A comprehensive evaluation method and system for the multi - voltage regulation characteristics of a deep peak - shaving thermal power unit

Abstract

The present disclosure belongs to the technical field of voltage regulation characteristic evaluation, and specifically relates to a comprehensive evaluation method and system for multi-element voltage regulation characteristics of deep peaking thermal power units, comprising: obtaining voltage regulation characteristic parameters of an excitation system of a deep peaking thermal power unit; constructing a multi-element voltage regulation characteristic comprehensive evaluation index system based on the obtained voltage regulation characteristic parameters; determining the objective weight and subjective weight of each indicator in the multi-element voltage regulation characteristic comprehensive evaluation index system, and calculating the combined weight of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weight and the subjective weight; comprehensively evaluating each deep peaking thermal power unit based on the combined weight of each indicator to obtain a comprehensive evaluation result, thereby completing the comprehensive evaluation of the multi-element voltage regulation characteristics of the deep peaking thermal power unit; constructing a multi-layer index system, and using the combined weight based on the objective weight and the subjective weight to realize the comprehensive evaluation of the deep peaking thermal power unit, effectively combining actual operation experience with objective data analysis, and meeting the stable operation requirements of the power system.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of voltage regulation characteristic evaluation, and specifically relates to a method and system for comprehensively evaluating the multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] As the deep peak-shaving capabilities of thermal power units are gradually being exploited, an increasing number of units are undergoing flexibility retrofits to become deep peak-shaving units. To ensure that the voltage regulation characteristics of these retrofitted deep peak-shaving units meet the requirements for safe and stable power system operation, enhance the system's peak-shaving capabilities, and ensure the absorption of new energy, the excitation system's voltage regulation characteristics must first meet operational requirements.

[0004] The excitation system of a thermal power unit is crucial for maintaining generator terminal voltage stability, controlling reactive power distribution between parallel units, and improving power system operational stability. This system is crucial for the stable operation of both the unit and the power system. Similarly, the excitation system plays a crucial role in the grid-connected operation of deep-peaking thermal power units. When a thermal power unit enters deep-peaking operation, the special operating conditions place higher demands on the excitation system's voltage regulation characteristics. Therefore, a reasonable assessment of the voltage regulation characteristics of the excitation system of deep-peaking thermal power units is crucial to ensuring their safe and stable operation during grid connection. This requires in-depth research into comprehensive evaluation methods and indicator systems.

[0005] The excitation system of a deep-peaking thermal power unit operates under special operating conditions. The better the excitation system's voltage regulation characteristics, while meeting the basic operational requirements of the power system, the better the evaluation results. The indicators selected during the evaluation process must be comprehensive and reasonable to establish a comprehensive indicator system. Furthermore, a comprehensive evaluation method that incorporates both subjective experience and objective analysis, tailored to the indicator system and actual needs, should be selected to ensure that the evaluation results have more practical guidance value. Existing research suffers from an incomplete indicator system, requiring further expansion. The selected evaluation methods also fall into extremes of being either too subjective or too objective, failing to effectively integrate actual operating experience with objective data analysis. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a comprehensive evaluation method and system for the multi-dimensional voltage regulation characteristics of deep peak-shaving thermal power units, establishes a complete indicator system for the voltage regulation characteristics of the excitation system of deep peak-shaving thermal power units, selects a reasonable empowerment strategy, and combines an efficient multi-attribute decision-making method for comprehensive evaluation to ensure that the voltage regulation characteristics of the thermal power units after the flexibility transformation into deep peak-shaving thermal power units meet the voltage regulation characteristics of the operating requirements, thereby ensuring the safe and stable operation of the power system.

[0007] According to some embodiments, the first solution of the present disclosure provides a comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit, which adopts the following technical solutions:

[0008] A comprehensive evaluation method for multi-element voltage regulation characteristics of deep peak-shaving thermal power units, comprising:

[0009] Obtain voltage regulation characteristic parameters of the excitation system of deep peak-shaving thermal power units;

[0010] Based on the obtained voltage regulation characteristic parameters, a comprehensive evaluation index system of multiple voltage regulation characteristics is constructed;

[0011] Determining the objective weight and subjective weight of each indicator in the multi-element voltage regulation characteristic comprehensive evaluation index system, and calculating the combined weight of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weight and the subjective weight;

[0012] Based on the combined weights of various indicators, a comprehensive evaluation is conducted on each deep peak-shaving thermal power unit to obtain a comprehensive evaluation result, thus completing a comprehensive evaluation of the multi-dimensional voltage regulation characteristics of the deep peak-shaving thermal power unit.

