Equipment state evaluation method based on DHGF algorithm

Abstract

The invention provides an equipment state evaluation method based on a DHGF algorithm. The method comprises the following steps: step 1, selecting evaluation indexes; 2, obtaining an index sample matrix; step 3, whitening the evaluation indexes; 4, constructing a fuzzy evaluation matrix; 5, carrying out fuzzy evaluation analysis to obtain a weight; step 6, determining an evaluation standard; step 7, solving an evaluation result; according to the method, the limitation of a single method can be avoided, and the advantages of multiple methods are fully utilized, so that the equipment state evaluation method based on the DHGF algorithm is an effective state evaluation method, and the equipment operation state can be well evaluated.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a device status evaluation method based on a DHGF algorithm. Background Art

[0002] With the vigorous development of my country's power industry, a low-carbon and efficient power system has become the common aspiration of the people. In order to achieve balanced development of energy production and power consumption, we need to adopt high-voltage direct current transmission technology for large-scale, long-distance AC power grids. In the field of long-distance power transmission in my country, this technology is playing an increasingly important role. Compared with AC transmission, DC transmission has many advantages, such as no stability problems of the AC system, low line loss, low line cost under the same transmission capacity, and stronger transmission capacity.

[0003] The DC transmission converter station is set up at the starting and end points of the system to play the role of energy conversion and control, regulation and control, and can also balance the supply and demand of electricity and adjust the voltage and frequency. By controlling the operation of the DC transmission converter station, the stable operation and reliable power supply of the power system can be achieved.

[0004] During the operation of the DC transmission converter station, the switching operation and nonlinear characteristics of the power electronic equipment will cause harmonic currents and harmonic voltages in the system. Harmonic signals will propagate in the power system and affect the system equipment and loads. They will also cause problems such as increased operating temperature of power equipment, magnetic flux saturation of transformers, and increased loss of capacitors. This will reduce the reliability and life of the equipment and have adverse effects on user load equipment, such as causing vibration of power equipment, motor torque fluctuations, and interference with communication systems, directly affecting power quality and normal operation of equipment. This requires in-depth analysis of the AC and DC harmonics formed in the DC transmission converter station, and evaluation of the operating status of key equipment in the converter station, such as converter transformers, rectifiers, and inverters, under the AC and DC harmonic superposition conditions, to facilitate the guidance of intelligent operation and maintenance strategies. Summary of the invention

[0005] The purpose of the present invention is to overcome the problems of the prior art and provide a device status evaluation method based on the DHGF algorithm.

[0006] To achieve the above object, the present invention provides the following technical solution: a device status evaluation method based on DHGF algorithm, comprising the following steps:

[0007] Step 1: Select evaluation indicators;

[0008] Step 2: Get the indicator sample matrix;

[0009] Step 3: whitening the evaluation index;

[0010] Step 4: Construct fuzzy evaluation matrix;

[0011] Step 5: Calculate weights through fuzzy evaluation analysis;

[0012] Step 6: Determine the evaluation criteria;

[0013] Step 7: Obtain the evaluation results.

[0014] Preferably, in step one, the specific method for selecting the evaluation index is: conducting statistical analysis on the equipment and querying the current regulations, determining the index reflecting the operating status of the equipment, and establishing a status evaluation index system, wherein the status evaluation index system includes 4 first-level evaluation indicators, and each of the first-level indicators includes a number of second-level indicators.

[0015] Preferably, in step 2, the specific method for obtaining the indicator sample matrix is ​​as follows: combining the status information of the evaluation indicators collected in step 1, inviting m experts to evaluate the n evaluation indicators of the equipment, and forming a sample matrix D with the evaluation data of all experts on the evaluated object, where d mn It represents the score given by the mth expert to the nth evaluation index, that is:

[0016]

[0017] Preferably, in step 3, the specific method of whitening the evaluation index is as follows: after selecting the evaluation index and the state scoring matrix, the evaluation index is pre-processed by whitening. According to the degree of degradation of the equipment, the evaluation gray class is set to 4 levels, namely good, attention, abnormal, and severe, and the gray number of the gray class j=1, 2, 3, 4; if the state evaluation of the equipment is based on the 10-point evaluation level, the corresponding whitening weight function can be obtained;

[0018] The first gray class is "good", gray class j = 1, gray number The corresponding whitening weight function is

[0019]

[0020] The second gray class is "attention", gray class j = 2, gray number The corresponding whitening weight function is

[0021]

[0022] The third gray class is "abnormal", gray class j = 3, gray number The corresponding whitening weight function is

[0023]

[0024] The fourth gray class is "serious", gray class j = 4, gray number The corresponding whitening weight function is

[0025]

[0026] As a preferred method, in step 4, the specific method of constructing the fuzzy evaluation matrix is: using the standard whitening weight function of each evaluation index to determine, calculate d li The weight f belonging to the j-type evaluation criteria j (d lj ), based on which the grey statistics N can be obtained ij And the total grey statistics N i ;

[0027]

[0028] Determine the grey weight of the i-th evaluation index belonging to the j-th evaluation grey class.

