CN105807190A - GIS partial discharge ultrahigh frequency live-line detection method - Google Patents
GIS partial discharge ultrahigh frequency live-line detection method Download PDFInfo
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Abstract
The invention provides a GIS partial discharge ultrahigh frequency live-line detection method. The GIS partial discharge ultrahigh frequency live-line detection method is characterized in that S1, an ultrahigh frequency sensor is used to receive electromagnetic pulse signals generated by GIS partial discharge, and the electromagnetic pulse signals can be converted into high frequency voltage signals, and then can be transmitted to a partial discharge detector by a shielding cable; and at the same time, a wireless power frequency signal generating device is used to transmit power frequency voltage signals to the partial discharge detector; S2, the partial discharge detector is used for the data analysis of the high frequency voltage signals and the power frequency voltage signals to acquire a PRPD discharge map and an ultrahigh frequency discharge pulse waveform; S3, the partial discharge detector is used for the PRPD cluster analysis and the pulse waveform time frequency analysis of the PRPD discharge map and the ultrahigh frequency discharge pulse waveform, and according to the analysis result, the discharge type of the GIS partial discharge can be identified. By adopting the technical scheme provided by the invention, the GIS partial discharge ultrahigh frequency live-line detection method is advantageous in that the structure is simple, the operation is convenient, and the accuracy and the reliability of the GIS partial discharge detection can be improved.
Description
Technical field
The present invention relates to a kind of detection method, in particular to a kind of GIS partial discharge SHF band electro-detection method.
Background technology
GIS is one of most important power equipment in network system, the feature such as little with its floor space, the class of insulation is strong obtains and uses more and more widely, fast development along with electric power network technique, the electric pressure of GIS is more and more higher, if it is impaired that it occurs insulation fault will directly endanger transformer station's main equipment, causing power failure, bring large area region to have a power failure, impact is lived normally, is produced even social stability.
Insulating properties are the key factors determining GIS safe and stable operation, all can cause that local field strength raises owing to the bubble manufacturing, producing in installation and operation process, burr, cut, loosened screw even come off etc., generation shelf depreciation.Such as, domestic Jiangmen 500kVGIS due on insulating bar defect after putting into operation, namely there is flashover soon, Daya Gulf 400kVGIS finds there is obvious tracking at the insulator of GIS and bus junction after Insulation Test.It is thus desirable to the shelf depreciation of detection GIS device.
During GIS internal discharge, rapid transfer due to point of discharge place electric charge, form the current impulse of nanosecond persistent period, and produce the electromagnetic signal that frequency component is extremely enriched, Partial Discharge Detection is carried out to sensing the signal of telecommunication produced by shelf depreciation, sensitivity can not only be improved, and the shelf depreciation of early stage can be found in time.It is mainly in bus corona discharge, radio wave, carrier communication and system switch motion etc. yet with on-the-spot signal of telecommunication interference, these interference are concentrated mainly on below 300MHz, conventional pulse current method and radio frequency detection method all can not eliminate this type of interference well, and ultra-high-frequency detection method adopts the mode of antenna coupled electromagnetic wave, its detection frequency band is concentrated mainly within the scope of 300MHz-3000MHz, can effectively avoid the interference suffered by general pulse electric current testing, be effectively improved detection sensitivity.
Existing ultra-high-frequency detection method adopts uhf sensor monitoring GIS partial discharge, by uhf sensor, the local discharge signal of test point is sent to Partial discharge detector by cable, voltage transformer the power-frequency voltage phase signal of test point is sent to Partial discharge detector by cable；By Partial discharge detector's device, power-frequency voltage phase signal and local discharge signal are carried out data parsing again and obtain PRPD spectrogram, according to the shelf depreciation type of PRPD spectrogram diagnosis GIS.
In practice, GIS is at initial period many generations corona discharge of electric discharge, PRPD spectrogram produced by different defects is more similar, and when there is multiple discharge source inside a GIS device simultaneously, the PRPD spectrogram of shelf depreciation can present the even all of superposition of part, so only judge the way of the shelf depreciation type of GIS device according to PRPD collection of illustrative plates by Partial discharge detector's device, it is possible to cause causing due to the overlap of spectrogram failing to judge or judging by accident.
