CN106570644A - Power transmission and transformation equipment quantization evaluation method based on statistical tool - Google Patents

Abstract

The invention discloses a quantitative evaluation method for power transmission and transformation equipment based on statistical tools, including: establishing a quantitative index system for power transmission and transformation equipment, wherein the evaluation factors for the health status of power transmission and transformation equipment specifically include oil-immersed power transmission and transformation equipment Evaluation factors and other evaluation factors of power transmission and transformation equipment; collect historical state data and current state data of power transmission and transformation equipment; use the statistical tool SPSS to normalize the collected data and test data, and preprocess the abnormal values. Retain the out-of-range abnormal value caused by the failure of power transmission and transformation equipment; through the method of public coefficient analysis, determine the correlation of each variable in the collected data; through the public factor correlation analysis method, it is obtained that each evaluation index is Contribution value when evaluating the overall health status of power transmission and transformation equipment; establish a health index evaluation model for power transmission and transformation equipment, calculate the health index of power transmission and transformation equipment, and evaluate the overall health status of power transmission and transformation equipment based on the health index. The invention can comprehensively evaluate the overall health status of the transformer.

Description

A quantitative evaluation method for power transmission and transformation equipment based on statistical tools

technical field

The invention relates to the technical field of state security of power transmission and transformation equipment, in particular to a quantitative evaluation method for power transmission and transformation equipment based on statistical tools.

Background technique

The direct and indirect costs generated by planned power outages and maintenance every year are huge, and its social impact is even more difficult to measure. In order to accurately and efficiently discover the defects of the commissioning and transportation transformation equipment, reduce the failure rate, and reduce the operation risk, at present, the state-of-the-art maintenance method is adopted at home and abroad. This maintenance method relies on a scientific and comprehensive evaluation index system and calculation method for the health status and risk of power equipment. Therefore, the health status assessment of power transmission and transformation equipment is an important link in the life cycle assessment of power transmission and transformation equipment.

Some studies have used a certain parameter of equipment as the main characteristic quantity to evaluate the insulation aging life of power transmission and transformation equipment. However, the characteristic parameter reflecting the health status of power transmission and transformation equipment is an index system involving many aspects and variables. Using a single parameter Evaluation cannot fully realize the accurate assessment of the health status of power transmission and transformation equipment.

At present, the determination of the weight of each indicator is usually based on expert experience or technical standards, and no in-depth study of the relationship between indicators has been conducted. Human subjective factors are relatively large, which in turn leads to large errors in the evaluation of the health status of power transmission and transformation equipment.

Contents of the invention

In order to solve the deficiencies in the prior art, the present invention discloses a quantitative evaluation method for power transmission and transformation equipment based on statistical tools. On the basis of the relationship between the parameters of the power transmission and transformation equipment, the correlation degree of the public parameters and the contribution value to the health index are obtained, and finally the quantitative weight is determined by statistical methods and the health index evaluation model based on the contribution analysis is used to improve the power transmission and transformation equipment. Prediction accuracy ensures reliable and stable operation of power transmission and transformation equipment.

To achieve the above object, the specific scheme of the present invention is as follows:

A quantitative evaluation method for power transmission and transformation equipment based on statistical tools, comprising the following steps:

Establish a quantitative index system for power transmission and transformation equipment, among which, the health status evaluation factors of power transmission and transformation equipment specifically include the evaluation factors of oil-immersed power transmission and transformation equipment and the evaluation factors of other power transmission and transformation equipment;

Collect historical state data and current state data of power transmission and transformation equipment;

Use the statistical tool SPSS to normalize the collected data and test data, preprocess the abnormal values, and retain the excessively high abnormal values caused by the failure of the power transmission and transformation equipment;

Through the method of public coefficient analysis, to determine the correlation of each variable in the collected data;

Through the analysis method of the correlation degree of public factors, the contribution value of each evaluation index in evaluating the overall health status of power transmission and transformation equipment is obtained;

Establish the health index evaluation model of power transmission and transformation equipment, calculate the health index of power transmission and transformation equipment, and evaluate the overall health status of power transmission and transformation equipment according to the health index.

