Multi-objective optimization method and apparatus for determining the coordination relationship of bulb turbine units

Abstract

This invention discloses a multi-objective optimization method and apparatus for determining the coordination relationship of a bulb turbine generator unit. The method includes: acquiring field test data; analyzing the field test data to obtain the relationship between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane opening degrees; based on the comparison results of the optimized head and the critical discharge head, and the relationship between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane opening degrees, with the highest turbine efficiency or maximum turbine output as the first objective and optimal unit stability as the second objective, a multi-objective optimization algorithm is used to obtain the turbine coordination relationship curve. Through this method, this invention can accurately obtain the turbine coordination relationship curve, better adapting to the operating characteristics of bulb turbine generator units and improving the overall power generation efficiency of hydropower stations.

Description

Technical Field

[0001] This invention relates to the field of hydro-generator technology, specifically to a method and apparatus for determining the cooperative relationship of a bulb turbine generator set through multi-objective optimization. Background Technology

[0002] Due to their superior hydraulic performance and economic efficiency, bulb turbines have become one of the main types of turbines for developing low-head hydropower resources. As bulb turbines are dual-regulating units, both the guide vane opening and blade angle can be adjusted during operation, maintaining a certain coordination relationship between them. This results in excellent power output, efficiency, and stability characteristics. When the turbine leaves the factory, the manufacturer provides the optimal coordination relationship between the guide vanes and blades based on theoretical research and model tests. However, due to factors such as unit installation and commissioning, this coordination relationship is not necessarily the optimal one for the actual unit. It needs to be optimized and determined through on-site testing to obtain the optimal coordination relationship suitable for the actual unit.

[0003] In the optimization test of bulb turbine co-operation, it is necessary to conduct unit output tests, turbine relative efficiency tests, and stability tests. The optimal co-operation relationship is determined by analyzing the test results. Currently, bulb turbine co-operation optimization is mainly based on manual enumeration and is a single-objective optimization, primarily using the highest turbine efficiency as the objective function. However, in actual unit operation, there are often contradictions between the objective functions of turbine efficiency, turbine output, and unit stability. Furthermore, bulb turbine units are often run-of-river power plants, which frequently open their gates to release water under low head conditions, resulting in significant water wastage. In such cases, using the highest turbine efficiency as the objective function to determine the co-operation relationship is clearly detrimental to reducing water wastage and is highly uneconomical. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a method and apparatus for determining the cooperative relationship of a bulb turbine generator set with multi-objective optimization, which overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a multi-objective optimization method for determining the coordination relationship of a bulb turbine generator unit is provided. The method includes: acquiring field test data, including: optimized head, turbine efficiency, turbine output, and unit stability data under different guide vane openings and blade openings; analyzing the field test data to obtain the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings; and, based on the comparison results between the optimized head and the critical discharge head, and the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, using a multi-objective optimization algorithm to obtain the turbine coordination relationship curve with the highest turbine efficiency or the largest turbine output as the first objective and the optimal unit stability as the second objective.

[0006] In one optional approach, the analysis of the field test data to obtain the relationship between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings includes: grouping the field test data according to the guide vane opening, with different groups corresponding to different guide vane openings; generating a blade opening matrix based on the blade opening in each group; generating a turbine efficiency matrix, a turbine output matrix, and a water guide bearing vibration matrix based on the turbine efficiency, turbine output, and water guide bearing vibration value (as unit stability data) under different guide vane openings and blade openings; and obtaining the relationship between the blade opening and the turbine efficiency, turbine output, and water guide bearing vibration value based on the blade opening matrix, the turbine efficiency matrix, the turbine output matrix, and the water guide bearing vibration matrix.

[0007] In one optional approach, obtaining the relationship between the blade opening and the turbine efficiency, turbine output, and water guide bearing vibration value based on the blade opening matrix, the turbine efficiency matrix, the turbine output matrix, and the water guide bearing vibration matrix includes: fitting a quadratic function relationship between the blade opening and the turbine efficiency using the least squares method based on the blade opening matrix and the turbine efficiency matrix; fitting a linear function relationship between the blade opening and the turbine output based on the blade opening matrix and the turbine output matrix; and fitting a linear function relationship between the blade opening and the water guide bearing vibration based on the blade opening matrix and the water guide bearing vibration matrix.