[0029]

[0030] Thus, the fuzzy evaluation weight matrix R can be constructed;

[0031]

[0032] As a preferred method, in step 5, the specific method of constructing the fuzzy evaluation matrix is: using the fuzzy judgment matrix to calculate the weight of each indicator, first establish the fuzzy judgment matrix, assume that there are n indicator elements in the target system X, recorded as X = {x1, x2, ..., x n}, then the complementary judgment matrix of system X is

[0033]

[0034] Among them, a ij Represents element a i Than element a j Important membership:

[0035] After that, the consistency of the matrix is ​​checked and judged. The fuzzy hierarchical analysis method judges the matrix A = (a ij ) n×n The consistency should satisfy have

[0036] a ij =a ik -a jk +0.5

[0037] If the consistency verification conditions are met, the weight calculation of each element can be performed. If not, the judgment matrix needs to be adjusted to meet the consistency conditions. The fuzzy weight calculation method used is:

[0038]

[0039] Among them, β is the weight resolution coefficient. By increasing the β value, the resolution of each element weight can be improved. β is selected according to the requirements of the equipment. According to the reference, β can be taken as e e (e is the natural logarithm);

[0040] Calculate the combined weights for each indicator in turn, and set the final weight matrix as

[0041]

[0042] As a preference, in step 6, the specific method for determining the evaluation criteria is: for the equipment status evaluation classification status, the equipment status evaluation criteria set V can be determined as

[0043] V = {v1, v2, v3, v4}

[0044] Among them, v1, v2, v3, and v4 correspond to the good state, attention state, abnormal state, and eye state respectively; when performing quantitative calculations, each state has a corresponding range, and the range of the good state is specified to be [8, 10], the range of the attention state is [6, 8), the range of the abnormal state is [4, 6), and the range of the serious state is [0, 4).

[0045] Finally, the comprehensive evaluation results of the equipment are calculated:

[0046] Z=(W·R)·V T .

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The DHGF algorithm, which integrates the Delphi method, grey theory analysis method and fuzzy hierarchical analysis method, fully considers the structural characteristics, operating characteristics and sample characteristics of the equipment to achieve equipment status evaluation; the theoretical basis of the DHGF algorithm is composed of fuzzy mathematics, grey theory, comprehensive integration methods from qualitative to quantitative, and physical-reasoning-human analysis method; the DHGF algorithm is an integrated algorithm that combines the Delphi method, grey theory analysis method and fuzzy hierarchical analysis method to solve complex decision-making problems; its main idea is to form a comprehensive decision-making framework by combining the expert opinions of the Delphi method, the criterion weights of the fuzzy hierarchical analysis method, the correlation analysis of the grey theory and the evaluation mechanism of the fuzzy judgment, and combine the results of the above methods to make a final comprehensive evaluation and decision to obtain the best decision-making plan or strategy;

[0049] This method can avoid the limitations of a single method and make full use of the advantages of multiple methods. Therefore, the converter station equipment status evaluation method based on the DHGF algorithm is an effective status evaluation method that can better evaluate the equipment operating status. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a flowchart of the overall process of the present invention;

[0052] Figure 2 It is the equipment evaluation index system of the present invention; DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the related art, the main idea of ​​the DHGF algorithm is to combine the advantages of the Delphi method, the gray theory analysis method and the fuzzy hierarchical analysis method, and optimize the decision-making process and improve the quality of the solution by integrating these methods. Combine the expert opinions of the Delphi method, the criterion weights of the hierarchical analysis method, the association analysis of the gray theory and the comprehensive evaluation mechanism of the fuzzy hierarchical analysis method to form a comprehensive decision-making framework. Combine the results of the above methods, make a final comprehensive evaluation and decision, and obtain the best decision-making plan or strategy. Among them, the Delphi method is used to collect expert opinions and suggestions, obtain preliminary information and expert evaluation on decision-making problems, and help form preliminary decision criteria and weights; the gray theory analysis method is to process incomplete and uncertain information, evaluate the advantages and disadvantages of each plan through gray correlation analysis, and provide analysis and processing capabilities for decision data. Fuzzy analytic hierarchy process is a decision analysis method that combines fuzzy theory and analytic hierarchy process. It is mainly used to deal with complex decision problems and provide a more flexible and agile evaluation mechanism.

