Evaluation method and analysis system for power load supply guarantee capability of thermal power generating unit

Abstract

The invention discloses an evaluation method and an analysis system for the power load supply guarantee capability of a thermal power generating unit, and belongs to the technical field of power guarantee capability evaluation. A three-level evaluation index system is constructed based on actual operation report data of a power plant unit; correcting the objective weight of each index by using the subjective weight of each index and the accurate expert coefficient range and adopting a linear weighting method to obtain a more reasonable comprehensive weight; a foundation is laid for accurately evaluating the electric load supply guarantee capability of the thermal power generating unit; determining the basic supply guaranteeing capacity of the electric load of the thermal power generating unit through step-by-step recursion by utilizing the comprehensive weight of each index; a mathematical basis is provided for accurately evaluating the power load supply guarantee capability of the thermal power generating unit; and correcting the basic supply guarantee capability of the thermal power generating unit electrical load in combination with the flexible transformation layer, finally obtaining the actual supply guarantee capability of the thermal power generating unit electrical load, and accurately evaluating the supply guarantee capability of the thermal power generating unit electrical load.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply guarantee capability assessment, and in particular relates to an assessment method and analysis system for the power load supply guarantee capability of a thermal power unit. Background Art

[0002] At present, although my country's power supply guarantee capacity is good and the power supply and demand are generally balanced, there are still some problems in the power supply guarantee during the peak load period. For example, under extremely high temperatures, the power generation capacity of wind power may be significantly reduced, and solar power generation cannot supply power at night, which limits the supply guarantee capacity of new energy sources during peak power consumption periods. In addition, the reduction of coal production capacity and the fluctuation of international market prices have also put pressure on the supply guarantee capacity of thermal power companies. These factors have combined to affect the stability and reliability of power supply.

[0003] Up to now, the assessment of power plant supply guarantee capacity still faces a series of problems, including inaccurate or insufficient data, single models and methods, insufficient emergency plans, and unreasonable maintenance and overhaul plans. In particular, there is a lack of quantitative assessment and evaluation mechanism for the power load supply guarantee capacity of thermal power units. The main manifestation is that the assessment index system for thermal power units has not been established, the weight of the index system has not been involved, and the assessment method for the power load supply guarantee capacity of thermal power units has not been determined. These factors and existing problems will affect the accuracy of the assessment results of the power load supply guarantee capacity of thermal power units. The superior management departments of the power grid and thermal power units cannot effectively monitor the changes in the power supply guarantee capacity of the units and then propose adjustment measures, which may endanger the safe and reliable operation of the power grid.

[0004] At present, there is no method for determining a more reasonable expert coefficient. In addition, the existing method cannot directly evaluate the performance of the unit's supply guarantee capability based solely on basic indicator data. Even if the weights of each indicator are calculated, only the basic supply guarantee capability can be obtained, without considering the impact of flexibility modifications on the boiler side, turbine side, generator side, and control system side on the unit's supply guarantee capability.

[0005] Therefore, a new technical solution is urgently needed in the prior art to solve this problem. Summary of the invention

[0006] In order to overcome the deficiencies in the prior art, the present invention provides an evaluation method and analysis system for the power load supply guarantee capability of a thermal power unit, which are used to solve the problem that the power load supply guarantee capability of a thermal power unit cannot be accurately evaluated.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the power load supply guarantee capability of a thermal power unit, comprising the following steps:

[0008] S1. Construct a three-level evaluation index system based on the actual operation report data of the power plant units;

[0009] S2. Based on the actual reported operation and maintenance data of the power plant, each indicator in the three-level evaluation index system constructed in S1 is normalized, and then the objective weight of each indicator based entirely on the power plant unit data is determined according to the entropy weight method;

[0010] S3. Establish a hierarchical structure of the system: target layer, criterion layer and solution layer; experts conduct analysis of factors S1, S2, ... S at the same level. n For a factor E in the previous layer i The relative importance of the indicators is compared pairwise, and a proportional scale is given to the relative importance, so as to construct a judgment matrix; and then the subjective weight and expert coefficient of each indicator are determined according to the hierarchical analysis method;

[0011] S4, using the subjective weights and expert coefficients of each indicator obtained in S3, adopt the linear weighting method to correct the objective weights of each indicator obtained in S2 to obtain the comprehensive weights of each indicator;

[0012] S5. Use the comprehensive weights of the indicators obtained in S4 to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data; determine the basic supply capacity of the thermal power unit's electric load through step-by-step recursion;

[0013] S6. Combined with the impact of the flexibility transformation, the basic power supply capacity of the thermal power units obtained in S5 is corrected, and finally the actual power supply capacity of the thermal power units is obtained.