Additionally, existing ultra-high-frequency detection method utilizes cable to obtain power-frequency voltage phase signal from the voltage transformer of test point, during practice, is not necessarily present available voltage transformer near Site Detection point, if there being voltage transformer, also because long cable connects causes that measurement is inconvenient for.
It is therefore desirable to study a kind of new shelf depreciation live detection method, to reduce the detection difficulty of GIS partial discharge, improve the detection efficiency of GIS partial discharge, improve the pattern recognition accuracy rate of GIS partial discharge.
Summary of the invention
In order to solve the problems referred to above existing in prior art, the present invention provides a kind of GIS partial discharge SHF band electro-detection method.
Present invention provide the technical scheme that a kind of GIS partial discharge SHF band electro-detection method, it thes improvement is that: described method comprises the steps:
Step S1 uhf sensor receives the electromagnetic pulse signal that described GIS partial discharge produces, and is transferred to Partial discharge detector by shielded cable after described electromagnetic pulse signal is converted into high-frequency voltage signal；Meanwhile, wireless power frequency component generating means launches power-frequency voltage signal to described Partial discharge detector；
Described high-frequency voltage signal and described power-frequency voltage signal are carried out data parsing and obtain PRPD electric discharge collection of illustrative plates and hyperfrequency Discharge pulse waveform by Partial discharge detector described in step S2；
Step S3, described Partial discharge detector described PRPD is discharged collection of illustrative plates and described hyperfrequency Discharge pulse waveform carries out PRPD cluster analysis and impulse waveform time frequency analysis respectively, and according to described PRPD cluster analysis result and described impulse waveform time frequency analysis result identification the electric discharge type of GIS partial discharge.
Preferably, in described step S1, described uhf sensor is hyperfrequency micro-strip paster antenna, and the length of described high-frequency microstrip paster antenna is 5cm, and wide for 3.5cm, thickness is 0.2cm, and its bandwidth is 0.4GHz～2.7GHz；Described high-frequency microstrip paster antenna is pasted onto the surfaces externally and internally of described GIS.
Preferably, in described step S1, described wireless power frequency component generating means is arranged on by described hyperfrequency microband paste, adding low frequency signal receptor in described Partial discharge detector, described Partial discharge detector receives, with described low frequency signal receptor, the power-frequency voltage signal that described wireless power frequency component generating means sends.
Preferably, in described step S2, the amplitude of described high-frequency voltage signal and the phase place of frequency and described power-frequency voltage signal are carried out data parsing by described Partial discharge detector, obtain described PRPD electric discharge collection of illustrative plates and hyperfrequency Discharge pulse waveform.
Preferably, the method for the PRPD cluster analysis in described step S3 is: extracting the characteristic parameter of PRPD electric discharge collection of illustrative plates, described characteristic parameter includes: type charcteristics parameter, moment characteristics parameter and statistical nature parameter；
Described type charcteristics parameter includes: Image Fractal dimension Features normalized value f1 is put in power frequency positive half-wave office, high level gray level image fractal dimension normalized value f2 is put in power frequency positive half-wave office, image second order Generalized Dimension normalized value f3 is put in power frequency positive half-wave office, Image Fractal dimension Features normalized value f4 is put in the negative half-wave office of power frequency, high level gray level image fractal dimension normalized value f5 is put in the negative half-wave office of power frequency, and image second order Generalized Dimension normalized value f6 is put in the negative half-wave office of power frequency；
Described moment characteristics parameter includes: gradation of image barycentric coodinates f7 is put in power frequency positive half-wave office, gradation of image barycentric coodinates f8 is put in the negative half-wave office of power frequency, image major axes orientation characteristic parameter f9 is put in power frequency positive half-wave office, and image major axes orientation characteristic parameter f10 is put in the negative half-wave office of power frequency；
Described statistical nature parameter includes: the ratio f11 of power frequency positive half-wave discharge capacity and total discharge capacity, the ratio f12 of power frequency positive half-wave discharge time and total discharge time, the normalized value f13 of power frequency positive half-wave initial discharge phase place, the normalized value f14, the correlation coefficient f15 of the positive and negative half-wave discharge image of power frequency of the negative half-wave initial discharge phase place of power frequency.
