CN105784959A - Distribution transformer winding material detection method - Google Patents

Abstract

The embodiment of the invention discloses a distribution transformer winding material detection method.The method comprises the steps of objective function determination, optimal solution acquisition and quantitative investigation, wherein in the step of objective function determination, transformer design parameters and external characteristic parameters are considered; in the step of optimal solution acquisition, a correlation function of the external characteristic of the detected transformer serves as an optimization objective, completely same constraint conditions and fitness functions are adopted, the winding material adopts copper wires and aluminum wires for design respectively, transformer design is optimized by applying a genetic algorithm, and an optimal solution WCu and an optimal solution WAl of a W-Cu initial population and a W-Al initial population are solved respectively; in the step of quantitative investigation, quantitative representation is conducted through the distance from an actual measurement point Wm of the detected transformer to the consideration weight coefficient of the optimal solution WCu and the optimal solution WAl, the probability value of the detected transformer belonging to a copper winding transformer or an aluminum winding transformer is obtained, and convenient, rapid and accurate analytical judgment on the detected transformer winding material is achieved.

Description

Distribution transformer winding material detection method

Technical Field

The invention relates to the technical field of power supply equipment detection, in particular to a distribution transformer winding material detection method.

Background

With the rapid development of social economy, the power consumption of the whole society is larger and larger, and the demand on power distribution equipment, particularly on a power distribution transformer is larger and larger. However, some distribution transformer manufacturers have been under various counterfeiting strategies, such as false alarm capacity, dummy nameplates, use of inferior materials, etc., in order to obtain a higher market share with a smaller market profit. The deceptiveness that the aluminum wire replaces the copper wire to wind the transformer winding, even the external winding is the copper wire, and the internal winding is the aluminum wire is the strongest, wherein, the deception comprises adopting a semi-aluminum wire or a full-aluminum wire, the primary winding is the aluminum wire, the secondary winding is the copper wire, the primary winding is the copper wire, the secondary winding is the aluminum wire, and part of production enterprises even use the full-aluminum wire as the primary winding and the secondary winding. The distribution transformer with the aluminum wires is adopted to pretend to be a copper wire distribution transformer to take good action, so that consumers with unknown phase suffer from economic loss, potential safety hazards are brought to the operation of the distribution transformer, and the detection of the material of the distribution transformer winding is very necessary.

In the prior art, the following methods are generally used for detecting the material of the distribution transformer winding: firstly, destroy the sample to distribution transformer winding, carry out metal chemistry analysis, but this kind of destruction detection method needs distribution transformer to stop the fortune and carries out the disintegration and detect, carries out destructive disintegration inspection not only to distribution transformer and is difficult for obtaining manufacturer's consent, influences the follow-up use that is detected distribution transformer moreover. And the other method is to photograph the distribution transformer winding by using a radiographic inspection machine and an industrial radiographic film, and compare the X-ray attenuation coefficient transillumination thickness curve of the measured distribution transformer winding with the X-ray attenuation coefficient transillumination thickness curve of copper and the X-ray attenuation coefficient transillumination thickness curve of aluminum respectively.

Disclosure of Invention

The embodiment of the invention provides a method for detecting the material of a distribution transformer winding, which aims to solve the problem that the existing method for replacing a copper wire with an aluminum wire for a transformer winding is difficult to inspect on site.

In order to solve the technical problem, the embodiment of the invention discloses the following technical scheme:

a distribution transformer winding material detection method comprises the following steps:

selecting transformer design parameters as genotype codes of population individuals and transformer external characteristic parameters as phenotype codes of the population individuals;

respectively establishing a W-Cu initial population of the copper winding transformer and a W-Al initial population of the aluminum winding transformer, which take the genotype codes as function inputs and the phenotype codes as function outputs, according to the design indexes and parameter requirements of the tested transformer;

respectively obtaining the optimal solution W corresponding to the W-Cu initial population and the W-Al initial population by using a genetic algorithm by taking the corresponding performance requirement of the tested transformer as a constraint condition and the optimized objective function of the tested transformer as an adaptive function_CuAnd W_Al；