[0014] An analysis system for the power load guarantee capability of a thermal power unit, using the above-mentioned evaluation method for the power load guarantee capability of a thermal power unit, comprising an index system management unit, an evaluation criterion management unit, a weight algorithm management unit and a data processing unit; the index system management unit generates an evaluation index system for the power load guarantee capability of a thermal power unit, and is also used to save, load and export the generated evaluation index system for the power load guarantee capability of a thermal power unit;

[0015] The evaluation criteria management unit inputs and saves the evaluation criteria; the evaluation criteria are used to determine the indicator weights;

[0016] The weight algorithm management unit calls the subjective and objective weighting algorithm, and determines the indicator weight through the called subjective and objective weighting algorithm, wherein the subjective and objective weighting algorithm includes: expert evaluation method, judgment matrix analysis method, hierarchical analysis method and entropy value method;

[0017] The data processing unit uses the subjective weights and expert coefficients of each indicator obtained by S3, and adopts the linear weighting method or the least squares method to correct the objective weights of each indicator obtained by S2 to obtain the comprehensive weights of each indicator; the data processing unit uses the comprehensive weights of each indicator obtained by S4 to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data, and determines the basic power supply capacity of the thermal power unit's electric load by recursive step by step; the data processing unit corrects the basic power supply capacity of the thermal power unit's electric load obtained by S5 in combination with the influence of the flexibility transformation level, and finally obtains the actual power supply capacity of the thermal power unit's electric load.

[0018] A computer-readable storage medium, characterized in that it is used to store computer-readable instructions; when the computer-readable instructions are executed, they include the steps described above.

[0019] Through the above design scheme, the present invention can bring the following beneficial effects:

[0020] 1. Using the subjective weight of each indicator and the precise range of expert coefficients, the linear weighting method is used to correct the objective weight of each indicator to obtain a more reasonable comprehensive weight of each indicator, laying the foundation for accurately evaluating the power load supply capacity of thermal power units;

[0021] 2. Use the comprehensive weight of each indicator to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data; determine the basic power supply capacity of the thermal power unit through step-by-step recursion; and provide a mathematical basis for accurately evaluating the power supply capacity of the thermal power unit;

[0022] 3. Combined with the impact of flexibility transformation, the basic power supply capacity of thermal power units is corrected, and finally the actual power supply capacity of thermal power units is obtained, so as to accurately evaluate the power supply capacity of thermal power units. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a schematic diagram of an evaluation index system for an evaluation method of the electric load supply guarantee capability of a thermal power unit.

[0024] Figure 2 The present invention is a flow chart of a method for evaluating the electric load supply guarantee capability of a thermal power unit.

[0025] Figure 3 The present invention is a schematic diagram of a weight analysis calculation flow of a method for evaluating the electric load supply guarantee capability of a thermal power unit.

[0026] Figure 4 The present invention is a structural block diagram of a system for analyzing the power load supply guarantee capability of a thermal power unit.

[0027] In the figure, 1-indicator system management unit, 2-evaluation criteria management unit, 3-weight algorithm management unit, 4-data processing unit. DETAILED DESCRIPTION

[0028] The following is a detailed description of the specific implementation of the present invention in conjunction with the accompanying drawings.

[0029] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with the preferred embodiments. It should be understood by those skilled in the art. The content described in detail below is illustrative rather than restrictive, and the user may make various changes to the following parameters without departing from the inventive mechanism and scope set forth in the claims. In order to avoid confusing the essence of the present invention, known methods and processes are not described in detail.

[0030] By the attached Figures 1 to 4 As shown: A method for evaluating the power load supply capability of a thermal power unit includes the following steps:

[0031] S1. Construct a three-level evaluation index system based on the actual operation report data of the power plant units;

[0032] S2. Based on the actual reported operation and maintenance data of the power plant, each indicator in the three-level evaluation index system constructed in S1 is normalized, and then the objective weight of each indicator based entirely on the power plant unit data is determined according to the entropy weight method;

[0033] S3. Establish a hierarchical structure of the system: target layer, criterion layer and solution layer; experts conduct analysis of factors S1, S2, ... S at the same level. n For a factor E in the previous layer i The relative importance of the indicators is compared pairwise, and a proportional scale is given to the relative importance, so as to construct a judgment matrix; and then the subjective weight and expert coefficient of each indicator are determined according to the hierarchical analysis method;

[0034] S4, using the subjective weights and expert coefficients of each indicator obtained in S3, adopt the linear weighting method to correct the objective weights of each indicator obtained in S2 to obtain the comprehensive weights of each indicator;

[0035] S5. Use the comprehensive weights of the indicators obtained in S4 to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data; determine the basic supply capacity of the thermal power unit's electric load through step-by-step recursion;

[0036] S6. Combined with the impact of the flexibility transformation, the basic power supply capacity of the thermal power units obtained in S5 is corrected, and finally the actual power supply capacity of the thermal power units is obtained.