Further, the historical status data of the power transmission and transformation equipment includes inspection data, family defects, basic equipment information, and load data of the power transmission and transformation equipment, and the current status data of the power transmission and transformation equipment includes power transmission and transformation equipment. Routine test data of equipment.

Furthermore, when determining the correlation of variables in the collected data through the method of public coefficient analysis, first determine the unquantified factors and public factors that have a certain impact on the test results, the higher the degree of correlation between the public factors and each variable coefficient The larger the value, the degree of association between common factors is mutual.

Further, the analysis method of the correlation degree of public factors is specifically, using statistical tools to obtain the analysis graph of the correlation degree of public factors among multiple variables, and according to the graph, the relationship between each parameter and the evaluation index of each evaluation index are obtained in evaluating the overall power transmission and transformation equipment. Contribution value at health status.

Further, the steps for calculating the health index of power transmission and transformation equipment are mainly as follows:

1) On the basis of the correlation analysis of public factors, analyze the contribution value of public factors to the health index;

2) Determine the weight of each health indicator;

3) Formulate correction factors according to expert experience and standard regulations;

4) Correct the weight by the correction factor;

5) Obtain the health index to evaluate the overall health status of power transmission and transformation equipment.

Further, the health status of oil-immersed power transmission and transformation equipment is evaluated hierarchically according to different components, and the calculation formula is as follows:

Other power transmission and transformation equipment conducts a hierarchical assessment of the health status according to different components, and the calculation formula is as shown in formula (2):

In the formula, K _i is the weight, HIF _i is the health index, and HIF _max is the corresponding health index when the health status is the best.

Further, the evaluation factors of oil-immersed power transmission and transformation equipment include: paper insulation polymerization degree, furfural, dissolved gas in oil, moisture, acid value, breakdown voltage, oil dielectric loss factor, infrared temperature measurement, defect data, load history , visual inspection, basic equipment information and routine tests, bushing-appearance, bushing-infrared temperature measurement, bushing-dissolved gas in oil, bushing-insulation resistance, cooling system-inspection record, tap changer-inspection record , Tap changer-operating data, tap changer-action characteristics, tap changer-decomposition gas in oil and non-electricity protection-inspection records.

Further, other evaluation factors of power transmission and transformation equipment in the health status evaluation factors of power transmission and transformation equipment include: infrared temperature measurement, defect data, load history, appearance inspection, basic information, routine tests and non-electricity protection-inspection records.

Furthermore, the defect data includes two parts: the equipment itself and family defects, and the defect records should include the defects found during the operation inspection, maintenance inspection, live inspection, inspection, defect classification and defect elimination.

Further, the basic information of the equipment mainly includes nameplate information of power transmission and transformation equipment, voltage level, capacity, short-circuit impedance; routine tests include winding DC resistance, winding dielectric loss factor, capacitance, frequency response test, winding insulation resistance, absorption ratio or Polarization index and leakage current.

Further, the health status of power transmission and transformation equipment is classified according to the health index:

The health index is 85-100, and the health status of power transmission and transformation equipment: the condition is excellent;

The health index is 70-85, and the health status of power transmission and transformation equipment: good condition;

The health index is 50-70, and the health status of power transmission and transformation equipment: good;

Health index 30-50, health status of power transmission and transformation equipment: poor;

Health index 0-30, health status of power transmission and transformation equipment: serious deterioration.

Beneficial effects of the present invention:

The invention comprehensively considers the history and current state data of the transformer, including basic information, routine tests, and operation and maintenance data.

The present invention adopts statistical tools, and based on test data, analyzes and studies the relationship between parameters of the transformer and the contribution value of each parameter to the total health index, deduces the weight of each index, and provides the calculation process of the health index.