[0013] As a further technical limitation, the constructed multi-element voltage regulation characteristic comprehensive evaluation index system includes three layers of comprehensive evaluation indicators, namely, target layer evaluation indicators, criterion layer evaluation indicators and indicator layer evaluation indicators; the criterion layer evaluation indicators include steady-state performance comprehensive evaluation indicators, dynamic performance comprehensive evaluation indicators and transient performance comprehensive evaluation indicators.

[0014] As a further technical limitation, the information content of each indicator is obtained based on the correlation coefficient and variation coefficient of each indicator, and the objective weight of each indicator is determined; specifically,

[0015] Perform dimensionless processing on each evaluation index;

[0016] The mean and standard deviation of each indicator are calculated using dimensionless alternative data;

[0017] The coefficient of variation was calculated based on the mean and standard deviation of each indicator;

[0018] Calculate the correlation coefficient between indicators based on the mean of each indicator and the data of alternative solutions;

[0019] Calculate the information content of indicators based on the coefficient of variation and correlation coefficient;

[0020] Calculate the objective weight of each indicator based on the amount of information.

[0021] As a further technical limitation, the best indicator comparison vector and the worst indicator comparison vector are obtained based on the comparison criteria, and the optimal aggregation weight reflecting the overall preference of all evaluators is determined through the Bayesian hierarchical model to determine the subjective weight of each indicator; specifically,

[0022] Obtain the best and worst indicators and construct an indicator comparison vector;

[0023] Use multinomial distribution as the probability distribution assumption for the input and output of the Bayesian BWM model;

[0024] Based on Bayesian inference, the weight judgment problem is converted into a probability statistics problem of approximately estimating the distribution of the optimal weight vector, the optimal weight vector for group decision-making is calculated, and the subjective weight of each indicator is determined.

[0025] As a further technical limitation, in the process of calculating the combined weights of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weights and the subjective weights, game theory is used as a combined weighting method to optimize and solve the combined coefficients of the subjective weights and the objective weights, obtain the optimal linear combination coefficients, determine the combined weights of each indicator, and realize combined weighting.

[0026] Furthermore, the specific process of determining the combined weights of each indicator is as follows:

[0027] Establish a weight vector set Q c ={q1,q2}; where q1 and q2 represent the subjective weight vector and the objective weight vector respectively, and any linear combination thereof is: Among them, {α1,α2} are linear combination coefficients;

[0028] According to the idea of the game aggregation model, the two linear combination coefficients α1 and α2 are optimized with the goal of minimizing the deviation to obtain reasonable weights and determine the objective function, namely min‖Qq i ‖2,i=1,2;

[0029] According to the differential properties of the matrix, the objective function is transformed equivalently to obtain the linear equations satisfied by the optimal first-order derivative conditions, that is, Find the optimal combination coefficients α1 and α2;

[0030] Normalize α1 and α2 to obtain the combined weight Q based on game theory combination weighting:

[0031]

[0032]

[0033] Get the index z j The final weight of (j=1,2,…,m), that is, w zj = c ×Q j Among them, Q c Indicates index zj Corresponding criterion layer indicator y l The combined weight of Q j Indicates index z j The combined weight of .

[0034] As a further technical limitation, the PROMETHEEⅡ method is used for comprehensive evaluation to form a set of alternative plans and evaluation indicators. Appropriate preference functions are selected to compare the alternative plans pairwise. The degree of preference is obtained by combining the indicator weights. The comprehensive evaluation results of the alternative plans are obtained by calculating the net flow to complete the comprehensive evaluation.

[0035] According to some embodiments, the second solution of the present disclosure provides a comprehensive evaluation system for multi-element voltage regulation characteristics of deep peak-shaving thermal power units, which adopts the following technical solutions:

[0036] A comprehensive evaluation system for multi-element voltage regulation characteristics of deep peak-shaving thermal power units, comprising:

[0037] An acquisition module configured to acquire voltage regulation characteristic parameters of an excitation system of a deep peak-shaving thermal power unit;

[0038] A construction module configured to construct a multi-element voltage regulation characteristic comprehensive evaluation index system based on the acquired voltage regulation characteristic parameters;

[0039] a calculation module configured to determine an objective weight and a subjective weight of each indicator in the multi-element voltage regulation characteristic comprehensive evaluation index system, and calculate a combined weight of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weight and the subjective weight;

[0040] The evaluation module is configured to conduct a comprehensive evaluation of each deep peak-shaving thermal power unit based on the combined weight of each indicator, obtain a comprehensive evaluation result, and complete a comprehensive evaluation of the multi-element voltage regulation characteristics of the deep peak-shaving thermal power unit.