[0037] Further, the three-level evaluation index system of S1 includes five first-level indicators; the first-level indicators include twenty-three second-level indicators; the second-level indicators include forty-two third-level indicators, which are specifically distributed as follows:

[0038] 1) First-level indicator of power load supply capacity of thermal power units

[0039] There are five first-level indicators for the power load supply capacity of thermal power units, including: equipment reliability evaluation index 1, which mainly represents the impact of equipment reliability on supply capacity; operation capacity evaluation index 2, which mainly represents the impact of operation conditions on supply capacity; power coal evaluation index 3, which mainly represents the impact of power coal quality on supply capacity; management evaluation index 4, which mainly represents the impact of management factors on supply capacity; maintenance evaluation index 5, which mainly represents the impact of equipment maintenance level on supply capacity.

[0040] 2) Secondary indicators of thermal power unit load supply capacity

[0041] There are 23 secondary indicators for the power load supply capacity of thermal power units, including:

[0042] The first-level index, equipment reliability evaluation index 1, includes 9 second-level indicators, namely: boiler and its auxiliary equipment reliability index 1.1; turbine and its auxiliary equipment reliability index 1.2; primary and secondary power equipment reliability index 1.3; control system reliability index 1.4; chemical water system reliability index 1.5; coal transportation system reliability index 1.6; network security system reliability index 1.7; switchyard reliability evaluation index 1.8; equipment fault elimination evaluation index 1.9;

[0043] The first-level indicator, operating capacity evaluation index 2, includes 5 second-level indicators, namely: maximum output capacity evaluation index 2.1; minimum output capacity evaluation index 2.2; grid-related performance evaluation index 2.3; unit non-stop evaluation index 2.4; unit output obstruction index 2.5;

[0044] The first-level indicator, thermal coal evaluation index 3, includes two second-level indicators, namely: thermal coal quality index 3.1; thermal coal inventory index 3.2;

[0045] The first-level indicator, management evaluation indicator 4, includes 5 second-level indicators, namely: production safety management 4.1; emergency management 4.2; production environment management 4.3; personnel management 4.4; coal yard management 4.5;

[0046] The first-level indicator, maintenance evaluation indicator 5, includes two second-level indicators, namely: regular maintenance evaluation indicator 5.1; fault maintenance evaluation indicator (temporary maintenance) 5.2,

[0047] 3) Level 3 indicators of thermal power unit load supply capacity

[0048] There are 42 three-level indicators for the power load supply capacity of thermal power units, including:

[0049] The second-level index, boiler and its auxiliary equipment reliability index 1.1, includes two third-level indicators, namely: boiler body life assessment index 1.1.1; boiler and its auxiliary equipment test index 1.1.2;

[0050] The second-level index, steam turbine and its auxiliary equipment reliability index 1.2, includes two third-level indexes, namely: steam turbine body life assessment index 1.2.1; steam turbine and its auxiliary equipment test index 1.2.2;

[0051] Secondary indicators, primary and secondary power equipment reliability indicators 1.3, include 8 third-level indicators, namely: generator main equipment life assessment indicator 1.3.1; generator test indicator 1.3.2; main transformer main equipment life assessment indicator 1.3.3; transformer test indicator 1.3.4; reactor test indicator 1.3.5; transformer test indicator 1.3.6; main secondary equipment test indicator 1.3.7; excitation system test indicator 1.3.8;

[0052] The second-level index, control system reliability index 1.4, includes two third-level indexes, namely: control system life assessment index 1.4.1; control system test index 1.4.2;

[0053] The second-level indicator chemical water system reliability index is 1.5, which includes three third-level indicators, namely: chemical water vapor inspection qualified rate index 1.5.1; chemical online instrument input qualified rate 1.5.2; chemical online instrument accuracy qualified rate 1.5.3;