The present invention starts with establishing a power transformer index system in layers, and then analyzes 87 groups of transformer oil sample test data by using the statistical tool SPSS, and studies the relationship between various factors and the contribution value of each parameter to the total health index. The statistical tool SPSS can be used to weight the index system and establish a health index calculation model. In the future, this research can be extended to the field of power transmission and transformation equipment evaluation for promotion and application.

The health index evaluation model based on SPSS analysis is extended and applied to power transmission and transformation equipment. SPSS is used to analyze various types of equipment, assign weights, and conduct a comprehensive hierarchical and sub-indicator evaluation of their health status.

Description of drawings

Fig. 1 Analysis diagram of correlation degree of public factors;

Figure 2 Health index evaluation model based on contribution analysis;

Figure 3 Health status evaluation of power transmission and transformation equipment.

detailed description:

The present invention is described in detail below in conjunction with accompanying drawing:

The present invention establishes a comprehensive health evaluation model for power transmission and transformation equipment based on the health index. The model performs multi-parameter statistical analysis on the layers and sub-components of power transmission and transformation equipment, and aims at historical data (operation inspection data, family defects, basic equipment information, Load) and status data (routine test data), as well as different components, establish a quantitative index system. It is proposed to use the statistical tool SPSS (statistical product and service solutions), on the basis of studying the relationship between the parameters of the power transmission and transformation equipment, to obtain the correlation degree of the public parameters and the contribution value to the health index, and finally use the statistical method to determine the quantitative weight and Health index evaluation model based on contribution analysis. Finally, taking the transformer in actual operation of Shandong Power Grid as an example, the effectiveness of the state evaluation model based on health index for power transmission and transformation equipment is verified, which provides a scientific basis for the state evaluation and maintenance of power transmission and transformation equipment.

In order to study the health status of power transmission and transformation equipment, it is first necessary to establish an index system. The characteristic quantities reflecting the health status of power transmission and transformation equipment include electrical characteristics, chemical characteristics, mechanical characteristics and appearance. From the component level, it is divided into body, bushing, tap changer, cooling system and non-electrical protection. The proportion of each part in the evaluation is reflected by the calculation of the final health index.

1. Oil-paper insulation system test

Since the oil paper degrades under the conditions of electricity and heat operation, accompanied by excessive oxygen and moisture in the environment, the aging substances (such as CO, CO ₂ , furfural, etc.) generated by the degradation in the oil and the insulation polymerization degree of the paper can be tested. Measure transformer health.

(1) Polymerization degree of paper insulation

The degree of polymerization reflects the mechanical properties of the tensile strength of the paper. With the application of new materials, the criterion of the degree of polymerization will also be continuously updated. At present, it is generally believed that the degree of polymerization of new transformer paper insulation is mostly around 1000. The criteria for the degree of polymerization of paper insulation are shown in Table 1.

Table 1 Limits of Polymerization Degree of Paper Insulation

(2) Furfural

In the process of cardboard deterioration, a main oxygen heterocyclic compound is formed after the degradation of cellulose macromolecules. During the operation of power transmission and transformation equipment, there are many factors that affect the furfural content, including the ratio of oil to paper and its own manufacturing process, the running time, operating conditions, load rate of power transmission and transformation equipment, and the treatment method of transformer oil. Faults such as local overheating, aging, and overload of insulation in power transmission and transformation equipment will also cause an increase in furfural content. The furfural content is measured by sampling transformer oil. A low content indicates that the power transmission and transformation equipment oil paper has high strength and good performance, and the overall condition of the power transmission and transformation equipment is good.

(3) Dissolved gas in oil

Dissolved gas in oil is a widely used means of fault monitoring and prevention. Under the long-term action of electrothermal stress, the oil and oil-paper insulation system of power transmission and transformation equipment undergoes a change in molecular structure, and produces various characteristic gases through a series of chemical reactions, such as hydrogen, hydrocarbons, and hydrocarbons.

