Method for evaluating synchronous grid connection process of water-turbine generator set

Abstract

A method for evaluating the synchronous grid connection process of a water-turbine generator set comprises the steps that 1, corresponding performance indexes are selected according to the national standard, the industrial standard, the characteristics of synchronous grid connection of a generator and the requirements in the actual working condition, the performance indexes serve as evaluation indexes, and an evaluation index system is established; 2, determining the weight of each evaluation index by adopting an analytic hierarchy process; and 3, measuring the relationship between the evaluation object and the ideal reference sequence by using a grey correlation analysis method, and carrying out comprehensive evaluation on each synchronous grid-connected process. The method can help a power station operator to evaluate the quality of the same period, determines that the power generation equipment is damaged due to low quality of the same period, and carries out the guidance of the optimization and transformation of the same period in a targeted manner.

Description

Technical Field

[0001] The invention belongs to the technical field of hydroelectric power generation, and in particular relates to a method for evaluating the synchronous grid-connected process of a hydroelectric generator set. Background Art

[0002] With the continuous development of modern power systems and the large-scale grid connection of wind and solar power, the task of hydropower regulation has become heavier, and the units need to be connected to and disconnected from the grid more frequently. At the same time, the capacity of the system and the capacity of the generator are constantly upgrading, and the poor quality of the unit grid connection has a more serious impact on related power equipment and the power grid. For example, excessive grid-connected impact current may cause damage to equipment such as generators, circuit breakers and transformers, and may also cause a greater impact on the power grid, affecting the safe and stable operation of the system. Therefore, quality assessment of the synchronous grid connection process of hydropower generator sets is of great significance to the extension of the life of related power and the safe and stable operation of the power grid.

[0003] At present, most of the research on synchronous grid connection of hydro-turbine generator sets focuses on dynamic behavior analysis, control strategy optimization and system stability, while there are few studies on the performance evaluation and quality assessment of the synchronous grid connection process of hydro-turbines.

[0004] In summary, the shortcomings of the existing technology are: quality assessment relies on the experience and judgment of technicians, and the evaluation results are not objective enough. Usually, they only focus on one aspect of performance and lack a comprehensive evaluation system. There is a lack of systematic evaluation methods, which makes it difficult to accurately reflect the comprehensive performance of the grid connection process. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for evaluating the synchronous grid-connected process of a hydro-turbine generator set, which can help power station operators evaluate the quality of synchronization, determine the damage to power generation equipment caused by poor synchronization quality, and provide targeted synchronization optimization and transformation guidance.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for evaluating the synchronous grid connection process of a hydro-generator set, comprising the following steps:

[0008] Step 1: Select corresponding performance indicators according to national standards, industry standards, characteristics of synchronous grid connection of generators and requirements in actual working conditions, use performance indicators as evaluation indicators and establish an evaluation indicator system;

[0009] Step 2: Use the analytic hierarchy process to determine the weight of each evaluation index;

[0010] Step 3: Use the grey correlation analysis method to measure the relationship between the evaluation object and the ideal reference sequence, and conduct a comprehensive evaluation of each synchronous grid-connected process.

[0011] Preferably, in step 1, according to national standards, industry standards, actual operation requirements of synchronous grid-connected power generation and key domestic and foreign practical experience, key performance indicators are selected with the goal of accurately, reliably and quickly connecting the generator to the grid; and then a synchronous grid-connected performance evaluation index system that meets actual application requirements is constructed; the performance indicators include primary indicators and secondary indicators, and each primary indicator is provided with a number of secondary indicators.

[0012] Preferably, in step 2, in order to quantify the evaluation index system, it is necessary to assign a weight value to each indicator based on the evaluation index system established in step 1, and determine the degree of influence of the evaluation indicator on the evaluation target according to the weight of the evaluation indicator, so as to directly reflect the role of the evaluation indicator in the evaluation index system.

[0013] Preferably, in step 2, the weights of the performance indicators of the same period are determined by using the hierarchical analysis method, a complex multi-criteria decision-making problem is decomposed into relevant factors, and the subordinate relationships between different factors are then formed into a hierarchical structure, and finally the decision-making problem is transformed into a problem of determining the importance weight of the object to be evaluated relative to the evaluation target;

[0014] The steps to build the AHP model are as follows:

[0015] Step 2.1: First, the problem to be evaluated should be subdivided into several levels, and the relationship between the factors at each level and the subordinate factors should be combined into a hierarchical structure that can explain the decision-making problem. The top layer in the structure is the target layer, and the following layers correspond to the criterion layer and the indicator layer respectively.

