CN110031729B - Detection method and system of partial discharge signal source and data fusion analysis unit - Google Patents
Detection method and system of partial discharge signal source and data fusion analysis unit Download PDFInfo
- Publication number
- CN110031729B CN110031729B CN201811498702.1A CN201811498702A CN110031729B CN 110031729 B CN110031729 B CN 110031729B CN 201811498702 A CN201811498702 A CN 201811498702A CN 110031729 B CN110031729 B CN 110031729B
- Authority
- CN
- China
- Prior art keywords
- signal
- partial discharge
- data
- signal data
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 126
- 238000004458 analytical method Methods 0.000 title claims abstract description 50
- 230000004927 fusion Effects 0.000 title claims abstract description 31
- 230000001360 synchronised effect Effects 0.000 claims abstract description 48
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 37
- 238000010219 correlation analysis Methods 0.000 claims abstract description 30
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims description 56
- 238000012545 processing Methods 0.000 claims description 41
- 230000015654 memory Effects 0.000 claims description 19
- 238000001914 filtration Methods 0.000 claims description 15
- 238000012216 screening Methods 0.000 claims description 11
- 230000003321 amplification Effects 0.000 claims description 10
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 10
- 230000035945 sensitivity Effects 0.000 abstract description 12
- 238000005070 sampling Methods 0.000 description 30
- 238000010586 diagram Methods 0.000 description 17
- 230000008569 process Effects 0.000 description 9
- 230000000694 effects Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000009413 insulation Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000000737 periodic effect Effects 0.000 description 4
- 230000000630 rising effect Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000000875 corresponding effect Effects 0.000 description 3
- 238000010295 mobile communication Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000003750 conditioning effect Effects 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 230000001629 suppression Effects 0.000 description 2
- 238000012731 temporal analysis Methods 0.000 description 2
- 238000000700 time series analysis Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 101000798707 Homo sapiens Transmembrane protease serine 13 Proteins 0.000 description 1
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 102100032467 Transmembrane protease serine 13 Human genes 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 description 1
- 235000019580 granularity Nutrition 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Relating To Insulation (AREA)
Abstract
本发明提供了一种局部放电信号源的检测方法、系统及数据融合分析单元，该局部放电信号源的检测方法包括：获取部署于各监测点的各特高频局放传感器的同步采集信号，生成样本信号数据；采用预设的聚类算法对样本信号数据进行聚类分组，生成多组聚类信号数据；采用时域相关分析算法分别对各组聚类信号数据进行信号筛选，生成备选局部放电信号数据组；对备选局部放电信号数据组进行定位分析，确定局部放电源的位置。通过实施本发明，实现了对局部放电源的粗定位，并可以有效识别各类电磁干扰信号，提高局放检测的可靠性，提高了对局放信号源进行检测的灵敏度和检测效率，并为对局放信号源进行精确定位提供了准确的数据基础。
The invention provides a detection method, a system and a data fusion analysis unit for a partial discharge signal source. The detection method for a partial discharge signal source includes: acquiring synchronous acquisition signals of each UHF partial discharge sensor deployed at each monitoring point; Generate sample signal data; use a preset clustering algorithm to cluster and group the sample signal data to generate multiple groups of clustered signal data; use a time domain correlation analysis algorithm to screen each group of clustered signal data separately to generate alternatives Partial discharge signal data set; perform positioning analysis on the candidate partial discharge signal data set to determine the position of the partial discharge source. By implementing the present invention, the rough positioning of the partial discharge source is realized, various electromagnetic interference signals can be effectively identified, the reliability of partial discharge detection is improved, the sensitivity and detection efficiency of the partial discharge signal source detection are improved, and the Precise positioning of the PD signal source provides an accurate data basis.
Description
Technical Field
The invention relates to the technical field of high-voltage equipment fault detection, in particular to a method and a system for detecting a partial discharge signal source and a data fusion analysis unit.
Background
The insulation deterioration is one of the important causes of the failure of the substation power system, and the main causes of the failure include poor contact, burrs, impurities inside, and the like. The high-voltage power transmission and distribution equipment generates partial discharge signals due to insulation faults, then the insulation is further degraded, insulation breakdown is easily caused after vicious circulation, and larger accidents are caused. Therefore, in order to ensure the normal operation of the substation power system, the detection and positioning of the partial discharge (partial discharge) signal of the high-voltage equipment is an important task.
The ultrahigh frequency (UHF) frequency band wireless receiver is used for detecting the partial discharge signal radiation energy, the sensitivity is high, the deployment is flexible, the space coverage is wide, and the method is an effective means for online partial discharge detection and positioning. However, the existing ultrahigh frequency partial discharge signal source detection methods are partial discharge detection methods based on time domain waveform characteristics, and because the existing detection methods lack effective interference suppression means, high-pass and band-pass filters are often adopted to filter out interference signals of low frequency bands and specific frequency bands more extensively, the method cannot effectively remove the interference signals, but also excludes partial high-energy partial discharge signals, and greatly limits the sensitivity and detection efficiency of detecting the partial discharge signals.
Disclosure of Invention
The invention aims to solve the technical problem that the ultrahigh frequency partial discharge signal source detection method in the prior art is an partial discharge detection method based on time domain waveform characteristics, and is easy to remove partial discharge signals when interference signals are removed, so that the detection sensitivity and detection efficiency of the partial discharge signal source are influenced. Therefore, a method and a system for detecting a partial discharge signal source and a data fusion analysis unit are provided.
The embodiment of the invention provides a method for detecting a partial discharge signal source, which comprises the following steps: acquiring synchronous acquisition signals of all ultrahigh frequency partial discharge sensors deployed at all monitoring points to generate sample signal data; clustering and grouping the sample signal data by adopting a preset clustering algorithm to generate a plurality of groups of clustered signal data; respectively carrying out signal screening on each group of clustering signal data by adopting a time domain correlation analysis algorithm to generate an alternative partial discharge signal data group; and carrying out positioning analysis on the alternative partial discharge signal data set to determine the position of a partial discharge source.
Optionally, the acquiring a synchronous acquisition signal of each uhf partial discharge sensor deployed at each monitoring point, and generating sample signal data includes: acquiring the synchronous acquisition signal, and generating a standard analog signal according to the synchronous acquisition signal; carrying out digital sampling on the standard analog signal to obtain sampling data; and carrying out signal detection on the sampling data, and removing noise data to obtain the sample signal data.
Optionally, the generating a standard analog signal according to the synchronous acquisition signal includes: filtering the synchronous acquisition signal to obtain a filtered acquisition signal; and carrying out low-noise amplification processing on the filtering acquisition signal to obtain the standard analog signal.
Optionally, after the acquiring synchronous acquisition signals of the ultrahigh frequency partial discharge sensors deployed at each monitoring point and generating sample signal data, before clustering and grouping the sample signal data by using a preset clustering algorithm and generating multiple groups of clustered signal data, the method for detecting the partial discharge signal source further includes: time stamping each pulse signal in the sample signal data; and grouping the sample signal data according to a preset time difference and the time stamp of each pulse signal to obtain grouped sample signal data.
