CN113722328B - Multisource space-time analysis algorithm for faults of high-voltage switch equipment - Google Patents
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Abstract
The invention discloses a multisource space-time analysis algorithm for faults of high-voltage switch equipment, which comprises the following steps: firstly, acquiring various fault characterization quantity data of high-voltage switch equipment, and storing the fault characterization quantity data into a database; the invention provides theoretical support for realizing multi-dimensional comprehensive analysis of the high-voltage switch equipment by multi-dimensionally analyzing the association relation between various fault characterization quantities and the association relation between the association relation and the fault characteristics, simultaneously, the analysis of the fault characterization quantities is not limited to the comparison of absolute threshold values, and the comparison of transverse data and longitudinal data is carried out, so that the fault judgment is more stereoscopic and accurate, finally, an algorithm model between the fault characterization quantities and the fault characteristics of the high-voltage switch equipment is established, and a multi-source space-time analysis algorithm of the switch cabinet temperature and the insulation defects is formed, thereby improving the accuracy of defect alarm, reducing false alarm and missing report, being beneficial to improving the operation and maintenance efficiency and reducing the safety risk.
Description
Technical Field
The invention belongs to the field of fault analysis of high-voltage switching equipment, and particularly relates to a multisource space-time analysis algorithm of high-voltage switching equipment faults.
Background
In the prior art, the high-voltage switch equipment refers to electric appliances with rated voltage of 1kV and above and is mainly used for switching on and off a conductive loop, is a generic term for the high-voltage switch and corresponding control, measurement, protection and regulation devices, accessories, shells, supporting parts and other parts and electric and mechanical connection components thereof, and is important control equipment for switching on and off the loop, cutting off and isolating faults, wherein the faults of the high-voltage switch equipment are phenomena frequently occurring in the operation process of the high-voltage switch equipment, and in practical application, the faults of the high-voltage switch equipment are often required to be comprehensively analyzed by using an analysis algorithm.
However, in actual use, the existing high-voltage switching equipment fault analysis algorithm cannot carry out multidimensional analysis on the association relation between various characterization quantities of the high-voltage switching equipment faults and the relation between the association relation and fault characteristics, so that theoretical support cannot be provided for comprehensive analysis, and meanwhile, in actual application, the existing analysis algorithm cannot carry out comparative analysis on transverse data and longitudinal data, so that an algorithm model between the characterization quantities and the fault characteristics cannot be formed, and when the algorithm runs, the accuracy of fault alarm is poor, false alarm and false alarm easily occur, operation and maintenance efficiency is not improved, and meanwhile, a certain safety risk exists, so that the actual application is not facilitated.
The invention comprises the following steps:
the object of the present invention is to solve the above-mentioned problems by providing a multisource spatio-temporal analysis algorithm of high voltage switchgear faults, solving the problems mentioned in the background art.
In order to solve the problems, the invention provides a technical scheme that:
a multi-source spatio-temporal analysis algorithm of high voltage switchgear faults, comprising the steps of:
s1, firstly, acquiring various fault characterization quantity data of high-voltage switch equipment, and storing the fault characterization quantity data into a database;
s2, acquiring fault characteristics of the high-voltage switch equipment, storing the fault characteristics into a database, and simultaneously carrying out multidimensional analysis and association analysis on the fault characteristics and the acquired various fault characterization quantity data respectively to obtain association relations among various characterization quantities and relations between the association relations and the fault characteristics;
s3, respectively collecting fault characterization quantity data and transverse data and longitudinal data of fault characteristics, and respectively carrying out comparison analysis on the fault characterization quantity data and the transverse data and the longitudinal data of the fault characteristics;
s4, establishing an algorithm model between fault characterization quantity data and fault characteristics of the high-voltage switch equipment according to multidimensional analysis and correlation analysis results and comparison analysis results of transverse data and longitudinal data, training and optimizing the algorithm model to obtain a trained model, and forming a multisource space-time analysis algorithm of the temperature and insulation defects of the switch cabinet;
and S5, finally substituting the real-time fault characterization quantity data into an algorithm model between the fault characterization quantity data and the fault characteristics of the high-voltage switching equipment to obtain the real-time fault characteristics of the high-voltage switching equipment.