Acquiring an actually measured external characteristic parameter Wm of the measured transformer;

constructing an objective function F according to the closeness degree of individuals in the W-Cu final generation population and the W-Al final generation population to the external characteristic parameter Wm;

obtaining the probability that the transformer to be tested is a copper winding transformer and the probability of an aluminum winding transformer according to the target function F;

wherein the objective functionThe first term corresponds to the temperature rise index in the external characteristic parameter of the transformer, and the second term corresponds to other indexes in the external characteristic parameter of the transformer, p_iAnd p_jRespectively is the optimal solution W_CuAnd W_AlMedium temperature rise index external characteristic parameter and other index external characteristic parameters, p_jCSFor the corresponding p in the measured external characteristic parameter Wm_jParameter of (a), r_i、r_jIs a parameter p_i、p_jThe weight coefficient of (c).

Preferably, the method further comprises:

respectively with the optimal solution W_CuAnd W_AlObtaining the W-Cu last generation population of the copper winding transformer and the W-Al last generation population of the aluminum winding transformer by taking the spherical center and the set deviation value as the radius;

respectively calculating the frequency distribution of the ratio of the number of individuals in the W-Cu final population and the W-Al final population, within F '~ F' + delta F ', to the total number of individuals, which changes with F';

obtaining the probability that the transformer to be tested is a copper winding transformer and the probability of an aluminum winding transformer according to the frequency distribution;

wherein, $F^{\′}=F^{\′}\left(P\right)=\underset{i=1}{\overset{m}{\Σ}}{r}_i{p_i}^{\′}+\underset{j=1}{\overset{n}{\Σ}}{r}_j\left|{p_j}^{\′}-p_{jCS}\right|,{p_i}^{\′}=\frac{p_i}{{10}^{\[lg\left(p_i\right)\]+1}},{p_j}^{\′}=\frac{p_j}{{10}^{\[lg\left(p_j\right)\]+1}}.$

preferably, the design parameters of the transformer comprise an iron core design coefficient K, an iron core working magnetic flux density B and a high-voltage winding wire current density design value J_HLow voltage winding wire current density J_LAnd each layer of low-voltage winding has n turns_LAnd n turns per layer of the high-voltage winding_H。

Preferably, the design parameter of the transformer comprises a no-load current percentage value I₀% load loss P_KHigh voltage phase winding resistor R_RHIron core to oil temperature rise delta theta_coOil temperature rise delta theta of low-voltage winding_WHLTotal weight G of transformer_TTotal oil weight Go, no load loss P₀Per unit value u of short circuit impedance_KPercent, low voltage phase winding resistance R_RLOil temperature rise delta theta of high-voltage winding_WHHOil to air temperature rise delta theta_oAnd body weight G_B。

Preferably, the W-Cu initial population and the W-Al initial population are both 200-400 in scale.

Preferably, a genetic algorithm is utilized to respectively obtain the optimal solutions W corresponding to the W-Cu initial population and the W-Al initial population_CuAnd W_AlAnd then, includes:

the generation number of the population is set to be 40-80, the cross probability is set to be 0.75-0.90, and the variation probability is set to be 0.02-0.05.

According to the technical scheme, the power distribution transformer provided by the embodiment of the inventionThe method for detecting the winding material comprises the steps of determining a target function, obtaining an optimal solution and quantitatively investigating. The design parameters and the external characteristic parameters of the transformer are considered in the determination of the objective function; in the optimal solution acquisition, a correlation function of the external characteristics of the tested transformer is taken as an optimization target, the completely same constraint condition and fitness function are adopted, the winding material is respectively designed by adopting a copper wire and an aluminum wire, the process of designing the transformer is optimized by applying a genetic algorithm, and the respective optimal solutions W in the W-Cu initial population and the W-Al initial population are solved_CuAnd W_Al(ii) a Applying the actual measuring point Wm of the measured transformer to the optimal solution W in quantitative investigation_CuAnd W_AlThe distance after the weight coefficient is considered is quantitatively expressed, mapping of the external characteristic parameter space W to the 1-dimensional real number space R is achieved, and then the probability value that the tested transformer belongs to a copper winding transformer or an aluminum winding transformer is obtained. The detection method provided by the embodiment of the invention can conveniently, quickly and accurately analyze and judge the winding material of the detected transformer under the condition of not checking the disassembly of the detected transformer, provides the probability value that the winding material of the detected transformer belongs to a copper winding or an aluminum winding, and can meet the requirement of practical detection and application of the winding material of the distribution transformer.