The embodiment of the present invention further provides a data fusion analysis unit, including: the clustering signal data generating subunit is used for acquiring sample signal data of each monitoring point, clustering and grouping the sample signal data by adopting a preset clustering algorithm, and generating a plurality of groups of clustering signal data; the alternative partial discharge signal data group generation subunit is used for respectively carrying out signal screening on each group of clustering signal data by adopting a time domain correlation analysis algorithm to generate an alternative partial discharge signal data group; and the partial discharge source position determining subunit is used for performing positioning analysis on the alternative partial discharge signal data set by adopting a preset positioning method to determine the position of the partial discharge source.
The embodiment of the invention also provides a detection system of a partial discharge signal source, which comprises: the system comprises a plurality of ultrahigh frequency partial discharge sensors respectively deployed at monitoring points and a data fusion analysis unit in the embodiment of the invention, wherein each ultrahigh frequency partial discharge sensor is respectively connected with the data fusion analysis unit; the ultrahigh frequency partial discharge sensor is used for acquiring synchronous acquisition signals of the monitoring points, generating sample signal data according to the synchronous acquisition signals and sending the sample signal data to the data fusion analysis unit; the data fusion analysis unit is used for receiving the sample signal data and determining the position of the partial discharge source according to the sample signal data.
Optionally, the uhf partial discharge sensor includes: the device comprises a broadband antenna, an analog front end unit, an A/D converter and a digital signal processing unit, wherein the broadband antenna is used for acquiring the synchronous acquisition signal in a preset frequency range; the analog front end unit is used for generating a standard analog signal according to the synchronous acquisition signal; the A/D converter is used for carrying out digital sampling on the standard analog signal to obtain sampling data; and the digital signal processing unit is used for carrying out signal detection on the sampling data, removing noise data and obtaining the sampling signal data.
Optionally, the analog front end unit includes: the band-pass filter is used for filtering the synchronous acquisition signal to obtain a filtered acquisition signal; the low-noise amplifier is used for carrying out low-noise amplification processing on the filtering acquisition signal to obtain an amplified signal; and the adjustable gain amplifier is used for carrying out gain control on the amplified signal to obtain the standard analog signal which is adaptive to the amplitude range of the input signal of the A/D converter.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions for causing the computer to execute the above-mentioned method for detecting a partial discharge signal source.
An embodiment of the present invention further provides a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executable by the at least one processor to cause the at least one processor to perform the above-mentioned method for detecting the source of the partial discharge signal.
The technical scheme of the invention has the following advantages:
the embodiment of the invention provides a detection method of a partial discharge signal source, which comprises the steps of obtaining sample signal data from synchronous acquisition signals acquired by ultrahigh frequency partial discharge sensors deployed at monitoring points, clustering and grouping the data, screening the clustered signal data of each group by adopting a time domain correlation analysis algorithm to obtain an alternative partial discharge signal data group, and then carrying out positioning analysis by using a preset positioning algorithm to obtain the position of the partial discharge source. Therefore, the rough positioning of the partial discharge source is realized by utilizing the preset clustering algorithm and the time domain correlation analysis algorithm, various electromagnetic interference signals can be effectively identified, the reliability of partial discharge detection is improved, the sensitivity and the detection efficiency of detecting the partial discharge signal source are improved, and an accurate data base is provided for accurately positioning the partial discharge signal source.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for detecting a partial discharge signal source according to an embodiment of the present invention;
fig. 2 is a specific flowchart for acquiring acquisition signals of the ultrahigh frequency partial discharge sensors disposed at each monitoring point and generating sample signal data according to the embodiment of the present invention;
FIG. 3 is another flowchart of a method for detecting a partial discharge signal source according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a principle of pulse detection based on a moving average window according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating the results of two UHF sensors acquiring synchronous acquisition signals in the embodiment of the present invention;
FIG. 6 is a diagram illustrating clustering and grouping results according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a data fusion analysis unit according to an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a detection system of a partial discharge signal source according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a specific structure of a UHF sensor in an embodiment of the present invention;
FIG. 10 is a schematic diagram of an exemplary analog front end unit according to the present invention;
FIG. 11A is a diagram illustrating an original waveform of a partial discharge signal according to an embodiment of the present invention;
FIG. 11B is a diagram illustrating an original waveform of an impulsive interference signal according to an embodiment of the present invention;
FIG. 11C is a diagram of a power detection envelope waveform of a partial discharge signal in an embodiment of the present invention;
FIG. 11D is a waveform of a power detection envelope of an impulsive interference signal in accordance with an embodiment of the invention;
fig. 12 is a schematic structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment of the invention provides a method for detecting a partial discharge signal source, as shown in fig. 1, the method for detecting a partial discharge signal source includes:
step S1: and acquiring synchronous acquisition signals of all ultrahigh frequency partial discharge sensors (hereinafter referred to as UHF sensors) deployed at all monitoring points to generate sample signal data. Specifically, the UHF sensors are distributed and deployed in a certain distance range around the high-voltage power equipment, such as a transformer, a GIS, a switch cabinet and the like, different UHF sensors are mutually synchronized, and receive, filter, sample, digitally process and the like the partial discharge and electromagnetic interference signals in a wireless non-contact manner.
Step S2: and clustering and grouping the sample signal data by adopting a preset clustering algorithm to generate a plurality of groups of clustered signal data. Specifically, the cluster grouping process is cluster grouping from multiple dimensions according to spatial and temporal dimension information and signal waveform feature information provided by sample signal data.
Step S3: and respectively carrying out signal screening on each group of clustering signal data by adopting a time domain correlation analysis algorithm to generate an alternative partial discharge signal data group. Specifically, the time domain correlation analysis algorithm distinguishes partial discharge signals and common pulse interference signals according to the occurrence rule of each group of clustering signal data in the time domain and the difference of different signal generation mechanisms, so as to obtain an alternative partial discharge signal data group.
Step S4: and carrying out positioning analysis on the alternative partial discharge signal data set to determine the position of a partial discharge source. Specifically, the method for performing location analysis is to perform location calculation on the signal source based on the energy amplitude and/or arrival time difference of signals received by different UHF sensors, and determine the position of the partial discharge signal source.
Through the steps S1 to S4, in the detection method of the partial discharge signal source provided in the embodiment of the present invention, sample signal data are obtained from the synchronous acquisition signals acquired by the UHF sensors disposed at the monitoring points, then the data are clustered and grouped, a time domain correlation analysis algorithm is used for signal screening in each group of clustered signal data, an alternative partial discharge signal data group is obtained, and then positioning analysis is performed to obtain the position of the partial discharge source. Therefore, the rough positioning of the partial discharge source is realized by utilizing the preset clustering algorithm and the time domain correlation analysis algorithm, various electromagnetic interference signals can be effectively identified, compared with the detection method based on the waveform in the prior art, partial effective signals can be filtered out, the condition of the detection result is influenced, the reliability of partial discharge detection is improved, the sensitivity and the detection efficiency of detecting the partial discharge signals are improved, and an accurate data base is provided for accurately positioning the partial discharge signal source.