[0041] According to some embodiments, a third solution of the present disclosure provides a computer-readable storage medium, which adopts the following technical solution:

[0042] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as described in the first aspect of the present disclosure.

[0043] According to some embodiments, a fourth solution of the present disclosure provides an electronic device, which adopts the following technical solution:

[0044] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the comprehensive evaluation method for the multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as described in the first aspect of the present disclosure are implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This paper selects a comprehensive set of evaluation indicators and constructs a multi-layer indicator system. This complete indicator system serves as the basis for comprehensive evaluation, making the evaluation results more practical and guiding. It ensures that the voltage regulation characteristics of deep peak-shaving thermal power units are evaluated in many aspects, ensuring that deep peak-shaving thermal power units connected to the grid meet the requirements for stable operation of the power system.

[0047] The CRITIC method is adopted as the objective weighting method, which comprehensively measures the objective weights based on the comparison intensity and conflict between evaluation indicators. It can take into account both the variability of indicators and the correlation between indicators, and deeply explore the objective attributes of the data itself for scientific evaluation. The Bayesian BWM method is adopted as the subjective weighting method, which has the outstanding advantages of fewer comparisons and shorter time, while ensuring a high consistency ratio and weight reliability. Game theory is used to integrate subjective weights and objective weights to achieve combined weighting, organically combining actual operating experience with objective data analysis.

[0048] The PROMETHEEⅡ method is used to combine a complete indicator system with subjective and objective weighting results to conduct a comprehensive evaluation of multiple alternative plans. This multi-attribute decision-making method is not fully compensatory and can well reflect the status of alternative plans. The evaluation results can be obtained with relatively small amounts of input data, ultimately achieving the goal of obtaining a ranking result that comprehensively integrates multiple indicators and subjective and objective weights. The evaluation results can be used as a guide for further improvement of the voltage regulation characteristics of deep peak-shaving thermal power units. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0050] Figure 1 This is a flow chart of the comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit in the first embodiment of the present disclosure;

[0051] Figure 2 Schematic diagram of the comprehensive evaluation index system for multiple voltage regulation characteristics of deep peak-shaving thermal power units in the first embodiment of the present disclosure;

[0052] Figure 3 This is a flow chart of the PROMETHEEⅡ multi-attribute decision-making algorithm based on the combined weighting method in the first embodiment of the present disclosure;

[0053] Figure 4 The evaluation results of each alternative solution under each indicator in the first embodiment of the present disclosure;

[0054] Figure 5 The evaluation results of the alternative solution GS 4 under each indicator in the first embodiment of the present disclosure are as follows;

[0055] Figure 6 Rank the net dominant flows of the alternative solutions in the first embodiment of the present disclosure;

[0056] Figure 7 This is a structural block diagram of the comprehensive evaluation system for multi-element voltage regulation characteristics of deep peak-shaving thermal power units in the second embodiment of the present disclosure. DETAILED DESCRIPTION

[0057] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0058] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0059] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0060] In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are merely relational words determined for the convenience of describing the structural relationships of the various parts or elements of the present disclosure, and do not specifically refer to any part or element in the present disclosure, and should not be understood as limitations on the present disclosure.

[0061] In this disclosure, terms such as "fixed connection," "connected," and "connection" should be interpreted broadly to mean a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediary. Relevant researchers or technicians in this field may determine the specific meanings of these terms in this disclosure based on specific circumstances, and they should not be construed as limitations on this disclosure.

[0062] In the absence of conflict, the embodiments of the present disclosure and the features thereof may be combined with each other.

[0063] Example 1

[0064] Embodiment 1 of the present disclosure introduces a comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit.

[0065] like Figure 1 A comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit is shown, comprising the following steps:

[0066] Step S01: constructing a comprehensive evaluation index system for multiple voltage regulation characteristics of deep peak-shaving thermal power units;

[0067] Step S02: Using the CRITIC method as an objective weighting method to determine the objective weight;

[0068] Step S03: Using the Bayesian BWM method as a subjective weighting method to determine the subjective weight;

[0069] Step S04: using game theory as a combination weighting method to determine the combination weight;

[0070] Step S05: Perform comprehensive evaluation using the PROMETHEE II method to obtain comprehensive evaluation results.