Drawings

In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.

FIG. 1 is a diagram illustrating a mathematical description method for detecting winding material in accordance with an embodiment of the present invention;

fig. 2 is a schematic flow chart of a method for detecting a material of a winding of a distribution transformer according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the conversion of the population from the extrinsic characteristic parameter space to the "distance" space according to the embodiment of the present invention;

fig. 4 is a frequency distribution of a last generation group of a copper winding transformer and an aluminum winding transformer in a one-dimensional "distance" space according to an embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in FIG. 1, a distribution transformer includes an external characteristic parameter space T_C(parameters include no-load performance, quality, temperature rise and the like of the transformer) and design parameter space T_D(parameters include wire gauge, core leg diameter, etc.). Under specific design indexes, all feasible schemes of the copper winding transformer are in the external characteristic parameter space T_CAnd a design parameter space T_DMiddle component set T_C(Cu) and T_D(Cu), all possible variants of the same aluminum-wound transformer form a set T_C(Al) and T_D(Al)。

In a copper winding transformer, the simple replacement of a copper wire by an aluminum wire can cause the obvious deviation of parameters such as direct current resistance, load loss, temperature rise and the like of the winding; the deviation is corrected through design, and the no-load characteristic, the short-circuit impedance and the overall dimension of the transformer are greatly changed. This phenomenon can be summarized as the impossibility of complete overlapping of the outer characteristic parameter spaces of copper-winding transformers and aluminum-winding transformers under the same performance requirements, i.e. T_C(Cu) and T_C(Al) is incompleteAnd (4) fully overlapping. Measured transformer T is at T_CAnd T_DIs represented by point T_CSAnd T_DS，T_CSKnown or measurable, T_DSUnknown, so the analysis of the winding material of the tested transformer T is to judge T_CSAnd T_C(Cu) and T_C(Al) in the same category.

The embodiment of the invention is to apply the characteristics to detect the winding material of the tested transformer.

Referring to fig. 2, a schematic flow chart of a method for detecting a material of a distribution transformer winding according to an embodiment of the present invention includes the following steps:

s110: and selecting the design parameters of the transformer as the genotype codes of the population individuals, and the external characteristic parameters of the transformer as the phenotype codes of the population individuals.

Specifically, according to the study on the transformer design method, the individual genotype code is represented as G ═ (K, B, J)_H,J_L,n_L,n_H) Wherein K is the design coefficient of the diameter of the iron core, B is the working magnetic flux density of the iron core, J_HDesigned value for high voltage winding wire current density, J_LFor the current density of the low-voltage winding wire, n_LFor each layer of low voltage winding, n_HThe number of turns per layer of the high voltage winding.

Meanwhile, since the phenotype code sufficiently reflects the difference in the external characteristics between the copper winding transformer and the aluminum winding transformer, it is expressed as P ═ I (I)_O％，P₀，P_K，u_K％，R_RH，R_RL，Δθ_co，Δθ_WHL，Δθ_WHH，Δθ_o，G_T，G_B，G_O) Wherein, I₀% is percentage value of no-load current, P₀For no-load losses, P_KFor load loss, R_RH、R_RLHigh and low voltage phase winding resistances; delta theta_coFor core temperature rise, Δ θ_WHH、Δθ_WHLFor temperature rise of high and low voltage windings, Delta theta_oFor increasing oil temperature G_T、G_B、G_OThe total weight of the transformer, the weight of the transformer body and the weight of the oil.