The following describes a method for detecting a partial discharge signal source according to an embodiment of the present invention in detail with reference to specific examples.
In a preferred embodiment, as shown in fig. 2, the step S1 mentioned above, obtaining synchronous acquisition signals of the vhf partial discharge sensors disposed at each monitoring point, and generating sample signal data specifically includes:
step S11: and acquiring a synchronous acquisition signal, and generating a standard analog signal according to the synchronous acquisition signal. In practical applications, the broadband omnidirectional antenna may be deployed around the target power device to receive wireless radio frequency signals from different directions and propagated through free space, that is, the above synchronous acquisition signals. The frequency detection range of the broadband omnidirectional antenna can be set according to actual needs, and the VHF/UHF frequency band of 30 MHz-1 GHz is generally recommended. Although the frequency range of the partial discharge signal can be from tens of MHz to the highest 3GHz, after the radio propagation attenuation, the energy of the partial discharge signal which can be received by the broadband omnidirectional antenna is mostly concentrated below 1GHz, especially in the low frequency band below 500 MHz.
Specifically, in an embodiment, as shown in fig. 2, the step S11 mentioned above, generating the standard analog signal according to the synchronous acquisition signal specifically includes:
step S111: and filtering the synchronous acquisition signal to obtain a filtered synchronous acquisition signal. In practical application, according to the frequency distribution range of the actual partial discharge signal source, a band-pass filter is adopted to perform frequency selection on the synchronous acquisition signal, so as to obtain a filtering acquisition signal. The electromagnetic interference suppression device can suppress electromagnetic interference signals of a specific frequency band. Specifically, considering that a stronger mobile communication signal exists between 800MHz and 1GHz and a large number of corona discharge signals exist below 50MHz, the frequency selection range of the band-pass filter is recommended to be 50-800 MHz without loss of generality, and the specific frequency range can be determined according to the actual situation of a partial discharge detection site. The band-pass filtering selected in the embodiment of the invention can quickly avoid strong interference signals and improve the dynamic amplitude range of the partial discharge detection on the premise of not obviously reducing the receiving energy of the partial discharge signals.
Step S112: and carrying out low-noise amplification processing on the filtering acquisition signal to obtain a noise amplification signal. Specifically, the filtering collected signal of the low noise amplifier may be amplified and the signal-to-noise ratio thereof may be improved to obtain a noise amplified signal, and in practical applications, the adjustable attenuator and the adjustable gain amplifier may be further used to perform dynamic gain adjustment on the intensity of the noise amplified signal, so as to meet the requirements of the subsequent processing process on the signal. The low-noise amplifier and the adjustable gain amplifier amplify the weak signal detected by the UHF sensor, and when the input signal is too strong and exceeds the normal working range of the UHF sensor, the UHF sensor can reach a saturated nonlinear state so as to influence the performance of the UHF sensor; the adjustable attenuator adjusts the amplified signal output by the low noise amplifier to the normal input range of the adjustable gain amplifier. In practical partial discharge detection application, when the radio frequency signal strength output by the broadband omnidirectional antenna is too high, a stage of radio frequency attenuator can be added in front of the low noise amplifier. By reasonably combining and using the three components, the dynamic range of the UHF sensor for receiving the partial discharge signal can be enlarged, and the partial discharge detection sensitivity is improved. In practical applications, the UHF sensor has a received RF signal strength in the range of at least-60 dBm to 0dBm, preferably-70 dBm to 10dBm or higher.
Step S113: and carrying out power detection processing on the noise amplification signal to obtain a standard analog signal. In practical applications, the power detection process can convert the high-frequency rf signal, i.e., the noise amplification signal, into a low-frequency envelope signal by the power detector to meet the requirements of subsequent low-speed sampling and signal processing. Specifically, the input signal frequency range of the power detector is required to cover the partial discharge signal frequency detection range of the UHF sensor, and the input signal strength thereof can be set in a range of-50 dBm to 10dBm or higher, but the present invention is not limited thereto.
It should be noted that the step S113 is not essential in practical application, and may perform high-speed sampling and detection on the original signal, and the high-speed sampling generally requires a sampling rate of 2GSPS or more. Step S12: and carrying out digital sampling on the standard analog signal to obtain sampling data. In particular, in practical applications, the digital-to-analog converter can be used to meet the requirement of subsequent digital signal processing. Sampling requirements can be met by generally adopting an analog-to-digital converter with more than 20 MSPS.
Step S13: and carrying out signal detection on the sampling data, and removing noise data to obtain sample signal data. The purpose of the signal detection is to perform intelligent pulse detection on the sampled data, remove most of background noise data in a time domain, and only retain data samples related to pulse signals so as to reduce the amount of data stored and transmitted. Thereby improving the partial discharge detection efficiency and reducing the communication bandwidth requirement of the system.
In practical application, the signal detection is detection of a signal pulse, a moving average method can be used for tracking fluctuation change of a reference amplitude in real time, and then detection of a target pulse is triggered based on a user-adjustable pulse detection delay sum and a pulse detection threshold amplitude. The pulse detection delay is the time interval or number of samples from the last sample in the moving average window to the sample used for the threshold amplitude comparison. The user can control the detection of the pulse signal with specific rising edge steepness by setting two parameters of pulse detection time delay and pulse detection threshold amplitude, thereby filtering most background noise and interference signals with slow rising edge change and greatly reducing the local data volume.
In practical applications, the above-mentioned signal pulse detection can also be implemented by calculating the relative coefficient of the highest or average amplitude of two adjacent windows and the energy coefficient of the moving window relative to the background noise by moving. By setting the threshold value of the relative coefficient and the width of the detection window, the high-energy pulse signal with specific rising edge steepness can be detected. To reduce the time and computational resource overhead of data processing, the detection window may be moved with a certain percentage of overlap (e.g., 50%); or dividing the data samples collected in a period of time into a certain number of large windows, and then performing moving window pulse detection on the data in the large window with the highest amplitude greater than a certain amplitude threshold.
In a preferred embodiment, as shown in fig. 3, after the step S1 is executed and before the step S2 is executed, the method for detecting the partial discharge signal source further includes:
step S5: individual pulse signals in the sample signal data are time stamped. In practical application, due to the fact that clocks of different UHF sensors are highly synchronous, the time stamp marking is carried out on the pulse signals detected by each UHF sensor, and basis can be provided for subsequent time domain correlation analysis.
Specifically, a schematic diagram of a method for performing pulse detection based on a moving average window is shown in fig. 4, which calculates and tracks the fluctuation change of a reference amplitude value through the moving average window, and by averaging or maximizing sample data in the detection window and comparing the sample data with a pulse detection threshold value, a burst pulse signal can be detected and time stamps thereof can be marked. By setting parameters such as a moving average window, a detection window, a pulse detection threshold and the like, pulse signals with different rising edge steepnesses can be specially detected. Because the pulse detection is generally carried out in real time, the time delay caused by the detection algorithm is very small, the difference is not large in different UHF sensors, the time delay can be ignored under general conditions, and the time delay can be specially calculated and correspondingly compensated when the timestamp marks under special conditions, so that the invention is not limited to the above.