[0016] Step 2.2: Establish a judgment matrix;

[0017] Step 2.3: Calculate the indicator weights;

[0018] Step 2.4: Check the consistency of the judgment matrix.

[0019] Preferably, in step 2.2, on the established hierarchical structure, based on the elements of the previous layer, the common subordinate factors and the related factors are compared in pairs from top to bottom, thereby establishing a judgment matrix.

[0020] Preferably, in step 2.3, after constructing the judgment matrix, the relative weights between the elements of each layer and the elements of the next layer are calculated, and the calculated quantity has the largest characteristic root λ max The corresponding eigenvectors are calculated using the square root method, the sum method, and the least squares method;

[0021] The calculation process of the maximum eigenvalue and corresponding eigenvector of the judgment matrix J by the square root method is as follows:

[0022] Calculate the nth root W of the product of each row of the judgment matrix Ji , whose composition vector W=[W1,W2,…,W i ] T is the eigenvector, and the calculation formula is as follows:

[0023]

[0024] Among them, T represents the transition rank;

[0025] W i Normalization processing obtains the normalized vector The calculation formula is as follows:

[0026]

[0027] Calculate the maximum eigenvalue λ of the judgment matrix max , the calculation formula is as follows:

[0028]

[0029] Preferably, in step 2.4, a consistency check is performed on judgment matrices of different orders, and the random consistency ratio CR is used for representation, and the test formula is as follows:

[0030]

[0031] Where: CI represents the consistency index of the judgment matrix, and its calculation formula can be expressed as CI = λmax-n / (n-1), λ max represents the maximum eigenvalue of the judgment matrix, n represents the order of the judgment matrix, RI represents the average random consistency index of the same order of the judgment matrix, when the random consistency ratio CR<0.1, it is considered that the judgment matrix meets the consistency requirements, if CR>0.1, it is considered that the judgment matrix does not meet the consistency requirements, and the judgment matrix scale needs to be adjusted.

[0032] Preferably, in step 3, based on the indicator weights determined in step 2, a performance evaluation model for synchronous grid connection of a hydro-generator set is established using a grey correlation analysis method, and the steps are as follows:

[0033] Step 3.1: Construct the decision matrix:

[0034] X={X1,X2,…,X n};

[0035] Where X i ={x i (1),x i (2),...,x i (m)} represents the score of the i-th object on each indicator;

[0036] Step 3.2: Calculate the sequence difference:

[0037] Δ i (k)=|x0(k)-x i (k)|;

[0038] Among them, x0(k) is the value of the reference sequence at the kth index;

[0039] Step 3.3: Calculate the grey relationship number:

[0040]

[0041] Among them, ρ is the resolution coefficient;

[0042] Step 3.4: Calculate the comprehensive grey relational degree:

[0043]

[0044] Among them, w k is the weight value of the kth indicator. The grey correlation degree expresses the tendency degree of each secondary indicator to each grey class, reflecting the evaluation result of the secondary indicator.

[0045] Preferably, the value of ρ is 0.5.

[0046] A synchronous grid-connected process evaluation system for a hydro-generator set adopts the synchronous grid-connected process evaluation method for a hydro-generator set.

[0047] The present invention can achieve the following beneficial effects:

[0048] 1. The present invention proposes a method for evaluating the quality of the synchronous grid-connected process of a hydro-turbine generator set based on a grey correlation analysis method and a fuzzy comprehensive evaluation method, which makes an accurate judgment on the quality of the synchronous grid-connected process of a hydro-turbine generator set, realizes synchronous grid-connected quality assessment and synchronous optimization guidance, and helps power stations improve the level of refined management of power plants.

