CN111537850B - Big data identification management system for intelligent separation and classification diagnosis of partial discharge signals - Google Patents
Big data identification management system for intelligent separation and classification diagnosis of partial discharge signals Download PDFInfo
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- CN111537850B CN111537850B CN202010432994.XA CN202010432994A CN111537850B CN 111537850 B CN111537850 B CN 111537850B CN 202010432994 A CN202010432994 A CN 202010432994A CN 111537850 B CN111537850 B CN 111537850B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—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 of components, parts or materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1209—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 using acoustic measurements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention provides a big data identification management system for intelligent separation and classification diagnosis of partial discharge signals, which comprises: the acquisition module is used for acquiring the discharge information of any position point, and the acquisition time is longer than first preset time; the classification module is used for classifying the obtained discharge information according to a first strategy to generate classification data, wherein the classification data at least comprises first class data and second class data; and the comparison module is used for acquiring the classification data, comparing and identifying each group of data in the classification data with the target data in the database respectively, and outputting the state data corresponding to each identified group of data, wherein the state data comprises an insulation state and a non-insulation state. The intelligent detection can be carried out on the partial discharge signals, the signals are judged through multiple dimensions, accurate equipment information is obtained, and the system is suitable for a power generation system, a power supply system and a power consumption user, so that manpower, material resources and financial resources are saved.
Description
Technical Field
The invention relates to the technical field of electric energy detection, intelligent processing and big data, in particular to a big data identification management system for intelligent separation and classification diagnosis of partial discharge signals.
Background
The current online system and big data platform suitable for electric energy detection mainly have the problems that false alarm, false alarm and false alarm have no correct and definite detection result. The only solution in the market at present measures and reports the result by taking whether the discharge capacity exceeds the standard or not as a standard, and whether the discharge capacity exceeds the standard or not, noise, interference or other unknown signals exist, the alarm is given to workers to add a lot of unnecessary troubles and greatly reduce the working efficiency of the workers as long as the preset threshold value is exceeded, a lot of incorrect judgments are made, a lot of unnecessary maintenance and treatment are caused, and a lot of manpower, material resources and financial resources are wasted.
Disclosure of Invention
The invention provides a big data identification management system for intelligent separation and classification diagnosis of partial discharge signals, which can intelligently detect the partial discharge signals, judge the signals through multiple dimensions, obtain accurate equipment information and save manpower, material resources and financial resources.
A big data identification management system for intelligent separation and classification diagnosis of partial discharge signals comprises:
the acquisition module is used for acquiring the discharge information of any position point, and the acquisition time is longer than first preset time;
the classification module is used for classifying the obtained discharge information according to a first strategy to generate classification data, wherein the classification data at least comprises first class data and second class data;
and the comparison module is used for acquiring the classification data, comparing and identifying each group of data in the classification data with the target data in the database respectively, and outputting the state data corresponding to each identified group of data, wherein the state data comprises an insulation state and a non-insulation state.
Further, in the above-mentioned case,
the acquisition module comprises a wide passband sensor and a high-speed broadband sampling unit;
the discharge information includes any one or more of pulse information, voltage information, and current information.
Further, in the above-mentioned case,
the acquisition module comprises a waveform acquisition unit for acquiring a current pulse signal of any monitored point, wherein the current pulse signal is acquired as a complete time domain waveform of a 100MS/s broadband;
the classification module comprises a signal extraction unit, and is divided into at least first-class data and second-class data based on the density, the point number, the noise ratio, the frequency and the phase of discharge points in different periods according to a first strategy.
Further, in the above-mentioned case,
the comparison module comprises a data identification unit, a data processing unit and a data processing unit, wherein the data identification unit is used for comparing the first type data and the second type data with the data in the database respectively and outputting the first type data and the second type data with the labels corresponding to the same data in the database respectively;
the label is any one of normal discharge and abnormal discharge.
Further, in the above-mentioned case,
the acquisition module further comprises a display unit, a processing unit and a communication unit, the processing unit receives the discharge information and then controls the display unit to display the discharge information, and the processing unit receives the discharge information and then controls the communication unit to transmit data to the classification module or the comparison module.
Further, in the above-mentioned case,
the classification module and the comparison module are respectively positioned in the server, and the server receives the discharge information sent by the acquisition module;
the server is connected with the database.
In a further aspect of the present invention,
the system is suitable for a power generation system, a power supply system and a power consumer, wherein the power consumer comprises any one or more of a large conference, a convention and exhibition, a ship, a railway and an airplane.
In a further aspect of the present invention,
the first type of data corresponds to a first discharge type;
the second type of data corresponds to a second discharge type.
In a further aspect of the present invention,
the acquisition module comprises a pincer-shaped high-frequency current sensor, an ultrasonic sensor, a transient earth electric wave sensor, an ultrahigh frequency sensor, a synchronous signal sensor and a high-frequency partial discharge detection host;
the classification module and the comparison module are respectively a notebook;
the high-frequency partial discharge detection host is connected with the notebook.