Step S6: and grouping the sample signal data according to the preset time difference and the time stamp of each pulse signal to obtain grouped sample signal data. In practical application, the UHF sensors deployed at each monitoring point can receive a partial discharge signal with maximum energy within a target full-band range. Therefore, in most cases, the partial discharge pulse signal can be received by more than one UHF sensor at the same time, and only a part of the signal which is very weak or the partial discharge source is far away can be detected by only one UHF sensor. Under the condition that the number of pulse signals is large, in order to realize rapid and efficient partial discharge detection, the pulse signals detected by a plurality of UHF sensors at the same time can be preferentially selected, the pulse signals detected only in a single UHF sensor can be rapidly grouped according to the label of the UHF sensor, and then the sample signal data are grouped, so that the subsequent clustering grouping step is simplified, and the clustering grouping efficiency is further improved.
In the above process, it can be determined whether or not the pulse signals are simultaneously received by a plurality of UHF sensors according to the time stamp corresponding to each pulse signal in the above step S5. In practical application, taking 10 μ s as an example of a preset time difference, if the time stamps of pulse signals detected by different UHF sensors are within 10 μ s, the pulse signals can be considered to be generated by the same signal source. The higher the time synchronization precision between different UHF sensors is, the stronger the distinguishing capability of different signal sources is. Because the probability that the pulse signals generated by different signal sources repeatedly appear in the same 10 μ s time period for a long time is very low, in the embodiment of the present invention, the preset time difference is 10 μ s, and in practical applications, different time synchronization accuracies may also be set according to actual needs, which is not limited in this respect.
Specifically, in an embodiment, in the step S2, the sample signal data are clustered and grouped by using a preset clustering algorithm, so as to generate a plurality of groups of clustered signal data.
In practical application, because the generation of the partial discharge signal has a certain correlation with the 20ms power frequency period, the partial discharge signal of a specific type and the pulse-type interference signal strongly correlated with the power frequency signal generally appear repeatedly near a certain section or several sections of specific phases of the power frequency period, and most of the interference signals have no obvious time statistical regularity relative to the phase point appearing in the power frequency period. Based on the characteristics, all pulse signals collected by the plurality of UHF sensors can be mapped to a power frequency period of 20ms within a period of continuous monitoring time, and phase maps of all the pulse signals are obtained. Then, the pulse signals are clustered once according to the distribution and density of different phase regions in the phase map. The phase interval according to which the signal is clustered can be flexibly selected according to the distribution condition of the actual signal: if most pulse signals are obviously concentrated and distributed in a certain specific phase interval, the signals can be clustered according to the certain phase interval; on the contrary, if most pulse signals are distributed uniformly and dispersedly in the power frequency period, the pulse signals can be clustered according to a fixed phase interval.
Then, the pulse signal may be clustered a plurality of times in combination with other characteristic information. After multiple clustering, the pulse signal groups clustered once can be further refined into more groups, and the original groups can also be classified into the same large group. The preset clustering algorithm in the embodiment of the invention is a clustering method which classifies pulse signals generated by different signal sources through amplitude ratio information of synchronously received signals of multiple UHF sensors, and the method is referred to as amplitude ratio clustering for short. According to the attenuation characteristics of the radio frequency signals propagating in the air, the ratio of the received amplitudes of the signals generated by the same stationary signal source at a certain moment in different UHF sensors can be considered to be basically stable. The time synchronization precision of 10 mus between different UHF sensors can distinguish the pulse signals with the phase difference larger than 0.18 degrees, and the higher time synchronization precision (such as 1 mus) can achieve the better signal distinguishing effect. By calculating the amplitude ratio of all synchronous pulse signals received by the plurality of UHF sensors and marking the time and the amplitude, cluster analysis can be performed so as to distinguish the pulse signals generated by different signal sources. The clustering algorithm can also perform clustering analysis based on the arrival time of the same signal at different UHF sensors, and has higher requirements on digital sampling rate and clock synchronization precision in order to distinguish the arrival time of the signal at different UHF sensors.
In addition, the preset clustering algorithm in the embodiment of the present invention may also perform waveform characteristic correlation analysis and classification on the detected signal based on the time domain waveform characteristic information of the pulse signal. As above, the robustness of the clustering method based on time domain waveform features may be affected by the signal propagation channel, the data sampling rate, and the performance of the UHF sensor signal conditioning unit. Generally, clustering analysis based on the original waveform of the pulse signal has better signal discrimination effect than analysis based on the power detection waveform. It should be noted that signal clustering based on time-domain waveform features is optional in the embodiment of the present invention. According to the actual situation, if the signal distinguishing effect is good, the method can be adopted; if the clustering effect is not good, the clustering effect can be ignored. It should be further noted that, in the embodiment of the present invention, the partial discharge is not directly identified according to the time domain waveform characteristics, but only the characteristic information is utilized to perform signal clustering, and finally, the partial discharge signal can be further identified only by performing high-resolution correlation analysis in the time domain, so that the partial discharge signal cannot be easily misjudged even if the waveform characteristics are used.
In the clustering process, the use or the combined use of different clustering methods has no strict sequence, can be flexibly combined and used according to actual conditions, and can also continuously adjust and optimize clustering groups based on the result feedback of subsequent data analysis of the invention.
The above step S2 will be described in detail below by taking two UHF sensors as an example, the synchronous acquisition signals of the two UHF sensors are shown in fig. 5, and each UHF sensor individually detects a plurality of pulse signals; wherein, only part of the pulse signals are detected by two UHF sensors at the same time, such as (S1/P1, S2/P1) and (S1/P3, S2/P3), the clustering grouping and the time correlation analysis can be carried out on the part of the pulse signals detected together, and the possibility of partial discharge signals can be roughly eliminated by other pulse signals detected only on a single UHF sensor. The criterion for determining whether the pulse signal is detected by the plurality of UHF sensors at the same time is to see whether the timestamp difference is within a preset time difference, it should be noted that, in the embodiment of the present invention, the value of the preset time difference Δ t is within 10 μ s, and the preset time difference may be set according to needs in practical applications, which is not limited by the present invention. Although the method has a certain probability of eliminating a small amount of partial discharge signals which can only be detected in a single UHF sensor, the method provided by the embodiment of the invention is based on continuous long-time detection and can be adjusted according to the feedback of an analysis result, so that the effectiveness of partial discharge detection is not influenced basically.