Preferably, the fault characterizing data in the step S1 includes temperature data, arc data, insulation data, humidity data, and partial discharge data.
Preferably, the specific operation steps of the multidimensional analysis and association analysis in the step S2 are as follows:
s21, firstly, establishing a fact table and a dimension table according to the obtained various fault characterization quantity data of the high-voltage switch equipment and the fault characteristics of the high-voltage switch equipment, and establishing a multidimensional analysis model;
s22, sequentially substituting the obtained various fault characterization quantity data of the high-voltage switch equipment into a multidimensional analysis model for absolute threshold analysis and comparison, extracting various fault characteristics possibly attributed to the fault characterization quantity data, and marking the extracted fault characteristics as a set A;
s23, substituting the obtained fault characteristics of the high-voltage switch equipment into a multidimensional analysis model in sequence for absolute threshold analysis and comparison, extracting various fault characterization quantity data possibly occurring in the fault characteristics, and marking the extracted fault characterization quantity data as a set B;
s24, performing similarity association on various fault characteristics to which fault characterization quantity data belong and all fault characteristics in a database, extracting associated fault characteristics, and marking the associated fault characteristics as a set C;
and S25, finally performing similarity association on various fault characterization quantity data possibly occurring in fault characteristics and all fault characterization quantity data in the database, extracting associated fault characterization quantity data, and recording the extracted associated fault characterization quantity data as a set D.
Preferably, the lateral data in the step S3 is cross-section data, that is, a set of cases of different fault characterizations on the same fault characteristic, and the longitudinal data in the step S3 is longitude data, that is, a set of cases of the same fault characterization on different fault characteristics.
Preferably, the specific operation steps of the comparative analysis in the step S3 are as follows:
s31, firstly, acquiring the condition of each fault characterization quantity data on the same fault characteristic, and integrating and storing the condition as a set E;
s32, acquiring the conditions of the same fault representation on different fault characteristics, and integrating and storing the conditions as a set F;
s33, respectively comparing and analyzing the situations of a plurality of fault characterization quantity data on the same fault characteristic with the situations of the fault characterization on different fault characteristics;
s34, obtaining a comparison analysis result, and recording the same fault characterization quantity condition on the same fault characteristic as the condition of the fault characterization quantity on different fault characteristics, wherein the record is marked as a set E and F.
Preferably, in the step S4, the specific operation steps for training and tuning the algorithm model between the fault characterization value and the fault characteristic of the high-voltage switching device are as follows:
s41, substituting historical fault characterization data of the high-voltage switching equipment into an algorithm model between the preliminarily obtained fault characterization data and fault characteristics of the high-voltage switching equipment to train the fault characterization data;
s42, carrying out multidimensional analysis and association analysis on a plurality of historical fault characterization quantity data through an algorithm model between the fault characterization quantity and the fault characteristics of the high-voltage switching equipment to obtain association relations among various historical fault characterization quantity data of the high-voltage switching equipment and relations between the association relations and the fault characteristics;
s43, collecting various historical fault characterization quantity data of the high-voltage switch equipment and transverse data and longitudinal data of fault characteristics, and comparing and analyzing the data to obtain the fault characteristics corresponding to the various historical fault characterization quantity data of the high-voltage switch equipment;
s44, comparing the fault characteristics obtained through training with standard fault characteristics corresponding to various historical fault characterization quantity data of the high-voltage switch equipment, if the fault characteristics are the same, correcting and optimizing the result if the fault characteristics are different.
Preferably, the formula of the multi-source space-time analysis algorithm in the step S4 is as follows: v=angmax E [ a n B n C n D n (E n F) ].
Preferably, in the formula of the multi-source space-time analysis algorithm, V is fault characteristics, and angmax is fault characterization data.
