating electrical facility (transformer) or partial discharge generated inside. The apparatus includes a sound wave sensor 2, a waveform measuring device 3, an A / D converter 4, a communication device 5, a display device 6, an input device 7, a control circuit 8, and a storage device 9.

  The sonic sensor 2 is disposed in the vicinity of the electrical equipment, for example, the transformer 10 that is the object of the insulation abnormality diagnosis, detects an ultrasonic wave generated at the time of discharge, and outputs a voltage signal substantially proportional to the intensity. The waveform measuring device 3 (corresponding to the waveform measuring means) outputs a voltage signal obtained by converting the power supply voltage input to the transformer 10 into an appropriate magnitude. The A / D converter 4 converts the voltage signal output from the sound wave sensor 2 and the voltage signal output from the waveform measuring device 3 into a digital value and outputs the digital value to the control circuit 8.

  The communication device 5 (corresponding to a transmission means) transmits the output information of the control circuit 8 to the mobile terminal 11 such as a mobile phone or other external devices, or receives instructions from the mobile terminal 11 or other external devices. It is something to do. The display device 6 is for displaying output information of the control circuit 8, and is composed of, for example, a liquid crystal panel. This display device 6 also functions as an alarm generation means. The input device 7 is used to input operation instructions and data to the control circuit 8 and is constituted by a touch panel, for example.

  The control circuit 8 is configured by using a microcomputer, and includes a CPU 12, a RAM 13, a ROM 14, an input / output interface (not shown), a bus connecting them, a power supply device, and the like. As will be described in detail later, the control circuit 8 of the present embodiment functions as a sound wave signal filter unit, an envelope signal detection unit, an envelope signal frequency spectrum calculation unit, a Mahalanobis distance calculation unit, and an insulation abnormality determination unit.

  A storage device 9 (corresponding to data storage means) is connected to the control circuit 8 as an external storage device. The storage device 9 stores measurement data (acoustic wave waveform data) and analysis data (judgment result and calculated Mahalanobis distance), and also stores a program for operating the insulation abnormality diagnosis system 1 in advance. It consists of a magnetic disk device.

Next, the Mahalanobis distance calculated when the insulation abnormality diagnosis system 1 of the present embodiment performs facility abnormality diagnosis will be described.
In MT method (Mahalanobis-Taguchi method), which is one of the quality engineering methods used for pattern recognition for prediction and diagnosis, how similar to this reference data group is based on a certain state described in multivariate. A measure that expresses whether or not it takes into account the correlation between variables is called the Mahalanobis distance. In the present embodiment, a plurality of sound wave waveform data in a state where a discharge is generated is obtained, a specific frequency component such as 40 kHz is extracted for each of the sound wave waveform data, and the specific frequency component An envelope signal is detected from the signal, and a frequency spectrum of the waveform of the obtained envelope signal is calculated. In this frequency spectrum, the spectrum intensity of a frequency k times the power supply frequency (k is an integer of 1 or more) is used as a variable to be a reference data group.

  The spectrum distribution at the time of occurrence of discharge is not constant, and various changes are observed in the strength of discharge depending on, for example, how dust adheres to the surface of the electrical equipment and how wet it is. Therefore, in order to improve the diagnostic accuracy, it is preferable to include the pattern of the spectrum distribution under such various situations in the reference data group. The number n of data in the reference data group may be about several tens as an example.

A procedure for calculating the Mahalanobis distance using the reference data group will be described.
FIG. 2 shows the spectral intensities yi1, yi2, yi3 (i = 1, 2,..., N) of 60 Hz, 120 Hz, and 180 Hz (1 times, 2 times, and 3 times the power frequency) as variable 1, variable 2, respectively. A reference data group in the case of variable 3 is shown. Here, averages m1 to m3 of variables 1 to 3 and standard deviations σ1 to σ3 are calculated for the prepared n pieces of reference data.

Subsequently, for each of the spectral intensities yi1, yi2, yi3 (i = 1, 2,..., N) of the n pieces of reference data, the following formula (1) is used by using averages m1 to m3 and standard deviations σ1 to σ3. Normalize. FIG. 3 shows a reference data group based on normalized spectral intensities Yi1, Yi2, Yi3 (i = 1, 2,..., N).

  A correlation coefficient is calculated from the normalized variables shown in FIG. 3, and a correlation coefficient matrix R is created. FIG. 4 shows an equation (Equation (2)) for deriving the correlation coefficient matrix R and its element rpq (= rqp). Here, since there are three variables, the correlation coefficient matrix R is a 3 × 3 matrix.

