CN112946442B - Switch cabinet partial discharge detection method, terminal equipment and storage medium - Google Patents
Switch cabinet partial discharge detection method, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a switch cabinet partial discharge detection method, terminal equipment and a storage medium, wherein the method comprises the following steps: s1: collecting voice print information of a partial discharge signal and a non-partial discharge signal of a target switch cabinet; s2: carrying out noise reduction processing on the voiceprint information; s3: carrying out short-frame data analysis on the voice print information subjected to noise reduction, and extracting energy characteristics based on power frequency characteristics; s4: constructing a data short window by combining a DFT algorithm to perform spectrum analysis on the voiceprint information, and acquiring a pilot frequency band of the voiceprint information; s5: after filtering is carried out according to the pilot frequency band, extracting the maximum energy value of each frequency point, carrying out analysis according to the extracted maximum energy values of partial discharge and non-partial discharge, and obtaining an empirical energy coefficient alpha value and a partial discharge judgment parameter threshold value; s6: and judging whether the signal to be detected is a partial discharge signal or not according to the empirical energy coefficient alpha value and the partial discharge judging parameter threshold. The invention has low requirements on the aspects of the tightness of equipment to be detected and the ultrasonic electromagnetic environment, and has wider application scenes.
Description
Technical Field
The invention relates to the technical field of partial discharge detection, in particular to a switch cabinet partial discharge detection method, terminal equipment and a storage medium.
Background
With the rapid development of urban power grids in China in recent years, power transmission systems of the urban power grids begin to use cross-linked polyethylene (XLPE) power cables in large quantities. The main function of the switch gear is to open and close, control and protect electric equipment in the process of power generation, power transmission, power distribution and electric energy conversion of an electric power system. The components in the switch cabinet mainly comprise a circuit breaker, a disconnecting switch, a load switch, an operating mechanism, a mutual inductor, various protection devices and the like, and the components are key equipment in a power grid system. According to authoritative statistics, the insulation fault accounts for 37.3 percent and occupies a large proportion, and a discharge phenomenon is possibly generated in the latent period of the insulation fault, so that how to detect the partial discharge signal has important significance for comprehensively and deeply knowing the running state of the switch cabinet and the 'health' detection of the switch cabinet.
The insulation fault of the expert switch cabinet mainly comes from partial discharge in the expert switch cabinet, and the existing methods applied to the partial discharge detection mainly include an ultrasonic wave (AE) method, a Transient Earth Voltage (TEV), an Ultra High Frequency (UHF) method and the like. However, these detection methods are mainly applied to devices with good internal sealing and ideal grounding systems, such as high-voltage switchgear such as GIS, and are not suitable for medium-voltage switchgear with grounding and severe electromagnetic environment operating on site. The switch cabinet is not a closed cavity, the field electromagnetic interference problem is serious, the discharge capacity of 50-100 PC is in the operating environment, even higher, the false alarm rate is extremely high for detection methods with high sensitivity such as UHF or AE and higher grounding requirements such as TEV, and the method causes great trouble for normal and accurate intelligent operation and maintenance and state maintenance of equipment.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for detecting partial discharge of a switch cabinet.
The specific scheme is as follows:
a switch cabinet partial discharge detection method comprises the following steps:
s1: acquiring the tone pattern information of a partial discharge signal and a non-partial discharge signal of a target switch cabinet through a tone pattern acquisition device;
s2: carrying out noise reduction processing on the sound and grain information of the partial discharge signal and the non-partial discharge signal to eliminate interference of power frequency noise;
s3: carrying out short-frame data analysis on the voiceprint information of the noise-reduced partial discharge signal and the non-partial discharge signal, and extracting energy characteristics based on power frequency characteristics;
s4: according to the energy characteristics of the power frequency characteristics, a data short window is constructed by combining a DFT algorithm to carry out spectrum analysis on the voiceprint information, and a main pilot frequency band of the voiceprint information is obtained;
s5: filtering the voiceprint information of the local discharge signal and the non-local discharge signal after being denoised through a band-pass filter according to the main pilot frequency band of the voiceprint information, extracting the maximum energy value of each frequency point in the corresponding main pilot frequency band, analyzing according to the extracted maximum energy values of the local discharge signal and the non-local discharge signal, and obtaining an empirical energy coefficient alpha value and a local discharge distinguishing parameter threshold value;
S6: and carrying out noise reduction processing on the signal to be detected, extracting the energy characteristics of the power frequency characteristics of the signal to be detected, and judging whether the signal to be detected is a partial discharge signal or not according to the empirical energy coefficient alpha value and the partial discharge judgment parameter threshold.