The beneficial effects of the invention are as follows: according to the invention, through multidimensional analysis of association relations among various fault characterization quantities and relations between the association relations and fault characteristics, theoretical support is provided for realizing multidimensional comprehensive analysis of high-voltage switch equipment, meanwhile, analysis of the fault characterization quantities is not limited to comparison of absolute thresholds, and comparison of transverse data and longitudinal data is carried out, so that fault judgment is more stereoscopic and accurate, finally, an algorithm model between the fault characterization quantities and the fault characteristics of the high-voltage switch equipment is established, and a multisource space-time analysis algorithm of switch cabinet temperature and insulation defects is formed, so that accuracy of defect alarm is improved, false alarm and missing report are reduced, and operation and maintenance efficiency and safety risks are improved.
Description of the drawings:
for ease of illustration, the invention is described in detail by the following detailed description and the accompanying drawings.
Fig. 1 is a flow chart of a multi-source spatio-temporal analysis algorithm of the high voltage switchgear fault of the present invention.
The specific embodiment is as follows:
as shown in fig. 1, the present embodiment adopts the following technical scheme:
examples:
a multi-source spatio-temporal analysis algorithm of high voltage switchgear faults, comprising the steps of:
s1, firstly, acquiring various fault characterization quantity data of high-voltage switch equipment, and storing the fault characterization quantity data into a database;
s2, acquiring fault characteristics of the high-voltage switch equipment, storing the fault characteristics into a database, and simultaneously carrying out multidimensional analysis and association analysis on the fault characteristics and the acquired various fault characterization quantity data respectively to obtain association relations among various characterization quantities and relations between the association relations and the fault characteristics;
s3, respectively collecting fault characterization quantity data and transverse data and longitudinal data of fault characteristics, and respectively carrying out comparison analysis on the fault characterization quantity data and the transverse data and the longitudinal data of the fault characteristics;
s4, establishing an algorithm model between fault characterization quantity data and fault characteristics of the high-voltage switch equipment according to multidimensional analysis and correlation analysis results and comparison analysis results of transverse data and longitudinal data, training and optimizing the algorithm model to obtain a trained model, and forming a multisource space-time analysis algorithm of the temperature and insulation defects of the switch cabinet;
and S5, finally substituting the real-time fault characterization quantity data into an algorithm model between the fault characterization quantity data and the fault characteristics of the high-voltage switching equipment to obtain the real-time fault characteristics of the high-voltage switching equipment.
The fault characterization data in the step S1 include temperature data, arc data, insulation data, humidity data and partial discharge data.
The specific operation steps of the multidimensional analysis and the association analysis in the step S2 are as follows:
s21, firstly, establishing a fact table and a dimension table according to the obtained various fault characterization quantity data of the high-voltage switch equipment and the fault characteristics of the high-voltage switch equipment, and establishing a multidimensional analysis model;
s22, sequentially substituting the obtained various fault characterization quantity data of the high-voltage switch equipment into a multidimensional analysis model for absolute threshold analysis and comparison, extracting various fault characteristics possibly attributed to the fault characterization quantity data, and marking the extracted fault characteristics as a set A;
s23, substituting the obtained fault characteristics of the high-voltage switch equipment into a multidimensional analysis model in sequence for absolute threshold analysis and comparison, extracting various fault characterization quantity data possibly occurring in the fault characteristics, and marking the extracted fault characterization quantity data as a set B;
s24, performing similarity association on various fault characteristics to which fault characterization quantity data belong and all fault characteristics in a database, extracting associated fault characteristics, and marking the associated fault characteristics as a set C;
and S25, finally performing similarity association on various fault characterization quantity data possibly occurring in fault characteristics and all fault characterization quantity data in the database, extracting associated fault characterization quantity data, and recording the extracted associated fault characterization quantity data as a set D.
The lateral data in the step S3 are cross-section data, that is, a set of conditions of different fault characterizations on the same fault characteristic, and the longitudinal data in the step S3 are longitude data, that is, a set of conditions of the same fault characterizations on different fault characteristics.
The specific operation steps of the comparative analysis in the step S3 are as follows:
s31, firstly, acquiring the condition of each fault characterization quantity data on the same fault characteristic, and integrating and storing the condition as a set E;
s32, acquiring the conditions of the same fault representation on different fault characteristics, and integrating and storing the conditions as a set F;
s33, respectively comparing and analyzing the situations of a plurality of fault characterization quantity data on the same fault characteristic with the situations of the fault characterization on different fault characteristics;
s34, obtaining a comparison analysis result, and recording the same fault characterization quantity condition on the same fault characteristic as the condition of the fault characterization quantity on different fault characteristics, wherein the record is marked as a set E and F.
The specific operation steps for training and optimizing the algorithm model between the fault characterization quantity and the fault characteristic of the high-voltage switch equipment in the step S4 are as follows:
s41, substituting historical fault characterization data of the high-voltage switching equipment into an algorithm model between the preliminarily obtained fault characterization data and fault characteristics of the high-voltage switching equipment to train the fault characterization data;
s42, carrying out multidimensional analysis and association analysis on a plurality of historical fault characterization quantity data through an algorithm model between the fault characterization quantity and the fault characteristics of the high-voltage switching equipment to obtain association relations among various historical fault characterization quantity data of the high-voltage switching equipment and relations between the association relations and the fault characteristics;
s43, collecting various historical fault characterization quantity data of the high-voltage switch equipment and transverse data and longitudinal data of fault characteristics, and comparing and analyzing the data to obtain the fault characteristics corresponding to the various historical fault characterization quantity data of the high-voltage switch equipment;
s44, comparing the fault characteristics obtained through training with standard fault characteristics corresponding to various historical fault characterization quantity data of the high-voltage switch equipment, if the fault characteristics are the same, correcting and optimizing the result if the fault characteristics are different.
Wherein, the formula of the multisource space-time analysis algorithm in the step S4 is as follows: v=angmax E [ a n B n C n D n (E n F) ].
In the formula of the multi-source space-time analysis algorithm, V is fault characteristics, and angmax is fault characterization quantity data.
Specific: in practical application, firstly, an algorithm model between fault characterization quantity data and fault characteristics of high-voltage switching equipment needs to be established, and the specific operation steps are as follows: acquiring various fault characterization data of the high-voltage switch equipment and storing the fault characterization data into a database; then, acquiring fault characteristics of the high-voltage switch equipment, storing the fault characteristics into a database, and simultaneously carrying out multidimensional analysis and association analysis on the fault characteristics and the acquired various fault characterization quantity data respectively to obtain association relations among various characterization quantities and relations between the association relations and the fault characteristics; then, respectively acquiring fault characterization data and transverse data and longitudinal data of fault characteristics, and respectively comparing and analyzing the fault characterization data and the transverse data and the longitudinal data of the fault characteristics; according to multidimensional analysis and correlation analysis results, transverse data and longitudinal data comparison analysis results, an algorithm model between fault characterization quantity data and fault characteristics of the high-voltage switching equipment is established, training and optimizing are carried out to obtain a trained model, and a multisource space-time analysis algorithm of the temperature and insulation defects of the switch cabinet is formed, wherein the specific operation steps of training and optimizing the algorithm model between the fault characterization quantity and the fault characteristics of the high-voltage switching equipment are as follows: substituting the historical fault characterization data of the high-voltage switching equipment into an algorithm model between the preliminarily obtained fault characterization and fault characteristics of the high-voltage switching equipment to train the fault characterization data; firstly, carrying out multidimensional analysis and association analysis on a plurality of historical fault characterization data through an algorithm model between the fault characterization data and the fault characteristics of the high-voltage switching equipment to obtain association relations among various historical fault characterization data of the high-voltage switching equipment and relations between the association relations and the fault characteristics; collecting various historical fault characterization data of the high-voltage switch equipment and transverse data and longitudinal data of fault characteristics, and comparing and analyzing the data to obtain the fault characteristics corresponding to the various historical fault characterization data of the high-voltage switch equipment; and comparing the fault characteristics obtained through training with standard fault characteristics corresponding to various historical fault characterization quantity data of the high-voltage switch equipment, if the fault characteristics are the same, correcting and optimizing the result if the fault characteristics are different.
When the method is used, the real-time fault characterization quantity data of the high-voltage switching equipment is firstly obtained and then substituted into an algorithm model between the established fault characterization quantity data and fault characteristics of the high-voltage switching equipment for calculation and analysis;
firstly, multidimensional analysis and association analysis are carried out, and the specific operation steps are as follows: firstly, establishing a fact table and a dimension table according to the acquired real-time fault characterization quantity data of the high-voltage switch equipment and the fault characteristics of the high-voltage switch equipment, and establishing a multidimensional analysis model; substituting the obtained real-time fault characterization quantity data of the high-voltage switch equipment into a multidimensional analysis model for absolute threshold analysis and comparison, extracting various fault characteristics possibly attributed to the fault characterization quantity data, and marking the fault characterization quantity data as a set A; then substituting the obtained fault characteristics of the high-voltage switch equipment into a multidimensional analysis model in sequence for absolute threshold analysis and comparison, extracting various fault characterization quantity data possibly occurring in the fault characteristics, and marking the fault characterization quantity data as a set B; meanwhile, performing similarity association on various fault characteristics to which the real-time fault characterization quantity data belong and all fault characteristics in a database, extracting the associated fault characteristics, and marking the fault characteristics as a set C; performing similarity association on various fault characterization quantity data which possibly occur in fault characteristics and all fault characterization quantity data in a database, extracting associated fault characterization quantity data, and recording the extracted associated fault characterization quantity data as a set D;
then, carrying out contrast analysis on transverse data and longitudinal data, wherein the specific operation steps are as follows: firstly, acquiring the condition of each fault characterization quantity data on the same fault characteristic, and integrating, processing and storing the condition as a set E; then acquiring the conditions of the real-time fault characterization on different fault characteristics, and integrating, processing and storing the conditions as a set F; then, comparing and analyzing the condition of a plurality of fault characterization quantity data on the same fault characteristic with the condition of the real-time fault characterization on different fault characteristics respectively; obtaining a comparison analysis result, recording that the fault characterization quantity condition on the same fault characteristic is the same as the real-time fault characterization quantity condition on different fault characteristics, and recording as a set E and F;
and carrying out multi-source space-time analysis on the algorithm model between the fault characterization quantity and the fault characteristic of the high-voltage switching equipment after training and tuning, wherein V=angmax epsilon [ A n B n C n D n (E n F) ] is the fault characteristic, angmax is the fault characterization quantity data, and the fault characteristic of the high-voltage switching equipment corresponding to the real-time fault characterization quantity data can be obtained through calculation.
In the description of the present invention, it should be understood that the terms "coaxial," "bottom," "one end," "top," "middle," "another end," "upper," "one side," "top," "inner," "front," "center," "two ends," etc. indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," "third," "fourth," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, whereby features defining "first," "second," "third," "fourth" may explicitly or implicitly include at least one such feature.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "configured," "connected," "secured," "screwed," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intermediaries, or in communication with each other or in interaction with each other, unless explicitly defined otherwise, the meaning of the terms described above in this application will be understood by those of ordinary skill in the art in view of the specific circumstances.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. The multisource space-time analysis algorithm for the faults of the high-voltage switch equipment is characterized by comprising the following steps of:
s1, firstly, acquiring various fault characterization quantity data of high-voltage switch equipment, and storing the fault characterization quantity data into a database;
s2, acquiring fault characteristics of the high-voltage switch equipment, storing the fault characteristics into a database, and simultaneously carrying out multidimensional analysis and association analysis on the fault characteristics and the acquired various fault characterization quantity data respectively to obtain association relations among various characterization quantities and relations between the association relations and the fault characteristics;
s3, respectively collecting fault characterization quantity data and transverse data and longitudinal data of fault characteristics, and respectively carrying out comparison analysis on the fault characterization quantity data and the transverse data and the longitudinal data of the fault characteristics;
s4, establishing an algorithm model between fault characterization quantity data and fault characteristics of the high-voltage switch equipment according to multidimensional analysis and correlation analysis results and comparison analysis results of transverse data and longitudinal data, training and optimizing the algorithm model to obtain a trained model, and forming a multisource space-time analysis algorithm of the temperature and insulation defects of the switch cabinet;
s5, substituting the real-time fault characterization data into an algorithm model between the fault characterization data and the fault characteristics of the high-voltage switching equipment to obtain the real-time fault characteristics of the high-voltage switching equipment;
the specific operation steps of multidimensional analysis and association analysis in the step S2 are as follows:
s21, firstly, establishing a fact table and a dimension table according to the obtained various fault characterization quantity data of the high-voltage switch equipment and the fault characteristics of the high-voltage switch equipment, and establishing a multidimensional analysis model;
s22, sequentially substituting the obtained various fault characterization quantity data of the high-voltage switch equipment into a multidimensional analysis model for absolute threshold analysis and comparison, extracting various fault characteristics possibly attributed to the fault characterization quantity data, and marking the extracted fault characteristics as a set A;
s23, substituting the obtained fault characteristics of the high-voltage switch equipment into a multidimensional analysis model in sequence for absolute threshold analysis and comparison, extracting various fault characterization quantity data possibly occurring in the fault characteristics, and marking the extracted fault characterization quantity data as a set B;
s24, performing similarity association on various fault characteristics to which fault characterization quantity data belong and all fault characteristics in a database, extracting associated fault characteristics, and marking the associated fault characteristics as a set C;
s25, finally performing similarity association on various fault characterization quantity data with possible fault characteristics and all fault characterization quantity data in the database, extracting associated fault characterization quantity data, and recording the extracted associated fault characterization quantity data as a set D;
the transverse data in the step S3 are section data, namely, a set of conditions of different fault characterizations on the same fault characteristic, and the longitudinal data in the step S3 are longitude data, namely, a set of conditions of the same fault characterizations on different fault characteristics;
the specific operation steps of the comparative analysis in the step S3 are as follows:
s31, firstly, acquiring the condition of each fault characterization quantity data on the same fault characteristic, and integrating and storing the condition as a set E;
s32, acquiring the conditions of the same fault representation on different fault characteristics, and integrating and storing the conditions as a set F;
s33, respectively comparing and analyzing the situations of a plurality of fault characterization quantity data on the same fault characteristic with the situations of the fault characterization on different fault characteristics;
s34, obtaining a comparison analysis result, and recording the same fault characterization quantity condition on the same fault characteristic as the condition of the fault characterization quantity on different fault characteristics, wherein the record is marked as a set E and F.
2. The multi-source spatio-temporal analysis algorithm of high voltage switchgear failure according to claim 1, characterized in that the failure characterizing data in step S1 comprises temperature data, arc data, insulation data, humidity data and partial discharge data.
3. The multi-source space-time analysis algorithm for high-voltage switchgear fault according to claim 2, wherein the specific operation steps of training and optimizing the algorithm model between the high-voltage switchgear fault characterization quantity and the fault characteristic in step S4 are as follows:
s41, substituting historical fault characterization data of the high-voltage switching equipment into an algorithm model between the preliminarily obtained fault characterization data and fault characteristics of the high-voltage switching equipment to train the fault characterization data;
s42, carrying out multidimensional analysis and association analysis on a plurality of historical fault characterization quantity data through an algorithm model between the fault characterization quantity and the fault characteristics of the high-voltage switching equipment to obtain association relations among various historical fault characterization quantity data of the high-voltage switching equipment and relations between the association relations and the fault characteristics;
s43, collecting various historical fault characterization quantity data of the high-voltage switch equipment and transverse data and longitudinal data of fault characteristics, and comparing and analyzing the data to obtain the fault characteristics corresponding to the various historical fault characterization quantity data of the high-voltage switch equipment;
s44, comparing the fault characteristics obtained through training with standard fault characteristics corresponding to various historical fault characterization quantity data of the high-voltage switch equipment, if the fault characteristics are the same, correcting and optimizing the result if the fault characteristics are different.
4. The multi-source spatio-temporal analysis algorithm of high voltage switchgear failure according to claim 1, characterized in that the formula of the multi-source spatio-temporal analysis algorithm in step S4 is: v=angmax E [ a n B n C D n (E n F) ], where V is a fault characteristic and angmax is fault characterization data.
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