[0071] As one or more implementation methods, in step S01, the comprehensive evaluation results of the multi-element voltage regulation characteristics of the deep peak-shaving thermal power unit are set as the overall goal of the index system, which is taken as the target layer; the steady-state performance, dynamic performance and transient performance are selected as the three major categories of voltage regulation characteristics comprehensive evaluation directions, which are taken as the criterion layer; under the steady-state performance, three evaluation indicators are set: steady-state gain, voltage regulation rate and voltage static difference rate; under the dynamic performance, four evaluation indicators are set: overshoot, adjustment time, rise time and oscillation number; under the transient performance, three evaluation indicators are set: excitation system peak voltage multiple, excitation voltage response ratio and excitation system voltage response time; the above ten evaluation indicators are taken as the index layer, and the three-layer evaluation index system is constructed as follows: Figure 2 As shown, the criterion layer is represented as Y = {y1,…,y l}, where l represents the number of criteria layer indicators, and l = 3; the indicator layer is represented by Z = {z1, z2, …, z m}, where m represents the number of indicators in the indicator layer, and m=10.

[0072] As one or more implementation methods, in step S02, the CRITIC method is used as an objective weighting method, and the objective weight is determined by obtaining the indicator information amount based on the correlation coefficient and the coefficient of variation of the indicator; specifically,

[0073] Step S201: Perform forward or reverse processing on each evaluation indicator of the indicator layer.

[0074] In order to eliminate the influence of different dimensions on the comprehensive evaluation results, it is necessary to perform dimensionless processing on each evaluation indicator, and perform positive or negative processing on the positive and negative indicators respectively. The processing formula can be expressed as:

[0075]

[0076] Among them, x ij is the dimensionless processing result of the jth indicator of the i-th alternative; b ij is the actual parameter of the jth indicator of the i-th alternative.

[0077] Step S202: Calculate the mean and standard deviation of each indicator using the dimensionless alternative solution data. The calculation formula can be expressed as:

[0078]

[0079] in, is the mean value of the jth index; s j is the standard deviation of the jth indicator; n is the total number of alternatives.

[0080] Step S203: Calculate the coefficient of variation based on the mean and standard deviation of each indicator to measure the comparison strength of the values within each indicator. The calculation formula can be expressed as:

[0081]

[0082] Among them, j is the coefficient of variation of the j-th indicator.

[0083] Step S204: Calculate the correlation coefficient between indicators based on the mean value of each indicator and the alternative solution data to measure the correlation between indicators and serve as the basis for determining the conflict between indicators. The calculation formula can be expressed as:

[0084]

[0085] Among them, r jh is the correlation coefficient between the jth indicator and the hth indicator, where j≠h.

[0086] The conflict between any indicator and other indicators can be quantified by the correlation between the indicators. The calculation formula can be expressed as:

[0087]

[0088] Among them, v j is the quantitative value of the conflict between the jth indicator and other indicators, where j≠h.

[0089] Step S205: Calculate the index information content based on the coefficient of variation and the correlation coefficient, so that the objective weight includes both the contrast intensity and conflict information in the data. The calculation formula can be expressed as:

[0090] c j =o j v j (6)

[0091] Among them, c j is the amount of information contained in the j-th indicator.

[0092] Step S206: Calculate the index weight based on the amount of information.

[0093] The greater the amount of information in the evaluation indicator, the greater the role of the indicator in the entire evaluation indicator system, and the greater the weight value should be assigned. The indicator weight calculation formula can be expressed as:

[0094]

[0095] Among them, w j,obj is the objective weight value of the j-th indicator.

[0096] In one or more implementations, in step S03, the Bayesian BWM method is used as a subjective weighting method. The evaluators obtain the best and worst indicators based on their experience, obtain the best indicator comparison vector and the worst indicator comparison vector through a comparison criterion, and finally determine the optimal aggregation weight that reflects the overall preference of all evaluators through a Bayesian hierarchical model. Specifically:

[0097] Step S301: The evaluator selects the best indicator and the worst indicator from the evaluation indicator set based on experience, and constructs an indicator comparison vector.

[0098] According to the actual engineering experience of the evaluator, in the evaluation index set E={e1,e2,…,e n}Select the optimal index as e B , the worst indicator is e w ,Afterwards, the evaluator continued to construct the indicator comparison vector based on ,actual engineering experience, and selected the nine-scale method to ,compare the indicators pairwise to determine the importance relationship.