[0054] Secondary indicators, network-related performance evaluation indicators 2.3, include 4 third-level indicators, namely: primary frequency regulation performance test indicator 2.3.1; primary frequency regulation performance indicator 2.3.2 under deep regulation conditions; AGC performance test indicator 2.3.3; AGC performance indicator 2.3.4 under deep regulation conditions;

[0055] The second-level indicator, unit non-stop assessment indicator 2.4, includes two third-level indicators, namely: unit non-stop number assessment indicator 2.4.1; unit non-stop time assessment indicator 2.4.2;

[0056] The second-level indicator, unit output obstruction index 2.5, includes three third-level indicators, namely: unit output obstruction frequency assessment index 2.5.1; unit output obstruction time index 2.5.2; unit output obstruction degree assessment index 2.5.3;

[0057] The second-level indicator, power coal quality indicator 3.1, includes two third-level indicators, namely: power coal quality assessment indicator 3.1.1 under small load (load rate is less than 50% load); power coal quality assessment indicator 3.1.2 under large load (load rate is less than 50% load);

[0058] The second-level indicator, production safety management 4.1, includes three third-level indicators, namely: production safety responsibility system implementation indicator 4.1.1; production safety accident assessment indicator 4.1.2; safety inspection found hidden danger rectification rate assessment indicator 4.1.3;

[0059] The second-level indicator, emergency management 4.2, includes two third-level indicators, namely: emergency plan evaluation indicator 4.2.1; emergency plan drill evaluation indicator 4.2.2;

[0060] The second-level indicator, production environment management 4.3, includes four third-level indicators, namely: on-site hygiene and working environment assessment indicator 4.3.1; hazardous chemicals management compliance assessment indicator 4.3.2; safety sign setting assessment indicator 4.3.3; fire protection assessment indicator 4.3.4;

[0061] The second-level indicator, personnel management 4.4, includes three third-level indicators, namely: personnel certification rate assessment indicator 4.4.1; on-the-job personnel health status assessment indicator 4.4.2; occupational disease prevention and control assessment indicator 4.4.3;

[0062] The second-level indicator, coal yard management 4.5, includes two third-level indicators, namely: coal yard management evaluation indicator 4.5.1; coal yard prevention of spontaneous combustion, fire prevention, and explosion prevention evaluation indicator 4.5.2.

[0063] Further, the method for normalizing each indicator described in S2 is as follows:

[0064]

[0065] Where: P ij represents the normalized value of the i-th evaluation object on the j-th indicator; x ij It represents the performance value of the i-th evaluation object on the j-th indicator; m represents the number of indicators. When calculating the third-level indicator, m=42, when calculating the second-level indicator, m=23, and when calculating the first-level indicator, m=5; N is the number of units, N=Z, and Z represents the number of thermal power units;

[0066] The entropy weight method determines the objective weights of various indicators based entirely on power plant unit data as follows:

[0067]

[0068] Where: constant k = (lnN) -1 ; E m is the entropy of the indicator output, let the deviation d m =1-E m , then the objective weight of each indicator W m for:

[0069]

[0070] Where: d m is the deviation, W m is the objective weight of each indicator.

[0071] The steps of determining the subjective weight of indicators by analytic hierarchy process are as follows:

[0072] 1) Clarify the research problem and establish a hierarchical structure:

[0073] Establish a hierarchical structure of the system: goal layer, criterion layer and solution layer,

[0074] 2) Construct a judgment matrix:

[0075] Experts or decision makers conduct analysis of factors S1, S2, ...S at the same level. n For a factor E in the previous layer i The relative importance of the two is compared, and a proportional scale is given to the relative importance, so as to construct the judgment matrix K, where K ij The values ​​are shown in Table 1, where 2, 4, 6, and 8 can be used to quantify between any two levels.

[0076] Table 1 Relative importance scale

[0077]

[0078] The sum method or root method is used to obtain the eigenvector corresponding to the maximum eigenvalue of the judgment matrix K, and normalized to obtain the weight vector. Then the research objectives and evaluation factor set are determined. First, the research objectives should be clarified, the influencing factors should be analyzed, and the evaluation indicators should be screened by factor analysis to form the evaluation factor set U. Then the judgment matrix is ​​constructed: A represents the objective, u represents j represents the evaluation factor, u j ∈U,i=1,2,…,n,u ij Indicator u i For factor u j Relative importance value, u ij The values ​​are shown in Table 1, and the judgment matrix P can be constructed as follows:

[0079]