Next, pulse signals detected by the two UHF sensors at the same time are mapped to a phase map, and one-time clustering is performed quickly based on a phase interval in which different signals appear. The phase intervals of different pulse signals appearing in the phase map may be less distinct, and clustering may be performed according to the fixed phase intervals. Table 1 lists several typical fixed phase interval combinations, which can quickly cluster and group all pulse signals, and in practical applications, the fixed phase interval combinations with different granularities can be selected according to specific situations, and in many cases, the fixed phase interval combinations may need to be adjusted through multiple iterations and feedback.
| |
|
Phase interval 3 | Phase interval 4 | |
| Combination of one | [0°,90°] | [90°,180°] | [180°,270°] | [270°,360°] |
| Combination two | [-45°,45°] | [45°,135°] | [135°,225°] | [225°,315°] |
| Combination III | [-60°,60°] | [60°,120°] | [120°,240°] | [240°,300°] |
And performing secondary and multiple clustering on the grouped signals subjected to primary clustering according to other space and time domain characteristic information to obtain more reasonable signal grouping. Taking a common amplitude ratio clustering method as an example, as shown in fig. 6, different signals may be grouped according to the amplitude ratio of the signals synchronously received by two UHF sensors. The pulse signals (P0, P2, P4, P6) received very similar amplitude ratios (both close to 2:1) at the two UHF sensors, and can be considered as coming from the same source and grouped together, denoted by Cluster 1; similarly, the amplitude ratio of the pulse signals (P1, P3, P5) received at both UHF sensors is close to 1:2, and can be classified as another group represented by Cluster 2.
Specifically, in an embodiment, in step S3, a time domain correlation analysis algorithm is used to perform signal screening on each group of clustered signal data, so as to generate a candidate partial discharge signal data group. In practical application, after the clustering grouping of the pulse signals is completed, the time domain correlation analysis can be performed on the pulse signals of each group to distinguish partial discharge signals. In the time domain correlation analysis in the embodiment of the invention, the occurrence rule of each group of signals on the time domain is subjected to correlation analysis, statistics and feature extraction by adopting higher time resolution, so that a basis is provided for diagnosis and identification of typical partial discharge and interference pulse signals. The time domain correlation analysis method is various, for example, the time sequence analysis may be performed microscopically within a period of continuously monitored time by using a preset power frequency period as a reference, or the distribution statistical analysis may be performed macroscopically based on a phase map or a pulse signal arrival time interval, which is not limited in the present invention.
Specifically, in practical application, by performing continuous time series analysis on each grouped pulse signal, the occurrence rule of the group of pulse signals in a single power frequency cycle and the evolution rule of the group of pulse signals changing along with time in a subsequent preset power frequency cycle can be observed. For example: the preset power frequency period is 20ms, and according to the physical generation mechanism of partial discharge signals, the partial discharge signals do not always appear in each 20ms power frequency period, but once appear, the partial discharge signals continuously appear for multiple times at an extremely short time interval in two phase intervals in the power frequency period, and the difference between the two phase intervals is about 180 degrees; thereby screening to obtain the above-mentioned alternative partial discharge signal data group. Because periodic pulse signals caused by other specific events (such as power frequency signal zero crossing points) hardly have similar occurrence rules, the periodic pulse signals can be easily distinguished.
In particular, in practical applications, the statistical regularity of the occurrence of the group of pulse signals in the time domain can be observed by performing distribution statistics on the pulse signals of each group through a phase map or an arrival time interval. The time jitter of the general partial discharge signal in a specific phase region is relatively random, and the presented distribution is relatively uniform and dispersed; and because the periodic pulse signal triggered by a specific event (such as a power frequency signal zero crossing point) has smaller jitter of the occurrence time, the presented distribution is relatively concentrated, and the alternative partial discharge signal data group is obtained by screening. In actual partial discharge detection, by comprehensively using various time domain related analysis means, periodic pulse interference signals obviously not conforming to the occurrence rule of partial discharge can be eliminated to the greatest extent, so that partial discharge signals can be identified more accurately.
As described above, the time domain correlation analysis can be performed by microscopically performing continuous time series analysis on each grouped pulse signal with reference to a 20ms power frequency period within a continuously monitored time range, or by macroscopically performing statistical distribution analysis on each group of pulse signals based on a phase map or a pulse signal arrival time interval.
Specifically, in an embodiment, in step S4, the candidate partial discharge signal data set is subjected to positioning analysis, and the position of the partial discharge source is determined. In practical application, the candidate partial discharge signal data group screened by the clustering and time domain correlation analysis is used as a suspected partial discharge signal to perform key positioning analysis. The UHF sensor used in the embodiment of the invention is generally arranged at different positions at a certain distance around the power transmission and transformation equipment, can be used for roughly positioning a signal source, and the positioning result can be used as a basis for further distinguishing the partial discharge signal. If the positioning result of the pulse signal source points to a position which is obviously deviated from the power transmission and transformation equipment, the possibility of partial discharge signals can be eliminated, and the accuracy of partial discharge detection is further improved. The coarse positioning of the partial discharge source can be realized based on the strength or arrival time difference of pulse signals received by a plurality of UHF sensors. The positioning method based on the pulse signal strength can be realized by using a low-cost UHF sensor with a power detector, for example, 10 mu s is taken as the synchronization precision, which can ensure that the pulse signals used for calculating the positioning come from the same signal source in most cases; the positioning method based on the time difference of arrival generally needs to use a UHF sensor with a higher sampling rate to obtain the original waveform of the partial discharge signal and use higher clock synchronization precision to reduce the error.
In practical applications, the above steps S2 to S4 may be repeated multiple times, and multiple feedback optimization adjustments are often required to obtain the final result in the actual field test. For example, when performing the time domain correlation analysis on each pulse signal packet, if all pulse signal packets cannot find an obvious rule in the time domain, the result may be an improper signal cluster packet, and it is necessary to go back to step S2 to adjust the signal cluster packet and perform the time domain correlation analysis again. For another example, after the signal source positioning calculation result and the signal characteristic comparison result are comprehensively analyzed, a plurality of pulse signal groups are determined to be pulse signals from the same signal source, clustering grouping and time domain correlation analysis can be performed again, the relationship among the generating phases of the pulse signals in the groups is further obtained, and more dimensional information and basis are provided for the subsequent accurate position identification of the partial discharge signal source.
In summary, the embodiment of the present invention finally eliminates the pulse interference signal and identifies the position of the critical partial discharge signal source by using the pulse signal characteristic information of the space and time dimensions and combining the signal clustering, the time domain correlation analysis, the signal source positioning and other analysis means.
Through the steps S1 to S4, in the method for detecting a partial discharge signal source according to the embodiment of the present invention, sample signal data are obtained from synchronously acquired signals acquired by UHF sensors disposed at each monitoring point, then the data are clustered and grouped, a time domain correlation analysis algorithm is adopted to perform signal screening on each group of clustered signal data, an alternative partial discharge signal data group is obtained, and then positioning analysis is performed to obtain the position of the partial discharge source. Therefore, the rough positioning of the partial discharge source is realized by utilizing the preset clustering algorithm and the time domain correlation analysis algorithm, various electromagnetic interference signals can be effectively identified, the reliability of partial discharge detection is improved, the sensitivity and the detection efficiency of detecting the partial discharge signal source are improved, and an accurate data base is provided for accurately positioning the partial discharge signal source.