Further, the noise reduction in step S2 is performed by high-pass filtering.
Further, the pass band of the bandpass filter in step S4 is 3-7 Khz.
Further, the power frequency energy characteristics comprise average frame energy, average pulse frame energy and a pulse signal to noise ratio SIR of the average pulse frame energy and the average energy.
Further, the average frame energy is calculated by: and setting the sum of the absolute values of the energy corresponding to all the sampling points contained in each frame as the frame energy of the frame, and setting the average value of the frame energy of all the frames as the average frame energy.
Further, the method for calculating the average pulse frame energy comprises the following steps: obtaining a pulse energy threshold according to the product of the average energy and a preset energy coefficient; and taking the frame energy which is greater than the pulse energy threshold in all the frame energies as pulse energy, and taking the average value of all the pulse energies as average pulse frame energy.
Further, the threshold of the partial discharge discrimination parameter includes a total pulse number threshold, a 20ms pulse number threshold, a 10ms pulse number threshold and a pulse signal-to-noise ratio threshold.
Further, the method for determining whether the partial discharge signal is present is as follows: if the total pulse frame number is larger than the total pulse number threshold, the 10ms pulse frame number is larger than the 10ms pulse number threshold, the 20ms pulse frame number is larger than the 20ms pulse number threshold and the pulse signal-to-noise ratio is larger than the pulse signal-to-noise ratio threshold, the partial discharge signal is judged, and if not, the non-partial discharge signal is judged.
A switch cabinet partial discharge detection terminal device comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above for an embodiment of the invention.
By adopting the technical scheme, the invention can replace the ears of experts, is applied to solving the problem of high false alarm rate of partial discharge detection in intelligent operation and detection of the switch cabinet, has relatively low requirements on the aspects of the tightness and the ultrasonic electromagnetic environment of equipment to be detected, and has wider application scenes compared with other detection methods.
Drawings
Fig. 1 is a flowchart illustrating a first embodiment of the present invention.
Fig. 2 shows a schematic diagram of power frequency characteristic energy extraction.
FIG. 3 is a diagram illustrating window shifting.
FIG. 4 is a schematic diagram of time-frequency energy waveforms of DFT-5ms power frequency partial discharge signals.
FIG. 5 is a diagram showing the energy waveform of DFT-5ms non-partial discharge signal.
Fig. 6 is a schematic diagram showing an energy waveform of a power frequency partial discharge signal.
Fig. 7 is a schematic diagram of the energy waveform of the power frequency non-partial discharge signal.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides a method for detecting partial discharge of a switch cabinet, which is a flow chart of the method for detecting partial discharge of the switch cabinet, as shown in fig. 1, and the method comprises the following steps:
S1: acquiring the tone pattern information of a partial discharge signal and a non-partial discharge signal of a target switch cabinet through a tone pattern acquisition device;
s2: carrying out noise reduction processing on the sound and vein information of the partial discharge signal and the non-partial discharge signal to eliminate interference of power frequency noise;
s3: carrying out short-frame data analysis on the voiceprint information of the noise-reduced partial discharge signal and the non-partial discharge signal, and extracting energy characteristics based on power frequency characteristics;
s4: according to the energy characteristics of the power frequency characteristics, a data short window is constructed by combining a DFT algorithm to carry out spectrum analysis on the voiceprint information, and a main pilot frequency band of the voiceprint information is obtained;
s5: filtering the voiceprint information of the local discharge signal and the non-local discharge signal after being denoised through a band-pass filter according to the main pilot frequency band of the voiceprint information, extracting the maximum energy value of each frequency point in the corresponding main pilot frequency band, analyzing according to the extracted maximum energy values of the local discharge signal and the non-local discharge signal, and obtaining an empirical energy coefficient alpha value and a local discharge distinguishing parameter threshold value;
s6: and carrying out noise reduction processing on the signal to be detected, extracting the energy characteristics of the power frequency characteristics of the signal to be detected, and judging whether the signal to be detected is a partial discharge signal or not according to the empirical energy coefficient alpha value and the partial discharge judgment parameter threshold.
Further, in step S2, noise reduction is performed by using high-pass filtering.
Further, in step S4, the pass band of the bandpass filter is 3-7 Khz.