[0080] Calculate the importance ranking. According to the A-U judgment matrix, find the eigenvector corresponding to the maximum eigenvalue. The eigenvector is the importance ranking of each evaluation, that is, the weight distribution. The method of finding the eigenvalue and eigenvector of the A-U judgment matrix is ​​often very complicated. The following is a calculation derivation for the sum-product method, and normalize each column of the judgment matrix:

[0081]

[0082] Among them, U ij Represents the normalized value of the evaluation index, n represents the number of evaluation indicators, and each column of the normalized judgment matrix is ​​added row by row:

[0083]

[0084] in, Represents the row-wise addition value of the evaluation index, for vector The normalization process is as follows:

[0085]

[0086] Where: V n is the subjective weight of the indicator calculated by the hierarchical analysis method.

[0087] S4 further uses a linear weighting method to correct the objective weights of each indicator obtained in S2 to obtain the comprehensive weights of each indicator; the linear weighting method is as follows:

[0088] O=αV n +(1-α)W n 4

[0089] Where: O represents the comprehensive weight of the indicator; V n is the subjective weight of the indicator calculated by the hierarchical analysis method, W n is the objective weight of each indicator, α represents the expert coefficient, α∈(0,1), which is used to weigh the proportion of subjective and objective weights when assigning weights.

[0090] Further, the expert coefficient described in S3 has a value range of 0.8 to 0.9. The specific determination steps are as follows: the expert coefficient α should satisfy the following conditions:

[0091] 1) For some normal operating conditions, the comprehensive weight calculation results should meet the evaluator's preliminary judgment of the results. For example, when the small load accounts for more than 70% and the large load accounts for less than 30%, the corrected small load weight should be greater than 0.7.

[0092] 2) Several extreme operating conditions should be assumed to measure the rationality of the comprehensive weight calculation method. The evaluator believes that when the small load accounts for 1, the small load weight should be greater than 0.9; conversely, when the large load accounts for 1, the large load weight should be greater than 0.9.

[0093] 3) When the operating time proportions of small load and large load are both 0.5, the small load weight and large load weight in the comprehensive weight calculation results should be close to 0.5.

[0094] For the above conditions and comprehensive analysis of the calculation results of small load and large load weights obtained under different intervals of large load operation time and different expert coefficients, it can be seen that when the expert coefficient is greater than 0.8, it can meet the preliminary judgment of the results; when the expert coefficient is 0.9, the small load and large load operation time account for 1 each, this extreme operating condition, the corresponding weights are close to 1, indicating that when the expert coefficient is 0.9, the objective weight obtained by the entropy weight method has too little effect on the results. In summary, the expert coefficient should be greater than 0.8 and less than 0.9.

[0095] Finally, the expert coefficient is taken as 0.85. From the comprehensive weights of small load and large load obtained when the expert coefficient is 0.85, it can be seen that under the extreme operating conditions when the operating time of small load and large load accounts for 1 respectively, the weights of both meet the actual requirements. This shows that when the expert coefficient is 0.85, while the subjective weight obtained by the hierarchical analysis method has a major influence on the comprehensive weight calculation result, the objective weight obtained by the entropy weight method also has a certain influence on the comprehensive weight calculation. The subjective and objective weights are combined by the linear weighting method of the expert coefficient to make the final comprehensive weight calculation result more accurate.

[0096] Further, S5 uses the following method to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data:

[0097]

[0098] Where: Collect data for the third-level indicators; The power plant data of the second-level indicators calculated from the third-level indicators; is the weight of the third-level indicators calculated by the entropy weight method; n is the number of indicators,

[0099]

[0100] Where: The power plant data of the first-level index calculated through the second-level index; is the secondary index weight calculated by entropy weight method,

[0101] The basic power supply capacity of thermal power units determined by the primary indicator data and weights is:

[0102]

[0103] Where: The basic power supply capacity of thermal power units calculated by the first-level index. It is the first-level indicator weight calculated by entropy weight method.

[0104] The further flexibility transformation level described in S6 includes the boiler side, turbine side, generator side and control system side; the above four levels of correction are made to the basic power supply capacity of the thermal power unit, and the correction equation is as follows:

[0105]

[0106] Among them, K1 represents the impact of boiler flexibility transformation on the unit's power supply capability, K2 represents the impact of turbine flexibility transformation on the unit's power supply capability, K3 represents the impact of generator flexibility transformation on the unit's power supply capability, K4 represents the impact of control system flexibility transformation on the unit's power supply capability, and P represents the power generation capacity.

