Design method for parameters of short-term and long-term potential development water turbine

Abstract

The invention discloses a method for designing parameters of a short-term and long-term potential development water turbine. The method comprises the following steps: acquiring basic parameters of a hydropower station; the method comprises the following steps: setting the capacity of a single unit and the number of installed mixed-flow water turbines needing to be installed with a certain model, and totally obtaining a plurality of model selection schemes; determining mixed-flow water turbine prototype machine parameters in each type selection scheme, and calculating a parameter range according to a plurality of statistical formulas corresponding to each parameter; obtaining an objective function, and seeking a group of optimal water turbine prototype machine parameters of each type selection scheme based on a mayfly naiad optimization algorithm; obtaining a function fitness value according to the target function, sorting the fitness value corresponding to each type selection scheme, and selecting an optimal type selection scheme; and checking the working range of the mixed-flow water turbine for the optimal type selection scheme, and exporting optimal water turbine prototype machine parameters corresponding to the optimal type selection scheme. According to the method, the precision and efficiency of mixed-flow water turbine model selection calculation are improved, and the design period of mixed-flow water turbine model selection is shortened.

Description

Technical Field

[0001] The invention relates to the technical field of water turbine selection for a hydropower station, and in particular to a design method for water turbine parameters for developing near- and long-term potential. Background Art

[0002] Francis turbine has a compact structure, high efficiency, and can adapt to a wide range of water heads. It is one of the turbine types currently widely used in countries around the world. As an important part of hydropower station design, whether the Francis turbine selection calculation is reasonable is directly related to the kinetic energy economic indicators, operational stability and reliability of the power station.

[0003] Due to the complexity of fluid movement in turbines and the imperfection of their theories, as well as the limited current technical level, it is still not possible to use purely theoretical calculation methods to ultimately determine the optimal turbine solution. In the prior art, designers can only determine the best solution by comparing several different solutions. Usually, for a given design head, there are several or even dozens of model runners that can be applied. To select the most suitable runner from them, designers need to repeat the manual trial calculation process dozens or even hundreds of times. The design work is quite cumbersome, inefficient, and the accuracy of the results is also poor.

[0004] The mayfly optimization algorithm is a new type of intelligent optimization algorithm proposed by Greek scholar Zervoudakis in 2020 by combining genetic algorithm and particle swarm algorithm. It combines the advantages of swarm intelligence and evolutionary algorithm. It has good performance in terms of optimal solution quality, success rate, convergence speed, stability, etc., and the courtship dance and random flight in the solution process strengthen the balance between local exploration and global development of the algorithm. At the same time, some improved versions, discretization and multi-objective mayfly optimization algorithms have been proposed one after another, which have improved the optimization ability of the algorithm and expanded the application field of the mayfly optimization algorithm. At present, the mayfly optimization algorithm has been successfully applied to optimization problems in many fields such as fault diagnosis, feature selection, wind power system prediction, economic dispatch, medical image detection, wireless sensing, path planning, etc., and has made good progress. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a design method for turbine parameters with near- and long-term potential development, improves the quality and efficiency of Francis turbine selection calculations, shortens the design cycle of Francis turbine selection, improves the economy and safety of Francis turbines, and makes up for the shortcomings of human factors.

[0006] To achieve the above object, the present invention proposes a design method for turbine parameters for near- and long-term potential development, which is particularly characterized by comprising the following steps:

[0007] S1) obtaining basic parameters of the hydropower station, wherein the basic parameters of the hydropower station include maximum head, design head, weighted average head, minimum head, downstream tailwater level, model turbine runner diameter, maximum efficiency of turbine model, optimal unit speed of turbine model, rated operating efficiency of turbine model, total installed capacity, safety factor, generator efficiency, grid frequency, and efficiency correction factor;

[0008] S2) According to the head variation range of the hydropower station, the model of the Francis turbine is proposed, and then according to the total installed capacity of the hydropower station, the single unit capacity and the number of installed units of a certain type of Francis turbine to be installed are proposed, and the proposed single unit capacity and the number of installed units of a type of Francis turbine are used as a selection scheme, and a total of multiple selection schemes are obtained;

[0009] S3) determining the parameters of the Francis turbine prototype in each selection scheme, and calculating the parameter range according to multiple statistical formulas corresponding to each parameter;

[0010] The parameters include: specific speed, specific speed coefficient, unit speed, unit flow rate, device cavitation coefficient, model critical cavitation coefficient, suction height, runner diameter, maximum efficiency of turbine prototype, turbine prototype speed, generator synchronous speed;

[0011] S4) selecting the maximum efficiency and suction height of the turbine prototype from the parameters of the Francis turbine prototype as two evaluation indicators, assigning weights to the two evaluation indicators to obtain an objective function, and seeking a set of optimal turbine prototype parameters for each selection scheme based on the Mayfly Optimization Algorithm; obtaining a function fitness value from the objective function, and sorting the fitness values ​​corresponding to each selection scheme to select the best selection scheme; the objective function is expressed by the following formula:

[0012] F=aη pmax +bH s

[0013] In the formula,

[0014] F is the objective function, a is the weight factor,

[0015] b is the weight factor, and a+b=1,

[0016] η pmax The highest efficiency for the turbine prototype,

[0017] H s is the suction height;

[0018] S5) testing the best selected scheme within the working range of the Francis turbine, and deriving the best turbine prototype parameters corresponding to the best selected scheme.

[0019] Furthermore, in S1), the basic parameters of the hydropower station also include an assumed efficiency correction value, and the maximum efficiency of the turbine prototype is calculated based on the maximum efficiency of the turbine model, and then the efficiency correction value of the turbine prototype is obtained from the maximum efficiency of the turbine prototype and the maximum efficiency of the turbine model, and the efficiency correction value and the assumed efficiency correction value must be within a preset error range.

[0020] Furthermore, in S2), when formulating the selection plan, three or less plans are formulated at a time for comparison.

[0021] Furthermore, in S4), the steps of seeking a set of optimal turbine prototype parameters for each selection scheme based on the mayfly optimization algorithm are as follows:

[0022] S41) performs mayfly initialization to obtain the randomly generated mayfly position, i.e., the known parameters of the Francis turbine unit, and uses the decision variables to bring into the objective function to calculate the maximum efficiency η of the turbine prototype pmax and suction height H s ; According to the optimization target value, the parameters of each group of turbines are Pareto non-dominated sorted and the congestion degree is calculated, and the first-level Pareto non-dominated solution is stored in the Pareto optimal solution set;

[0023] S42) randomly selecting turbine parameters in the Pareto optimal solution set as the global optimal position of the mayfly group, and updating the position and speed of the mayfly according to the movement law of the mayfly;

[0024] S43) the mayflies in the Pareto optimal solution set mate to produce offspring female mayflies and male mayflies, perform mutation operation on the offspring mayflies, calculate optimization target values ​​using turbine parameters represented by positions of the offspring mayflies, perform Pareto non-dominated sorting on the male mayfly group and the female mayfly group after the offspring are added using the optimization target values, and calculate the crowding degree;

[0025] S44) sorting the male mayfly swarm and the female mayfly swarm according to the Pareto non-dominated level from high to low and the crowding degree from large to small, removing the mayfly individuals exceeding the population upper limit according to the sorting result, performing Pareto sorting on the obtained mayfly swarm and the individuals stored in the Pareto optimal solution set, and storing the obtained first-level Pareto non-dominated solution in the Pareto optimal solution set;

[0026] S45) returns to step S42) and iterates in turn until the upper limit of the number of iterations is reached, and the final Pareto optimal solution set is given, which can make the turbine prototype machine have the highest efficiency η pmax and suction height H s Francis turbine parameters that can reach the Pareto frontier.

[0027] Furthermore, in S5), the method for defining the working range of the mixed flow turbine is to draw the maximum unit flow straight line, the maximum unit speed straight line and the minimum unit speed straight line of the turbine prototype on the comprehensive characteristic curve of the turbine model, and the rectangular range surrounded by these three straight lines and the horizontal axis is the working range of the turbine.

[0028] Furthermore, in S5), according to the selected runner diameter D 1 and synchronous speed n m The maximum unit flow, maximum unit speed and minimum unit speed of the turbine prototype can be calculated using the following formula:

[0029]

[0030]

[0031]

[0032] In the formula,

[0033] Q 1 ' max is the maximum unit flow rate,

[0034] D 1 D is the diameter of the turbine runner 1 ,

[0035] N T is the unit capacity of the turbine,

[0036] H r To design the water head,

[0037] η is the rated operating efficiency of the turbine prototype, η=η m +Δη',

[0038] η m is the rated operating efficiency of the turbine model,

[0039] Δη' is the assumed efficiency correction value,

[0040] n m is the synchronous speed of the generator,

[0041] H min is the minimum water head of the hydropower station,

[0042] n 1 ' max is the maximum unit speed,

[0043] n' min is the minimum unit speed,

[0044] H maxIt is the maximum water head of the hydropower station.

[0045] The advantages of the present invention are:

[0046] 1. The present invention first inputs the basic parameters of the power station, and formulates the single-unit capacity and the number of installed units of each selection scheme. The system calculates the optional range of turbine parameters under each selection scheme according to a variety of empirical formulas. Then the user selects the evaluation index and assigns weights to each evaluation index to obtain the objective function. Under the specified objective function, the system will give a set of optimal turbine parameters for each scheme based on the mayfly optimization algorithm. Finally, the selection schemes are sorted according to their fitness values ​​and the best selection scheme is recommended.

[0047] 2. The present invention introduces the mayfly optimization algorithm by means of the powerful computing function of the computer when selecting the turbine, which makes up for the shortcomings of cumbersome work, low efficiency and poor result accuracy in manual selection, effectively improves the quality and efficiency of the Francis turbine selection calculation, shortens the design cycle of the Francis turbine selection, and improves the economy and safety of the turbine;

[0048] 3. The present invention establishes an open and sustainable system through a near- and long-term potential development turbine parameter design method, provides a technical platform for the selection of hydropower station turbines, and lays a technical support for the intelligent and digital selection of hydropower station turbines;

[0049] The present invention provides a design method for developing turbine parameters with near- and long-term potential, which makes up for the shortcomings of cumbersome work, low efficiency and poor result accuracy during manual selection, improves the accuracy and efficiency of Francis turbine selection calculation, shortens the design cycle of Francis turbine selection, and improves the economy and safety of Francis turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The overall framework diagram of the design method for developing turbine parameters for the near and long term potential of the present invention;

[0051] Figure 2 Flowchart of the float optimization algorithm;

[0052] Figure 3 A flow chart of optimizing turbine parameters based on the mayfly optimization algorithm in the present invention;

[0053] Figure 4 are the basic parameters of the hydropower station in this embodiment;

[0054] Figure 5 There are three selection schemes in this embodiment;

[0055] Figure 6 are the parameter ranges corresponding to the three selection schemes in this embodiment;

[0056] Figure 7 A set of optimal turbine prototype parameters corresponding to the three selection schemes after the evaluation indexes are weighted;

[0057] Figure 8 Sort the fitness values ​​of the three selection schemes in this embodiment;

[0058] Fig. 9 This is a schematic diagram of testing the working range of the best selection scheme in this embodiment. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0060] like Figure 1 As shown, the present invention provides a method for designing parameters of a hydraulic turbine for developing near- and long-term potential, comprising the following steps:

[0061] S1) Obtaining basic parameters of the hydropower station, which include the maximum water head H max , Design head H r , weighted average water head H av , minimum water head H min , downstream tailwater level height Δ w , Model turbine runner diameter D 1m 、The maximum efficiency of the turbine model η mmax , the optimal unit speed of the turbine model, the rated operating efficiency η of the turbine model m , total installed capacity P, safety factor K σ , generator efficiency η g , grid frequency f, efficiency correction factor K.

[0062] Preferably, the basic parameters of the hydropower station also include an assumed efficiency correction value Δη', which is calculated based on the maximum efficiency η of the turbine model. mmax Calculate the maximum efficiency η of the turbine prototype pmax , and then the maximum efficiency η of the turbine prototype pmax The maximum efficiency η of the turbine model mmax The efficiency correction value Δη of the turbine prototype is obtained, and the efficiency correction value Δη and the assumed efficiency correction value Δη' must be within a preset error range.

[0063] like Figure 4 As shown, the basic parameters of the hydropower station in this embodiment.

[0064] S2) According to the head variation range of the hydropower station, the model of the Francis turbine is proposed, and then according to the total installed capacity P of the hydropower station, the capacity N of a single unit of a certain type of Francis turbine to be installed is proposed Tand the number of installed units, the proposed single unit capacity P and the number of installed units of a type of Francis turbine are taken as a selection scheme, and a total of multiple selection schemes are obtained.

[0065] Preferably, when formulating selection plans, three or less plans are formulated at a time for comparison.

[0066] Francis turbines are widely used in water heads ranging from about 20 to 700 m. Figure 5 As shown in the figure, there are three selection schemes in this embodiment. The first selection scheme: single unit capacity N T The second option is: single unit capacity N T The third option is: the capacity of a single unit is N T It is 80MW, with 10 installed units.

[0067] S3) Determine the prototype parameters of the Francis turbine in each selection scheme, and calculate the parameter range according to multiple statistical formulas corresponding to each parameter.

[0068] The parameters include: specific speed n s , speed coefficient k, unit speed n 110 , unit flow Q 11 , device cavitation coefficient σ p , model critical cavitation coefficient σ c , Suction height H s , runner diameter D 1 , Maximum efficiency η of turbine prototype pmax , turbine prototype speed n r , generator synchronous speed n m .

[0069] Turbine specific speed n s The specific speed coefficient k is one of the important characteristic parameters that characterize the comprehensive technical and economic level of the turbine. Improving the specific speed can reduce the size of the unit, reduce the unit cost and plant construction costs, and has significant economic benefits. However, it is also restricted by factors such as efficiency, cavitation, silt wear and stability, and must be considered comprehensively based on the specific conditions of the hydropower station and the manufacturing level of the unit. According to the characteristic head of the hydropower station, the rated specific speed of the turbine n s , the calculation method of the specific speed coefficient k is shown in Table 1.

[0070] Table 1 Statistical formula calculation table of specific speed and specific speed coefficient

[0071]

[0072]

[0073] Turbine unit parameters (including unit speed n 110 and unit flow Q 11 ) and turbine specific speed n s The parameters, power station water head conditions and operation mode, turbine hydraulic design and safe and economical operation of the unit are closely related. Domestic and foreign manufacturers have proposed the unit parameters and specific speed n of the Francis turbine based on the summary of many Francis turbine performance parameters. s The statistical analysis relationship between the reasonable matching and the unit parameter statistical calculation formula are shown in Table 2.

[0074] Table 2 Statistical calculation formula of unit parameters of hydropower station

[0075]

[0076] Device cavitation coefficient σ p and the model critical cavitation coefficient σ c The size of is related to the installation height of the turbine, the operating stability of the turbine, and the service life of the turbine. The cavitation coefficient of the device σ p , model critical cavitation coefficient σ c Specific speed to turbine n s Generally, with the specific speed n s The cavitation coefficient will also increase with the increase of the pressure. The statistical formula of the cavitation coefficient of some domestic and foreign turbines is shown in Table 3.

[0077] Table 3 Statistical formulas of cavitation coefficient of domestic and foreign turbines

[0078]

[0079]

[0080] According to the cavitation coefficient σ of the device obtained above p , the suction height can be calculated. Suction height H s The calculation formula is:

[0081]

[0082] In the formula,

[0083] H s is the suction height, in m,

[0084] Δ w is the downstream tailwater level height, in m,

[0085] K σ is the safety factor,

[0086] σ p is the cavitation coefficient,

[0087] Hr is the design head, in m.

[0088] The unit capacity of the hydro-generator set is N T Refers to the output power of the unit, which is equal to the rated power N of the generator g If we use η g represents the generator efficiency, then the unit capacity of the turbine is N T =N g / η g . Because N T =9.81Q r H r η, and therefore So the turbine runner diameter is D 1 It can be calculated as follows:

[0089]

[0090] In the formula,

[0091] D 1 D is the diameter of the turbine runner 1 ,

[0092] N T is the unit capacity of the turbine,

[0093] Q 11 is the unit flow rate,

[0094] H r To design the water head,

[0095] η is the rated operating efficiency of the turbine prototype, η=η m +Δη',

[0096] η m is the rated operating efficiency of the turbine model,

[0097] Δη' is the assumed efficiency correction value.

[0098] The efficiency of the turbine directly affects the power generation efficiency of the power station. At present, the efficiency of the turbine model has a trend of further improvement. According to the maximum efficiency η of the turbine model mmax , the maximum efficiency η of the prototype can be calculated pmax The maximum efficiency of the Francis turbine prototype is η pmax The calculation formula of the efficiency correction value Δη is as follows:

[0099]

[0100] Δη=K(η pmax -η mmax )

[0101] In the formula,

[0102] η pmax The highest efficiency for the turbine prototype,

[0103] η mmax The highest efficiency of the turbine model.

[0104] D 1 D is the diameter of the turbine runner 1 , unit m,

[0105] D 1m is the runner diameter of the model turbine, in m,

[0106] K is the efficiency correction coefficient, K=0.5~1.0.

[0107] This step needs to determine whether the efficiency correction value Δη is equal to the assumed efficiency correction value Δη' or whether it is within a preset error range. If the conditions are not met, the efficiency correction value Δη needs to be recalculated.

[0108] For the turbine prototype speed n r The determination of depends on the turbine prototype speed n r Is correction needed? It needs to be corrected, the speed of the turbine prototype n r By calculating the following formula,

[0109]

[0110] n 11r =n 110 +Δn'

[0111]

[0112] In the formula,

[0113] n r is the speed of the turbine prototype,

[0114] n 11r is the corrected unit speed,

[0115] H av is the weighted mean water head,

[0116] n 110 is the unit speed,

[0117] Δn' is the unit speed correction value,

[0118] η pmax The highest efficiency for the turbine prototype,

[0119] η mmaxThe turbine model has the highest efficiency.

[0120] If the turbine prototype speed n r No correction is required, then the prototype speed of the turbine is n r By calculating the following formula,

[0121]

[0122] In the formula,

[0123] n r is the speed of the turbine prototype,

[0124] n 110 is the unit speed,

[0125] H av is the weighted mean water head,

[0126] D 1 D is the diameter of the turbine runner 1 .

[0127] Generator synchronous speed n m Should be close to and slightly larger than the turbine prototype speed n r , as shown in Table 4, according to the turbine prototype speed n r Determine the synchronous speed n of the generator m , and calculate the corresponding number of magnetic pole pairs. The calculation formula is as follows:

[0128]

[0129] In the formula,

[0130] n m is the synchronous speed of the generator,

[0131] f is the grid frequency,

[0132] p is the number of motor pole pairs.

[0133] Table 4 Generator synchronous speed n m Selection

[0134] Number of magnetic pole pairs 3 4 5 6 7 8 9 Synchronous speed (r / min) 1000 750 600 500 428.6 375 333.3 Number of magnetic pole pairs 10 12 14 16 18 20 22 Synchronous speed (r / min) 300 250 214.3 187.5 166.7 150 136.4 Number of magnetic pole pairs 24 26 28 30 32 34 36 Synchronous speed (r / min) 125 115.4 107.1 100 93.8 88.2 83.3 Number of magnetic pole pairs 38 40 42 44 46 48 50 Synchronous speed (r / min) 79 75 71.4 68.2 65.2 62.5 60

[0135] like Figure 6 As shown, the parameter ranges corresponding to the three selection schemes in this embodiment are shown.

[0136] S4) Select the maximum efficiency and suction height of the turbine prototype from the parameters of the Francis turbine prototype as two evaluation indicators, obtain the objective function after weighting the two evaluation indicators, and seek a set of optimal turbine prototype parameters for each selection scheme based on the mayfly optimization algorithm; and obtain the function fitness value from the objective function, and sort the fitness values ​​corresponding to each selection scheme to select the best selection scheme.

[0137] like Figure 2 As shown in Figure 2, the steps of the Mayfly optimization algorithm are as follows:

[0138] Step 1: Define the objective function, set the mayfly population size, problem dimension, maximum number of iterations, social positive attraction coefficient, visibility coefficient, and random walk coefficient;

[0139] Step 2: Initialize the position vector and velocity vector of the mayfly individual, evaluate the quality of the mayfly individual according to the objective function and update the global optimal mayfly individual;

[0140] Step 3: Start iteration, update the velocity vector and position vector of the male mayfly, evaluate the quality of individual mayflies according to the objective function; update the global optimal mayfly individual and the personal historical optimal position vector, and the global optimal mayfly individual performs a unique up-and-down wedding dance;

[0141] Step 4: Update the velocity vector and position vector of the female mayfly, and evaluate the quality of the individual mayfly according to the objective function;

[0142] Step 5: Sort the male and female populations according to the fitness values ​​obtained by the objective function, mate and produce offspring in the order of the best male and the best female, the second-best male and the second-best female, and so on, and update the global optimal mayfly individual;

[0143] Step 6: Sort by fitness value and select male and female mayflies for the next generation;

[0144] Step 7: Determine whether the number of iterations has reached the maximum value. If not, continue to iterate in step 3; if so, terminate and return the position vector and fitness value of the global optimal mayfly individual.

[0145] In the present invention, the objective function is expressed by the following formula:

[0146] F=aη pmax +bH s

[0147] In the formula,

[0148] F is the objective function,

[0149] a is the weight factor,

[0150] b is the weight factor, and a+b=1,

[0151] η pmax The highest efficiency for the turbine prototype,

[0152] H s For the suction height.

[0153] Specifically, Figure 3 As shown in the figure, the steps of seeking a set of optimal turbine prototype parameters for each selection scheme based on the mayfly optimization algorithm are as follows:

[0154] S41) performs mayfly initialization to obtain the randomly generated mayfly position, i.e., the known parameters of the Francis turbine unit, and uses the decision variables to bring into the objective function to calculate the maximum efficiency η of the turbine prototype pmax and suction height H s ; According to the optimization target value, the parameters of each group of turbines are Pareto non-dominated sorted and the congestion degree is calculated, and the first-level Pareto non-dominated solution is stored in the Pareto optimal solution set;

[0155] S42) randomly selecting turbine parameters in the Pareto optimal solution set as the global optimal position of the mayfly group, and updating the position and speed of the mayfly according to the movement law of the mayfly;

[0156] S43) the mayflies in the Pareto optimal solution set mate to produce offspring female mayflies and male mayflies, perform mutation operation on the offspring mayflies, calculate optimization target values ​​using turbine parameters represented by positions of the offspring mayflies, perform Pareto non-dominated sorting on the male mayfly group and the female mayfly group after the offspring are added using the optimization target values, and calculate the crowding degree;

[0157] S44) sorting the male mayfly swarm and the female mayfly swarm according to the Pareto non-dominated level from high to low and the crowding degree from large to small, removing the mayfly individuals exceeding the population upper limit according to the sorting result, performing Pareto sorting on the obtained mayfly swarm and the individuals stored in the Pareto optimal solution set, and storing the obtained first-level Pareto non-dominated solution in the Pareto optimal solution set;

[0158] S45) returns to step S42) and iterates in turn until the upper limit of the number of iterations is reached, and the final Pareto optimal solution set is given, which can make the turbine prototype machine have the highest efficiency η pmax and suction height H s Francis turbine parameters that can reach the Pareto frontier.

[0159] In this embodiment, the maximum efficiency weight of the turbine prototype is set to 0.7, the suction height weight is set to 0.3, and the three selection schemes correspond to a set of optimal turbine prototype parameters as follows: Figure 7shown.

[0160] like Figure 8 As shown in the figure, the fitness values ​​of the three selection schemes in this embodiment are ranked. Among them, the fitness value of the first scheme is 0.2769, the fitness value of the second scheme is -0.8439, and the fitness value of the third scheme is -1.7190. That is, the first scheme (single unit capacity N T 200MW, 4 installed units) as the best selection solution.

[0161] S5) testing the best selected scheme within the working range of the Francis turbine, and deriving the best turbine prototype parameters corresponding to the best selected scheme.

[0162] Specifically, the method for defining the working range of the Francis turbine is to plot the maximum unit flow Q of the turbine prototype on the comprehensive characteristic curve of the turbine model. 1 ' max Linear, maximum unit speed n 1 ' max Linear and minimum unit speed n' min Straight lines, the rectangular range enclosed by these three straight lines and the horizontal axis is the working range of the turbine.

[0163] According to the selected runner diameter D 1 and synchronous speed n m The maximum unit flow Q of the turbine prototype can be calculated 1 ' max , maximum unit speed n 1 ' max and minimum unit speed n' min The calculation formula is as follows:

[0164]

[0165]

[0166]

[0167] In the formula,

[0168] Q 1 ' max is the maximum unit flow rate,

[0169] D 1 D is the diameter of the turbine runner 1 ,

[0170] N T is the unit capacity of the turbine,

[0171] H r To design the water head,

[0172] η is the rated operating efficiency of the turbine prototype, η=η m +Δη',

[0173] η m is the rated operating efficiency of the turbine model,

[0174] Δη' is the assumed efficiency correction value,

[0175] n m is the synchronous speed of the generator,

[0176] H min is the minimum water head of the hydropower station,

[0177] n 1 ' max is the maximum unit speed,

[0178] n' min is the minimum unit speed,

[0179] H max It is the maximum water head of the hydropower station.

[0180] Fig. 9 The figure is a schematic diagram of the working range test of the first selection scheme in this embodiment. The selected runner diameter D according to the first selection scheme 1 Synchronous speed of the generator n m Find the maximum unit flow rate Q of the turbine prototype 1 ' max , maximum unit speed n 1 ' max and minimum unit speed n' min The rectangular range enclosed by these three straight lines and the horizontal axis is located on the comprehensive characteristic curve of the turbine model. Therefore, the optimal turbine prototype parameters corresponding to the first selection scheme are derived as the turbine parameters of the hydropower station in this embodiment.

[0181] The present invention provides a design method for developing turbine parameters with near- and long-term potential, which makes up for the shortcomings of cumbersome work, low efficiency and poor result accuracy during manual selection, improves the accuracy and efficiency of Francis turbine selection calculation, shortens the design cycle of Francis turbine selection, and improves the economy and safety of Francis turbines.

[0182] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A design method for turbine parameters for near- and long-term potential development, characterized in that: The steps include: S1) obtaining basic parameters of the hydropower station, wherein the basic parameters of the hydropower station include maximum head, design head, weighted average head, minimum head, downstream tailwater level, model turbine runner diameter, maximum efficiency of turbine model, optimal unit speed of turbine model, rated operating efficiency of turbine model, total installed capacity, safety factor, generator efficiency, grid frequency, and efficiency correction factor; S2) According to the head variation range of the hydropower station, the model of the Francis turbine is proposed, and then according to the total installed capacity of the hydropower station, the single unit capacity and the number of installed units of a certain type of Francis turbine to be installed are proposed, and the proposed single unit capacity and the number of installed units of a type of Francis turbine are used as a selection scheme, and a total of multiple selection schemes are obtained; S3) determining the parameters of the Francis turbine prototype in each selection scheme, and calculating the parameter range according to multiple statistical formulas corresponding to each parameter; The parameters include: specific speed, specific speed coefficient, unit speed, unit flow rate, device cavitation coefficient, model critical cavitation coefficient, suction height, runner diameter, maximum efficiency of turbine prototype, turbine prototype speed, generator synchronous speed; S4) selecting the maximum efficiency and suction height of the turbine prototype from the parameters of the Francis turbine prototype as two evaluation indicators, weighting the two evaluation indicators to obtain an objective function, and seeking a set of optimal turbine prototype parameters for each selection scheme based on the Mayfly optimization algorithm; and obtaining a function fitness value from the objective function, and sorting the fitness values ​​corresponding to each selection scheme to select the best selection scheme; The objective function is expressed by the following formula F=aη pmax +bH s In the formula, F is the objective function, a is the weight factor, b is the weight factor, and a+b=1, η pmax The highest efficiency for the turbine prototype, H s is the suction height; S5) testing the best selected scheme within the working range of the Francis turbine, and deriving the best turbine prototype parameters corresponding to the best selected scheme.

2. The method for designing parameters of a hydraulic turbine for developing near- and long-term potential according to claim 1 is characterized by: S1), the basic parameters of the hydropower station also include an assumed efficiency correction value, the maximum efficiency of the turbine prototype is calculated according to the maximum efficiency of the turbine model, and then the efficiency correction value of the turbine prototype is obtained from the maximum efficiency of the turbine prototype and the maximum efficiency of the turbine model, and the efficiency correction value and the assumed efficiency correction value must be within a preset error range.

3. The design method of turbine parameters for developing near- and long-term potential according to claim 1 is characterized by: In S2), when formulating the selection plan, three or less plans are formulated at a time for comparison.

4. The method for designing parameters of a hydraulic turbine for developing near- and long-term potential according to claim 3 is characterized by: In S4), the steps of seeking a set of optimal turbine prototype parameters for each selection scheme based on the mayfly optimization algorithm are as follows: S41) performs mayfly initialization to obtain the randomly generated mayfly position, i.e., the known parameters of the Francis turbine unit, and uses the decision variables to bring into the objective function to calculate the maximum efficiency η of the turbine prototype pmax and suction height H s ; According to the optimization target value, the parameters of each group of turbines are Pareto non-dominated sorted and the congestion degree is calculated, and the first-level Pareto non-dominated solution is stored in the Pareto optimal solution set; S42) randomly selecting turbine parameters in the Pareto optimal solution set as the global optimal position of the mayfly group, and updating the position and speed of the mayfly according to the movement law of the mayfly; S43) the mayflies in the Pareto optimal solution set mate to produce offspring female mayflies and male mayflies, perform mutation operation on the offspring mayflies, calculate optimization target values ​​using turbine parameters represented by positions of the offspring mayflies, perform Pareto non-dominated sorting on the male mayfly group and the female mayfly group after the offspring are added using the optimization target values, and calculate the crowding degree; S44) sorting the male mayfly swarm and the female mayfly swarm according to the Pareto non-dominated level from high to low and the crowding degree from large to small, removing the mayfly individuals exceeding the population upper limit according to the sorting result, performing Pareto sorting on the obtained mayfly swarm and the individuals stored in the Pareto optimal solution set, and storing the obtained first-level Pareto non-dominated solution in the Pareto optimal solution set; S45) returns to step S42) and iterates in turn until the upper limit of the number of iterations is reached, and the final Pareto optimal solution set is given, which can make the turbine prototype machine have the highest efficiency η pmax and suction height H s Francis turbine parameters that can reach the Pareto frontier.

5. The method for designing parameters of a hydraulic turbine for developing near- and long-term potential according to claim 4 is characterized by: S5), the method for defining the working range of the mixed flow turbine is to draw the maximum unit flow straight line, the maximum unit speed straight line and the minimum unit speed straight line of the turbine prototype on the comprehensive characteristic curve of the turbine model, and the rectangular range surrounded by these three straight lines and the horizontal axis is the working range of the turbine.

6. The method for designing parameters of a hydraulic turbine for developing near- and long-term potential according to claim 5 is characterized by: In S5), according to the selected wheel diameter D1 and synchronous speed n m The maximum unit flow, maximum unit speed and minimum unit speed of the turbine prototype can be calculated using the following formula: In the formula, Q1' max is the maximum unit flow rate, D1 is the diameter of the turbine runner, N T is the unit capacity of the turbine, H r To design the water head, η is the rated operating efficiency of the turbine prototype, η=η m +Δη',η m is the rated operating efficiency of the turbine model, Δη' is the assumed efficiency correction value, n m is the synchronous speed of the generator, H min is the minimum water head of the hydropower station, n1' max is the maximum unit speed, n' min is the minimum unit speed, H max It is the maximum water head of the hydropower station.

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