S120: and respectively establishing a W-Cu initial population of the copper winding transformer and a W-Al initial population of the aluminum winding transformer by taking the genotype codes as function inputs and the phenotype codes as function outputs according to the design indexes and parameter requirements of the tested transformer.

It is obvious that the process of calculating the phenotype from the genotype of an individual is a mapping from one point in the design parameter space V to the corresponding point in the external characteristic parameter space W, i.e. the design process of the transformer.

When the iron core is designed specifically, the diameter of the iron core column can be initially determined according to a related empirical formula after the silicon steel sheet material and the iron core structural type are selected. The empirical formula is:

$D=K^4\sqrt{P}---\left(1\right)$

wherein, P is the capacity of each core limb (unit: kVA, determined according to the transformer capacity and the core structure), K is the core limb diameter design coefficient, the value is generally 52-57, and D is the nominal diameter (unit: mm) of the core limb. After the D is initially determined, the section S (unit: m) of the core limb can be determined according to the related design manual²)。

The voltage Et of each turn of the winding can be calculated according to the winding voltage UL (unit: V) of the low-voltage winding according to the formula (2) and the formula (3) after the maximum magnetic density Bm of the initially fixed core limb (determined according to the magnetic property of a silicon steel sheet and mostly below 1.75T), and the number of turns NL of the low-voltage winding is adjusted to be an integer (f in the formula is power frequency and 50 Hz).

E_t＝4.44fB_mS (2)

$N_L=\frac{U_L}{E_t}---\left(3\right)$

Voltage U according to high voltage winding_H(unit: V) determining and checking the number of turns N of the high-voltage winding_HThen, the number of turns of each layer of the winding is roughly arranged, and the positions of the voltage regulating winding and each tap are arranged.

The wire for the winding can be selected according to the winding current I after selecting the wire type (enameled, paper-covered round wire or flat wire)_H、I_LThe value and the economic current density of the lead (the copper lead is 3-5A/mm2, the aluminum lead is 2-3A/mm2, the smaller value is favorable for heat dissipation and load loss reduction) determine the cross section of the lead, and various geometric dimensions of the lead can be selected by looking up related manuals.

The radial size and the axial size of the winding can be determined according to the geometric size of the lead and the turn number arrangement of the winding, the whole geometric size of the winding, the direct current resistance and the winding loss of the winding and the weight of the winding can be determined according to the design of an insulating structure between the windings and between the winding and the iron core (if necessary, an interlayer oil passage of the winding is included), and the short-circuit impedance of the transformer and a formula (4) can be calculated.

$u_{kx}\%=\frac{49.6{\mathrm{f\ρI}}_H{N}_H\ΣD}{E_{t}H\×{10}^6}\%---\left(4\right)$

I in formula (4)_HN_HThe magnetic potential (unit: A) of the high-voltage winding can also be calculated according to the low-voltage winding; h is the reduced reactance height (unit: c) of the high-low voltage windingm, calculated according to the winding entity height); rho is the Rowgowski coefficient of the winding and is calculated according to the size of the winding; sigma D is equivalent leakage area (unit: cm) of the high-low voltage winding²) Calculated according to the size of the winding and the oil passage.

After the winding size is determined, the center distance MO, the window height HW and other geometric dimensions of the iron core can be determined according to the interphase insulation distance and the insulation structure of the winding to the iron yoke, and the total weight and the excitation loss P of the iron core can be determined by combining the characteristic parameters of the silicon steel sheet_OAnd no-load current I_O。

After the sizes of the winding and the iron core are determined, the size of the oil tank body can be determined according to the insulation distance of the winding and the lead wire to the oil tank wall and by considering the structure of the tap switch if necessary.

After the indexes (short-circuit impedance, no-load characteristic and load loss) are determined, the temperature rise of each component to oil can be calculated according to a related formula, and all parameters of the radiator are calculated and determined according to the type of the radiator. The whole design process may need to be repeatedly calculated and corrected for many times, and the design process is basically completed after the obtained design result meets the design requirement and the relevant standard.