Subsequently, an inverse matrix R ⁻¹ of the correlation coefficient matrix R is calculated. FIG. 5 shows a determinant of the correlation coefficient matrix R, | R | (formula (3)), and an inverse matrix R- ¹ . Mahalanobis distance ^{D 2} is variable number (here, 3), that there normalized data Y1, Y2, Y3, using an inverse matrix ^{R -1} is calculated by the following equation (4).

FIG. 6 shows the generation of a correlation coefficient matrix R and its inverse matrix R- ¹ using the intensity of the frequency spectrum calculated from the sound wave signal at the time of occurrence of discharge as reference data, and using that to generate sound waves when discharge is generated. It shows the result of calculating Mahalanobis distance D2 for each of the signal (data numbers ² to 36 on the horizontal axis) and the sound wave signal (data numbers 1 and 37 to 40 on the horizontal axis) when no discharge occurs. . The reference data group used for obtaining the correlation coefficient matrix R is a sound wave signal (data numbers 2 to 36) when discharge is occurring. Mahalanobis distance D ² when the discharge has occurred while is calculated as 0 to 1.5, the Mahalanobis distance D ² when the discharge is not generated is greater than 2.0. Therefore, in this example, if the threshold value is around 2.0, the presence or absence of discharge can be determined.

Next, the control in which the insulation abnormality diagnosis system 1 of this embodiment performs equipment abnormality diagnosis will be described. FIG. 7 is a flowchart showing the control contents executed by the control circuit 8 after creating the inverse matrix R ⁻¹ of the correlation coefficient matrix R based on the reference data group. In the first step S1, the voltage signal output from the sound wave sensor 2 is converted into a digital signal by the A / D converter 4, and is taken into the RAM 13 as sound wave waveform data. If there is sound wave waveform data already converted into a digital signal and stored in the storage device 9, it may be read out to the RAM 13. The length of the sound wave waveform data to be captured is, for example, 20 cycles of the power supply voltage input to the transformer 10.

  In step S2, a specific frequency component is extracted from the sound wave waveform data. FIG. 8A shows a sound wave waveform, and FIG. 8B shows a waveform obtained by extracting a sound wave component of, for example, 40 kHz from the sound wave waveform. In step S3, an envelope signal is detected from the signal of the specific frequency component obtained in step 2. FIG. 8C shows the waveform of the envelope signal detected from the 40 kHz sound wave component shown in FIG. In step S4, the frequency spectrum of the envelope signal waveform is calculated. FIG. 8D shows the frequency spectrum distribution of the envelope signal shown in FIG.

In step S5, k times the power supply frequency in the frequency spectrum of the envelope signal (k is an integer of 1 or more), for example 60 Hz, 120 Hz, and calculates the Mahalanobis distance ^{D 2} by using the spectral intensity of 180 Hz. In step S6, comparing the Mahalanobis distance D ² calculated in step S5 a predetermined threshold (e.g., as discussed above 2.0). When the Mahalanobis distance ^{D 2} is smaller than the threshold value: in (Step S6 YES) it determines that there is abnormality insulated step S7, if the above threshold value (step S6: NO) in the step S8 Judge that there is no insulation abnormality.

As described above, in the present embodiment, detection of the sound wave signal generated from the transformer 10 that is the object of the insulation abnormality diagnosis, extraction of the specific frequency component from the sound wave signal, and the envelope signal from the signal of the specific frequency component detection, the Mahalanobis distance calculation D ² with calculated and the frequency spectrum intensity of the frequency spectrum of the envelope signal performed. Pattern of the frequency spectrum distribution is different depending on the presence or absence of the discharge due to insulation abnormalities can quantitatively evaluate the pattern of the frequency spectrum distribution Using the above Mahalanobis distance D ^2. Since the Mahalanobis distance D ² at the time of occurrence of the discharge is limited to a certain range regardless of the strength of the discharge, comparing the magnitude of the Mahalanobis distance D ² with a predetermined threshold value, the strength of the discharge can be reduced. Regardless of this, or even when the distance between the acoustic wave sensor 2 and the discharge location is large, it is possible to accurately determine the presence or absence of an abnormal insulation location.

The correlation coefficient matrix R (inverse matrix R ⁻¹ ) used when calculating the Mahalanobis distance D ² is created in advance by including in the reference data group the pattern of the spectrum distribution at the time of occurrence of discharge under various conditions. Yes. Then, the threshold value is because it is set on the basis of the Mahalanobis distance D ² calculated for the respective reference data, it can be grasped reliably discharge generated under various conditions.