Further, in the above-mentioned case,
the device also comprises a reminding unit and a memory, wherein the memory is used for storing the first type of data and the second type of data;
the reminding unit is used for setting reference data, and outputting a reminding signal when any one of the first type data and the second type data is larger than the reference data.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of a big data identification management system for intelligent separation and classification diagnosis of partial discharge signals;
FIG. 2 is a schematic structural diagram of a first embodiment of an acquisition module;
fig. 3 is a schematic structural diagram of a second embodiment of the acquisition module.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a big data identification management system for intelligent separation, classification and diagnosis of partial discharge signals, which is shown in a schematic structural diagram of fig. 1 and comprises an acquisition module, a classification module and a comparison module. The signals are judged through multiple dimensions, accurate equipment information is obtained, and manpower, material resources and financial resources are saved
The acquisition module is used for acquiring the discharge information of any position point, and the acquisition time is longer than first preset time. The acquisition module acquires the discharge information for more than the first preset time, so that the acquired discharge information can be in a periodic state. Wherein the discharge information includes any one or more of pulse information, voltage information, and current information.
And the classification module is used for classifying the obtained discharge information according to a first strategy to generate classification data, wherein the classification data at least comprises first class data and second class data. The discharge information is classified, so that data with different discharge information are respectively aggregated, and statistics and problem search are facilitated.
And the comparison module is used for acquiring the classification data, comparing and identifying each group of data in the classification data with the target data in the database respectively, and outputting the state data corresponding to each identified group of data, wherein the state data comprises an insulation state and a non-insulation state. By contrast, the discharge information of the device in a different state represented by each discharge information, for example, in a non-insulated state, is obtained, which is different from the discharge information of the device in an insulated state. Through the mode, the obtained discharge information is compared with the data in the database, and whether the equipment in the state is in the insulation state or not is further obtained.
The system can detect the partial discharge signal in a strong noise environment, can distinguish the partial discharge signal from noise, can distinguish different partial discharge types, and provides an objective and reasonable diagnosis conclusion.
The real intellectualization of the discharge pulse is realized, and the automatic separation, identification and diagnosis technology is as follows: acquiring a complete time domain waveform of a signal by carrying out high-speed (100MS/s) broadband sampling on a discharged current pulse signal; various signal characteristics are extracted according to the difference between different discharges and noises, so that different discharges are separated; on the basis, each type of discharge signal is discriminated, and the insulation state of the equipment is diagnosed.
The system can further optimize, construct a data tunnel technology, complete the incremental data capturing task of the database, transmit the incremental data capturing task to the core system to complete the real-time processing of the data, and has a data interface with extremely high consistency, timeliness and reliability.
In one embodiment, the acquisition module includes a wide-passband sensor and a high-speed wideband sampling unit.
The system adopts a wide passband sensor and a high-speed broadband sampling unit to obtain enough discharge information and provide effective diagnosis basis; by comparing the differences of waveform characteristics among different discharge pulse signals and between discharge and interference, different discharge pulses can be effectively separated and clustered, and different discharge types can be distinguished; the system has strong interference processing capacity, so that the equipment can be measured in a non-power-off state; the database of the system collects the actual fault discharge pulse waveform fingerprint characteristics of a large number of power equipment, and a powerful expert database system and a fuzzy logic diagnosis method are established, so that each type of discharge separated from the system can be identified respectively.
In one embodiment, the acquisition module comprises a waveform acquisition unit for acquiring a current pulse signal of any monitored point, wherein the current pulse signal is acquired as a complete time domain waveform of a 100MS/s broadband; the classification module comprises a signal extraction unit, and is divided into at least first-class data and second-class data based on the density, the point number, the noise ratio, the frequency and the phase of discharge points in different periods according to a first strategy. Wherein the first strategy may be a comparison of thresholds, for example, the degree of density has a density threshold, the density threshold may be 3 discharge points per 1 second period, the number of points may be the number of discharge points of the total period, the number of points threshold may be 100 discharge points per 30 second total period, the noise ratio may be the ratio of the noise amplitude to the normal signal amplitude, the noise ratio threshold may be 20 percent, the frequency may be the frequency of the waveform is opposite to the period, the frequency threshold may be 2, the phase may be forward, reverse, etc., the phase threshold may be between-1 and +1, etc., for example, data that is greater than the dense threshold, the point threshold, the noise ratio threshold, the frequency threshold, and the phase threshold is classified as the first type data, and data that is less than the dense threshold, the point threshold, the noise ratio threshold, the frequency threshold, and the phase threshold is classified as the second type data.
Through comprehensive analysis of a plurality of parameters such as discharge quantity, phase, discharge waveform and the like, the false alarm rate and the missing report rate are greatly reduced. The result given to the user is a discharge result in the true sense such as (internal discharge, surface discharge, corona, etc.) and not generally just the magnitude of the discharge amount to gauge whether there is a problem. The accuracy of problem determination is improved.
In one embodiment, the comparison module includes a data identification unit, configured to compare the first type of data and the second type of data with data in a database, and output a tag corresponding to the same data in the database, where the tag is any one of normal discharge and abnormal discharge. The first type of data and the second type of data are respectively compared with the data in the database to generate corresponding labels, so that a debugger can find out problem data immediately.