Preferably, the impulse waveform time frequency analysis in described step S3 comprises the steps:
1) adopt equation below (1) that described hyperfrequency Discharge pulse waveform z (t) is carried out Gobor conversion, obtain time frequency analysis collection of illustrative plates Gf(a, b, ω):
Wherein:For Gauss function ga(t-b) conjugate function；T is the time, and ω is angular frequency, and f is frequency, and b is window translation parameters, and a is that window size adjusts parameter；
2) extracting the characteristic parameter of described time frequency analysis collection of illustrative plates, described characteristic parameter includes: GfTime t corresponding to (a, b, ω) maximum amplitude pointm,GfFrequency f corresponding to (a, b, ω) maximum amplitude pointm, the difference Δ t of discharge pulse initial time and maximum amplitude place timem。
Preferably, in described step S3, described Partial discharge detector adopts the BP artificial neural network characteristic parameter of PRPD electric discharge collection of illustrative plates to extracting and the characteristic parameter of described time frequency analysis collection of illustrative plates to be trained, the electric discharge type according to the described GIS partial discharge of training result output correspondence.
Further, described BP neutral net includes input layer, intermediate layer and output layer；Described input layer includes 18 input layers corresponding respectively with 3 characteristic parameters of 15 characteristic parameters of described PRPD electric discharge collection of illustrative plates and described time frequency analysis collection of illustrative plates；The built-in 5 kinds of typical discharges sample datas in described intermediate layer；Described output layer includes 5 the output layer neurons corresponding with the 5 of described intermediate layer kinds of typical discharges sample datas.
Further, 5 typical discharges sample datas in described intermediate layer include noise sample data, needle point electric discharge sample data, creeping discharge sample data, bubble-discharge sample data and suspended discharge sample data；
The characteristic parameter of PRPD electric discharge collection of illustrative plates and the characteristic parameter of time frequency analysis collection of illustrative plates are input to the input layer of described BP neutral net, at described input layer after normalized, it is trained with 5 kinds of typical discharges sample datas in described BP neutral net intermediate layer, draw the electric discharge type corresponding with the characteristic parameter of the characteristic parameter of described PRPD electric discharge collection of illustrative plates and described time frequency analysis collection of illustrative plates, and exported by the output layer of described BP neutral net.
Further, described electric discharge type includes noise, needle point electric discharge, creeping discharge, bubble-discharge and suspended discharge.
Compared with immediate technical scheme, the present invention has following marked improvement:
1) PRPD is discharged collection of illustrative plates and hyperfrequency Discharge pulse waveform combines by technical scheme provided by the invention, corresponding characteristic parameter is extracted by PRPD cluster analysis and impulse waveform time frequency analysis, and utilize BP artificial neural network that characteristic parameter is trained, obtain the electric discharge type of GIS partial discharge, improve accuracy and the reliability of GIS partial discharge detection；
2) adopt the electromagnetic pulse signal that hyperfrequency micro-strip paster antenna detection GIS partial discharge produces compared with traditional ultra-high frequency antenna, there is less volume, it is possible to aspect is pasted onto the surfaces externally and internally of GIS cavity, reduces detection difficulty；
3) Partial discharge detector's built-in wireless reception device, receive the power-frequency voltage phase signal that on-site wireless power frequency component generating means produces, Partial discharge detector is provided only with the cable being connected with uhf sensor, facilitates execute-in-place, reduces detection difficulty.
Accompanying drawing explanation
Fig. 1 is the structural representation of micro-strip paster antenna；
Fig. 2 is that high-frequency local discharging measuring device with electricity tests schematic diagram on a GIS；
Fig. 3 is Gabor time-frequency conversion result figure；
Fig. 4 is BP artificial neural network's structure chart.
Detailed description of the invention
In order to be more fully understood that the present invention, below in conjunction with Figure of description and example, present disclosure is described further.
Fig. 1 is the schematic diagram of the micro-strip paster antenna designed by the present invention, this length of antenna is 5cm, width is 3.5cm, thickness is 0.2cm, bandwidth is 0.4GHz～2.7GHz, both can be arranged in GIS cavity as built-in sensors, it is also possible to be arranged on GIS disc insulator outer surface or GIS chamber outer surface as outer sensor.