The discharge due to the insulation abnormality often has a specific pattern that occurs in a certain number (for example, twice) during one cycle of the AC voltage applied to the transformer 10. In the present embodiment, one times the power supply frequency to the manifestation of salient features in the frequency spectrum, twice, so to calculate a Mahalanobis distance D ² of the spectral intensity of the triple as a variable, liable to determine the presence or absence of discharge generation, The reliability of diagnosis results is improved.

(Second Embodiment)
Next, a second embodiment of the present invention will be described with reference to FIG.
In this embodiment, when the discharge does not continue for a long time and the occurrence and stop of the discharge are repeated, the acoustic wave waveform data is periodically acquired and analyzed over a long period (for example, one week or one month), and the insulation abnormality The presence or absence of this is judged. The hardware configuration of the insulation abnormality diagnosis system is the same as that shown in FIG.

  FIG. 9 is a flowchart showing the control contents executed by the control circuit 8, and the same step numbers are assigned to the steps that execute the same processing as the steps shown in FIG. In order to acquire sound wave waveform data at predetermined time intervals, the control circuit 8 operates a timer (timer) and waits until a predetermined time (for example, 10 minutes) elapses in step S9. When a predetermined time elapses, the process proceeds to step S10, the power supply voltage detected by the waveform measuring device 3 is input via the A / D converter 4, and the power supply voltage is predetermined from the time when the power supply voltage reaches a predetermined phase (for example, 0 degrees). Sound wave waveform data for a period (for example, 20 periods) is input and written in the RAM 13.

In step S5 from step S2, and calculates the Mahalanobis distance D ² from the sound wave waveform data measured as described in the first embodiment. In step S6, and compares the calculated Mahalanobis distance D ² and the threshold value. Here, if the Mahalanobis distance ^{D 2} is smaller than the threshold (step S6: YES), it is determined that there is abnormal insulation at step S7. In step S 12, the determination result and the Mahalanobis distance D ² are stored in the storage device 9. In a succeeding step S13, an alarm notifying the occurrence of discharge is displayed on the display device 6. In step S 14, the determination result and Mahalanobis distance D ² are transmitted to the mobile terminal 11 via the communication device 5. Then, it returns to step S9 again.

On the other hand, the Mahalanobis distance D ² is greater than the threshold: (step S6 NO), it is determined that there is no insulation abnormality in step S8. Then, in step S15, it stores the determination result and the Mahalanobis distance D ² to the storage device 9, returns to step S9.

As described above, the Mahalanobis distance D ² is calculated at predetermined time intervals, since it is determined whether the discharge, even when the discharge is repeatedly generated a stop without continuous, it can be determined whether the insulation abnormal point . Further, since the calculated Mahalanobis distance D ² is stored in the storage device 9, the time transition of the Mahalanobis distance D ² becomes clear, and it is possible to determine the presence or absence of an insulation abnormality part later. It is possible to perform insulation abnormality diagnosis by changing various criteria (for example, the threshold value used in step S6).

In addition to the determination result and the Mahalanobis distance D ^2, the signal waveform of the specific frequency component extracted from the sound wave waveform (FIG. 8 (b) refer) or envelope signal waveform (see FIG. 8 (c)) and the supply voltage waveform (Fig. 10 (b)) is associated (synchronized) with each other and stored in the storage device 9, so that it is possible to know at which phase the sound wave signal is strong. As described above, since the discharge due to the insulation abnormality often has a specific pattern that occurs near the peak of the applied AC voltage, whether the diagnosis result is correct can be confirmed later based on the waveform, Reliability can be further enhanced.

Determining the presence or absence of insulating abnormal point immediately after calculating the Mahalanobis distance D ² at predetermined time intervals, since the emit an alarm when it is determined that the insulation abnormality location is present, a maintenance person abnormal insulation easily Can be recognized. Further, since it transmits the determination result and the Mahalanobis distance D ² to the portable terminal 11, can also maintenance person at a location remote from the electrical equipment to be diagnosed (the transformer 10) informs the determination result.

(Other embodiments)
The present invention is not limited to the embodiments described above and shown in the drawings, and can be modified or expanded as follows, for example.
As reference data group for calculating the Mahalanobis distance ^{D 2,} 60 Hz at the time of discharge generation, 120 Hz, although the variable spectral intensity of 180Hz, when the discharge is not generated 60 Hz, 120 Hz, the spectrum intensity of 180Hz A reference data group may be prepared as a variable. In this case, the Mahalanobis distance D ² calculated from the detected acoustic signal is determined that there is a discharge when greater than the threshold value. In this case and in each of the above embodiments, a spectral intensity such as 240 Hz, 300 Hz, 360 Hz,... (4 times, 5 times, 6 times,.