In this embodiment, a genetic algorithm is applied to completely algorithmize the process to meet the requirements of computer processing, wherein, in order to make the algorithm have better performance and stability, the sizes of the W-Cu initial population and the W-Al initial population are both 200-400, but are not limited to the numerical range.

S130: respectively obtaining the optimal solution W corresponding to the W-Cu initial population and the W-Al initial population by using a genetic algorithm by taking the corresponding performance requirement of the tested transformer as a constraint condition and the optimized objective function of the tested transformer as an adaptive function_CuAnd W_Al。

Environmental selective stress on individuals includes two aspects: one is that each transformer must meet the performance requirements set forth by the relevant standards, and the other is that the environment tends to select individuals that better meet optimization objectives (e.g., cost minimization or other objectives). The former requirement is mandatory, that is, individuals not meeting the performance requirement are directly eliminated, so that the performance requirement proposed by each standard is used as a constraint condition, and the individuals not meeting the performance requirement are directly eliminated when the fitness value of the individuals is finally determined.

After explicit mandatory constraints, the individual can adapt to the environment to be represented as close to the optimization goal. In general, the optimization objective is often a function of the individual phenotype, and here we use the expression of the optimization objective in combination with the characteristics of the algorithm as a fitness function.

After the constraint conditions and the fitness function are determined, calculation is performed according to the flow of a general genetic algorithm, and in order to enable the algorithm of the embodiment to have better performance and stability, the generation number of the population is set to be 40-80, the cross probability is set to be 0.75-0.90, and the variation probability is set to be 0.02-0.05, but the method is not limited to the numerical range.

S140: and acquiring the actually measured external characteristic parameter Wm of the measured transformer.

S150: and constructing an objective function F according to the closeness degree of the individuals in the W-Cu final generation population and the W-Al final generation population to the external characteristic parameter Wm.

And converting the problem into a single-target optimization problem with the minimum distance according to the closeness degree of the individuals in the W-Cu final generation population and the W-Al final generation population to the external characteristic parameter Wm.

In the 13 external characteristic indexes, except that 4 temperature rise indexes are indexes with smaller and better values, the other indexes are the indexes with better values close to the external characteristic parameter Wm. Wherein the form of the objective function F is as follows:

$F=F\left(P\right)=\underset{i=1}{\overset{4}{\Σ}}{r}_i{p}_i+\underset{j=1}{\overset{9}{\Σ}}{r}_j\left|p_j-p_{jCS}\right|---\left(5\right)$

in the formula (5), the first term corresponds to the temperature rise index in the external characteristic parameter of the transformer, and the second term corresponds to other indexes, p, in the external characteristic parameter of the transformer_iAnd p_jRespectively is the optimal solution W_CuAnd W_AlMedium temperature rise index external characteristic parameter and other index external characteristic parameters, p_jCSFor the corresponding p in the external characteristic parameter Wm_jParameter of (a), r_i、r_jIs a parameter p_i、p_jThe weight coefficient of (c).

To transform the parameters to the same order of magnitude and to obtain the probability function, the actual parameter p in equation (5) is used_i、p_jConversion to parameter p within interval (0,1)_i′、p_j', the details are as follows:

$p_i=\frac{p_i}{{10}^{\[lg\left(p_i\right)\]+1}}---\left(6\right)$

in formula (6), the integer function f (x) ═ x is defined as taking an integer not greater than x. The objective function F, i.e. the "distance", is therefore defined as:

$F^{\′}=F^{\′}\left(P\right)=\underset{i=1}{\overset{4}{\Σ}}{r}_i{p_i}^{\′}+\underset{j=1}{\overset{9}{\Σ}}{r}_j\left|{p_j}^{\′}-p_{jCS}\right|---\left(7\right)$

wherein, each weight coefficient is determined according to the constraint condition and the parameter characteristic of the transformer. The selected weight coefficients are shown in table one, but not limited to the values, and the test results show that small-range changes in the weight coefficients have little influence on the optimization results.