Further, the power frequency energy characteristics comprise average frame energy, average pulse frame energy and a pulse signal to noise ratio SIR of the average pulse frame energy and the average energy.
Further, the average frame energy is calculated by: and setting the sum of the absolute values of the energy corresponding to all the sampling points contained in each frame as the frame energy of the frame, and setting the average value of the frame energy of all the frames as the average frame energy.
Further, the method for calculating the average pulse frame energy comprises the following steps: obtaining a pulse energy threshold according to the product of the average energy and a preset energy coefficient; and taking the frame energy which is greater than the pulse energy threshold in all the frame energies as pulse energy, and taking the average value of all the pulse energies as average pulse frame energy.
Further, the threshold of the partial discharge discrimination parameter includes a total pulse number threshold, a 20ms pulse number threshold, a 10ms pulse number threshold and a pulse signal-to-noise ratio threshold.
Further, the method for determining whether the partial discharge signal is present is as follows: if the total pulse frame number is larger than the total pulse number threshold, the 10ms pulse frame number is larger than the 10ms pulse number threshold, the 20ms pulse frame number is larger than the 20ms pulse number threshold and the pulse signal-to-noise ratio is larger than the pulse signal-to-noise ratio threshold, the partial discharge signal is judged, and if not, the non-partial discharge signal is judged.
In the embodiment, MATLAB software is adopted to perform simulation analysis on the performance of the method, the simulation is performed on a 10kv high-voltage switch cabinet, a main control CPU adopts an STM32F407VGT6 chip of STM32 series, an ARM 32-bit Cortex-M4CPU of an FPU, an adaptive real-time accelerator (ART accelerator) for realizing zero-waiting-state running performance in a Flash memory, the main frequency is up to 168MHz, the sampling rate is 96KHz, and the acquisition of local discharge signals of the switch cabinet is performed on a target switch cabinet.
Because the power frequency current exists for a long time in the operation process of the switch cabinet, for the voiceprint information of the switch cabinet, the alternating power frequency source is the largest interference source, and in order to improve the accuracy of voice analysis, a high-pass filter is preferably adopted in the embodiment to filter the power frequency interference in the voiceprint information. The passband of the high-pass filter is 100Hz-48 KHz.
The generation of partial discharge under the power frequency voltage is regular, two times of discharge occur in one power frequency period, the duration of the partial discharge signal is generally 1-3 ms, and the partial discharge frequency is more at the wave crest and the wave trough of the power frequency sine wave. Based on the characteristic, in the embodiment, each frame is set to be 2ms when the noise-reduced voiceprint information is segmented, and the power frequency energy of each frame is calculated according to the Pasaval theorem. Because each sampling point in the window of each frame corresponds to the same power frequency energy, according to the discrete signal energy formula (Pasaval theorem):
Obtaining energy E of each short data framejWhere x (k) is a discrete signal, N is the number of sampling points, and j is the number of frames. Each frame corresponds to one frame energy, then the quotient of the sum of the frame energies of all the frames divided by the total frame number is obtained as the average frame energy, and the average frame energy is multiplied by an energy coefficient alpha to obtain the pulse frame energy threshold. And finally, extracting the pulse frame characteristics. If the frame energy value of the current frame is greater than the pulse frame energy threshold, setting the frame as 1; if the energy value of the current frame is smaller than the energy threshold of the pulse frame, setting the frame as 0, and recording the frame index, and correspondingly taking out all the pulse frames (pulse frame characteristics). If the pulse frame has an interval rule of 10ms/20ms, the energy pulse is considered to have power frequency characteristics. Dividing the sum of the frame energy of all the pulse frames by the total frame number to obtain the average pulse frame energy, wherein the pulse signal-to-noise ratio SIR and the energy coefficient alpha are defined as follows:
further, the power frequency partial discharge signal feature extraction steps are as follows:
1) collecting and introducing a power frequency signal;
2) filtering and framing preprocessing are carried out on the introduced power frequency signals;
3) extracting frame energy characteristics according to an energy formula;
4) and comparing the frame energy characteristics with the pulse frame energy threshold, and extracting all pulse frame energy characteristics.
The four steps correspond to waveforms in the schematic diagram of extracting the power frequency characteristics of the partial discharge signal in fig. 2 respectively. (wherein the upper solid line in the energy waveform diagram is the pulse energy threshold curve, the middle solid line is the pulse frame energy curve, and the lower dotted line is the average frame energy curve).