Fig. 2 is that the present invention is to a single-phase GIS Site Detection schematic diagram of 110kV.Adopt the electric discharge type of uhf sensor, wireless power frequency component generating means and Partial discharge detector's detection GIS partial discharge；
Uhf sensor adopts micro-strip paster antenna, uhf sensor is pasted onto outside GIS cavity during detection, wireless power frequency component generating means is arranged on by uhf sensor, by uhf sensor, the electromagnetic wave signal that GIS internal flaw causes is converted into high-frequency voltage signal, it is sent to Partial discharge detector, the built-in low frequency signal receptor of Partial discharge detector, the power-frequency voltage signal that wireless power frequency component generating means sends is received by low frequency signal receptor, Partial discharge detector is to the amplitude of the high-frequency voltage signal that uhf sensor sends and frequency, and the phase place of power-frequency voltage signal carries out data parsing, obtain PRPD spectrogram and hyperfrequency Discharge pulse waveform；Respectively PRPD electric discharge collection of illustrative plates and hyperfrequency Discharge pulse waveform are carried out PRPD cluster analysis and impulse waveform time frequency analysis again；Obtain PRPD collection of illustrative plates and the characteristic parameter of time frequency analysis collection of illustrative plates, then utilize BP artificial neural network that characteristic parameter is trained, identifying the electric discharge type of GIS partial discharge, recognition result is divided into the electric discharge of noise, needle point, creeping discharge, bubble-discharge and five kinds of types of suspended discharge.
As shown in table 1 below: PRPD cluster analysis specifically includes that the extraction type charcteristics of PRPD collection of illustrative plates, moment characteristics and statistical nature amount to 15 characteristic parameters.
Table 1PRPD chromatogram characteristic parameter
Impulse waveform time frequency analysis includes two steps:
1) adopt formula (1) that hyperfrequency Discharge pulse waveform z (t) is carried out Gobor conversion, obtain the time frequency analysis collection of illustrative plates G of z (t)f(a, b, ω):
For Gauss function ga(t-b) conjugate function；T is the time, and f is frequency, and w is angular frequency, and b is window translation parameters, and a is that window size adjusts parameter；
2), after obtaining time frequency analysis collection of illustrative plates, the characteristic parameter in time frequency analysis collection of illustrative plates is extracted, as shown in table 2:
Table 2 time frequency analysis TuPu method parameter
| Characteristic parameter | Concrete meaning |
| tm | Time corresponding to maximum amplitude point |
| fm | Frequency corresponding to maximum amplitude point |
| Δtm | The difference of discharge pulse initial time and maximum amplitude place time |
Fig. 3 be the present invention is directed to test a kind of discharge pulse signal time-frequency conversion as a result, it is possible to find out that time-frequency combination analysis has good time-frequency locality.
After obtaining the characteristic parameter of PRPD electric discharge collection of illustrative plates and the characteristic parameter of time frequency analysis collection of illustrative plates, Partial discharge detector adopts the BP artificial neural network characteristic parameter of PRPD electric discharge collection of illustrative plates to extracting and the characteristic parameter of described time frequency analysis collection of illustrative plates to be trained, the electric discharge type according to the GIS partial discharge of training result output correspondence.
As shown in Figure 4: BP artificial neural network includes input layer, intermediate layer and output layer；Extracting cluster feature parameter due to PRPD data and impulse waveform data are extracted time-frequency combination distribution characteristics and amounted to 18 parameters, the input layer of BP neuroid is 18；Intermediate layer is built-in noise, needle point electric discharge, creeping discharge, bubble-discharge and five kinds of typical discharges sample datas of suspended discharge；The output layer neuron of BP neuroid is 5, and output mode is noise, needle point electric discharge, creeping discharge, bubble-discharge and suspended discharge five kinds.After input layer inputs discharge characteristic data, it is trained with sample data, until output error is less than system step-up error, then is exported the electric discharge type corresponding with discharge characteristic data by output layer, it is achieved the pattern recognition of GIS partial discharge.
These are only embodiments of the invention, be not limited to the present invention, all within the spirit and principles in the present invention, any amendment of making, equivalent replacements, improvement etc., all applying within the scope of the presently claimed invention awaited the reply.