[0099] The optimal indicator e B The comparison matrix is represented as D B ={D B1 ,D B2 ,…,D Bj ,…,D Bm}, the importance scale is determined by Table 1.

[0100] Table 1 Importance scale

[0101]

[0102] Among them, the worst indicator e w The comparison matrix is represented as D w ={D w1 ,D w2 ,…,D wj ,…,D wm}, the importance scale is determined by Table 2.

[0103] Table 2 Importance scale

[0104]

[0105] Step S302: Use the multinomial distribution as the probability distribution hypothesis of the input and output of the Bayesian BWM model.

[0106] Determine D B 、D w The input and output of the Bayesian BWM model are modeled using multinomial distribution. e The multinomial probability distribution formula is:

[0107]

[0108]

[0109]

[0110] Where w is the probability distribution; Formula (9) represents w j represents the probability w of event j j It is proportional to the total number of events; w w The worst indicator e w Probability of occurrence.

[0111] The worst index e can be calculated by formula (9) and formula (10): w The proportional relationship between weight and expert rating:

[0112]

[0113] Similarly, the optimal indicator e B The proportional relationship between the weight and the expert score can be expressed as:

[0114]

[0115] Therefore, the worst indicator e w and the optimal index e B The probability distribution of is assumed to be:

[0116] Dw |~multinomial()(13)

[0117]

[0118] Step S303: Based on Bayesian inference, the weight judgment problem is converted into a probability statistics problem of approximate estimation of the optimal weight vector distribution, and the optimal weight vector w for group decision is calculated. agg .

[0119] Suppose there are K (k = 1, ..., K) evaluation experts, then the kth evaluation expert determines the best and worst comparison matrix according to the evaluation index and The set of the best and worst comparison matrices of K evaluation experts is: and Then the optimal weight vector w for group decision making is agg The joint probability distribution of is:

[0120]

[0121] Where w 1: Represents the set of weights of various indicators determined by K evaluation experts.

[0122] Applying the Bayesian approach to the joint probability distribution (15) based on the chain rule, the independence conditions of different variables, and each decision maker’s preference for a criterion yields:

[0123]

[0124] Where, represents w agg The posterior distribution of P(w agg ) represents w agg The prior distribution of express Multinomial distribution assumption, P(w k |w agg ) represents w k The posterior distribution of .

[0125] Calculate w using the uninformative Diricht distribution with parameter α = 1 agg The prior distribution on , that is:

[0126] w agg ~Dir(α) (17)

[0127] Solve for w agg 、w k The process of posterior distribution involves high-dimensional integrals, which are difficult to calculate. Therefore, the Markov chain Monte Carlo method under the Gibbs sampling environment is used to fit w agg 、wk The posterior distribution of w agg The sample mean of the posterior distribution is used as the optimal weight result.

[0128] In one or more implementation methods, in step S04, game theory is used as a combination weighting method. According to the idea of the game theory aggregation model, the combination coefficients of the subjective and objective weights are optimized and solved to obtain the optimal linear combination coefficients, and then the comprehensive weight based on the game theory combination weighting is determined; specifically:

[0129] Step S401: Establish a weight vector set Q c ={q1,q2}, where q1 and q2 represent the weight vectors obtained in step 3 and step 2 respectively; then any linear combination of these vectors is:

[0130]

[0131] Among them, {α1,α2} are linear combination coefficients.

[0132] Step S402: Based on the idea of the game aggregation model, the two linear combination coefficients α1 and α2 are optimized with the goal of minimizing the deviation to obtain the most reasonable weight. The objective function is determined by this method as follows:

[0133] min‖Qq i ‖2,i=1,2 (19)

[0134] According to the differential properties of the matrix, the above equation is equivalently transformed into a linear equation system that satisfies the optimal first-order derivative condition:

[0135]

[0136] The optimal combination coefficients α1 and α2 can be obtained from formula (20).

[0137] Step S403: Normalize α1 and α2 to obtain the combined weight Q based on game theory combined weighting:

[0138]

[0139]

[0140] Step S404: Index z j The final weight of (j=1,2,…,m) is:

[0141] w zj =Q c ×Q j (twenty three)

[0142] Where Q cIndicates index z j Corresponding criterion layer indicator y l The combined weight of Q j Indicates index z j The combined weight of .