The amplitude of oscillation waves caused by discharge at the wave crest and the wave trough of the power frequency voltage is large, so that the formed voiceprint information intensity and frequency spectrum information can relatively reflect the characteristic of partial discharge, and the example constructs a data short window based on a theory combined DFT algorithm to perform frequency spectrum analysis on the voiceprint information and acquire a main pilot frequency band of the voiceprint information;
the period of partial discharge under the power frequency characteristic is 20ms (1920 sampling points), and in order to research the content of each harmonic wave near the wave crest and the wave trough of the partial discharge. In this embodiment, the 5ms window designed by combining with the DFT algorithm is a 480-point DFT transform of the sampled data, and the fundamental frequency of the sine wave is fsThe frequency/N is 96000/480 Hz, i.e. 200Hz, the analysis of other frequency points x (m) is integral multiple of the fundamental frequency, and the half values of the sampling frequency, i.e. 200Hz, 400Hz, 600Hz, … Hz, 48000Hz, are obtained according to the nyquist criterion and DFT symmetry. And then calculating the gradient value corresponding to each frequency point, and further solving the corresponding amplitude value according to the gradient value of each frequency point. The frequency points correspond to the amplitude values one by one, and a 5ms window contains 240 frequency points, so that more frequency points are inconvenient to observe. Therefore, it is necessary to accurately analyze the amplitude content of each frequency point in a single periodic wave of a partial discharge signal by slicing the frequency domain.
In this embodiment, firstly, 200Hz, 400Hz, 600Hz, …, 1000Hz, and every 5 frequency points are used as slicing references, that is, the slicing is 1KHz, and similarly, until 48000Hz contains 48 slicing frequency points, that is, 1KHz, 2KHz, 3KHz, … …, 48KHz, in order to avoid data interference between adjacent data windows, the frequency domain performs time domain non-overlapping window shift analysis by using a frequency 1KHz slicing function, and further obtains the frequency spectrum information change rule at the power frequency partial discharge position. As shown in fig. 3, there is no overlapping window shift in the short-time window of 5ms of a single cycle of the power frequency partial discharge signal, and with 2.5ms before the peak or trough of the power frequency partial discharge signal as the starting point, the window shifts one cycle (i.e. 4 data short windows) continuously, and the data short windows appear alternately at the peak and trough of the power frequency partial discharge.
In this embodiment, first, a data short window shift of a cycle is performed on a power frequency partial discharge signal, as shown in fig. 4, when the power frequency discharge is 600pC, the left graph in fig. 4 is frequency spectrum information at a peak of a power frequency voltage wave, from which it can be seen that an energy value corresponding to a 4KHz frequency point is the largest, and an energy value corresponding to a frequency point in a 3-7KHz frequency band is significantly higher than that of other frequency bands. The right graph in fig. 4 is the spectral information at non-peaks (valleys). It can be seen that the energy value corresponding to the frequency points of 5KHz is maximum, the high-energy frequency points are all concentrated in the frequency band of 3-7KHz, but the energy value is much smaller, so that the partial discharge main frequency band is 3-7 KHz.
Through comparative analysis, the following results are found: the maximum energy value of the frequency point corresponding to the crest (valley) is obviously larger than that of the frequency point at the non-crest (valley), and the frequency points corresponding to the maximum energy value are all near 5 KHz. The frequency band is the characteristic frequency band of the voiceprint partial discharge. In order to verify the universality of the frequency spectrum rule of the power frequency partial discharge signal model, the following 1000 groups of data of different discharge modes, discharge positions and discharge intensities are subjected to frequency spectrum analysis, and the conclusion is met.
For non-partial discharge voiceprint signals, namely background interference, voiceprint information such as fans, human and animal voices, automobiles, music and the like, the extraction of power frequency characteristics is unsuccessful, and the voiceprint signals cannot directly enter the voiceprint partial discharge discrimination logic to diagnose non-partial discharge. As shown in fig. 5, the frequency point distribution difference corresponding to the maximum characteristic energy of each data short window of the non-power frequency partial discharge signal is large, almost runs through the whole frequency band, and is irregular compared with the power frequency partial discharge signal.