[0049] 2. Calculate the correlation by mathematical methods to reduce the impact of human factors. It can consider multiple performance indicators at the same time to achieve comprehensive evaluation. It is suitable for power systems of different sizes and types and has good adaptability. It can accurately identify the key factors affecting the synchronous grid connection process and improve the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0051] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0052] The preferred solution is Figure 1As shown, a method for evaluating the synchronous grid-connected process of a hydro-turbine generator set adopts the grey relational analysis method. The grey relational analysis method (Grey Relational Analysis, GRA) is a method for dealing with uncertainty and multivariate relationships in a system. It was proposed by Chinese scholar Deng Julong in the 1980s and is an important part of the grey system theory. GRA analyzes the relationship between the variables of the system by calculating the correlation between different factors (sequences), thereby helping decision-making and evaluation. The core idea of ​​the grey relational analysis method is to measure the correlation between different variables (sequences) according to their development trends. By calculating the grey correlation between each sequence and the reference sequence, the degree of correlation can be determined. The present invention selects this evaluation method to construct an evaluation model that can accurately find out the key factors affecting the synchronous grid-connected process and realize the comprehensive evaluation of the synchronous grid-connected process of the hydro-turbine generator set. The specific scheme is as follows:

[0053] Step 1: The selection of synchronous grid-connected performance evaluation indicators of hydro-turbine generator sets is the basis for realizing the synchronous grid-connected performance evaluation of hydro-turbine generator sets. First, it is necessary to refer to key bases such as relevant national and industry standards, actual operation requirements of synchronous grid-connected operation, and domestic and foreign practical experience, and select key performance indicators with the goal of accurate, reliable and fast grid-connected operation of the generator, and then construct a synchronous grid-connected performance evaluation indicator system that meets actual application needs. In order to accurately evaluate the synchronous grid-connected process of hydro-turbine generator sets, the present invention selects synchronous performance and synchronous stability as first-level indicators. Each first-level indicator has several second-level indicators, and each first-level and second-level indicator is shown in Table 1 below.

[0054] Table 1 Key performance indicators of turbine synchronous grid connection process

[0055]

[0056] Step 2: In order to quantify the evaluation index system, it is necessary to assign weight values ​​to each index based on the evaluation index system established in step 1. The degree of influence of the index on the evaluation target can be determined according to the weight of the index, which directly reflects the role of the index in the evaluation system. The present invention adopts the hierarchical analysis method to determine the weights of the performance indicators of the same period. The core idea is to decompose a complex multi-criteria decision-making problem into related factors, and then form a hierarchical structure of the subordinate relationships between different factors, and finally transform the decision-making problem into a problem of determining the importance weight of the object to be evaluated relative to the evaluation target. The steps to establish the hierarchical analysis method model are as follows:

[0057] Step 2.1: First, the problem to be evaluated should be subdivided into several levels, and the relationship between the factors at each level and the subordinate factors should be combined into a hierarchical structure that can explain the decision-making problem. The top layer in the structure is the target layer, and the following layers correspond to the criterion layer and the indicator layer respectively.

[0058] Step 2.2: Establish a judgment matrix:

[0059] On the established hierarchical structure, the common factors and related factors should be compared from top to bottom based on the elements of the previous layer, so as to establish a judgment matrix. Taking the relationship between the target layer G and the criterion layer A as an example, the judgment matrix is ​​shown in Table 1, and the judgment matrix of the target layer and the criterion layer is shown in Table 2.

[0060] Table 2 Judgment moments of target layer and criterion layer

[0061] G <![CDATA[A1]]> <![CDATA[A2]]> … <![CDATA[A n ]]> <![CDATA[A1]]> <![CDATA[A 11 ]]> <![CDATA[A 12 ]]> … <![CDATA[A 1n ]]> <![CDATA[A2]]> <![CDATA[A 21 ]]> <![CDATA[A 22 ]]> … <![CDATA[A 2n ]]> ┇ ┇ ┇ ┇ ┇ <![CDATA[A n ]]> <![CDATA[A n1 ]]> <![CDATA[A n2 ]]> … <![CDATA[A nn ]]>

[0062] In the judgment matrix of the target layer and the criterion layer in Table 2, Aij represents the ratio scale of the criterion and the importance of the criterion relative to the target, using the 1-9 scale method. The detailed meaning is shown in Table 3.

[0063] Table 3 The relative importance of the two factors

[0064] meaning <![CDATA[A ij Scale]]> Element i is as important as element j 1 Element i is slightly more important than element j 3 Element i is significantly more important than element j 5 Element i is more important than element j 7 Element i is extremely more important than element j 9 Between {1,3,5,7,9} 2,4,6,8

[0065] The judgment matrix J is constructed according to Table 2 and Table 3.