[0107] in, The effect of boiler efficiency on the power generation of the unit can be approximately characterized by: η B is the boiler thermal efficiency, Δη B is the change in boiler thermal efficiency, The influence of the absolute internal efficiency of the steam turbine on the power generation of the unit can be approximately characterized by: η T is the absolute internal efficiency of the steam turbine, Δη T is the change in absolute internal efficiency of the steam turbine, The effect of the electrical efficiency of the generator on the power generation of the unit can be approximately characterized by: η G is the generator electrical efficiency, Δη G is the change in the electrical efficiency of the generator, The effect of the unit load change rate on the unit power generation can be approximately characterized by: υ is the unit load change rate, Δυ is the change amount of the unit load change rate, the above All of them can be modified and calculated using the test parameters. Finally, the ranking is based on the data of the actual power supply capacity of the thermal power units. The larger the value, the stronger the power supply capacity of the power load.

[0108] In the specific implementation, the indicator weights are calculated based on the data reported by the three units in the plant.

[0109] By using the entropy weight method and combining the original data of the three-level indicators, we can get the objective weights of the three-level indicators on the secondary indicators calculated by the entropy weight method. By using the analytic hierarchy process principle, we can get the subjective weights. Based on equation 4, the objective weights calculated by the entropy weight method and the subjective weights calculated by the analytic hierarchy process are linearly coupled, and the expert coefficient is taken as 0.85. Finally, we can get the revised comprehensive weights of the three-level indicators on the secondary indicators. The weight calculation results are summarized in Table 2.

[0110] Table 2 Summary of the comprehensive weights of all third-level indicators of the three units to the second-level indicators

[0111]

[0112] Based on equation 5, combined with the original data of the third-level indicators and the comprehensive weight of the third-level indicators to the second-level indicators, the original data of the second-level indicators can be deduced. The calculation results are summarized in Table 3.

[0113] Table 3 Summary of secondary index data of three units

[0114]

[0115] Based on equations 1 to 3, the entropy weight method is used for calculation, and combined with the calculation data of the secondary indicators, the weights of the secondary indicators to the primary indicators can be obtained. The weight calculation results are summarized in Table 4.

[0116] Table 4 Summary of entropy weights of all secondary indicators of the three units to the primary indicators

[0117]

[0118] Based on equation 6, combined with the original data of the secondary indicators and the entropy weight of the secondary indicators to the primary indicators, the original data of the primary indicators can be deduced. The calculation results are summarized in Table 5.

[0119] Table 5 Summary of primary index data of three units

[0120] index Unit 1 Unit 2 Unit 3 1 Equipment level 0.12656 0.43336 0.44008 2 Operational Level 0.42767 0.44982 0.12252 3. Power coal level 0.13745 0.36647 0.49609 4. Management Level 0.22009 0.70547 0.07445 5 Maintenance level 0.32599 0.2092 0.46482

[0121] Based on equations 1 to 3, the entropy weight method is used for calculation, and combined with the calculation data of the first-level indicator, the entropy weight of the first-level indicator for the basic power supply capacity of the thermal power unit can be obtained. The weight calculation results are summarized in Table 6.

[0122] Table 6 Summary of entropy weights of all primary indicators of the three units on the basic power supply capacity of thermal power units

[0123] First level indicator 1 Equipment level 2 Operational Level 3. Power coal level 4. Management Level 5. Management Level First-order entropy weight 0.1579 0.1654 0.1535 0.4539 0.0694

[0124] Based on equation 7, combined with the original data of the first-level index and the entropy weight of the first-level index on the basic power supply capacity of the thermal power unit, the data of the basic power supply capacity of the thermal power unit can be calculated. The calculation results are summarized in Table 7.

[0125] Table 7 Summary of the basic power supply capacity of the three units

[0126]

[0127] Calculation of the actual power supply capacity of thermal power units

[0128] Unit 1 implemented flexibility modification on the boiler side, Unit 2 implemented flexibility modification on the turbine side, and Unit 3 did not implement flexibility modification.

[0129] Before the implementation of the flexibility transformation on the boiler side of unit 1, the rated power of the unit was 300MW and the boiler thermal efficiency was 88.5%. After the implementation of the flexibility transformation on the boiler side, the rated power of the unit was 302MW and the boiler thermal efficiency was 92.7%. Therefore, the actual power supply capacity of unit 1 is:

[0130]

[0131] Before the implementation of the flexibility modification on the steam turbine side of unit 2, the rated power of the unit was 660MW, and the absolute internal efficiency of the steam turbine was 46.5%. After the implementation of the flexibility modification on the steam turbine side, the rated power of the unit was 662MW, and the absolute internal efficiency of the steam turbine was 42.0%. Therefore, the actual power supply capacity of unit 2 is:

[0132]

[0133] Since Unit 3 did not implement flexibility transformation, the supply capacity of Unit 3 is still 0.2319.

