CN105242129A - Fault probability determination method for transformer winding - Google Patents

Abstract

The invention relates to a fault probability determination method for a transformer winding, and belongs to the technical field operation state assessment of transformers. The method comprises the following steps that (1) different fault modes caused by the transformer winding are divided; (2) according to a fault determination guide rule of the transformer winding, a fault characteristic parameter that reflects the transformer winding is input, and correspondence between the different fault modes and the fault characteristic parameter of the transformer winding is established; and (3) a fault probability of each fault mode is determined. The fault probability of each fault mode of the transformer winding is determined, so that an assessment result approaches the practical condition of the transformer winding more, and thus, the operation state of a transformer can be determined more reasonably.

Description

A kind of transformer winding fault probability determination method

Technical field

The invention belongs to converting equipment running status assessment technology field, particularly a kind of transformer winding fault probability determination method.

Background technology

Transformer is made up of according to certain institutional framework many parts, its unreliable degree or fault rate are always closely related with the unreliable degree of each building block or fault rate, and the basic element of character that winding transmits as voltage transformation and energy, it is the important component part of transformer, transformer is probably caused to be stopped transport if it breaks down, therefore, the probability obtaining transformer winding fault generation is the basis obtaining transformer whole system dependability, and the main method judged according to fuzzy comprehensive evoluation determines fault rate at present, but subordinate function has diversity in the choice, or need to determine fuzzy membership functions according to expertise, result of calculation has randomness and cannot obtain unified, therefore need to provide a kind of new probability of malfunction defining method.

Summary of the invention

Object of the present invention is the problems referred to above solving prior art, and provide a kind of transformer winding fault probability determination method, to achieve these goals, the technical solution used in the present invention is as follows:

A kind of transformer winding fault probability determination method, is characterized in that: described probability of malfunction defining method comprises the following steps:

(1) the various fault modes that Transformer Winding causes are divided;

(2) according to the differentiation directive/guide of transformer winding fault, the Failure Characteristic Parameter of input reflection Transformer Winding, and set up the corresponding relation between the various fault mode of Transformer Winding and Failure Characteristic Parameter;

(3) to divided fault mode, all Failure Characteristic Parameters of its correspondence are formed a stack features Vector Groups $Y_m={\left[\begin{array}{cccc}{Y}_{m.1}^X& Y_{m.1}^X& ...& Y_{m.n}^X\end{array}\right]}^T,$ By contrast current signature Vector Groups Y _mto the Vector Groups that the demand value of each characteristic parameter forms $Y_{thr}={\left[\begin{array}{cccc}{Y}_{thr.1}^X& Y_{thr.1}^X& ...& Y_{thr.n}^X\end{array}\right]}^T$ Distance and the proper vector group of initial value $Y_{inv}={\left[\begin{array}{cccc}{Y}_{inv.1}^X& Y_{inv.1}^X& ...& Y_{inv.n}^X\end{array}\right]}^T$ To demand value Y _thrratio between the distance of the Vector Groups of composition determines the probability of malfunction under each fault mode of Transformer Winding, its probability of malfunction P _xexpression formula be:

$P_x=1-\frac{\sqrt{\underset{i=1}{\overset{n}{\Σ}}{(Y_{m.i}^x-Y_{thr.i}^x)}^2}}{\sqrt{\underset{i=1}{\overset{n}{\Σ}}{(Y_{inv.i}^x-Y_{thr.i}^x)}^2}},$

In expression formula, for the initial value of i-th index state parameter in fault mode x, for the demand value of i-th index state parameter in fault mode x, for i-th index state parameter actual amount measured value in fault mode x.

Preferably, described probability of malfunction P _xspan be [0,1], work as P _xwhen being 0, showing that index is in optimum state, work as P _xshow that index is in worst state when being 1.

Preferably, described fault mode comprises insulation decline fault, direct current resistance abnormal failure, discharge fault fault mode, short trouble, winding deformation fault, oil duct obstruction fault and overload fault.