[0143] As one or more implementation methods, in step S05, a comprehensive evaluation is performed using the PROMETHEE II method. First, a set of alternatives and evaluation indicators is formed, and a suitable preference function is selected to compare each alternative two by two. Then, the preference degree is obtained by combining the indicator weights. Finally, the comprehensive evaluation result of the alternatives is obtained by calculating the net flow. Figure 3 As shown, the specific process is:

[0144] Step S501: Select appropriate criterion function to determine alternative plan A under each indicator i To A k (k=1,2,…,n) preference:

[0145] There are six forms of preference functions in the PROMETHEE II method, and the specific preference functions are as follows:

[0146] The general guidelines are:

[0147]

[0148] The U-shaped criterion is:

[0149]

[0150] The linear criterion is:

[0151]

[0152] The multi-level criteria are:

[0153]

[0154] The linear indifference interval criterion is:

[0155]

[0156] Gauss's criterion is:

[0157]

[0158] In formula (23)-(28), d j (A i ,A k )=f j (A i )-f j (A k ), indicating that any two alternatives havej The difference in assessed values.

[0159] Step S502: Calculate the overall preference index using the weight coefficient of each indicator:

[0160]

[0161] Where w zj Indicates index z j The final combined weight.

[0162] Step S503: Calculate the positive and negative dominant flows of each alternative solution:

[0163]

[0164]

[0165] Step S504: Calculate the net dominant flow of each alternative solution, and obtain the ranking of the alternative solutions based on the net dominant flow:

[0166]

[0167] Case Analysis

[0168] Taking 10 typical deep peak-shaving thermal power units as an example, the specific implementation process of this embodiment is further described. In various standards, the limit values of various indicators of the multi-element voltage regulation characteristics of the thermal power unit excitation system are shown in Table 3.

[0169] Table 3 Multi-element voltage regulation characteristic index limits

[0170]

[0171] Within the index limit, the voltage regulation characteristic data of the excitation systems of 10 typical deep peak-shaving thermal power units are taken, as shown in Table 4.

[0172] Table 4 Voltage regulation characteristics of excitation systems of 10 typical deep peak-shaving thermal power units

[0173]

[0174]

[0175] Indicators z1, z2, z8, and z9 were positively processed, and the remaining six indicators were negatively processed. The CRITIC method was used to calculate the objective weights of the processed data. Ten experts were invited to fully investigate industry data, evaluate each indicator at the criterion level and indicator level, and bring the evaluation results into the Bayesian BWM model to obtain the subjective weight of each indicator. Finally, game theory was used to calculate the comprehensive weight of each indicator. The results of all weight calculations are shown in Table 5.

[0176] Table 5 Weights of multivariate voltage regulation characteristic indicators

[0177]

[0178]

[0179] When using the PROMETHEEⅡ method for comprehensive evaluation, the z7 index uses the U-type criterion; z3, z5, z6, z8, z9, z 10 The linear criterion is used for the indicators; the linear indifference interval criterion is used for the indicators z1, z2, and z4. The evaluation results of each unit under each indicator are as follows: Figure 4 As shown in the figure, the evaluation results of GS 4 unit under various indicators are as follows Figure 5 As shown in Table 6, GS 4 is superior in the voltage regulation characteristic evaluation. Its various performances are balanced, especially the transient performance is outstanding, but the steady-state performance is slightly insufficient. According to the final weight results, the overall preference index and the net dominant flow of each unit are calculated. The results are shown in Table 6. The ranking of the evaluation objects is obtained according to the net dominant flow. Figure 6 As shown in the figure, the comprehensive evaluation results of the multi-element voltage regulation characteristics of deep peak-shaving thermal power units are: GS 4>GS10>GS 8>GS2>GS 7>GS 6>GS 9>GS 3>GS 5>GS 1.

[0180] The evaluation results can reflect the overall performance of the excitation system voltage regulation of deep peak-shaving thermal power units, and provide a reliable basis for improving the voltage regulation performance of the excitation system. Table 6 Static current values of each unit Inflow value

[0181] and outflow value

[0182]

[0183]

[0184] This embodiment introduces a comprehensive evaluation method for the multi-element voltage regulation characteristics of deep peaking thermal power units, which comprehensively evaluates the voltage regulation characteristics of the excitation system of deep peaking thermal power units to ensure that the voltage regulation characteristics after the thermal power units are flexibly transformed into deep peaking thermal power units meet the voltage regulation characteristics of the operating requirements, thereby ensuring the safe and stable operation of the power system; multiple evaluation indicators are selected to construct a complete indicator system, and the subjective and objective weighting method is used to calculate the weight of each indicator based on actual operating experience and objective data analysis. The present invention also provides a preference sequence structure evaluation method to comprehensively evaluate the multi-element voltage regulation characteristics of deep peaking thermal power units.