According to the 3-7KHz of the main pilot frequency band of the voiceprint information and the maximum energy point corresponding to each data short window frequency point can better reflect the power frequency partial discharge characteristic, the voiceprint information of the partial discharge signal and the non-partial discharge signal after noise reduction is filtered by a band-pass filter, the maximum energy value of each frequency point in the corresponding main pilot frequency band is extracted, analysis is carried out according to the maximum energy value of the extracted partial discharge signal and the non-partial discharge signal,
The maximum energy characteristic point in a 5ms data short window of a 10kv high-voltage switch cabinet voiceprint partial discharge signal is shown in fig. 6. Wherein the abscissa represents the order of a short window of 5ms, the ordinate is the largest energy characteristic value within the window, the horizontal line below the middle in the figure represents the average energy value, and the horizontal line above is 120% of the average energy (set as the energy threshold). The period of the power frequency signal is 20ms, so that the pulse energy characteristic (representing adjacent wave crests or wave troughs) with the index difference of 4 of the abscissa window is marked as 20ms pulse, and the pulse energy characteristic (representing adjacent wave crests and wave troughs) with the index difference of 2 of the abscissa window is marked as 10ms pulse. Similarly, the maximum energy characteristic point in a 5ms data short window of the voiceprint non-partial discharge signal of the 10kv high-voltage switch cabinet is displayed as shown in fig. 7, and an empirical energy coefficient alpha value and a partial discharge discrimination parameter threshold value are obtained according to a large amount of data analysis of the pulse energy point pulse number.
The number of 10ms pulses and 20ms pulses which are larger than the energy threshold value is taken as a basis, so that the voiceprint characteristic of partial discharge can be effectively judged.
According to the power frequency energy characteristics and spectrum characteristics of the partial discharge signals, a partial discharge detection algorithm is set through analysis, firstly, the partial discharge signals are preprocessed, and amplitude reduction, framing and filtering are carried out on data.
Each frame is 2ms (192 samples), 500ms corresponding to 250 frames (48000 samples). The sum of the absolute values of the energy (amplitude) corresponding to all the sampling points contained in each frame is recorded as the frame energy of the frame, so that the total frame energy of 250 frames is 500 ms. The total energy is then summed over 250 frame energies and divided by 250 to obtain the average energy. In this embodiment, the energy coefficient α is obtained in advance by empirical training, the pulse energy threshold is obtained by multiplying the average energy by the frame energy coefficient, and the frame energy greater than the pulse energy threshold in all the frame energies is taken as the pulse energy. In this embodiment, a frame value corresponding to a frame energy greater than a pulse energy threshold is recorded as 1, otherwise, the frame index of each frame is recorded through a corresponding function, then a pulse period is determined according to the frame index of each frame, if frames with adjacent values of 1 and a frame index distance minus 10 frames (20ms) are greater than 1, a 20ms pulse is determined, and if frames with adjacent values of 1 and a frame index distance minus 5 frames (10ms) are greater than 1, a 10ms pulse is determined. Pulse thresholds were set for the 10ms pulse and the 20ms pulse, while the total pulse threshold was set to 20.
In order to improve the effectiveness of detecting the partial discharge signal, the determination condition of the ratio of the average pulse frame energy to the average energy is increased. In this embodiment, the ratio threshold SIR is set to 1.5 on the basis of N experiments. If the total number of pulses is more than 20 and the 20ms pulses are more than 10, the SIR reaches the threshold value, and the following conditions are met: it is determined as a partial discharge signal (program output is 1), whereas it is a non-partial discharge signal (program output is 0). If the following conditions occur:
1: the total number of pulses or the 20ms number of pulses is less than the corresponding threshold (too few pulses).
2: SNR is less than threshold (pulse is not significant).
If any of the above occurs, it is determined as a non-partial discharge signal.
The embodiment of the invention can replace the ears of experts, is applied to solving the problem of high false alarm rate of partial discharge detection in intelligent operation and detection of the switch cabinet, has relatively low requirements on the aspects of the tightness and the ultrasonic electromagnetic environment of equipment to be detected, and has wider application scenes compared with other detection methods.
Example two:
the invention further provides a switch cabinet partial discharge detection terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the switch cabinet partial discharge detection terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The switch cabinet partial discharge detection terminal equipment can comprise, but is not limited to, a processor and a memory. It is understood by those skilled in the art that the above-mentioned composition structure of the switch cabinet partial discharge detection terminal device is only an example of the switch cabinet partial discharge detection terminal device, and does not constitute a limitation to the switch cabinet partial discharge detection terminal device, and may include more or less components than the above, or combine some components, or different components, for example, the switch cabinet partial discharge detection terminal device may further include an input-output device, a network access device, a bus, and the like, which is not limited by the embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the switch cabinet partial discharge detection terminal equipment, and various interfaces and lines are used for connecting various parts of the whole switch cabinet partial discharge detection terminal equipment.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the switch cabinet partial discharge detection terminal equipment by running or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The present invention also provides a computer-readable storage medium, which stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method of an embodiment of the present invention.