In sum, invention has following beneficial effect: the present invention reflects with the initial value of parameter and the ratio of demand value distance the probability that winding breaks down by the distance of the Failure Characteristic Parameter to demand value corresponding to parameter that calculate actual measurement, actual measurement parameter distance demand value is nearer, the probability that breaks down is larger, tally with the actual situation, solve when adopting the method for fuzzy synthesis judgement to determine fault rate simultaneously and choose the skimble-scamble problem of fuzzy membership functions, different fault modes and Failure Characteristic Parameter can be normalized by the method, be conducive to the contrast of the monitoring variable of different faults pattern, be convenient to the winding practical operation situation that multiple characteristic parameter reflects by transformer operational management personnel effectively combine, thus the capable situation of transformation fortune device winding and fault rate are made more reasonably judge.

Accompanying drawing explanation

In order to be illustrated more clearly in example of the present invention or technical scheme of the prior art, introduce doing accompanying drawing required in embodiment or description of the prior art simply below, apparently, accompanying drawing in the following describes is only examples more of the present invention, to those skilled in the art, do not paying under creationary prerequisite, other accompanying drawing can also obtained according to these accompanying drawings.

Fig. 1 is the process flow diagram of a kind of transformer winding fault probability determination method of the present invention.

Fig. 2 is the analytical calculation process flow diagram of a kind of transformer winding fault probability determination method of the present invention.

Embodiment

Below in conjunction with the accompanying drawing in example of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

Fault mode is all issuable fault modes of each parts in analytical equipment, and by each fault mode Frequency, affect the order of severity and detect the complexity analytical approach of being classified, be applicable to the Life cycle of the products such as solution formulation, design, production and use, with the part of equipment, parts or system for analytic target, contingent problem and potential fault in assembling produced by predict element or part, study a question and the reason of fault, propose the preventive and improved measures that may take.

Composition graphs 1, a kind of transformer winding fault probability determination method, comprises the following steps:

(1) the various fault modes that Transformer Winding causes are divided; For selected Transformer Winding, Transformer Winding can cause different fault modes, and described fault mode comprises insulation decline fault, direct current resistance abnormal failure, discharge fault fault mode, short trouble, winding deformation fault, oil duct obstruction fault and overload fault.After above-mentioned fault appears in Transformer Winding, the change of some parameters of transformer (parameter) being caused, by detecting the change of these parameters (parameter), can judge whether Transformer Winding has occurred fault.

(2) according to the differentiation directive/guide of transformer winding fault, to transformer state evaluation and corresponding preventative analysis, the Failure Characteristic Parameter of input reflection Transformer Winding, set up the corresponding relation between the various fault mode of Transformer Winding and Failure Characteristic Parameter simultaneously, and preventive trial is carried out to obtain each characteristic parameter value to transformer; The corresponding relation of fault mode and Failure Characteristic Parameter is as shown in table 2, in the present invention, discriminatory analysis and preventive trial is carried out with reference to GB/T7252-2001 " Gases Dissolved in Transformer Oil analysis and judge directive/guide " and DL/T596-1996 " power equipment preventive trial code ", then select the Failure Characteristic Parameter of reflection Transformer Winding, described Failure Characteristic Parameter is as shown in table 1.

Table 1 transformer winding fault characteristic parameter collection

The corresponding relation of table 2 transformer winding fault pattern and characteristic quantity

(3) to divided fault mode, all Failure Characteristic Parameters of its correspondence are formed a stack features Vector Groups $Y_m={\left[\begin{array}{cccc}{Y}_{m.1}^X& Y_{m.1}^X& ...& Y_{m.n}^X\end{array}\right]}^T,$ By contrast current signature Vector Groups Y _mto the Vector Groups that the demand value of each characteristic parameter forms $Y_{thr}={\left[\begin{array}{cccc}{Y}_{thr.1}^X& Y_{thr.1}^X& ...& Y_{thr.n}^X\end{array}\right]}^T$ Distance and the proper vector group of initial value $Y_{inv}={\left[\begin{array}{cccc}{Y}_{inv.1}^X& Y_{inv.1}^X& ...& Y_{inv.n}^X\end{array}\right]}^T$ To demand value Y _thrratio between the distance of the Vector Groups of composition determines the probability of malfunction under each fault mode of Transformer Winding, its probability of malfunction P _xexpression formula be:

$P_x=1-\frac{\sqrt{\underset{i=1}{\overset{n}{\Σ}}{(Y_{m.i}^x-Y_{thr.i}^x)}^2}}{\sqrt{\underset{i=1}{\overset{n}{\Σ}}{(Y_{inv.i}^x-Y_{thr.i}^x)}^2}},$

In expression formula, for the initial value of i-th index state parameter in fault mode x, for the demand value of i-th index state parameter in fault mode x, for i-th index state parameter actual amount measured value in fault mode x, described probability of malfunction P _xspan be [0,1], work as P _xwhen being 0, showing that index is in optimum state, work as P _xshow that index is in worst state when being 1, after Transformer Winding breaks down, diverse ways and test item is had to the fault diagnosis of Transformer Winding, involved fault detection method is concluded, arrange, the characteristic parameter each detection method obtained is as Failure Characteristic Parameter, as shown in table 1, (such as winding is short-circuited after fault, its loss of function of insulating, hold and can't stand withstand voltage test, therefore, utilize winding whether can tolerate withstand voltage test and can judge whether it exists short trouble), and short trouble can not cause the change of other parameters, therefore be not suitable for short trouble to detect.

As shown in Figure 2, in order to analytic process fault of the present invention being sent out to method of determining probability is described further, choose 240MVA, 220kV transformer, model is SFPSZ1-240000/220 is example, with winding insulation decline fault mode for fault example, the specific implementation process that probability is determined is described respectively, when it puts into operation, the preventive trial data of (initial value when transformer comes into operation) and test period tested (as: 2014) are as shown in table 3, when putting into operation (initial value) and the chromatogram trace data tested by test period as shown in table 4.

Table 3 preventive trial data

Table 4 chromatogram trace data (× 10 ^-6)

Concrete analytical approach and the step of process as follows, for winding insulation decline fault mode and discharge fault pattern,

1). determine that winding insulation decline fault mode is fault mode to be evaluated, obtain the demand value and Failure Characteristic Parameter initial value that specify in the experiment value of the parameter of each feature of insulation drop mode, code;

2). determine that the characteristic parameter corresponding to fault mode is by table 2: micro-water content Y4, tan δ winding Y6, oily dielectric loss, the insulation resistance Y11 of winding, the leakage current Y14 of winding in the experiment of body oil chromatogram analysis Y1, oil degradation Y2, absorptance Y3, oil, namely proper vector group is:

$Y_m={\left[\begin{array}{ccccccc}{Y}_{m.1}^X& Y_{m.2}^X& Y_{m.3}^X& Y_{m.4}^X& Y_{m.6}^X& Y_{m.11}^X& Y_{m.14}^X\end{array}\right]}^T$

3). calculate the probability of malfunction of winding insulation decline fault mode:

$P_1=1-\frac{\sqrt{\underset{i=1}{\overset{14}{\Σ}}{(Y_{m.i}^1-Y_{thr.i}^1)}^2}}{\sqrt{\underset{i=1}{\overset{14}{\Σ}}{(Y_{inv.i}^1-Y_{thr.i}^1)}^2}}=0.6323,$

Therefore, the probability when this Measuring Time Transformer Winding generation winding insulation decline fault is 0.6323.