[0054] Please refer to Figure 1 The present invention is a method for evaluating the status of converter station equipment based on the DHGF algorithm. The equipment in this embodiment is a converter transformer. The implementation steps of the present invention are described in detail below in conjunction with the accompanying drawings:

[0055] S1 Step 1: Select evaluation indicators;

[0056] First, statistical analysis of the equipment and query of current regulations are carried out to determine reliable indicators that reflect the operating status of the equipment. These evaluation indicators need to comprehensively reflect the aging of the insulation life of the equipment, internal health status, electrical or mechanical characteristics from the aspects of the equipment itself, testing and operation, and analyze whether there are historical problems and family defects from a vertical and horizontal perspective. The established status evaluation index system is a three-layer hierarchical structure, including four first-level evaluation indicators, and each first-level indicator includes several second-level indicators.

[0057] S2 Step 2: Obtain the indicator sample matrix; Considering that the evaluation methods for different equipment status are also quite different, combined with the status information of the evaluation indicators of the converter transformer collected during the maintenance process, m experts are invited to evaluate the n evaluation indicators of the converter transformer, and the evaluation data of all experts on the evaluated objects are combined into a sample matrix D, where d mn It represents the score given by the mth expert to the nth evaluation index, that is:

[0058]

[0059] S3 Step 3, whitening of evaluation indexes: After selecting the evaluation indexes and the state scoring matrix, the evaluation indexes are pre-processed by whitening. According to the degree of deterioration of the converter transformer, the evaluation gray categories are set to 4 levels, namely good, attention, abnormal, and severe, and the gray number of the gray category j = 1, 2, 3, 4. If the equipment state evaluation is based on the 10-point evaluation level, the corresponding whitening weight function can be obtained.

[0060] The first gray class is "good", gray class j = 1, gray number The corresponding whitening weight function is

[0061]

[0062] The second gray class is "attention", gray class j = 2, gray number The corresponding whitening weight function is

[0063]

[0064] The third gray class is "abnormal", gray class j = 3, gray number The corresponding whitening weight function is

[0065]

[0066] The fourth gray class is "abnormal", gray class j = 4, gray number The corresponding whitening weight function is

[0067]

[0068] S4 Step 4: Construct a fuzzy evaluation matrix; Next, construct a fuzzy evaluation weight matrix, and use the grey statistical method to determine the standard whitening weight function of each evaluation index to obtain d li The weight f belonging to the j-type evaluation criteria j (d li ), based on which the grey statistics N can be obtained ij And the total grey statistics N i .

[0069]

[0070] The grey weight of the i-th evaluation index belonging to the j-th evaluation grey class is determined by formula (6).

[0071]

[0072] In this way, the fuzzy evaluation weight matrix R can be constructed.

[0073]

[0074] S5 Step 5: Fuzzy evaluation analysis to calculate weights; Next, the fuzzy judgment matrix is ​​used to calculate the weights of each indicator. First, a fuzzy judgment matrix is ​​established. Suppose that there are n indicator elements in the target system X, recorded as X = {x1, x2, ..., x n}, then the complementary judgment matrix of system X is

[0075]

[0076] Among them, a ij Represents element a i Than element a j Important membership degrees and commonly used value scales are shown in the following table:

[0077]

[0078] Obviously a ij +a ji =1, to ensure 0≤a ij ≤1, a≥81 must be satisfied. In order to fully reflect the fuzziness of the evaluation and the differences between the elements, a is set to 243 according to the reference.

[0079] After that, the consistency of the matrix is ​​checked and judged. The fuzzy hierarchical analysis method judges the matrix A = (a ij ) n×n The consistency should satisfy have

[0080] a ij =a ik -a jk +0.5

[0081] If the consistency verification conditions are met, the weight calculation of each element can be performed. If not, the judgment matrix needs to be adjusted to meet the consistency conditions.

[0082] The fuzzy weight calculation method used is:

[0083]

[0084] Among them, β is the weight resolution coefficient. By increasing the β value, the resolution of each element weight can be improved. β is selected according to the requirements of the equipment. According to the reference, β can be taken as e e (e is the natural logarithm).

[0085] Calculate the combined weights for each indicator in turn, and set the final weight matrix as

[0086]

[0087] S6 Step 6: Determine the evaluation standard; refer to the classification of power equipment status evaluation in related projects and combine with industry standards to determine the equipment status evaluation standard set V as

[0088] V={v1,v2,v3,v4}

[0089] Among them, v1, v2, v3, and v4 correspond to the good state, attention state, abnormal state, and in-eye state respectively.

[0090] S7 Step 7: Obtain the evaluation result. When the four status levels of the equipment are quantitatively calculated, each status has a corresponding range, and the range of the good status is [8, 10], the range of the caution status is [6, 8), the range of the abnormal status is [4, 6), and the range of the severe status is [0, 4).