Some methods have been developed at home and abroad, which can be used to judge the proportion of dissolved gas in oil to judge internal faults of transformers, such as: overheating, poor electrical contact, partial discharge, etc. CO/CO ₂ <5, indicating rapid degradation of oil paper. When CO ₂ /CO>7, it indicates insulation aging or large-area low-temperature overheating failure. Of course, when the power transmission and transformation equipment fails, the transformer oil will also decompose CO and CO ₂ gases. Whether it is oil paper deterioration or failure of power transmission and transformation equipment, it can be attributed to the health status of power transmission and transformation equipment. Therefore, CO and CO ₂ should be considered comprehensively.

2. Operation and maintenance data and routine tests

The basic equipment information mainly includes transformer nameplate information, voltage level, capacity, short-circuit impedance, etc. Routine tests include winding DC resistance, winding dielectric loss factor, capacitance, frequency response test, winding insulation resistance, absorption ratio or polarization index, leakage current, etc., whether it meets the technical requirements.

(1) Defect data

Including the device itself and family defects. Defect records shall include defects found during operation inspection, maintenance inspection, live inspection, inspection and repair, defect grading and defect elimination treatment.

(2) Load history

The maximum load value recorded monthly by the equipment in the past 12 months, overload or long-term emergency load operation regulations will degrade the performance of the transformer.

(3) Infrared temperature measurement

Infrared thermal imaging can detect the transformer body, lead joints, and cable terminals. The infrared thermal image shows that there should be no abnormal temperature rise, temperature difference and/or relative temperature difference, which can reveal hidden dangers inside the transformer.

(4) Visual inspection

Mainly by checking the inspection records, the appearance of the equipment is intact and free of foreign objects; whether the lead wires, busbars, and connectors are normal; whether the grounding is reliable; whether there is noise and vibration; whether the air duct is smooth and free of foreign objects, etc.

Three multivariate analysis based on statistical tools

(1) Data normalization

An important assumption in multivariate analysis is that the data are normally distributed. The test results are usually not standard normal distribution, but there are different degrees of deviation. First, through the SPSS tool, the non-normal data is normalized using the transformation tool. SPSS is a data statistical analysis tool, which is convenient for data docking, has built-in VBA language, and powerful statistical processing functions. After using SPSS to normalize the test data, there are excessively high outliers in the original data, which will be ignored in the multivariate analysis. It is necessary to preprocess the outliers and retain the abnormalities caused by the failure of power transmission and transformation equipment. value.

(2) Public coefficient analysis

The results of certain types of power transmission and transformation equipment test items may have unknown correlations, and the correlation of each variable is determined by the method of public coefficient analysis. This paper uses SPSS tools to conduct statistical analysis on the results of multiple tests, and obtains the relationship between different test items and the influence of public influencing factors on each item.

The boxes in Figure 1 represent the test results, and e1~e12 represent unquantified factors that have a certain impact on the test results, such as ambient temperature and humidity, electromagnetic environment interference, and test errors. C1~C5 are public factors, the higher the degree of correlation between them and each variable, the larger the coefficient value. The degrees of correlation between C1-C5 are mutual, indicated by double-headed arrows.

(3) Correlation analysis of public factors

In the evaluation of the health status of power transmission and transformation equipment, the performance of the oil-paper insulation system is one of the most important indicators. Usually, the life of the oil-paper insulation system directly determines the life of the oil-immersed transformer. Therefore, this paper mainly conducts statistical analysis on the test data of 87 sets of transformer oil samples. Firstly, the relationship between various parameters is studied. Secondly, the contribution value of each evaluation index in evaluating the overall health status of power transmission and transformation equipment is obtained. The correlation analysis of common factors among multiple variables is shown in Figure 1.

As shown in Figure 1, substances with similar properties or produced by the same chemical reaction are divided into one group. Under electrical and thermal operating conditions, when the oil paper is degraded and aged or the transformer fails, the decomposition gas CO and CO ₂ in the oil are usually accompanied by chemical products; the higher the impurity and moisture content in the transformer oil, the easier it is to break down. The parameters with a large influence relationship are divided into the same group. H ₂ , CH ₄ , and C ₂ H ₆ are a group of highly correlated parameters, which are related to the common factor C3.