Example 2
An embodiment of the present invention provides a data fusion analysis unit, as shown in fig. 7, the data fusion analysis unit includes:
and the clustering signal data generating subunit 21 is configured to obtain sample signal data of each monitoring point, and perform clustering grouping on the sample signal data by using a preset clustering algorithm to generate multiple groups of clustering signal data. For details, see the description related to steps S1 and S2 in example 1.
And the alternative partial discharge signal data group generation subunit 22 is configured to perform signal screening on each group of clustered signal data by using a time domain correlation analysis algorithm, and generate an alternative partial discharge signal data group. For details, see the description related to step S3 in embodiment 1.
And the partial discharge source position determining subunit 23 is configured to perform positioning analysis on the alternative partial discharge signal data set by using a preset positioning method, and determine a position of the partial discharge source. For details, see the description related to step S4 in embodiment 1.
Through the cooperative cooperation of the above components, the data fusion analysis unit of the embodiment of the invention realizes the coarse positioning of the local discharge source by using the preset clustering algorithm and the time domain correlation analysis algorithm, can effectively identify various electromagnetic interference signals, improves the reliability of the local discharge detection, improves the sensitivity and the detection efficiency of the detection of the local discharge signal, and provides an accurate data base for accurately positioning the local discharge signal source.
Example 3
An embodiment of the present invention provides a detection system for a partial discharge signal source, as shown in fig. 8, the detection system for a partial discharge signal source includes: the system comprises a plurality of UHF sensors 1 respectively deployed at monitoring points and a data fusion analysis unit 2 as described in embodiment 2, wherein each UHF sensor 1 is respectively connected with the data fusion analysis unit 2; the UHF sensor 1 is used for acquiring synchronous acquisition signals of monitoring points, generating sample signal data according to the synchronous acquisition signals and sending the sample signal data to the data fusion analysis unit 2; the data fusion analysis unit 2 is used for receiving the sample signal data and determining the position of the partial discharge source according to the sample signal data.
Through the cooperative cooperation of the above components, the detection system of the partial discharge signal source according to the embodiment of the present invention realizes the coarse positioning of the partial discharge source by using the UHF sensor 1 and the data fusion analysis unit 2, can effectively identify various electromagnetic interference signals, improves the reliability of the partial discharge detection, improves the sensitivity and the detection efficiency of the detection of the partial discharge signal, and provides an accurate data base for the accurate positioning of the partial discharge signal source.
The following describes the detection system of the partial discharge signal source according to an embodiment of the present invention in detail with reference to specific application examples.
Specifically, in practical applications, the UHF sensors 1 may be distributed and disposed in a certain distance range around the high-voltage power equipment, such as a transformer, a GIS, a switch cabinet, and the like, different UHF sensors 1 are synchronized with each other, and receive, filter, condition, sample, and digitally process partial discharge and electromagnetic interference signals in a wireless non-contact manner. The data fusion analysis unit 2 can be deployed in basic devices such as a comprehensive gateway, a personal computer and a server, and collects signal samples from different UHF sensors 1 in a wireless or wired network manner to perform data fusion, signal clustering and local discrimination. The data fusion analysis unit 2 may also be composed of a plurality of devices, and different algorithms and functions are implemented on different devices as required. In addition, in the above structure, the data fusion analysis unit 2 can also be used for remotely configuring the operation modes and key parameters of the plurality of UHF sensors 1. Based on the feedback of the data fusion analysis result in the data fusion analysis unit 2, parameters such as the sampling rate, the continuous sampling duration, the amplification or attenuation gain, the pulse detection threshold and the like of the UHF sensor 1 can be interactively configured, and the detection system of the partial discharge signal source is gradually adjusted to an optimal working state.
In a preferred embodiment, as shown in fig. 9, the UHF sensor 1 includes: the system comprises a broadband antenna 11, an analog front end unit 12, an A/D converter 13 and a digital signal processing unit 14, wherein the broadband antenna 11 is used for acquiring synchronous acquisition signals in a preset frequency range; the analog front end unit 12 is used for generating a standard analog signal according to the synchronous acquisition signal; the a/D converter 13 is configured to perform digital sampling on the standard analog signal to obtain sampling data; and the digital signal processing unit 14 is used for performing signal detection on the sampling data, removing noise data and obtaining sampling signal data.
In practical applications, the wideband antenna 11 is deployed around a target power device to receive radio frequency signals propagating through free space from different directions, the analog front end unit 12 performs filtering, gain control and other necessary conditioning on signals output by the wideband antenna 11 to meet the requirements of partial discharge detection, the a/D converter 13 performs analog-to-digital conversion on signals output by the analog front end unit 12, and the digital signal processing unit 14 performs real-time signal processing and pulse detection on signal samples from the a/D converter 13. Pulse detection in the UHF sensor 1 can reduce the bandwidth requirements for data transmission and improve system operating efficiency.
Specifically, as shown in fig. 9, one UHF sensor 1 may include a separate a/D converter 13 and a digital signal processing unit 14, and in practical applications, the UHF sensor 1 may also not include the digital signal processing unit 14, but jointly access the digital signal processing unit 14 including multiple analog-to-digital conversion channels together with other UHF sensors 1, and the invention is not limited thereto. Independent UHF sensor 1 can freely and flexibly carry out distributed wireless cable deployment in different types of transformer substations, and microsecond level or higher precision time sampling synchronization can be realized between different UHF sensor 1 devices through a GPS or wireless network bottom layer protocol. In this case, the UHF sensor 1 may further include a module (not shown) for synchronizing the clock and the sampling, and since the synchronizing module is not a main component of the present invention and is a general-purpose module, it will not be described herein. By default, circuit-level synchronization between multiple UHF sensors 1 sharing a multichannel digital signal processing unit 14 has been achieved, but the deployment is relatively inflexible due to the limitations of the connecting cables of the analog front end unit 12, and is therefore suitable for indoor substation applications.
In the above UHF sensor 1, the frequency detection range of the broadband antenna 11 is generally recommended to be in the VHF/UHF band of 30MHz to 1 GHz. Although the frequency range of the partial discharge signal can be from several tens of MHz to the highest 3GHz, after the radio propagation attenuation, the energy of the partial discharge signal that can be received by the broadband antenna 11 is mostly concentrated below 1GHz, especially in the low frequency band below 500 MHz.
In a preferred embodiment, as shown in fig. 10, the analog front end unit 12 includes: the band-pass filter 121 is used for filtering the synchronous acquisition signal to obtain a filtered acquisition signal; the low-noise amplifier 122 is configured to perform low-noise amplification processing on the filtered and acquired signal to obtain an amplified signal; the adjustable gain amplifier 123 is configured to perform gain control on the amplified signal to obtain an analog signal adapted to the amplitude range of the input signal of the a/D converter 13; and the power detector 124 is used for performing power detection processing on the analog signal to obtain a standard analog signal.
