CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of China application serial no. 202010455962.1, filed on May 26, 2020. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification.

BACKGROUND

Technical Field

The disclosure relates to a transformer state evaluation field, and in particular, relates to a transformer state evaluation method combining a fuzzy analytic hierarchy process (FAHP), a decision making trial and evaluation laboratory (DEMATEL) method, and a criteria importance through intercriteria correlation (CRITIC) method.

Description of Related Art

The safety of transformers is an important factor for the normal operation of a power grid. In order to prevent transformer failures, time for maintenance and repair of the transformers may be determined in a timely and reasonable manner, and the level of operation and maintenance of the power transformers may be improved, and these are important issues to be addressed. At present, among the several maintenance strategies which are currently available, the most advanced and cost-saving maintenance strategy is to rely on the state evaluation close to the actual operation state of the transformers, and that the maintenance time and plan may be accordingly selected.

Nevertheless, a transformer has a complex structure and exhibits a large number of state quantities, and further, interference occurs among indicator quantities. As affected by the above-mentioned problems, it is difficult to achieve accurate evaluation of transformer states. When calculating the weights of evaluation indicators through a conventional method, interference among indicators is ignored. In order to reduce the evaluation deviation caused by the mutual influence among evaluation indicators, in the disclosure, the fuzzy analytic hierarchy process (FAHP), a decision making trial and evaluation laboratory (DEMATEL) method, and the criteria importance through intercriteria correlation (CRITIC) method are combined. The weights of the indicators are calculated from a subjective level and an objective level to reduce the interference. In this way, the evaluation result that is closest to the actual health conditions of the transformers may be accordingly obtained.

SUMMARY

The disclosure aims to solve the technical problem of inaccurate final evaluation results caused by mutual interference among evaluation indicators and provides a transformer state evaluation method combining a fuzzy analytic hierarchy process (FAHP), a decision making trial and evaluation laboratory (DEMATEL) method, and a criteria importance through intercriteria correlation (CRITIC) method.

The technical solutions adopted by the disclosure includes the following.

The disclosure provides a transformer state evaluation method combining a FAHP method, a DEMATEL method, and a CRITIC method, and the method includes the following steps.

In step one, a plurality of groups of state quantities, which are most relevant to a transformer health condition is selected, and a hierarchical indicator system is built.

In step two, an influencing degree and an influenced degree of a plurality of state quantity indicators are calculated through the DEMATEL method, and a weight of each of the state quantity indicators are calculated through the FAHP method. Indicator subjective weights are obtained by combining the influencing degree and the influenced degree of the state quantity indicators. A plurality of groups of transformers and state quantity indicators corresponding to the plurality of groups of transformers are selected, and indicator objective weights are calculated through the CRITIC method.

In step three, an optimal weight is calculated according to the indicator subjective weights and the indicator objective weights, such that a total difference value between a weight vector obtained through the optimal weight and a weight vector obtained through each of methods is minimum.

In step four, a state score is calculated layer by layer from an indicator layer to a factor layer for each of the transformers to be evaluated according to the hierarchical indicator system and the optimal weights, which are obtained, the state score of each of the transformers is weighted and obtained, and an actual health condition of each of the transformers is determined through the state score of each of the transformers.

In an embodiment of the disclosure, a method of building the hierarchical indicator system in the step one specifically includes the following steps.

24 groups of the state quantities, which are most relevant to the transformer health condition are selected, and the hierarchical indicator system is built. The hierarchical indicator system is divided into three layers from top to bottom: a target layer, the factor layer, and the indicator layer. The target layer is a comprehensive state of the transformers. 9 failure types, which are most influential to the states of the transformers are selected in the factor layer, and a plurality of specific variables, which are most influential in the failure types, are selected in the indicator layer.

In an embodiment of the disclosure, in a method of building the target layer, the factor layer, and the indicator layer in the step one:

The target layer is the comprehensive state of the transformers.

The factor layer is: a winding failure, a core failure, arc discharging, partial discharging, oil discharging, insulation damping, insulation aging, insulation oil deterioration, and current loop overheating.

The indicator layer is: dielectric loss of insulation oil, a water content in oil, an oil breakdown voltage, an insulation resistance absorption ratio, a polarization indicator, a volume resistivity H2, a core ground current, a core insulation resistance, C2H6, C2H4, a mutual difference of winding direct current resistance, CO, a relative gas production rate, CO2, a relative gas production rate, an initial value difference of winding short-circuit impedance, winding insulation dielectric loss, an initial value difference of winding capacitance, C2H2, a partial discharging amount, a gas content in oil, CH4, a neutral point oil flow static current, a furfural content, and a cardboard polymerization degree.

In an embodiment of the disclosure, a method of obtaining the indicator subjective weights in the step two specifically includes the following steps.

The influencing degree and the influenced degree of indicators are calculated for the indicators under the factor layer by using the DEMATEL method. A direct influence matrix A of the indicators is determined for the indicators under the factor layer by using an expert scoring method, and a variable a is an element in the direct influence matrix A.