[0134] It can be seen from this that the actual supply capacity of the three units is: Unit 1 is greater than Unit 3, which is greater than Unit 2. If the flexibility transformation is not considered, the supply capacity obtained is: Unit 2 is greater than Unit 1, which is greater than Unit 3, which is obviously not in line with the actual working conditions. It can also be seen that not all flexibility transformations will improve the supply capacity, and some flexibility transformations will reduce the supply capacity, which has a good guiding significance in actual work.

[0135] The present application also discloses an analysis system for the power load supply guarantee capability of a thermal power unit, which adopts any of the above-mentioned evaluation methods for the power load supply guarantee capability of a thermal power unit, including an index system management unit 1, an evaluation criterion management unit 2, a weight algorithm management unit 3 and a data processing unit 4; the index system management unit 1 generates an evaluation index system for the power supply guarantee capability of a thermal power unit, and is also used to save, load and export the generated evaluation index system for the power supply guarantee capability of a thermal power unit;

[0136] The evaluation criteria management unit 2 inputs and saves the evaluation criteria; the evaluation criteria are used to determine the indicator weights;

[0137] The weight algorithm management unit 3 calls the subjective and objective weighting algorithm, and determines the indicator weight through the called subjective and objective weighting algorithm, wherein the subjective and objective weighting algorithm includes: expert evaluation method, judgment matrix analysis method, hierarchical analysis method and entropy value method;

[0138] The data processing unit 4 uses the subjective weights and expert coefficients of each indicator obtained by S3, and adopts a linear weighted method to correct the objective weights of each indicator obtained by S2 to obtain the comprehensive weights of each indicator; the data processing unit 4 uses the comprehensive weights of each indicator obtained by S4 to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data, and determines the basic power supply capacity of the thermal power unit's electric load by recursive step by step; the data processing unit 4 corrects the basic power supply capacity of the thermal power unit's electric load obtained by S5 in combination with the influence of the flexibility transformation level, and finally obtains the actual power supply capacity of the thermal power unit's electric load.

[0139] The present application also discloses a computer-readable storage medium for storing computer-readable instructions; the computer-readable instructions, when executed, include any of the steps described above.

[0140] Obviously, the above described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of 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.

[0141] The present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is performed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0142] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device to perform all or part of the steps of each embodiment method of the present application for a personal computer, server or TRP. The aforementioned memory includes: U disk, read-only memory ROM, Read-Only Memory, random access memory RAM, RandomAccess Memory, mobile hard disk, disk or optical disk and other media that can store program codes.

Claims

1. A method for evaluating the power load supply capability of a thermal power unit, characterized in that: The steps include: S1. Construct a three-level evaluation index system based on the actual operation report data of the power plant units; S2. Based on the actual reported operation and maintenance data of the power plant, each indicator in the three-level evaluation index system constructed in S1 is normalized, and then the objective weight of each indicator based entirely on the power plant unit data is determined according to the entropy weight method; S3. Establish a hierarchical structure of the system: target layer, criterion layer and solution layer; experts conduct analysis of factors S1, S2, ... S at the same level. n For a factor E in the previous layer i The relative importance of the indicators is compared pairwise, and a proportional scale is given to the relative importance, so as to construct a judgment matrix; and then the subjective weight and expert coefficient of each indicator are determined according to the hierarchical analysis method; S4, using the subjective weights and expert coefficients of each indicator obtained in S3, adopt the linear weighting method to correct the objective weights of each indicator obtained in S2 to obtain the comprehensive weights of each indicator; S5. Use the comprehensive weights of the indicators obtained in S4 to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data; determine the basic supply capacity of the thermal power unit's electric load through step-by-step recursion; S6. Combined with the impact of the flexibility transformation, the basic power supply capacity of the thermal power units obtained in S5 is corrected, and finally the actual power supply capacity of the thermal power units is obtained.

2. The method for evaluating the power load supply capability of a thermal power unit according to claim 1, characterized in that: S1 The three-level evaluation index system includes five first-level indicators; the first-level indicators include twenty-three second-level indicators; the second-level indicators include forty-two third-level indicators.