The sonic sensor 2 is not limited to ultrasonic waves (40 kHz in the above embodiment), and may be configured to generally detect sound waves. The sonic sensor 2 may be always installed in the vicinity of the electrical equipment, for example, outside the safety fence, in the panel, or the like.
A flash memory, an SD memory card or the like may be used as the data storage means.
The alarm generation means may be an audio output means.
The alarm generation means and the transmission means may be provided as necessary. In the first embodiment, the input processing of the power supply voltage waveform, when not executing the transmission processing of the determination result and the Mahalanobis distance D ² is a waveform measuring device 3, it may be omitted communication device 5.

The block block diagram which shows the insulation abnormality diagnostic system of the 1st Embodiment of this invention Diagram showing reference data group before normalization Diagram showing standard data group after normalization The figure which shows the correlation coefficient matrix R and the derivation formula of the element The figure which shows the inverse matrix R- ¹ of the correlation coefficient matrix R, and determinant | R | Shows the Mahalanobis distance D ² calculated for a plurality of acoustic waveform data Flow chart showing control details of insulation abnormality diagnosis (A) Sound wave waveform, (b) Waveform obtained by extracting 40 kHz sound wave component from sound wave waveform, (c) Envelope signal waveform, (d) Frequency spectrum distribution of envelope signal FIG. 7 equivalent view showing the second embodiment of the present invention FIG. 2 is a diagram showing a waveform obtained by extracting a 40 kHz sound wave component from a sound wave signal, and (b) an AC voltage waveform applied to electrical equipment, which is used for explaining the prior art.

Explanation of symbols

  In the drawings, 1 is an insulation abnormality diagnosis system, 2 is a sound wave sensor, 3 is a waveform measuring device (waveform measuring means), 5 is a communication device (transmitting means), 6 is a display device (alarm generating means), and 8 is a control circuit ( Sound wave signal filter means, envelope signal detection means, envelope signal frequency spectrum calculation means, Mahalanobis distance calculation means, insulation abnormality determination means, timing means), 9 storage device (data storage means), 10 transformer (electrical equipment) ).

Claims (6)

An insulation abnormality diagnosis system for electrical equipment that determines the presence or absence of an insulation abnormality by detecting creeping discharge generated on the surface of the electrical equipment in operation or partial discharge generated inside,
A sound wave sensor for detecting sound waves generated during the discharge;
A sound wave signal filter means for extracting a specific frequency component from the sound wave signal detected by the sound wave sensor;
An envelope signal detection means for detecting an envelope signal from the signal output by the sound wave signal filter means;
An envelope signal frequency spectrum calculating means for calculating the frequency spectrum from the envelope signal detected by the envelope signal detecting means;
Mahalanobis distance calculating means for calculating the Mahalanobis distance using the frequency spectrum calculated by the envelope signal frequency spectrum calculating means as a variable,
Electrical equipment comprising: an insulation abnormality determining means for determining the presence or absence of an insulation abnormality location based on a comparison between the Mahalanobis distance calculated by the Mahalanobis distance calculating means and a predetermined threshold value Insulation abnormality diagnosis system.   The Mahalanobis distance calculating means is configured to calculate the Mahalanobis distance using a spectrum intensity that is k times the power frequency (k is an integer of 1 or more) in the frequency spectrum calculated by the envelope signal frequency spectrum calculating means as a variable. The insulation abnormality diagnosis system for electrical equipment according to claim 1, wherein: A timing means and a data storage means,
The Mahalanobis distance calculating means calculates a Mahalanobis distance at a predetermined time interval timed by the time measuring means, and the calculated Mahalanobis distance is stored in the data storage means. The insulation abnormality diagnosis system for electrical equipment according to claim 1 or 2. Equipped with waveform measuring means to measure the waveform of power supply voltage,
The waveform of the signal output from the sound wave signal filter means or the waveform of the envelope signal detected by the envelope signal detection means and the waveform of the power supply voltage measured by the waveform measurement means are associated with each other and stored in the data storage means The insulation abnormality diagnosis system for electrical equipment according to claim 3, wherein the system is configured to do so. Equipped with alarm generating means,
After the Mahalanobis distance calculating means calculates the Mahalanobis distance at a predetermined time interval, when the insulation abnormality determining means determines the presence or absence of an insulation abnormality portion, and the insulation abnormality portion is present, the alarm generation means alerts The insulation abnormality diagnosis system for electrical equipment according to claim 3 or 4, characterized in that:   6. The electrical equipment insulation abnormality according to claim 3, further comprising transmission means for transmitting the Mahalanobis distance stored in the data storage means and the judgment result of the insulation abnormality judgment means. Diagnostic system.

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