[0185] Example 2

[0186] The second embodiment of the present disclosure introduces a comprehensive evaluation system for multi-element voltage regulation characteristics of deep peak-shaving thermal power units.

[0187] like Figure 7 The comprehensive evaluation system for multi-element voltage regulation characteristics of deep peak-shaving thermal power units shown in FIG. includes:

[0188] An acquisition module configured to acquire voltage regulation characteristic parameters of an excitation system of a deep peak-shaving thermal power unit;

[0189] A construction module configured to construct a multi-element voltage regulation characteristic comprehensive evaluation index system based on the acquired voltage regulation characteristic parameters;

[0190] a calculation module configured to determine an objective weight and a subjective weight of each indicator in the multi-element voltage regulation characteristic comprehensive evaluation index system, and calculate a combined weight of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weight and the subjective weight;

[0191] The evaluation module is configured to conduct a comprehensive evaluation of each deep peak-shaving thermal power unit based on the combined weight of each indicator, obtain a comprehensive evaluation result, and complete a comprehensive evaluation of the multi-element voltage regulation characteristics of the deep peak-shaving thermal power unit.

[0192] The detailed steps are the same as the comprehensive evaluation method for multi-element voltage regulation characteristics of deep peak-shaving thermal power units provided in Example 1, and will not be repeated here.

[0193] Example 3

[0194] A third embodiment of the present disclosure provides a computer-readable storage medium.

[0195] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of the comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as described in the first embodiment of the present disclosure.

[0196] The detailed steps are the same as the comprehensive evaluation method for multi-element voltage regulation characteristics of deep peak-shaving thermal power units provided in Example 1, and will not be repeated here.

[0197] Example 4

[0198] A fourth embodiment of the present disclosure provides an electronic device.

[0199] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for comprehensive evaluation of multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as described in the first embodiment of the present disclosure are implemented.

[0200] The detailed steps are the same as the comprehensive evaluation method for multi-element voltage regulation characteristics of deep peak-shaving thermal power units provided in Example 1, and will not be repeated here.

[0201] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present disclosure shall be included within the scope of protection of the present disclosure.

[0202] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A comprehensive evaluation method for multi-element voltage regulation characteristics of deep peak-shaving thermal power units, characterized in that: include: Obtain voltage regulation characteristic parameters of the excitation system of deep peak-shaving thermal power units; Based on the obtained voltage regulation characteristic parameters, a comprehensive evaluation index system of multiple voltage regulation characteristics is constructed; Determining the objective weight and subjective weight of each indicator in the multi-element voltage regulation characteristic comprehensive evaluation index system, and calculating the combined weight of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weight and the subjective weight; Comprehensively evaluate each deep peak-shaving thermal power unit based on the combined weight of each indicator, obtain comprehensive evaluation results, and complete the comprehensive evaluation of the multi-dimensional voltage regulation characteristics of the deep peak-shaving thermal power unit; According to the correlation coefficient and variation coefficient of each indicator, the information content of the indicator is obtained and the objective weight of each indicator is determined; specifically, Perform dimensionless processing on each evaluation index; The mean and standard deviation of each indicator are calculated using dimensionless alternative data; The coefficient of variation was calculated based on the mean and standard deviation of each indicator; Calculate the correlation coefficient between indicators based on the mean of each indicator and the data of alternative solutions; Calculate the information content of indicators based on the coefficient of variation and correlation coefficient; Calculate the objective weight of each indicator based on the amount of information; Based on the comparison criteria, the best indicator comparison vector and the worst indicator comparison vector are obtained. The optimal aggregation weight that reflects the overall preference of all evaluators is determined through the Bayesian hierarchical model, and the subjective weight of each indicator is determined. Specifically, Obtain the best and worst indicators and construct an indicator comparison vector; Use multinomial distribution as the probability distribution assumption for the input and output of the Bayesian BWM model; Based on Bayesian inference, the weight judgment problem is converted into a probability statistics problem of approximately estimating the distribution of the optimal weight vector, the optimal weight vector for group decision-making is calculated, and the subjective weight of each indicator is determined.