When Transformer Winding generation discharge fault, first, the demand value that specifies in each characteristic parameter initial value of discharge fault, code and Failure Characteristic Parameter measured value is determined, as shown in table 4, wherein, when measured value is the generation of transformer discharge fault, the discharge capacity of measured transformer;

Table 4 chromatogram trace data (× 10 ^-6)

Secondly, determine that the characteristic parameter corresponding to fault mode is by table 2: oil chromatogram analysis Y1, shelf depreciation Y7, namely proper vector group is $Y_m={\left[\begin{array}{cc}{Y}_{m.1}^X& Y_{m.7}^X\end{array}\right]}^T;$ Finally, determine the probability of malfunction of Transformer Winding discharge fault fault mode, its probability of malfunction is as follows:

$P_1=1-\frac{\sqrt{\underset{i=1}{\overset{2}{\Σ}}{(Y_{m.i}^1-Y_{thr.i}^1)}^2}}{\sqrt{\underset{i=1}{\overset{2}{\Σ}}{(Y_{inv.i}^1-Y_{thr.i}^1)}^2}}=0.224758,$

Therefore, the probability when this Measuring Time Transformer Winding generation discharge fault is 0.2248.

In sum, the present invention is by carrying out fault analysis to Transformer Winding, the various fault modes that Transformer Winding causes are divided, according to the differentiation directive/guide of transformer winding fault, the Failure Characteristic Parameter of input reflection Transformer Winding, and the corresponding relation set up between the various fault mode of Transformer Winding and Failure Characteristic Parameter, and preventive trial is carried out to winding, obtain each Failure Characteristic Parameter to determine the fault rate of each fault mode of Transformer Winding, of the present invention needs carries out preventive trial to equipment, test needs as often as possible to contain each Failure Characteristic Parameter, thus make assessment result more meet the actual state of Transformer Winding, thus the situation that transformer runs is made and more reasonably being judged.

The foregoing is only the preferred embodiment of invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (3)

1. a transformer winding fault probability determination method, is characterized in that: described probability of malfunction defining method comprises the following steps:

(1) the various fault modes that Transformer Winding causes are divided;

(2) according to the differentiation directive/guide of transformer winding fault, the Failure Characteristic Parameter of input reflection Transformer Winding, and set up the corresponding relation between the various fault mode of Transformer Winding and Failure Characteristic Parameter;

(3) to divided fault mode, all Failure Characteristic Parameters of its correspondence are formed a stack features Vector Groups $Y_m={\left[\begin{array}{cccc}{Y}_{m.1}^X& Y_{m.1}^X& ...& Y_{m.n}^X\end{array}\right]}^T,$ By contrast current signature Vector Groups Y _mto the Vector Groups that the demand value of each characteristic parameter forms $Y_{thr}={\left[\begin{array}{cccc}{Y}_{thr.1}^X& Y_{thr.1}^X& ...& Y_{thr.n}^X\end{array}\right]}^T$ Distance and the proper vector group of initial value $Y_{inv}={\left[\begin{array}{cccc}{Y}_{inv.1}^X& Y_{inv.1}^X& ...& Y_{inv.n}^X\end{array}\right]}^T$ To demand value Y _thrratio between the distance of the Vector Groups of composition determines the probability of malfunction under each fault mode of Transformer Winding, its probability of malfunction P _xexpression formula be:

$P_x=1-\frac{\sqrt{\underset{i=1}{\overset{n}{\Σ}}{(Y_{m.i}^x-Y_{thr.i}^x)}^2}}{\sqrt{\underset{i=1}{\overset{n}{\Σ}}{(Y_{inv.i}^x-Y_{thr.i}^x)}^2}},$

In expression formula, for the initial value of i-th index state parameter in fault mode x, for the demand value of i-th index state parameter in fault mode x, for i-th index state parameter actual amount measured value in fault mode x.

2. a kind of transformer winding fault probability determination method according to claim 1, is characterized in that: described probability of malfunction P _xspan be [0,1], work as P _xwhen being 0, showing that index is in optimum state, work as P _xshow that index is in worst state when being 1.

3. a kind of transformer winding fault probability determination method according to claim 1, is characterized in that: described fault mode comprises insulation decline fault, direct current resistance abnormal failure, discharge fault fault mode, short trouble, winding deformation fault, oil duct obstruction fault and overload fault.