3. The method for evaluating the power load supply capability of a thermal power unit according to claim 2, characterized in that: The method for normalizing each indicator described in S2 is as follows: Where: P ij represents the normalized value of the i-th evaluation object on the j-th indicator; x ij It represents the performance value of the i-th evaluation object on the j-th indicator; m represents the number of indicators. When calculating the third-level indicator, m=42, when calculating the second-level indicator, m=23, and when calculating the first-level indicator, m=5; N is the number of units, N=Z, and Z represents the number of thermal power units; The entropy weight method determines the objective weights of various indicators based entirely on power plant unit data as follows: Where: constant k = (lnN) -1 ; E m is the entropy of the indicator output, let the deviation d m =1-E m , then the objective weight of each indicator W m for: Where: d m is the deviation, W m is the objective weight of each indicator.

4. The method for evaluating the power load supply capability of a thermal power unit according to claim 2, characterized in that: S4 uses the linear weighting method to correct the objective weights of each indicator obtained in S2 to obtain the comprehensive weights of each indicator; the linear weighting method is as follows: O=αV n +(1-α)W n (4) Where: O represents the comprehensive weight of the indicator; V n is the subjective weight of the indicator calculated by the hierarchical analysis method, W n is the objective weight of each indicator, α represents the expert coefficient, α∈(0,1), which is used to weigh the proportion of subjective and objective weights when assigning weights.

5. The method for evaluating the power load supply capability of a thermal power unit according to claim 2, characterized in that: The expert coefficient described in S3 ranges from 0.8 to 0.

9.

6. The method for evaluating the power load supply capability of a thermal power unit according to claim 2, characterized in that: S5 uses the following method to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data: Where: Collect data for the third-level indicators; The power plant data of the second-level indicators calculated from the third-level indicators; is the weight of the third-level indicators calculated by the entropy weight method; n is the number of indicators, Where: The power plant data of the first-level index calculated through the second-level index; is the secondary index weight calculated by entropy weight method, The basic power supply capacity of thermal power units determined by the primary indicator data and weights is: Where: The basic power supply capacity of thermal power units calculated by the first-level index. It is the first-level indicator weight calculated by entropy weight method.

7. The method for evaluating the power load supply capability of a thermal power unit according to claim 2, characterized in that: The flexibility transformation level described in S6 includes the boiler side, turbine side, generator side and control system side; the above four levels of correction are made to the basic power supply capacity of the thermal power unit, and the correction equation is as follows: Among them, K1 represents the impact of boiler flexibility transformation on the unit's power supply capability, K2 represents the impact of turbine flexibility transformation on the unit's power supply capability, K3 represents the impact of generator flexibility transformation on the unit's power supply capability, K4 represents the impact of control system flexibility transformation on the unit's power supply capability, and P represents the power generation capacity.

8. An analysis system for the power load guarantee capability of a thermal power unit, using an evaluation method for the power load guarantee capability of a thermal power unit as claimed in any one of claims 1 to 7, characterized in that: It comprises an index system management unit (1), an evaluation criteria management unit (2), a weight algorithm management unit (3) and a data processing unit (4); the index system management unit (1) generates an evaluation index system for the supply guarantee capability of a thermal power unit, and is also used to save, load and export the generated evaluation index system for the supply guarantee capability of a thermal power unit; The evaluation criteria management unit (2) inputs and stores the evaluation criteria; the evaluation criteria are used to determine the indicator weights; The weight algorithm management unit (3) calls a subjective and objective weighting algorithm, and determines the indicator weight through the called subjective and objective weighting algorithm, wherein the subjective and objective weighting algorithm includes: expert evaluation method, judgment matrix analysis method, hierarchical analysis method and entropy value method; The data processing unit (4) uses the subjective weights and expert coefficients of the indicators obtained by S3 and adopts a linear weighting method to correct the objective weights of the indicators obtained by S2, thereby obtaining the comprehensive weights of the indicators. The data processing unit (4) uses the comprehensive weights of the indicators obtained by S4 to deduce and calculate the lower-level indicator data to obtain the upper-level indicator data, and determines the basic power supply capacity of the thermal power unit's electric load by recursive step by step. The data processing unit (4) corrects the basic power supply capacity of the thermal power unit's electric load obtained by S5 in combination with the influence of the flexibility transformation level, thereby finally obtaining the actual power supply capacity of the thermal power unit's electric load.

9. A computer-readable storage medium, characterized in that: Used to store computer-readable instructions; when the computer-readable instructions are executed, they include the steps as described in any one of claims 1 to 7.

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