If the module/unit integrated with the switch cabinet partial discharge detection terminal device is realized in the form of a software functional unit and sold or used as an independent product, the module/unit can be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for detecting partial discharge of a switch cabinet is characterized by comprising the following steps:
s1: acquiring voice print information of a partial discharge signal and a non-partial discharge signal of a target switch cabinet by a voice print acquisition device;
s2: carrying out noise reduction processing on the sound and grain information of the partial discharge signal and the non-partial discharge signal to eliminate interference of power frequency noise;
s3: carrying out short frame data analysis on the voiceprint information of the denoised partial discharge signal and non-partial discharge signal, and extracting energy characteristics based on power frequency characteristics, wherein the energy characteristics comprise average frame energy, average pulse frame energy and a pulse signal-to-noise ratio (SIR) of the average pulse frame energy and the average frame energy;
s4: according to the energy characteristics of the power frequency characteristics, a data short window is constructed by combining a DFT algorithm to carry out spectrum analysis on the voiceprint information, and a main pilot frequency band of the voiceprint information is obtained;
s5: filtering the voiceprint information of the local discharge signal and the non-local discharge signal after noise reduction through a band-pass filter according to the main pilot frequency band of the voiceprint information, extracting the maximum energy value of each frequency point in the corresponding main pilot frequency band, analyzing according to the extracted maximum energy values of the local discharge signal and the non-local discharge signal, and obtaining an energy coefficient alpha value and a local discharge distinguishing parameter threshold value, wherein the energy coefficient alpha is the pulse frame energy threshold divided by the average frame energy;
S6: and carrying out noise reduction processing on the signal to be detected, extracting the energy characteristics of the power frequency characteristics of the signal to be detected, and judging whether the signal to be detected is a partial discharge signal or not according to the partial discharge judgment parameter threshold.
2. The method for detecting partial discharge of switch cabinet according to claim 1, wherein: in step S2, noise reduction is performed by high-pass filtering.
3. The method for detecting partial discharge of switch cabinet according to claim 1, wherein: in step S4, the pass band of the band-pass filter is 3-7 Khz.
4. The method for detecting partial discharge of switch cabinet according to claim 1, wherein: the average frame energy is calculated by the following method: and setting the sum of the absolute values of the energy corresponding to all the sampling points contained in each frame as the frame energy of the frame, and setting the average value of the frame energy of all the frames as the average frame energy.
5. The method for detecting partial discharge of switch cabinet according to claim 1, characterized in that: the partial discharge distinguishing parameter threshold comprises a total pulse number threshold, a 20ms pulse number threshold, a 10ms pulse number threshold and a pulse signal-to-noise ratio threshold; the sequence of the 5ms short windows is used as an abscissa, the pulse energy characteristic with the abscissa window index difference of 4 is 20ms pulse, and the pulse energy characteristic with the abscissa window index difference of 2 is 10ms pulse.
6. The switch cabinet partial discharge detection method according to claim 5, characterized in that: the method for judging whether the signal is a partial discharge signal comprises the following steps: if the total pulse frame number is larger than the total pulse number threshold, the 10ms pulse frame number is larger than the 10ms pulse number threshold, the 20ms pulse frame number is larger than the 20ms pulse number threshold and the pulse signal-to-noise ratio is larger than the pulse signal-to-noise ratio threshold, the partial discharge signal is judged, and if not, the non-partial discharge signal is judged.
7. The utility model provides a cubical switchboard partial discharge detection terminal equipment which characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1 to 6 when executing the computer program.
8. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 6.
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| CN113792034B (en) * | 2021-08-30 | 2022-11-25 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Method and device for cleaning partial discharge data, computer equipment and storage medium |
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| CN113899991A (en) * | 2021-09-22 | 2022-01-07 | 珠海华网科技有限责任公司 | Partial discharge rapid identification method based on Internet of things online terminal node |
| CN114200260A (en) * | 2021-11-26 | 2022-03-18 | 山东泰开成套电器有限公司 | Audio-based switch cabinet partial discharge detection method |
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