The positive correlation between C2 and C3 is the strongest, which is 0.53; the correlation between C1 and C3 is next, and the correlation coefficient is 0.47; the relationship between C4 and C5 is negative correlation. The analysis of the relationship between each characteristic parameter and common factors lays the foundation for further determining the parameter weight and health index.

4. Evaluation model of health index for power transmission and transformation equipment

Establish the health index model based on SPSS analysis; The process of establishing the model includes:

Based on multivariate analysis of statistical tools, normalize non-normal data using transformation tools;

Use SPSS tool to conduct statistical analysis on multiple test results to obtain the relationship between different test items and the influence of public influencing factors on each item;

The calculation steps of power transmission and transformation equipment health index are as follows:

1) On the basis of the analysis of the relationship between public factors, analyze the contribution of public factors to the health index HI;

2) Further determine the weight of each health index;

3) Formulate correction factors according to expert experience and standard regulations;

4) Correct the weight by the correction factor;

5) Obtain the health index to evaluate the overall health status of power transmission and transformation equipment.

Among them, the first step is a key step. The health index evaluation model based on contribution analysis is shown in Figure 2.

As shown in Figure 2, C4 has the greatest impact on the health index, including the three parameters of breakdown voltage, furfural, and moisture, so it is given the highest weight in the evaluation system. Moisture in the oil of power transmission and transformation equipment plays an important role in the deterioration of transformer insulation characteristics. The furfural content reflects the performance of the oil-paper insulation system of power transmission and transformation equipment to a certain extent. The higher the furfural content, the worse the insulation performance. The grouping and weight obtained through statistical analysis are basically consistent with the existing conclusions. When evaluating the health status of power transmission and transformation equipment, the weight distribution needs to be further corrected by using correction factors to make the evaluation process closer to the actual production site. Finally, the weight of each major item and the health index HIF are obtained.

The health status of oil-immersed power transmission and transformation equipment is evaluated hierarchically according to different components, and the calculation formula is shown in formula (1).

For other power transmission and transformation equipment, the health status is evaluated hierarchically according to different components, and the calculation formula is shown in formula (2).

In the formula, K _i is the weight, HIF _i is the health index, and HIF _max is the corresponding health index when the health status is the best.

Table 2 Health Index Grading

Table 3 Weight and score of transformer health status evaluation factors

Table 4 Weights and scores of other power transmission and transformation equipment health status evaluation factors

A more detailed implementation example:

110kV Zhengyang Station No. 2 main transformer, the specification model is SZ10-50000/110, the voltage level is 220kV, the capacity is 50MVA, the cooling method is self-cooling, produced by Shandong Luneng Taishan Electric Power Equipment Co., Ltd., put into operation in April 2008, It has been in operation for 22 years, and the scrapping period is 30 years.

220kV Baodu Substation No. 2 main transformer, the specification model is SFPSZ9-150000/220, the voltage level is 220kV, the capacity is 150MVA, produced by Shandong Electric Power Equipment Co., Ltd., put into operation in December 2002, has been in operation for 14 years, and the scrapping period is 30 years year. The operation and test data of the two transformers are shown in Table 5.

The serial number (1) in Table 4 corresponds to the data of the No. 2 main transformer of Zhengyang Station. Using the formula (1), the calculated HIF% of the No. 2 transformer of the 110kV Zhengyang Station is 49.182, which is in the range of 30% to 50%, and the overall operating condition is poor , close to or slightly exceed the standard limit, the operation should be monitored, and power outage maintenance should be arranged in due course. The actual condition of the equipment: the hydrogen gas exceeds the standard seriously, and accounts for more than 90% of the total hydrogen hydrocarbons, the total hydrocarbons exceed the standard, CH ₄ accounts for more than 75% of the total hydrocarbons, and the characteristic gas criterion is judged to be partial discharge. The code of the three-ratio method is 010, which is judged as partial discharge. The dielectric loss of the neutral point bushing on the high voltage side exceeds the standard, the capacitance is close to the alarm value, and the routine test of the main transformer is normal. In summary, the overall operating condition of the equipment is poor, and it is close to the end of life. It can be concluded that the calculated value obtained by the health index method in this paper is basically consistent with the actual situation.