2. A comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as claimed in claim 1, characterized in that: The constructed multi-element voltage regulation characteristic comprehensive evaluation index system includes three layers of comprehensive evaluation indicators, namely target layer evaluation indicators, criterion layer evaluation indicators and indicator layer evaluation indicators; the criterion layer evaluation indicators include steady-state performance comprehensive evaluation indicators, dynamic performance comprehensive evaluation indicators and transient performance comprehensive evaluation indicators.

3. A comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as claimed in claim 1, characterized in that: In the process of calculating the combined weights of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weights and the subjective weights, game theory is used as a combined weighting method to optimize and solve the combined coefficients of the subjective weights and the objective weights, obtain the optimal linear combination coefficients, determine the combined weights of each indicator, and realize combined weighting.

4. A comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as claimed in claim 3, characterized in that: The specific process of determining the combined weights of each indicator is as follows: Establish a weight vector set Q c ={q1,q2}; where q1 and q2 represent the subjective weight vector and the objective weight vector respectively, and any linear combination thereof is: Among them, {α1,α2} are linear combination coefficients; According to the idea of the game aggregation model, the two linear combination coefficients α1 and α2 are optimized with the goal of minimizing the deviation to obtain reasonable weights and determine the objective function, namely min‖Qq i ‖2,i=1,2; According to the differential properties of the matrix, the objective function is transformed equivalently to obtain the linear equations satisfied by the optimal first-order derivative conditions, that is, Find the optimal combination coefficients α1 and α2; Normalize α1 and α2 to obtain the combined weight Q based on game theory combination weighting: Get the index z j The final weight of (j=1,2,…,m), that is, w zj =Q c ×Q j Among them, Q c Indicates index z j Corresponding criterion layer indicator y l The combined weight of Q j Indicates index z j The combined weight of .

5. A comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as claimed in claim 1, characterized in that: The PROMETHEEⅡ method is used for comprehensive evaluation to form a set of alternative plans and evaluation indicators. Appropriate preference functions are selected to compare the alternative plans pairwise. The degree of preference is obtained by combining the indicator weights. The comprehensive evaluation results of the alternative plans are obtained by calculating the net flow to complete the comprehensive evaluation.

6. A comprehensive evaluation system for multi-element voltage regulation characteristics of deep peak-shaving thermal power units, characterized in that: include: An acquisition module configured to acquire voltage regulation characteristic parameters of an excitation system of a deep peak-shaving thermal power unit; A construction module configured to construct a multi-element voltage regulation characteristic comprehensive evaluation index system based on the acquired voltage regulation characteristic parameters; a calculation module configured to determine an objective weight and a subjective weight of each indicator in the multi-element voltage regulation characteristic comprehensive evaluation index system, and calculate a combined weight of each indicator in the constructed multi-element voltage regulation characteristic comprehensive evaluation index system based on the objective weight and the subjective weight; An evaluation module is configured to perform a comprehensive evaluation of each deep peaking thermal power unit based on the combined weight of each indicator, obtain a comprehensive evaluation result, and complete a comprehensive evaluation of the multi-element voltage regulation characteristics of the deep peaking thermal power unit; According to the correlation coefficient and variation coefficient of each indicator, the information content of the indicator is obtained and the objective weight of each indicator is determined; specifically, Perform dimensionless processing on each evaluation index; The mean and standard deviation of each indicator are calculated using dimensionless alternative data; The coefficient of variation was calculated based on the mean and standard deviation of each indicator; Calculate the correlation coefficient between indicators based on the mean of each indicator and the data of alternative solutions; Calculate the information content of indicators based on the coefficient of variation and correlation coefficient; Calculate the objective weight of each indicator based on the amount of information; Based on the comparison criteria, the best indicator comparison vector and the worst indicator comparison vector are obtained. The optimal aggregation weight that reflects the overall preference of all evaluators is determined through the Bayesian hierarchical model, and the subjective weight of each indicator is determined. Specifically, Obtain the best and worst indicators and construct an indicator comparison vector; Use multinomial distribution as the probability distribution assumption for the input and output of the Bayesian BWM model; Based on Bayesian inference, the weight judgment problem is converted into a probability statistics problem of approximately estimating the distribution of the optimal weight vector, the optimal weight vector for group decision-making is calculated, and the subjective weight of each indicator is determined.

7. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit as described in any one of claims 1 to 5 are implemented.

8. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the comprehensive evaluation method for multi-element voltage regulation characteristics of a deep peak-shaving thermal power unit according to any one of claims 1 to 5 are implemented.

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