[0091] Finally, the comprehensive evaluation results of the equipment are calculated:

[0092] Z=(W·R)·V T

[0093] The fuzzy comprehensive evaluation matrix B = W·R = (b1, b2, b3, b4); and the elements of the matrix B satisfy:

[0094] This combination can avoid the limitations of a single method and make full use of the advantages of multiple methods. Therefore, the converter station equipment status evaluation method based on the DHGF algorithm is an effective status evaluation method that can better evaluate the equipment operating status.

[0095] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A device status evaluation method based on DHGF algorithm, characterized in that: The following steps are involved: Step 1: Select evaluation indicators; Step 2: Get the indicator sample matrix; Step 3: whitening the evaluation index; Step 4: Construct fuzzy evaluation matrix; Step 5: Calculate weights through fuzzy evaluation analysis; Step 6: Determine the evaluation criteria; Step 7: Obtain the evaluation results.

2. The device status evaluation method based on the DHGF algorithm according to claim 1 is characterized in that: In the step 1, the specific method for selecting the evaluation index is: conducting statistical analysis on the equipment and querying the current regulations, determining the index reflecting the operating status of the equipment, and establishing a status evaluation index system, wherein the status evaluation index system includes 4 first-level evaluation indicators, and each of the first-level indicators includes a number of second-level indicators.

3. The device status evaluation method based on the DHGF algorithm according to claim 1 is characterized in that: In step 2, the specific method of obtaining the indicator sample matrix is ​​as follows: combining the status information of the evaluation indicators collected in step 1, inviting m experts to evaluate the n evaluation indicators of the equipment, and forming a sample matrix D with the evaluation data of all experts on the evaluated object, where d mn It represents the score given by the mth expert to the nth evaluation index, that is:

4. The device status evaluation method based on the DHGF algorithm according to claim 1 is characterized in that: In the step 3, the specific method of whitening the evaluation index is as follows: after selecting the evaluation index and the state scoring matrix, the evaluation index is pre-processed by whitening. According to the degree of degradation of the equipment, the evaluation gray class is set to 4 levels, namely good, attention, abnormal, and severe, and the gray number of the gray class j=1, 2, 3, 4; if the state evaluation of the equipment is based on the 10-point evaluation level, the corresponding whitening weight function can be obtained; The first gray class is "good", gray class jj = 1, gray number The corresponding whitening weight function is The second gray class is "attention", gray class j = 2, gray number The corresponding whitening weight function is The third gray class is "abnormal", gray class j = 3, gray number The corresponding whitening weight function is The fourth gray class is "serious", gray class j = 4, gray number The corresponding whitening weight function is 5. The device status evaluation method based on the DHGF algorithm according to claim 1 is characterized in that: In step 4, the specific method of constructing the fuzzy evaluation matrix is: using the standard whitening weight function of each evaluation index to obtain d li The weight f belonging to the j-type evaluation criteria j (d li ), based on which the grey statistics Ni can be obtained j and the total grey statistics Ni; Determine the gray weight of the i-th evaluation index belonging to the ,th evaluation gray class. Thus, the fuzzy evaluation weight matrix R can be constructed; 6. The device status evaluation method based on the DHGF algorithm according to claim 1 is characterized in that: In step 5, the specific method of constructing the fuzzy evaluation matrix is ​​as follows: the fuzzy judgment matrix is ​​used to calculate the weight of each indicator. First, the fuzzy judgment matrix is ​​established. Assume that there are n indicator elements in the target system X, recorded as X = {x1, x2, ..., x n }, then the complementary judgment matrix of system X is Among them, a ij Represents element α i Than element a j Important membership: After that, the matrix consistency is tested and judged. The fuzzy hierarchical analysis method judges the matrix A = (α ij ) n×n The consistency should satisfy have a ij =a ik -a jk +0.5 If the consistency verification conditions are met, the weight calculation of each element can be performed. If not, the judgment matrix needs to be adjusted to meet the consistency conditions. The fuzzy weight calculation method used is: Among them, β is the weight resolution coefficient. By increasing the β value, the resolution of each element weight can be improved. β is selected according to the requirements of the equipment. According to the reference, β can be taken as e e (e is the natural logarithm); Calculate the combined weights for each indicator in turn, and set the final weight matrix as 7. The device status evaluation method based on the DHGF algorithm according to claim 1 is characterized in that: In step 6, the specific method for determining the evaluation standard is: according to the equipment status evaluation classification status, the equipment status evaluation standard set V can be determined as V = {v1, v2, v3, v4} Among them, v1, v2, v3, and v4 correspond to the good state, attention state, abnormal state, and eye state respectively; when performing quantitative calculations, each state has a corresponding range, and the range of the good state is specified to be [8, 10], the range of the attention state is [6, 8), the range of the abnormal state is [4, 6), and the range of the serious state is [0, 4). Finally, the comprehensive evaluation results of the equipment are calculated: Z=(W·R)·V T 。

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