Table one:

parameter(s)	Weight coefficient table	Parameter(s)	Value of weight coefficient
				Percentage value of no-load current I0％	0.4	No load loss P0	0.4
Load loss PK	0.4	Per unit value u of short circuit impedanceK％	0.4
				High voltage phase winding resistor RRH	1	Low voltage phase winding resistor RRL	1
Increase delta theta of oil temperature by iron coreco	0.5	Oil temperature rise delta theta of high-voltage windingWHH	0.5
				Oil temperature rise delta theta of low-voltage windingWHL	0.5	Oil to air temperature rise delta thetao	0.5
Total weight G of transformerT	1	Body weight GB	0.5
				Total oil weight Go	0.5

S160: and obtaining the probability that the transformer to be tested is a copper winding transformer and the probability of the aluminum winding transformer according to the target function F.

The probability that the transformer to be tested is a copper winding transformer and the probability that the transformer to be tested is an aluminum winding transformer are obtained according to the specific values of the transformed objective function F 'of the objective function F, for example, the calculation result of the transformed objective function F' is about 0.5, in which case, the transformer to be tested can be considered to be made of a copper-aluminum mixed material or other manufacturing means.

Further, when the population scale is large, the distribution of the copper winding transformer and the aluminum winding transformer can be represented by a final population, and the W-Cu final population or the W-Al final population is distributed near Wm, so that the method for calculating the probability that Wm belongs to the W-Cu final population and the W-Al final population is established, and the method specifically comprises the following steps:

s210: respectively with the optimal solution W_CuAnd W_AlAnd obtaining the W-Cu final generation population of the copper winding transformer and the W-Al final generation population of the aluminum winding transformer by taking the set deviation value as the radius for the spherical center.

I.e. after having found the optimal solution W_CuAnd W_AlOn the premise that the final population group is represented by W_CuAnd W_AlA centered distribution.

S220: and respectively calculating the frequency distribution of the ratio of the number of individuals in the W-Cu final population and the W-Al final population, within F ' to F ' + delta F ', to the total number of individuals, which changes with F.

Specifically, using equations (5) through (7), as shown in fig. 3, the last generation population distribution is converted into a distribution in a one-dimensional "distance" space, wherein the frequency distribution of the last generation population in the one-dimensional "distance" space is defined as being distant from the optimal solution W_CuAnd W_AlThe ratio of the number of individuals within F ' to F ' + Δ F ' to the total number of individuals varies with F.

S220: and obtaining the probability that the transformer to be detected is a copper winding transformer and the probability of the aluminum winding transformer according to the frequency distribution.

And estimating the probability of each point in individuals in the W-Cu final generation population and the W-Al final generation population to take values in F ' ~ F ' + delta F ' by using the frequency distribution, thereby calculating the probability of Wm taking values in the W-Cu final generation population and the W-Al final generation population, and further obtaining the probability of the transformer to be the copper winding transformer and the probability of the aluminum winding transformer.

As shown in fig. 4, in order to design a W-Cu initial population and a W-Al initial population containing 500 individuals for a certain distribution transformer, after 40 generations of evolution, the frequency distribution of the final population is obtained. Wherein a number of tests indicate that the frequency distribution is close to an exponential distribution, which can be used to describe the distribution of the final generation population.

It can be seen from the above technical solutions that, in this embodiment, based on deep analysis of the external characteristic difference between the copper winding transformer and the aluminum winding transformer, the same constraint conditions and fitness functions are adopted, the transformer design is performed by using the copper wires and the aluminum wires respectively, the functions related to the measured transformer characteristics are used as optimization targets, the transformer design is optimized by using the genetic algorithm, and the optimal solution W under two design schemes is obtained_CuAnd W_Al(ii) a And (4) quantitatively estimating the probability of the measured transformer parameter Wm belonging to W-Cu and W-Al by investigating the optimum value of the fitness function of each population, and obtaining a final detection conclusion. According to the embodiment, the material of the winding is reversely analyzed according to the external characteristic parameters of the transformer, the possible winding counterfeiting problem is identified, and the poor-quality products are prevented from entering the power grid.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In this document, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (6)