[0008] In one optional approach, the step of obtaining the turbine coordination curve using a multi-objective optimization algorithm, with the first objective being the highest turbine efficiency or the highest turbine output, and the second objective being optimal unit stability, includes: obtaining a first objective function based on the relationship between blade opening and turbine efficiency or turbine output under different guide vane openings; obtaining a second objective function based on the relationship between blade opening and unit stability under different guide vane openings; and using a multi-objective genetic algorithm based on a non-dominated sorting algorithm to obtain the blade opening that minimizes both the first and second objective functions, thus obtaining the turbine coordination curve.

[0009] Optionally, the step of obtaining the blade opening that minimizes both the first and second objective functions using a multi-objective genetic algorithm based on a non-dominated sorting algorithm, and thus obtaining the turbine coordination curve, includes: combining the first and second objective functions to obtain the fitness function of an individual, and performing non-dominated quicksorting and crowding calculation to obtain an initial population; using mutation and crossover operations to generate a new generation population; merging the parent and offspring populations, recalculating the objective function value, and then using non-dominated quicksorting and crowding calculation to obtain the next generation of individuals; determining whether the maximum number of generations has been reached, and if so, outputting the optimal solution set.

[0010] Optionally, the step of obtaining the turbine coordination relationship curve using a multi-objective optimization algorithm, based on the comparison results between the optimized head and the critical discharge head, and the relationship between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, with the highest turbine efficiency or maximum turbine output as the first objective and optimal unit stability as the second objective, includes: if the optimized head is greater than the critical discharge head, then based on the relationship between the blade opening and turbine efficiency and unit stability under different guide vane openings, the algorithm adopts the highest turbine efficiency as the first objective, and the optimal unit stability as the second objective. A multi-objective optimization algorithm with optimal unit stability as the second objective is used to obtain the blade opening that satisfies both the first and second objectives under different guide vane openings, thus obtaining the turbine coordination curve. If the optimized head is less than the critical discharge head, then based on the relationship between the blade opening and turbine output and unit stability under different guide vane openings, a multi-objective optimization algorithm with maximizing turbine output as the first objective and optimal unit stability as the second objective is used to obtain the blade opening that satisfies both the first and second objectives under different guide vane openings, thus obtaining the turbine coordination curve.

[0011] Optionally, the method for determining the cooperative relationship of the bulb turbine unit under multi-objective optimization further includes: repeatedly acquiring the turbine cooperative relationship curves under different cooperative optimization heads to obtain the turbine cooperative relationship curves under full head.

[0012] Based on the same inventive concept, a multi-objective optimization device for determining the cooperative relationship of a bulb turbine generator unit is provided, comprising: a data acquisition unit for acquiring field test data, including: cooperative optimization head, turbine efficiency, turbine output, and unit stability data under different guide vane openings and blade openings; a data analysis unit for analyzing the field test data to obtain the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings; and a cooperative relationship acquisition unit for obtaining the turbine cooperative relationship curve using a multi-objective optimization algorithm, based on the comparison results between the cooperative optimization head and the critical discharge head, and the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, with the highest turbine efficiency or maximum turbine output as the first objective and optimal unit stability as the second objective.

[0013] Based on the same inventive concept, this invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned method.

[0014] Based on the same inventive concept, embodiments of the present invention also propose a computer storage medium storing at least one executable instruction that causes a processor to execute the aforementioned method.

[0015] This invention, through the acquisition of field test data, including data on optimized head, turbine efficiency, turbine output, and unit stability under different guide vane and blade openings, analyzes the field test data to obtain the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings. Based on the comparison results between the optimized head and the critical discharge head, and the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, a multi-objective optimization algorithm is used to obtain the turbine coordination curve, with the highest turbine efficiency or maximum turbine output as the first objective and optimal unit stability as the second objective. This approach accurately obtains the turbine coordination curve, better adapts to the operating characteristics of bulb turbine units, improves turbine operating efficiency and water utilization rate at high heads, and increases turbine power generation and reduces water discharge at low heads, thereby improving the overall power generation efficiency of the hydropower station.