Claims (10)
1. a GIS partial discharge SHF band electro-detection method, it is characterised in that: described method comprises the steps:
Step S1 uhf sensor receives the electromagnetic pulse signal that described GIS partial discharge produces, and is transferred to Partial discharge detector by shielded cable after described electromagnetic pulse signal is converted into high-frequency voltage signal；Meanwhile, wireless power frequency component generating means launches power-frequency voltage signal to described Partial discharge detector；
Described high-frequency voltage signal and described power-frequency voltage signal are carried out data parsing and obtain PRPD electric discharge collection of illustrative plates and hyperfrequency Discharge pulse waveform by Partial discharge detector described in step S2；
Step S3, described Partial discharge detector described PRPD is discharged collection of illustrative plates and described hyperfrequency Discharge pulse waveform carries out PRPD cluster analysis and impulse waveform time frequency analysis respectively, and according to described PRPD cluster analysis result and described impulse waveform time frequency analysis result identification the electric discharge type of GIS partial discharge.
2. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 1, it is characterised in that:
In described step S1, described uhf sensor is hyperfrequency micro-strip paster antenna, and the length of described high-frequency microstrip paster antenna is 5cm, and wide for 3.5cm, thickness is 0.2cm, and its bandwidth is 0.4GHz～2.7GHz；Described high-frequency microstrip paster antenna is pasted onto the surfaces externally and internally of described GIS.
3. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 1, it is characterised in that:
In described step S1, described wireless power frequency component generating means is arranged on by described hyperfrequency microband paste, adding low frequency signal receptor in described Partial discharge detector, described Partial discharge detector receives, with described low frequency signal receptor, the power-frequency voltage signal that described wireless power frequency component generating means sends.
4. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 1, it is characterised in that:
In described step S2, the amplitude of described high-frequency voltage signal and the phase place of frequency and described power-frequency voltage signal are carried out data parsing by described Partial discharge detector, obtain described PRPD electric discharge collection of illustrative plates and hyperfrequency Discharge pulse waveform.
5. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 1, it is characterised in that:
The method of the PRPD cluster analysis in described step S3 is: extracting the characteristic parameter of PRPD electric discharge collection of illustrative plates, described characteristic parameter includes: type charcteristics parameter, moment characteristics parameter and statistical nature parameter；
Described type charcteristics parameter includes: Image Fractal dimension Features normalized value f1 is put in power frequency positive half-wave office, high level gray level image fractal dimension normalized value f2 is put in power frequency positive half-wave office, image second order Generalized Dimension normalized value f3 is put in power frequency positive half-wave office, Image Fractal dimension Features normalized value f4 is put in the negative half-wave office of power frequency, high level gray level image fractal dimension normalized value f5 is put in the negative half-wave office of power frequency, and image second order Generalized Dimension normalized value f6 is put in the negative half-wave office of power frequency；
Described moment characteristics parameter includes: gradation of image barycentric coodinates f7 is put in power frequency positive half-wave office, gradation of image barycentric coodinates f8 is put in the negative half-wave office of power frequency, image major axes orientation characteristic parameter f9 is put in power frequency positive half-wave office, and image major axes orientation characteristic parameter f10 is put in the negative half-wave office of power frequency；
Described statistical nature parameter includes: the ratio f11 of power frequency positive half-wave discharge capacity and total discharge capacity, the ratio f12 of power frequency positive half-wave discharge time and total discharge time, the normalized value f13 of power frequency positive half-wave initial discharge phase place, the normalized value f14, the correlation coefficient f15 of the positive and negative half-wave discharge image of power frequency of the negative half-wave initial discharge phase place of power frequency.
6. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 1, it is characterised in that:
Impulse waveform time frequency analysis in described step S3 comprises the steps:
1) adopt equation below (1) that described hyperfrequency Discharge pulse waveform z (t) is carried out Gobor conversion, obtain time frequency analysis collection of illustrative plates Gf(a, b, ω):
Wherein:For Gauss function ga(t-b) conjugate function；T is the time, and ω is angular frequency, and f is frequency, and b is window translation parameters, and a is that window size adjusts parameter；
2) extracting the characteristic parameter of described time frequency analysis collection of illustrative plates, described characteristic parameter includes: GfTime t corresponding to (a, b, ω) maximum amplitude pointm,GfFrequency f corresponding to (a, b, ω) maximum amplitude pointm, the difference Δ t of discharge pulse initial time and maximum amplitude place timem。
7. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 1, it is characterised in that:
In described step S3, described Partial discharge detector adopts the BP artificial neural network characteristic parameter of PRPD electric discharge collection of illustrative plates to extracting and the characteristic parameter of described time frequency analysis collection of illustrative plates to be trained, the electric discharge type according to the described GIS partial discharge of training result output correspondence.
8. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 7, it is characterised in that:
Described BP neutral net includes input layer, intermediate layer and output layer；Described input layer includes 18 input layers corresponding respectively with 3 characteristic parameters of 15 characteristic parameters of described PRPD electric discharge collection of illustrative plates and described time frequency analysis collection of illustrative plates；The built-in 5 kinds of typical discharges sample datas in described intermediate layer；Described output layer includes 5 the output layer neurons corresponding with the 5 of described intermediate layer kinds of typical discharges sample datas.
9. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 8, it is characterised in that:
5 typical discharges sample datas in described intermediate layer include noise sample data, needle point electric discharge sample data, creeping discharge sample data, bubble-discharge sample data and suspended discharge sample data；
The characteristic parameter of PRPD electric discharge collection of illustrative plates and the characteristic parameter of time frequency analysis collection of illustrative plates are input to the input layer of described BP neutral net, at described input layer after normalized, it is trained with 5 kinds of typical discharges sample datas in described BP neutral net intermediate layer, draw the electric discharge type corresponding with the characteristic parameter of the characteristic parameter of described PRPD electric discharge collection of illustrative plates and described time frequency analysis collection of illustrative plates, and exported by the output layer of described BP neutral net.
10. a kind of GIS partial discharge SHF band electro-detection method as claimed in claim 9, it is characterised in that:
Described electric discharge type includes noise, needle point electric discharge, creeping discharge, bubble-discharge and suspended discharge.
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| CN107831409A (en) * | 2017-09-22 | 2018-03-23 | 国网山东省电力公司电力科学研究院 | The method and method for detecting abnormality of superfrequency partial discharge detection TuPu method parameter extraction |
| CN107831409B (en) * | 2017-09-22 | 2020-08-18 | 国网山东省电力公司电力科学研究院 | Ultrahigh frequency partial discharge detection map characteristic parameter extraction method and anomaly detection method |
| CN112308049A (en) * | 2020-12-04 | 2021-02-02 | 广微(中山)智能科技有限公司 | Pulse signal feature extraction and identification method based on multiple types of particles in electrically sensitive area |
| CN112990655A (en) * | 2021-02-03 | 2021-06-18 | 中国长江电力股份有限公司 | GIS equipment partial discharge danger degree evaluation algorithm |
| CN113156284A (en) * | 2021-04-28 | 2021-07-23 | 西安西电开关电气有限公司 | Method and device for processing interference signals of GIS equipment switching action |
| CN114355114A (en) * | 2021-10-18 | 2022-04-15 | 广东电网有限责任公司 | GIS partial discharge detection method, device, medium and terminal equipment |
| CN114137369A (en) * | 2021-11-19 | 2022-03-04 | 国网山东省电力公司电力科学研究院 | Power equipment partial discharge mode identification method and system |
| CN114065824A (en) * | 2022-01-17 | 2022-02-18 | 陕西公众电气股份有限公司 | Method for quickly identifying type of partial discharge signal |
| CN115187527A (en) * | 2022-06-27 | 2022-10-14 | 上海格鲁布科技有限公司 | Separation and identification method for multi-source mixed ultrahigh frequency partial discharge spectrum |
| CN114994587A (en) * | 2022-08-04 | 2022-09-02 | 国网江西省电力有限公司电力科学研究院 | Anti-interference energy evaluation method for partial discharge detector |
| CN114994587B (en) * | 2022-08-04 | 2022-12-30 | 国网江西省电力有限公司电力科学研究院 | Energy evaluation method for anti-interference performance of partial discharge detection instrument |
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