Normalization is performed through

G=A/max1<i<n⁢∑j=1n⁢aij
to obtain a matrix G, where n is a number of the state quantity indicators under the factor layer. A comprehensive influence matrix is calculated through T=G(I−G)−1, where a variable t is an element in a comprehensive influence matrix T.

An influencing degree fiand an influenced degree eiare determined through a formula

fi=∑nj=1⁢tij,ei=∑ni=1⁢tij.
fiis a sum of row elements in the comprehensive influence matrix T, indicating a direct influencing degree or an indirect influencing degree of a state quantity indicator i on a state quantity indicator j, and eiis a sum of column elements in the comprehensive influence matrix T, indicating a value of the state quantity indicators corresponding to each row influenced by other state quantity indicators.

A state quantity weight W1is calculated through the FAHP method, a relationship matrix between the influencing degree and the influenced degree is obtained through the DEMATEL method according to a formula d=fT*e, a vector of the influencing degree d=fT*e of the indicators is formed through a diagonal line element, the influencing degree of the indicator i is calculated, and a corresponding weight W2is obtained through a formula

W2=di/∑i=1n⁢di.

The DEMATEL method and the FAHP method are combined, a weight W is obtained through a formula W=W1□W2, and a comprehensive weightW, which is, the indicator subjective weight which is obtained after performing normalization.

In an embodiment of the disclosure, a method of calculating the indicator objective weights in the step two specifically includes the following steps.

The plurality of groups of the transformers and the state quantities thereof are selected, standardization processing is performed on state quantity data of each of the transformers, and a formula thereof is: standardized quantity=(this value-lowest value)/(highest value-lowest value).

A contrast intensity σj, conflict Rj, and an information amount Cjare calculated through formulas provided as follows:

Rj=∑i=1n⁢(1-rij)Cj=σj⁢Rj

The contrast intensity is a difference between a same indicator of different individuals, and a standard deviation is used to represent the contrast intensity σj.

An objective weight Wjis obtained through calculating a formula provided as follows:

Wj=Cj∑j=1n⁢⁢Cj
where i and j represent the state quantities, n represents a total number of the state quantities, and rijrepresents a correlation coefficient provided between the state quantities i and j.

In an embodiment of the disclosure, a method of calculating the optimal weight in the step three specifically includes the following steps.

The optimal weight is calculated according to the indicator subjective weights and the indicator objective weights, which are obtained by calculating, based on a minimum-variance principle through a method of Lagrange multipliers for finding an extremum. The total difference value between the weight vector obtained through the optimal weight and the weight vector obtained through each of methods is minimum. A method of calculating the optimal weight is provided as follow.

A weight vector of a jthindicator of one weight calculation method is Wj=(Wj1, Wj2, Wj3, . . . , Wjn), a most reasonable attribute weight vector under weighting of two weight calculation methods is W=(W1, W2, W3, . . . Wm), m and n are both numbers of indicators of one factor layer, and a single-target planning model is accordingly built:

{min⁢⁢P=∑i=1m⁢∑k=1s⁢∑j=1n⁢(ak⁢λki-aj⁢λij)2∑k=1s⁢ak=1

A corresponding Lagrangian function is constructed, and the extremum is found:

L⁡(a,λ)=∑i=1m⁢∑k=1s⁢∑j=1n⁢(ak⁢uki-aj⁢uij)2+λ⁡(∑k=1s⁢ak-1),
and
the following may be derived:

∂L⁡(a,λ)∂ak=2⁢ak⁢∑i=1m⁢uki2-(a1⁢∑i=1m⁢u1⁢i⁢uki+a2⁢∑i=1m⁢u2⁢i⁢uki)+λ2=0∂L⁡(a,λ)∂λ=∑t=1s⁢at-1=0,
when k=1 and 2, a system of equations formed by 3 unknowns and 3 equations is constructed, the two methods respectively account for a=(a1, a2) of the weighting after solving the system of equations, and that an optimal weight vector is accordingly obtained.

In an embodiment of the disclosure, a method of calculating the state scores in the step four specifically includes the following steps.

The state scores are calculated in the indicator layer according to data values of the transformers, which are to be evaluated, and a calculation expression for calculating the state scores in the indicator layer is provided as follows:

xi=z-znz-zf,
where xiis the state score of an indicator, when xi<0, let xi=0, when xi>1, take xi=1, z is an attention value, znis an experimental value of this time, and zfis an initial value of the state quantity indicator.

A state score Xiof the factor layer is calculated through weighting by using known weights and the state scores of the indicator layer. A fuzzy determination matrix of the factor layer is built by using the state scores of the failure types of the factor layer, weights of the failure types of the factor layer are obtained according to the fuzzy determination matrix, the state scores of the transformers are finally obtained through weighting and calculating.

The fuzzy determination matrix of the failure types is:

RA=⁡(r1⁢1r1⁢2…r1⁢Nr2⁢1r2⁢2…r2⁢N⋮⋮⋮rN⁢⁢1rN⁢2…rN⁢N),
and
a calculation formula of an internal element rijis:

rij=XiXi+Xj.