In order to fully verify the validity of the model, 220kV Baodu substation main transformers with different voltage levels, different working conditions and different defects are selected, and the serial number (2) is its basic information and test data. Compared with Example 1, although it has the same defects, the properties of the two are different, and the test data are also quite different. After the model calculation, the HIF is 78.052, and the model results show that the equipment is in good condition. Actual operating status: The transformer has been in operation for 14 years, and all indicators are basically normal, and there is no bad working condition. Although there are serious defects in the subcooler, they have been eliminated and are currently operating normally. The evaluation results are in good agreement with the actual situation.

Table 5 Transformer operation and test data

In addition, in order to better verify the consistency of the model evaluation, 12 110kV transformers with different electrical parameters and defect properties were selected in Shandong Power Grid. The health index HIF of each transformer is shown in Figure 3.

In the calculation results, the minimum value is 54.346, the maximum value is 78.326, 25% are in the range of 50% to 70%, and 75% are in the range of 70% to 85%. The overhaul is about to expire, and the health index is low, which is basically in line with the actual situation. By selecting a number of transformers with different operating stages and electrical parameters and different evaluation states from Shandong Power Grid, they are calculated and compared to verify the effectiveness of the evaluation model and calculation method.

Although the specific implementation of the present invention has been described above in conjunction with the accompanying drawings, it does not limit the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, those skilled in the art do not need to pay creative work Various modifications or variations that can be made are still within the protection scope of the present invention.

Claims (10)