[0066]

[0067] Step 2.3: Calculation of indicator weights:

[0068] After constructing the judgment matrix, it is necessary to calculate the relative weights between each layer element and its adjacent next layer element. The calculated quantity has the largest characteristic root λ max The commonly used calculation methods for the corresponding eigenvectors are the square root method, the sum method, and the least squares method. This paper uses the square root method to calculate the maximum eigenroot and corresponding eigenvector of the judgment matrix J. The specific calculation process is as follows:

[0069] Calculate the nth root W of the product of each row of the judgment matrix J i , whose composition vector W=[W1,W2,…,W i ] T is the eigenvector, and the calculation formula is as follows:

[0070]

[0071] W i Normalization processing obtains the normalized vector The calculation formula is as follows:

[0072]

[0073] Calculate the maximum eigenvalue λ of the judgment matrix max , the calculation formula is as follows:

[0074]

[0075] Step 2.4: Judgment matrix consistency test

[0076] After the indicator weights are calculated, the judgment matrix needs to be checked for consistency because the scale value A ij It is obtained by experts based on their own knowledge and practical experience. If there are inconsistent or even contradictory indicator scale values, it is necessary to perform consistency test on the judgment matrix. For judgment matrices of different orders, consistency test is required, which is expressed by random consistency ratio CR. The test formula is as follows:

[0077]

[0078] Where: CI represents the consistency index of the judgment matrix, and its calculation formula can be expressed as CI = λmax-n / (n-1), λ max represents the maximum eigenvalue of the judgment matrix, n represents the order of the judgment matrix, RI represents the average random consistency index of the same order of the judgment matrix, and the RI values ​​corresponding to judgment matrices of different orders are shown in Table 4.

[0079] Table 4 RI values ​​of different order judgment matrices

[0080]

[0081] When the random consistency ratio CR<0.1, the judgment matrix is ​​considered to meet the consistency requirements. If CR>0.1, the judgment matrix is ​​considered to not meet the consistency requirements, and the judgment matrix scale needs to be adjusted.

[0082] Step 3: The indicators in the synchronous grid-connected evaluation index system of hydro-generator sets are interrelated, fully reflecting the system status of the entire synchronous process, and laying the foundation for subsequent evaluation work. The evaluation index system can only reflect the current energy efficiency status, and it is not intuitive. Therefore, the status currently reflected by the system must be reflected in specific values ​​so that the evaluator can objectively understand the operating status of the grid-connected process. Based on the indicator weights determined in step 2, the grey correlation analysis method is used to establish a synchronous grid-connected performance evaluation model for hydro-generator sets. The specific steps are as follows:

[0083] Step 3.1: Construct the decision matrix

[0084] X={X1,X2,...,X n};

[0085] Where X i ={x i (1),xi (2),...,x i (m)} represents the score of the i-th object on each indicator.

[0086] Step 3.2: Calculate the sequence difference:

[0087] Δ i (k)=|x0(k)-x i (k)|;

[0088] Among them, x0(k) is the value of the reference sequence (ideal target value) at the kth indicator.

[0089] Step 3.3: Calculate the grey relationship number:

[0090]

[0091] Here, ρ is the resolution coefficient, which is usually taken as 0.5.

[0092] Step 3.4: Calculate the comprehensive grey relational degree

[0093]

[0094] Among them, w k is the weight value of the kth indicator. The grey correlation degree expresses the tendency degree of each secondary indicator to each grey class, reflecting the evaluation result of the secondary indicator.

[0095] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for evaluating the synchronous grid connection process of a hydro-generator set, characterized in that The following steps are involved: Step 1: Select corresponding performance indicators according to national standards, industry standards, characteristics of synchronous grid connection of generators and requirements in actual working conditions, use performance indicators as evaluation indicators and establish an evaluation indicator system; Step 2: Use the analytic hierarchy process to determine the weight of each evaluation index; Step 3: Use the grey correlation analysis method to measure the relationship between the evaluation object and the ideal reference sequence, and conduct a comprehensive evaluation of each synchronous grid-connected process.

2. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 1, characterized in that: In step 1, based on national standards, industry standards, actual operation requirements of synchronous grid connection and key domestic and foreign practical experience, performance indicators are selected with the goal of accurate, reliable and fast grid connection operation of generators; and then a synchronous grid connection performance evaluation index system that meets actual application requirements is constructed; Performance indicators include primary indicators and secondary indicators, and each primary indicator has several secondary indicators.

3. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 1, characterized in that: In step 2, in order to quantify the evaluation index system, it is necessary to assign weight values ​​to each indicator based on the evaluation index system established in step 1, and determine the degree of influence of the evaluation indicator on the evaluation target according to the weight of the evaluation indicator, so as to directly reflect the role of the evaluation indicator in the evaluation index system.

4. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 3, characterized in that: In step 2, the analytic hierarchy process is used to determine the weights of the performance indicators of the same period, decomposing a complex multi-criteria decision-making problem into relevant factors, and then forming a hierarchical structure of the affiliation between different factors. Finally, the decision-making problem is transformed into a problem of determining the importance weight of the object to be evaluated relative to the evaluation target; The steps to build the AHP model are as follows: Step 2.1: First, the problem to be evaluated should be subdivided into several levels, and the relationship between the factors at each level and the subordinate factors should be combined into a hierarchical structure that can explain the decision-making problem. The top layer in the structure is the target layer, and the following layers correspond to the criterion layer and the indicator layer respectively. Step 2.2: Establish a judgment matrix; Step 2.3: Calculate the indicator weights; Step 2.4: Check the consistency of the judgment matrix.

5. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 4, characterized in that: In step 2.2, on the established hierarchical structure, from top to bottom, based on the elements of the previous layer, the common factors and related factors are compared pairwise, thereby establishing a judgment matrix.

6. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 4, characterized in that: In step 2.3, after constructing the judgment matrix, the relative weights between each layer element and its adjacent next layer element are calculated. The calculated quantity has the largest characteristic root λ max The corresponding eigenvectors are calculated using the square root method, the sum method, and the least squares method; The calculation process of the maximum eigenvalue and corresponding eigenvector of the judgment matrix J by the square root method is as follows: Calculate the nth root W of the product of each row of the judgment matrix J i , whose composition vector W=[W1,W2,…,W i ] T is the eigenvector, and the calculation formula is as follows: Where T represents the transition rank; W i Normalization processing obtains the normalized vector The calculation formula is as follows: Calculate the maximum eigenvalue λ of the judgment matrix max , the calculation formula is as follows:

7. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 4, characterized in that: In step 2.4, the consistency test is performed on judgment matrices of different orders, and the random consistency ratio CR is used to represent it. The test formula is as follows: Where: CI represents the consistency index of the judgment matrix, and its calculation formula can be expressed as CI = λ max -n / (n-1), λ max represents the maximum eigenvalue of the judgment matrix, n represents the order of the judgment matrix, RI represents the average random consistency index of the same order of the judgment matrix, when the random consistency ratio CR<0.1, it is considered that the judgment matrix meets the consistency requirements, if CR>0.1, it is considered that the judgment matrix does not meet the consistency requirements, and the judgment matrix scale needs to be adjusted.

8. The method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 1, characterized in that: In step 3, based on the index weights determined in step 2, a performance evaluation model for synchronous grid connection of hydro-generator units is established using the grey correlation analysis method. The steps are as follows: Step 3.1: Construct the decision matrix: X={X1,X2,...,X n }; Where X i ={x i (1),x i (2),...,x i (m)} represents the score of the i-th object on each indicator; Step 3.2: Calculate the sequence difference: Δ i (k)=|x0(k)-x i (k)|; Among them, x0(k) is the value of the reference sequence at the kth index; Step 3.3: Calculate the grey relationship number: Among them, ρ is the resolution coefficient; Step 3.4: Calculate the comprehensive grey relational degree: Among them, w k is the weight value of the kth indicator. The grey correlation degree expresses the tendency degree of each secondary indicator to each grey class, reflecting the evaluation result of the secondary indicator.

9. A method for evaluating the synchronous grid connection process of a hydro-generator set according to claim 1, characterized in that: The value of ρ is 0.

5.

10. A system for evaluating the synchronous grid connection process of a hydro-generator set, characterized in that: A method for evaluating the synchronous grid-connection process of a hydro-generator set according to any one of claims 1 to 9 is adopted.

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