In practical application, the band-pass filter 121 performs frequency selection on the output signal of the broadband antenna 11 as required, the low-noise amplifier 122 amplifies the output signal of the band-pass filter 121 and improves the signal-to-noise ratio thereof to adapt to the input requirement of the next-stage component, the adjustable gain amplifier 123 performs dynamic gain control on the output signal of the adjustable attenuator, and the power detector 124 converts the high-frequency radio-frequency signal output by the adjustable gain amplifier 123 into a low-frequency envelope signal to meet the requirements of low-speed sampling and signal processing. In addition, the analog front end unit 12 may also include a module for digitally adjusting and controlling the voltages applied to the adjustable attenuator and the adjustable gain amplifier 123, and since the voltage adjusting and controlling module is not a main component of the present invention and is a general-purpose module, the description thereof will not be provided here.
The band-pass filter 121 in the analog front-end unit 12 can suppress electromagnetic interference signals of a specific frequency band. Considering that a strong mobile communication signal exists between 800MHz and 1GHz in practice, and a large number of corona discharge signals exist below 50MHz, the frequency selection range of the band-pass filter 121 is recommended to be 50-800 MHz without loss of generality, and the specific frequency range can be determined according to the actual situation of a partial discharge detection site. The band-pass filtering is a compromise design, and can quickly avoid strong interference signals and improve the dynamic amplitude range of partial discharge detection on the premise of not obviously reducing the receiving energy of partial discharge signals.
The low noise amplifier 122 and the adjustable gain amplifier 123 in the analog front end unit 12 are all common devices in analog signal processing. Briefly described, the low noise amplifier 122 and the adjustable gain amplifier 123 amplify the weak signal detected by the UHF sensor 1, and when the input signal is too strong to exceed its normal operating range, a saturation nonlinear state is reached, thereby affecting its performance. In practical partial discharge detection applications, when the rf signal strength output by the wideband antenna 11 is too high, a stage of rf attenuator may be added in front of the low noise amplifier 122. In the analog front-end unit 12, the three components are reasonably combined and used, so that the dynamic range of the UHF sensor 1 for receiving the partial discharge signal can be expanded, and the partial discharge detection sensitivity can be improved. Without loss of generality, the reception intensity range of the radio frequency signal of the UHF sensor 1 is preferably at least in the range of-60 dBm to 0dBm, and more preferably in the range of-70 dBm to 10dBm or higher.
The power detector 124 in the analog front-end unit 12 performs power detection on the output signal of the adjustable gain amplifier 123, and converts the rf partial discharge signal into a low-frequency envelope detection signal. Fig. 11A and 11B are a diagram of an original waveform of an partial discharge signal and a diagram of an original waveform of an impulsive interference signal, respectively, and fig. 11C and 11D are a diagram of a power detection envelope waveform of the partial discharge signal and a diagram of a power detection envelope waveform of the impulsive interference signal, respectively; the frequency range of the partial discharge envelope detection signal is generally within 5MHz, and the a/D converter 13 with more than 20MSPS is generally adopted to meet the sampling requirement, so that the computing resource overhead and the cost of the digital signal processing unit 14 are also reduced. The input signal frequency range of the power detector 124 is required to cover the partial discharge signal frequency detection range of the UHF sensor 1, the input signal strength generally suggests a range of-50 dBm to 10dBm or higher, and the specific range can be adjusted accordingly according to the gains of the preamplifier and the attenuator.
In practical applications, the power detector 124 is not necessary, and the original signal may be sampled and detected at a high speed, and the high speed sampling generally requires a sampling rate of 2GSPS or more. In embodiments of the present invention, the power detector 124 of the analog front end unit 12 is not necessary, regardless of system implementation cost. The function of the power detector 124 is generally implemented by a commercial analog power detector, and may be implemented by a method based on real-time signal processing in the digital signal processing unit 14.
In practical applications, the digital signal processing unit 14 is responsible for performing real-time pulse detection on the sampled signal, and removing most of the data including background noise and general interference signals, thereby improving the partial discharge detection efficiency and reducing the communication bandwidth requirement of the system. The digital signal processing unit 14 can be implemented based on technologies such as FPGA, DSP or ASIC, and generally can also include data storage functions such as DRAM or FLASH. In addition, the data processing unit should also have wired or wireless network communication capability to transmit the data retained after pulse detection to the server. The digital signal processing unit 14 also needs to be able to integrate a clock synchronization module to ensure synchronous detection of the pulse signals by different UHF sensors 1 by time stamping samples of each detected pulse signal. Since the clock synchronization module is not a main component of the present invention and is a general-purpose module, it will not be described herein. As above, the accuracy requirement for clock synchronization is typically within 10 μ s.
Specifically, there are various methods for detecting the pulse in the digital signal processing unit 14, and the detection capability of different methods for pulse signals with different shapes or steepnesses may be different. The pulse detection in the digital signal processing unit 14 is generally performed in real time, and since the time delay caused by the detection algorithm itself is very small and is not very different among different UHF sensors 1, it can be ignored in general, and also can be specially calculated and correspondingly compensated in time stamp marking in special cases, and the invention is not limited thereto.
Through the cooperative cooperation of the above components, the detection system of the partial discharge signal source according to the embodiment of the present invention realizes the coarse positioning of the partial discharge source by using the UHF sensor 1 and the data fusion analysis unit 2, can effectively identify various electromagnetic interference signals, improves the reliability of the partial discharge detection, improves the sensitivity and the detection efficiency of the detection of the partial discharge signal, and provides an accurate data base for the accurate positioning of the partial discharge signal source.
Example 4
The embodiment of the invention provides a non-transitory computer storage medium, which stores a computer executable instruction, where the computer executable instruction can execute the detection method of the partial discharge signal source in any method embodiment, where the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), or a Solid-State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Those skilled in the art will appreciate that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
Example 5
An embodiment of the present invention provides a computer device, a schematic structural diagram of which is shown in fig. 12, where the computer device includes: one or more processors 410 and a memory 420, with one processor 410 being an example in fig. 12.
The computer device described above may further include: an input device 430 and an output device 440.
The processor 410, the memory 420, the input device 430, and the output device 440 may be connected by a bus or other means, as exemplified by the bus connection in fig. 12.
The memory 420 is a non-transitory computer-readable storage medium, and can be used to store a non-transitory software program, a non-transitory computer-executable program, and modules, such as program instructions/modules corresponding to the method for detecting a partial discharge signal source in the embodiment of the present application, and the processor 410 executes various functional applications and data processing of the server by executing the non-transitory software program, instructions, and modules stored in the memory 420, so as to implement the method for detecting a partial discharge signal source in the above-described method embodiment.
The memory 420 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of a processing device of a detection method of the partial discharge signal source, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 420 may optionally include memory located remotely from processor 410, which may be connected to the detection device of the source of the partial discharge signal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may receive input numeric or character information and generate key signal inputs related to user settings and function control related to a processing device of a detection operation of the partial discharge signal source. The output device 440 may include a display device such as a display screen.