In an embodiment, as shown in fig. 2, the obtaining module further includes a display unit, a processing unit, and a communication unit, the processing unit controls the display unit to display the discharging information after receiving the discharging information, and the processing unit controls the communication unit to transmit data to the classifying module or the comparing module after receiving the discharging information. The discharge information can be displayed through the display unit, so that a debugger can conveniently master the discharge information, and the use state of the equipment is considered.
In an embodiment, as shown in fig. 3, the system further includes a server, where the classification module and the comparison module are respectively located in the server, and the server receives the discharge information sent by the obtaining module; the server is connected with the database.
In one embodiment, the system is suitable for use in power generation systems, power supply systems, and electricity consumers, including any one or more of large conferences, exhibitions, ships, railroads, and airplanes.
In one embodiment, the first type of data corresponds to a first discharge type and the second type of data corresponds to a second discharge type.
In one embodiment, the acquisition module comprises a clamp-on high-frequency current sensor, an ultrasonic sensor, a transient earth electric wave sensor, an ultrahigh frequency sensor, a synchronous signal sensor and a high-frequency partial discharge detection host. The classification module and the comparison module are respectively a notebook; the high-frequency partial discharge detection host is connected with the notebook.
In one embodiment, the system further comprises a reminding unit and a memory, wherein the memory is used for storing the first type data and the second type data; the reminding unit is used for setting reference data, and when any one of the first type of data and the second type of data is larger than the reference data, the reminding unit outputs a reminding signal. Can reach the purpose of reminding the user through reminding the unit, both can scan the operation after interval second preset time by this system, need not respond to the person that detects and just work, increased this system's practicality. And the memory can store the first class data and the second class data machine, so that the user can look back conveniently, and the practicability of the system is improved.
The system can provide a storage service Web facing a local area network based on a storage device and a server. And the user can more easily calculate the network scale. Through a simple service interface, a user can access the same infrastructure with high expansibility, reliability, safety, rapidness and convenience by storing and retrieving data with any size on the Web at any time through the simple service interface.
And can build the big data analysis and identification platform of local discharge, this big data analysis and identification platform of local discharge is intelligent accomplishes collection, transmission, analysis and discernment overall process, great reduction user's maintenance cost, improve the efficiency of handling, play the effect of protecting driving and protecting the navigation for all consumer.
The system is widely applied in China, the technical advancement and the performance reliability of the product are fully embodied, and higher-quality technology and service are continuously provided for customers.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A big data identification management system for intelligent separation and classification diagnosis of partial discharge signals is characterized by comprising:
the acquisition module is used for acquiring the discharge information of any position point, and the acquisition time is longer than first preset time;
the classification module is used for classifying the obtained discharge information according to a first strategy to generate classification data, wherein the classification data at least comprises first class data and second class data;
the comparison module is used for acquiring classification data, comparing and identifying each group of data in the classification data with target data in a database respectively, and outputting state data corresponding to each identified group of data, wherein the state data comprises an insulation state and a non-insulation state;
the acquisition module comprises a waveform acquisition unit for acquiring a current pulse signal of any monitored point, wherein the current pulse signal is acquired as a complete time domain waveform of a 100MS/s broadband;
the classification module comprises a signal extraction unit, and is divided into at least first-class data and second-class data based on the density, the point number, the noise ratio, the frequency and the phase of discharge points in different periods according to a first strategy.
2. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the acquisition module comprises a wide passband sensor and a high-speed broadband sampling unit;
the discharge information includes any one or more of pulse information, voltage information, and current information.
3. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the comparison module comprises a data identification unit, a data processing unit and a data processing unit, wherein the data identification unit is used for comparing the first type data and the second type data with the data in the database respectively and outputting the first type data and the second type data with the labels corresponding to the same data in the database respectively;
the label is any one of normal discharge and abnormal discharge.
4. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the acquisition module further comprises a display unit, a processing unit and a communication unit, the processing unit receives the discharge information and then controls the display unit to display the discharge information, and the processing unit receives the discharge information and then controls the communication unit to transmit data to the classification module or the comparison module.
5. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the classification module and the comparison module are respectively positioned in the server, and the server receives the discharge information sent by the acquisition module;
the server is connected with the database.
6. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the system is suitable for a power generation system, a power supply system and a power consumer, wherein the power consumer comprises any one or more of a large conference, a convention and exhibition, a ship, a railway and an airplane.
7. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the first type of data corresponds to a first discharge type;
the second type of data corresponds to a second discharge type.
8. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the acquisition module comprises a pincer-shaped high-frequency current sensor, an ultrasonic sensor, a transient earth electric wave sensor, an ultrahigh frequency sensor, a synchronous signal sensor and a high-frequency partial discharge detection host;
the classification module and the comparison module are respectively a notebook;
the high-frequency partial discharge detection host is connected with the notebook.
9. The big data identification management system for intelligent separation and classification diagnosis of partial discharge signals according to claim 1,
the device also comprises a reminding unit and a memory, wherein the memory is used for storing the first type of data and the second type of data;
the reminding unit is used for setting reference data, and when any one of the first type of data and the second type of data is larger than the reference data, the reminding unit outputs a reminding signal.
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