1. A distribution transformer winding material detection method is characterized by comprising the following steps:

selecting transformer design parameters as genotype codes of population individuals and transformer external characteristic parameters as phenotype codes of the population individuals;

respectively establishing a W-Cu initial population of the copper winding transformer and a W-Al initial population of the aluminum winding transformer, which take the genotype codes as function inputs and the phenotype codes as function outputs, according to the design indexes and parameter requirements of the tested transformer;

so thatThe corresponding performance requirements of the tested transformer are used as constraint conditions, the optimized objective function of the tested transformer is used as a fitness function, and a genetic algorithm is utilized to respectively obtain the optimal solution W corresponding to the W-Cu initial population and the W-Al initial population_CuAnd W_Al；

Acquiring an actually measured external characteristic parameter Wm of the measured transformer;

constructing an objective function F according to the closeness degree of individuals in the W-Cu final generation population and the W-Al final generation population to the external characteristic parameter Wm;

obtaining the probability that the transformer to be tested is a copper winding transformer and the probability of an aluminum winding transformer according to the target function F;

wherein the objective functionThe first term corresponds to the temperature rise index in the external characteristic parameter of the transformer, and the second term corresponds to other indexes in the external characteristic parameter of the transformer, p_iAnd p_jRespectively is the optimal solution W_CuAnd W_AlMedium temperature rise index external characteristic parameter and other index external characteristic parameters, p_jCSFor the corresponding p in the measured external characteristic parameter Wm_jParameter of (a), r_i、r_jIs a parameter p_i、p_jThe weight coefficient of (c).

2. The method of claim 1, further comprising:

respectively with the optimal solution W_CuAnd W_AlObtaining the W-Cu last generation population of the copper winding transformer and the W-Al last generation population of the aluminum winding transformer by taking the spherical center and the set deviation value as the radius;

respectively calculating the frequency distribution of the ratio of the number of individuals in the W-Cu final population and the W-Al final population, within F '~ F' + delta F ', to the total number of individuals, which changes with F';

obtaining the probability that the transformer to be tested is a copper winding transformer and the probability of an aluminum winding transformer according to the frequency distribution;

wherein, $F^{\′}=F^{\′}\left(P\right)=\underset{i=1}{\overset{m}{\Σ}}{r}_i{p_i}^{\′}+\underset{j=1}{\overset{m}{\Σ}}{r}_j|{p_j}^{\′}-p_{jCS}|,{p_i}^{\′}=\frac{p_i}{{10}^{\[\lg \left(p_i\right)\]+1}},{p_j}^{\′}=\frac{p_j}{{10}^{\[\lg \left(p_j\right)\]+1}}.$

3. the distribution transformer winding material detection method of claim 1, wherein the transformer design parameters include core design factor K, core operating flux density B, and high-voltage winding wire current density design value J_HLow voltage winding wire current density J_LAnd each layer of low-voltage winding has n turns_LAnd n turns per layer of the high-voltage winding_H。

4. The method of claim 1, wherein the external transformer characteristic parameter comprises a percentage no-load current I₀% load loss P_KHigh voltage phase winding resistor R_RHIron core to oil temperature rise delta theta_coOil temperature rise delta theta of low-voltage winding_WHLTotal weight G of transformer_TTotal oil weight Go, no load loss P₀Per unit value u of short circuit impedance_KPercent, low voltage phase winding resistance R_RLOil temperature rise delta theta of high-voltage winding_WHHOil to air temperature rise delta theta o and body weight G_B。

5. The method as claimed in claim 1, wherein the W-Cu initial population and the W-Al initial population are both 200-400 in size.

6. The distribution transformer winding material detection method according to claim 1 or 5, characterized in that the optimal solution W corresponding to the W-Cu initial population and the W-Al initial population is obtained by using a genetic algorithm_CuAnd W_AlAnd then, includes:

the generation number of the population is set to be 40-80, the cross probability is set to be 0.75-0.90, and the variation probability is set to be 0.02-0.05.