One or more modules are stored in the memory 420, which when executed by the one or more processors 410 perform the methods illustrated in fig. 1-8.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For details of the embodiments of the present invention, reference may be made to the description of the embodiments shown in fig. 1 to 8.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811498702.1A CN110031729B (en) | 2018-12-08 | 2018-12-08 | Detection method and system of partial discharge signal source and data fusion analysis unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811498702.1A CN110031729B (en) | 2018-12-08 | 2018-12-08 | Detection method and system of partial discharge signal source and data fusion analysis unit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110031729A CN110031729A (en) | 2019-07-19 |
| CN110031729B true CN110031729B (en) | 2021-02-09 |
Family
ID=67235316
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811498702.1A Active CN110031729B (en) | 2018-12-08 | 2018-12-08 | Detection method and system of partial discharge signal source and data fusion analysis unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110031729B (en) |
Families Citing this family (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112543411B (en) * | 2019-09-20 | 2023-07-11 | 中兴通讯股份有限公司 | Interference positioning method, device and system of wireless communication system |
| CN110988619B (en) * | 2019-11-21 | 2022-05-10 | 苏州光格科技股份有限公司 | Multi-source discharge signal separation method and analysis and discrimination method |
| CN111478617B (en) * | 2020-04-02 | 2021-11-16 | 广东美的制冷设备有限公司 | Pulse circuit, half-bridge drive circuit, circuit board and air conditioner |
| CN112198399B (en) * | 2020-09-07 | 2023-12-19 | 红相股份有限公司 | Multi-source electromagnetic wave signal identification method and terminal |
| CN112051473A (en) * | 2020-09-15 | 2020-12-08 | 北京神州泰岳软件股份有限公司 | High-frequency partial discharge signal detection system and method |
| CN112505798B (en) * | 2020-11-27 | 2022-07-01 | 河北雄安太芯电子科技有限公司 | Object detection method based on terahertz |
| CN112698160A (en) * | 2020-12-01 | 2021-04-23 | 广东电网有限责任公司广州供电局 | Switch cabinet partial discharge fault identification method and device, computer equipment and storage medium |
| CN112763871A (en) * | 2020-12-30 | 2021-05-07 | 珠海华网科技有限责任公司 | Partial discharge classification identification method |
| CN113009290B (en) * | 2021-02-05 | 2022-12-16 | 江苏大学 | A method and system for identifying and locating partial discharge types of UHV GIS equipment |
| CN113866569B (en) * | 2021-04-01 | 2022-10-21 | 全球能源互联网研究院有限公司 | Multi-partial discharge source fingerprint positioning method and device based on broadband radio frequency detection |
| CN114201991B (en) * | 2021-12-06 | 2025-03-28 | 大连赛听科技有限公司 | A partial discharge signal detection method and system based on ultrasonic sensor array |
| CN114384383A (en) * | 2022-03-22 | 2022-04-22 | 东华理工大学南昌校区 | Circuit and method for positioning ultrahigh frequency partial discharge point |
| CN115356616B (en) * | 2022-07-28 | 2025-07-01 | 北京时代民芯科技有限公司 | A high-speed and high-precision analog-to-digital converter inter-chip synchronization performance detection system and method |
| CN115166453B (en) * | 2022-09-08 | 2023-01-24 | 国网智能电网研究院有限公司 | Partial discharge continuous monitoring method and device based on edge real-time radio frequency pulse classification |
| CN116318254A (en) * | 2023-03-17 | 2023-06-23 | 国网智能电网研究院有限公司 | Broadband radio frequency partial discharge detection method and device based on airspace information anti-interference |
| CN116973702B (en) * | 2023-07-31 | 2024-04-05 | 上海莫克电子技术有限公司 | Signal identification method and system applied to GIS partial discharge test |
| CN117538710B (en) * | 2023-12-14 | 2024-07-23 | 四川大唐国际甘孜水电开发有限公司 | Intelligent early warning method and system for local dynamic discharge monitoring |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101051099B1 (en) * | 2008-09-30 | 2011-07-21 | 한국전력공사 | Microwave partial discharge and discharge position measuring device of high voltage power equipment |
| CN101852836B (en) * | 2010-05-05 | 2012-07-25 | 华北电力大学 | System of detecting partial discharge in equipment |
| CN102645620B (en) * | 2012-05-17 | 2015-02-11 | 广东电网公司电力科学研究院 | Multisource partial discharge detection method and device of transformer substation based on time-frequency characteristic parameters |
| CN104459484B (en) * | 2014-11-13 | 2017-06-09 | 南方电网科学研究院有限责任公司 | Method for identifying interference signals in on-site converter station overhaul plant |
| CN108490325B (en) * | 2018-04-11 | 2020-05-05 | 上海交通大学 | A two-stage substation partial discharge signal localization method and system |
| CN108896891A (en) * | 2018-08-26 | 2018-11-27 | 国网天津市电力公司 | A kind of GIS multi-source shelf depreciation separation recognition methods based on superfrequency method |
-
2018
- 2018-12-08 CN CN201811498702.1A patent/CN110031729B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| Comparison of clustering techniques of multiple partial discharge sources in high voltage transformer windings;N.H. Nik Ali 等;《 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials》;20151015;第256-259页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110031729A (en) | 2019-07-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110031729B (en) | Detection method and system of partial discharge signal source and data fusion analysis unit | |
| CN114050951B (en) | Blind estimation method for parameters of ultrashort wave frequency hopping signals | |
| CN112307969B (en) | A method, device and computer equipment for classifying and identifying pulse signals | |
| CN106788506B (en) | A kind of broadband integration reconnaissance receiver and its working method | |
| US10237749B2 (en) | Dynamic frequency selection with discrimination | |
| CN109975772B (en) | Multi-system radar interference performance detection system | |
| CN102331542A (en) | On-line monitoring and location method of partial discharge signal in substation | |
| CN106597368A (en) | Method and system for locating indoor interference source | |
| CN104901754B (en) | Channel monitoring system based on channelization frequency spectrum perception | |
| Martone et al. | Fast technique for wideband spectrum sensing | |
| CN103926461A (en) | Frequency selection method for partial discharge signal spatial orientation receiving frequency | |
| CN103217672B (en) | Motor message detection method and device | |
| CA3215106A1 (en) | Mitigating strong nongaussian interference in ss receivers | |
| CN109639376B (en) | A kind of spectrum scanning method of power wireless private network system | |
| KR20140010825A (en) | Signal filtering method of radar warning receiver and apparatus thereof | |
| US20230148340A1 (en) | Performing environmental radio frequency monitoring | |
| Raman et al. | Distributed spatio-temporal spectrum sensing: An experimental study | |
| KR102204815B1 (en) | Method and apparatus for providing direction finding | |
| JP5631674B2 (en) | Radio wave receiver and signal analysis method | |
| CN102724001A (en) | Channel measurement system and method and device | |
| CN116087887A (en) | Radar pulse signal detection method and device based on spectrum sparse sensing | |
| CN103684497A (en) | Frequency band enfolding type digital channelization receiving method based on fractional order Fourier transform | |
| CN120185739A (en) | Interference diagnosis method and system for dual-mode communication | |
| Dong et al. | Algorithm of multi-signals separation for shortwave broadband receiver | |
| CN116953621A (en) | Radar RF shield signal identification method based on multi-domain feature association |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |