CN109507517B - Distribution transformer operation state analysis method based on double-side power big data comparison - Google Patents
Distribution transformer operation state analysis method based on double-side power big data comparison Download PDFInfo
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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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
Abstract
The invention provides a distribution transformer running state analysis method based on double-side power big data comparison. The acquired data file consists of bilateral power data, bilateral voltage values and bilateral calculated impedance curves, and the operation state and the future operation state of the transformer at the appearance stage are obtained by comparing the bilateral power parameters and the voltage parameters of the transformer with the outgoing power comparison file of the transformer and comparing the bilateral power parameters and the voltage parameters with the big data of the parameters of the transformer at the week, month and year. By utilizing the bilateral power symmetry principle of the transformer, the problem of inaccurate state evaluation of the conventional distribution transformer is effectively solved. The operation detectability of the distribution transformer is improved, and the probability of problems after the distribution transformer breaks down is reduced.
Description
Technical Field
The invention relates to the technical field of transformer state analysis and operation condition prediction, in particular to a distribution transformer operation state analysis method based on bilateral power big data comparison.
Background
The stable operation of the distribution transformer is an important condition in relation to the reliability and safety of power supply. In the past, the stable operation of the transformer is restricted due to the reasons of scattered installation of the distribution transformer, low value of a single transformer, untimely inspection and the like, and although a large number of transformer detection devices are developed for many years, a simple and reliable judgment method and judgment logic for the distribution transformer are lacked. The invention aims to provide an analysis method capable of analyzing internal faults and future faults of a transformer by detecting and analyzing double-side power of the transformer.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a distribution transformer operation state analysis method based on double-side power big data comparison, and by utilizing the principle of transformer double-side power symmetry, the invention provides a simple and feasible analysis method capable of detecting and analyzing the internal fault and the future fault probability of a transformer in real time, and effectively solves the problem of inaccurate state evaluation of the existing distribution transformer. The operation detectability of the distribution transformer is improved, and the probability of problems after the distribution transformer breaks down is reduced.
In order to achieve the purpose, the invention adopts the following technical scheme:
a distribution transformer operation state analysis method based on double-side power big data comparison comprises transformer delivery power experiment and filing data establishment, online test acquisition of transformer double-side power, recording and input database file formation. The construction and experiment of the transformer factory power data filing are to carry out group power test in the transformer factory experiment. The collected data file consists of bilateral power data, bilateral voltage values and bilateral calculated impedance curves, and the method for obtaining the operation state and the future operation state of the transformer in the appearing stage is realized by comparing the bilateral power parameters and voltage parameters of the transformer with the outgoing power comparison file of the transformer and comparing the bilateral power parameters and voltage parameters with the big data of the parameters of the transformer in cycles, months and years.
The method specifically comprises the following steps:
step one, when the distribution transformer leaves a factory, carrying out factory power test, wherein the test process is as follows:
1) taking n powers within the power range of the transformer to carry out an input power given experiment; the input power factor of the load end selects n values between 0.7 and 0.98 to carry out experiments, and the experiments record a primary power P1, a secondary power P2, a load power factor C, a primary voltage effective value U1 and a secondary voltage effective value U2;
2) forming the recorded parameters into the following matrix arrays Ac and Ap, and calculating the product Acp of the two matrices;
wherein: power factor matrix
Power electricityPressure matrix
Calculating a matrix product Acp to obtain a result matrix and storing the result matrix and the two matrixes together;
step two, when the transformer is actually operated, the power parameter of the on-line distribution transformer is measured to obtain a matrix Acc,
{C1}{P1 P2 U1 U2}＝Acc
storing it in a memory;
step three, comparing the matrix Acc with the matrix Acp to calculate and obtain a decision conclusion;
the calculation method comprises the following steps: calculating the coincidence probability Gb of the matrix Acc and the matrix Acp to obtain the highest value Ga of the element probability of a certain row in the matrix Acc and the matrix Acp; evaluating the running state of the transformer according to the values of Ga and Gb;
step four, combining the test data of m time points in the time period T of the operation of the storage transformer into a matrix Acpt, wherein the combination method comprises the following steps:
step five, carrying out differential operation on the Acpt and the Acp to obtain an average deviation matrix Acpp, calculating division operation of the Acp and the Acpp to obtain a coefficient K, wherein the K is Acpp/Acp;
and step six, estimating the fault rate of the future running state of the transformer according to the value of the coefficient K.
In the third step, the running state of the transformer is evaluated according to the values of Ga and Gb, and the method specifically comprises the following steps:
1) when Ga is more than a% or a plurality of coincidence probabilities Gb are more than b%, judging that the operation state of the transformer is excellent;
2) when Ga is larger than c% or a plurality of coincidence probabilities Gb are larger than d%, the transformer is judged to be in a good running state;
3) when Ga is larger than e% or a plurality of coincidence probabilities Gb are larger than f%, the operation state of the transformer is considered to be qualified;
4) the transformer is considered to be failed to operate under other conditions;
wherein, a, b, c, d, e, f are percentage constants, a, b value range: (100, 85], c and d have the value ranges of (85, 70), and e and f have the value ranges of (70, 60).
The data of the online test in the third step and the fourth step can be test data of a plurality of time points in a period of week, month or year.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a big data comparison operation state analysis and future fault rate prediction method based on distribution transformer online bilateral power test records, which is a method for obtaining the operation state and the future operation state of a transformer in an appearance stage by comparing online test transformer bilateral power parameters and voltage parameters with the transformer delivery power comparison file and comparing with big data of cycle, month and year parameters.
The invention provides a simple and feasible analysis method for the internal fault and the future fault probability of the transformer, which can detect and analyze in real time, by utilizing the bilateral power symmetry principle of the transformer, and effectively solves the problem of inaccurate state evaluation of the existing distribution transformer. The operation detectability of the distribution transformer is improved, and the probability of problems after the distribution transformer breaks down is reduced.
Drawings
Fig. 1 is a flowchart of a method for analyzing an operating state of a distribution transformer based on a double-side power big data comparison according to the present invention.
Detailed Description
The following describes in detail specific embodiments of the present invention.
As shown in fig. 1, a method for analyzing the operating state of a distribution transformer based on double-side power big data comparison includes a transformer factory power experiment, establishing archived data, collecting and recording double-side power of the transformer through online test, and forming an input database file. The construction and experiment of the transformer factory power data filing are to carry out group power test in the transformer factory experiment. The collected data file consists of double-side power data, double-side voltage values and double-side calculated impedance curves.
The method specifically comprises the following steps:
step one, when the distribution transformer leaves a factory, carrying out factory power test, wherein the test process is as follows:
1) taking n powers within the power range of the transformer to carry out an input power given experiment; the input power factor of the load end selects n values between 0.7 and 0.98 to carry out experiments, and the experiments record a primary power P1, a secondary power P2, a load power factor C, a primary voltage effective value U1 and a secondary voltage effective value U2;
2) forming the recorded parameters into the following matrix arrays Ac and Ap, and calculating the product Acp of the two matrices;
wherein: power factor matrix
Power voltage matrix
Calculating a matrix product Acp to obtain a result matrix and storing the result matrix and the two matrixes together;
step two, when the transformer is actually operated, the power parameter of the on-line distribution transformer is measured to obtain a matrix Acc,
{C1}{P1 P2 U1 U2}＝Acc
storing it in a memory;
step three, comparing the matrix Acc with the matrix Acp to calculate and obtain a decision conclusion;
the calculation method comprises the following steps: calculating the coincidence probability Gb of the matrix Acc and the matrix Acp to obtain the highest value Ga of the element probability of a certain row in the matrix Acc and the matrix Acp; evaluating the running state of the transformer according to the values of Ga and Gb;
step four, combining the test data of m time points in the time period T of the operation of the storage transformer into a matrix Acpt, wherein the combination method comprises the following steps:
step five, carrying out differential operation on the Acpt and the Acp to obtain an average deviation matrix Acpp, calculating division operation of the Acp and the Acpp to obtain a coefficient K, wherein the K is Acpp/Acp;
and step six, estimating the fault rate of the future running state of the transformer according to the value of the coefficient K.
The data of the online test in the third step and the fourth step can be test data of a plurality of time points in a period of week, month or year.
In the third step, the running state of the transformer is evaluated according to the values of Ga and Gb, and the method specifically comprises the following steps:
1) when Ga is more than a% or a plurality of coincidence probabilities Gb are more than b%, judging that the operation state of the transformer is excellent;
2) when Ga is larger than c% or a plurality of coincidence probabilities Gb are larger than d%, the transformer is judged to be in a good running state;
3) when Ga is larger than e% or a plurality of coincidence probabilities Gb are larger than f%, the operation state of the transformer is considered to be qualified;
4) the transformer is considered to be failed to operate under other conditions;
wherein, a, b, c, d, e, f are percentage constants, a, b value range: (100, 85], c and d have the value ranges of (85, 70), and e and f have the value ranges of (70, 60).
In the sixth step, the failure rate of the transformer in the future operating state is estimated according to the value of the coefficient K as follows:
when the coefficient K is less than or equal to m%, the future operation state of the transformer is considered to be excellent; k is more than or equal to m% and less than or equal to n%, the future running state of the transformer is considered to be good; and K is more than or equal to n% and less than or equal to p%, and the transformer is considered to be poor in future operation.
Wherein m, n and p are percentage constants, and the value range of m is as follows: (0, 10], n has the value range of (10, 20) and f has the value range of (20, 60).
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (2)
1. A distribution transformer operation state analysis method based on double-side power big data comparison is characterized by comprising the following steps:
step one, when the distribution transformer leaves a factory, carrying out factory power test, wherein the test process is as follows:
1) taking n powers within the power range of the transformer to carry out an input power given experiment; the input power factor of the load end selects n values between 0.7 and 0.98 to carry out experiments, and the experiments record a primary power P1, a secondary power P2, a load power factor C, a primary voltage effective value U1 and a secondary voltage effective value U2;
2) forming the recorded parameters into the following matrix arrays Ac and Ap, and calculating the product Acp of the two matrices;
wherein: power factor matrix
Power voltage matrix
Calculating a matrix product Acp to obtain a result matrix and storing the result matrix and the two matrixes together;
step two, when the transformer is actually operated, the power parameter of the on-line distribution transformer is measured to obtain a matrix Acc,
{C1}{P1 P2U1 U2}＝Acc
storing it in a memory;
step three, comparing the matrix Acc with the matrix Acp to calculate and obtain a decision conclusion;
the calculation method comprises the following steps: calculating the coincidence probability Gb of the matrix Acc and the matrix Acp to obtain the highest value Ga of the element probability of a certain row in the matrix Acc and the matrix Acp; evaluating the running state of the transformer according to the values of Ga and Gb;
step four, combining the test data of m time points in the time period T of the operation of the storage transformer into a matrix Acpt, wherein the combination method comprises the following steps:
step five, carrying out difference operation on the Acpt and the Acp to obtain an average deviation matrix Acpp; calculating division operation of the Acp and the Acpp to obtain a coefficient K, wherein K is the Acpp/Acp;
and step six, estimating the fault rate of the future running state of the transformer according to the value of the coefficient K.
2. The method for analyzing the operating state of the distribution transformer based on the bilateral power big data comparison as claimed in claim 1, wherein the operating state of the distribution transformer is evaluated according to the values of Ga and Gb in the third step, specifically as follows:
1) when Ga is more than a% or a plurality of coincidence probabilities Gb are more than b%, judging that the operation state of the transformer is excellent;
2) when Ga is larger than c% or a plurality of coincidence probabilities Gb are larger than d%, the transformer is judged to be in a good running state;
3) when Ga is larger than e% or a plurality of coincidence probabilities Gb are larger than f%, the operation state of the transformer is considered to be qualified;
4) the transformer is considered to be failed to operate under other conditions;
wherein, a, b, c, d, e, f are percentage constants, a, b value range: (100, 85], c and d have the value ranges of (85, 70), and e and f have the value ranges of (70, 60).
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| CN101404414B (en) * | 2008-09-28 | 2011-05-04 | 王磊 | Power distribution network electromagnetic optimization dynamic loss reduction method, system and synthetic loss reduction system |
| CN101902077A (en) * | 2010-07-13 | 2010-12-01 | 杭州交联电气工程有限公司 | Operating data monitoring and alarming system of converting stations |
| CN103198175B (en) * | 2013-03-04 | 2016-01-13 | 辽宁省电力有限公司鞍山供电公司 | Based on the Diagnosis Method of Transformer Faults of fuzzy clustering |
| CN103713210A (en) * | 2013-11-01 | 2014-04-09 | 天津工业大学 | Dry power transformer monitoring and diagnosis system |
| JP6340207B2 (en) * | 2014-02-24 | 2018-06-06 | パナソニック株式会社 | Nonlinear distortion detector and distortion compensation power amplifier |
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| CN104914327B (en) * | 2015-05-06 | 2018-01-30 | 北京航空航天大学 | Transformer fault maintenance Forecasting Methodology based on real-time monitoring information |
| CN104914362A (en) * | 2015-05-26 | 2015-09-16 | 深圳供电局有限公司 | Insulation state monitoring system and method of SF6 gas transformer |
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