1. A quantitative evaluation method for power transmission and transformation equipment based on statistical tools, characterized in that it comprises the following steps: Establish a quantitative index system for power transmission and transformation equipment, among which, the health status evaluation factors of power transmission and transformation equipment specifically include the evaluation factors of oil-immersed power transmission and transformation equipment and the evaluation factors of other power transmission and transformation equipment; Collect historical state data and current state data of power transmission and transformation equipment; Use the statistical tool SPSS to normalize the collected data and test data, preprocess the abnormal values, and retain the excessively high abnormal values caused by the failure of the power transmission and transformation equipment; Through the method of public coefficient analysis, to determine the correlation of each variable in the collected data; Through the analysis method of the correlation degree of public factors, the contribution value of each evaluation index in evaluating the overall health status of power transmission and transformation equipment is obtained; Establish the health index evaluation model of power transmission and transformation equipment, calculate the health index of power transmission and transformation equipment, and evaluate the overall health status of power transmission and transformation equipment according to the health index. 2. A kind of quantitative evaluation method for power transmission and transformation equipment based on statistical tools as claimed in claim 1, wherein the historical state data of said power transmission and transformation equipment includes inspection data, family Defects, basic equipment information, load data, current state data of power transmission and transformation equipment include routine test data of power transmission and transformation equipment. 3. a kind of method for quantitative evaluation of power transmission and transformation equipment based on statistical tools as claimed in claim 1, it is characterized in that, when determining the relevance of each variable in the collected data by the method of public coefficient analysis, at first determine the The unquantified factors and common factors that have a certain influence on the test results, the higher the degree of correlation between the common factors and each variable, the larger the coefficient value, and the degree of correlation between the common factors is mutual. 4. a kind of quantitative evaluation method for power transmission and transformation equipment based on statistical tools as claimed in claim 1, is characterized in that, the public factor correlation analysis method is specifically, utilizes statistical tools to draw the public factor correlation analysis between multivariables According to the figure, the relationship between each parameter and the contribution value of each evaluation index in evaluating the overall health status of power transmission and transformation equipment can be obtained. 5. A kind of quantitative evaluation method for power transmission and transformation equipment based on statistical tools as claimed in claim 1, wherein the calculation steps of power transmission and transformation equipment health index are mainly as follows: 1) On the basis of the correlation analysis of public factors, analyze the contribution value of public factors to the health index; 2) Determine the weight of each health index; 3) Formulate correction factors according to expert experience and standard regulations; 4) Correct the weight by the correction factor; 5) Obtain the health index to evaluate the overall health status of power transmission and transformation equipment. 6. A quantitative evaluation method for power transmission and transformation equipment based on statistical tools as claimed in claim 5, wherein the oil-immersed power transmission and transformation equipment performs hierarchical evaluation of the health status according to different components, and the calculation formula is as follows: $<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \%</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\left(\begin{array}{c}\mathrm{<span\; class="notranslate">\; 0.5</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 12</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 12</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.2</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 13</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 16</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 13</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 16</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.1</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 17</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 17</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 17</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 17</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.1</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 18</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 18</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.1</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}\end{array}\right)<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; 100</span>}\mathrm{<span\; class="notranslate">\; \%</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ Other power transmission and transformation equipment conducts a hierarchical assessment of the health status according to different components, and the calculation formula is as shown in formula (2): $<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \%</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\left(\begin{array}{c}\mathrm{<span\; class="notranslate">\; 0.2</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.2</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 3</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 3</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.1</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 3</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 4</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 3</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 4</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 5</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 5</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 6</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; HIF</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}\end{array}\right)<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; 100</span>}\mathrm{<span\; class="notranslate">\; \%</span>}$ In the formula, K _i is the weight, HIF _i is the health index, and HIF _max is the corresponding health index when the health status is the best. 7. A quantitative evaluation method for power transmission and transformation equipment based on statistical tools as claimed in claim 1, wherein the evaluation factors of oil-immersed power transmission and transformation equipment include: paper insulation polymerization degree, furfural, and dissolved gas in oil , moisture, acid value, breakdown voltage, oil dielectric loss factor, infrared temperature measurement, defect data, load history, appearance inspection, basic equipment information and routine test, bushing-appearance, bushing-infrared temperature measurement, bushing - Dissolved gas in oil, bushing - insulation resistance, cooling system - inspection record, tap changer - inspection record, tap changer - operating data, tap changer - action characteristics, tap changer - decomposed gas in oil and non-electricity protection - inspection records; Other evaluation factors of power transmission and transformation equipment in the health status evaluation factors of power transmission and transformation equipment include: infrared temperature measurement, defect data, load history, appearance inspection, basic information and routine tests, and non-electricity protection-inspection records. 8. A quantitative evaluation method for power transmission and transformation equipment based on statistical tools as claimed in claim 7, wherein the defect data includes two parts of the device itself and family defects, and the defect record should include operation inspection, maintenance inspection, live detection , Defects found in the maintenance process, defect grading and defect elimination treatment. 9. A method for quantitative evaluation of power transmission and transformation equipment based on statistical tools as claimed in claim 1, wherein the basic information of the equipment mainly includes nameplate information of power transmission and transformation equipment, voltage level, capacity, short-circuit impedance; routine test Including winding DC resistance, winding dielectric dissipation factor, capacitance, frequency response test, winding insulation resistance, absorption ratio or polarization index and leakage current. 10. A kind of quantitative evaluation method of power transmission and transformation equipment based on statistical tools as claimed in claim 1, characterized in that, according to the health index, the health status classification of power transmission and transformation equipment is carried out: The health index is 85-100, and the health status of power transmission and transformation equipment: the condition is excellent; The health index is 70-85, and the health status of power transmission and transformation equipment: good condition; The health index is 50-70, and the health status of power transmission and transformation equipment: good; Health index 30-50, health status of power transmission and transformation equipment: poor; Health index 0-30, health status of power transmission and transformation equipment: serious deterioration.