[0016] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0018] Figure 1 A flowchart illustrating the method for determining the cooperative relationship of a bulb turbine generator unit with multi-objective optimization provided in an embodiment of the present invention is shown.

[0019] Figure 2 This diagram illustrates the structure of the multi-objective optimization bulb turbine co-current relationship determination device provided in an embodiment of the present invention.

[0020] Figure 3 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0021] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0022] Figure 1 A flowchart illustrating the multi-objective optimization method for determining the coordination relationship of bulb turbine units provided in an embodiment of the present invention is shown. Figure 1 As shown, the multi-objective optimization method for determining the cooperative relationship of bulb turbine units includes:

[0023] Step S11: Obtain field test data, which includes: optimized head, turbine efficiency, turbine output and unit stability data under different guide vane opening and blade opening.

[0024] In this embodiment of the invention, field test data is acquired to obtain data on turbine efficiency, turbine output, and unit stability under coordinating and optimizing the head, different guide vane openings, and different blade openings. Specifically, the coordinating and optimizing head is fixed, and data on the relative efficiency, turbine output, and unit stability of the turbine under different guide vane openings and different blade openings are collected.

[0025] Step S12: Analyze the field test data to obtain the relationship between the blade opening and turbine efficiency, turbine output and unit stability under different guide vane opening degrees.

[0026] In this embodiment of the invention, the vibration value A of the turbine's water guide bearing is selected as a representative measurement point for the unit's stability. In step S12, the field test data is first grouped according to the guide vane opening degree, with different groups corresponding to different guide vane opening degrees. That is, the data is grouped according to different guide vane opening degrees, with each group having a guide vane opening degree of y. i There are n groups (i = 1, 2, 3, ..., n). Each group of guide vane openings has m groups of blade openings. (i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m). Then, a blade opening matrix is ​​generated based on the blade opening in each group.

[0027]

[0028] Then, based on the turbine efficiency, turbine output, and water guide bearing vibration values ​​(used as unit stability data) under different guide vane opening and blade opening, the turbine efficiency matrix η, turbine output matrix p, and water guide bearing vibration matrix A are generated respectively:

[0029]

[0030]

[0031]

[0032] Finally, the relationships between the blade opening matrix, turbine efficiency matrix, turbine output matrix, and water guide bearing vibration matrix are obtained. Optionally, a quadratic function relationship between the blade opening and turbine efficiency is fitted using the least squares method based on the blade opening matrix and turbine efficiency matrix; a linear function relationship between the blade opening and turbine output is fitted using the blade opening matrix and turbine output matrix; and a linear function relationship between the blade opening and water guide bearing vibration is fitted using the blade opening matrix and water guide bearing vibration matrix. That is, a quadratic function is used. For blade opening With turbine efficiency (η) i Fitting is performed using a linear function. For blade opening With the active power of the unit (p i Fitting is performed using a linear function. For blade opening Vibration of water-guided bearings (A) i The following uses the least squares method to fit the relationship between the blade opening and the turbine efficiency, which is a quadratic function. Let's take the fitting of ) as an example to illustrate:

[0033] Construct the quadratic error summation function W i As shown in the following formula:

[0034]

[0035] in, Given points i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., m, according to the principle of least squares, the system of equations is obtained by minimizing the sum of quadratic errors:

[0036]

[0037] Solving the above system of equations yields a i b i c i We obtain the fitting function, which is the relationship between the blade opening and the turbine efficiency.

[0038] The linear relationship between blade opening and turbine output, as well as the linear relationship between blade opening and water bearing vibration, can also be obtained by linear fitting using the least squares method.

[0039] Step S13: Based on the comparison results of the optimized head and the critical discharge head, and the relationship between the blade opening and turbine efficiency, turbine output and unit stability under different guide vane openings, the multi-objective optimization algorithm is used to obtain the turbine cooperative relationship curve with the highest turbine efficiency or the largest turbine output as the first objective and the optimal unit stability as the second objective.

[0040] In this embodiment of the invention, before step S13, the water head during flood discharge and water release at the power station over many years is statistically analyzed to find the minimum water head H. q This is taken as the critical head for water discharge.

[0041] In step S13, optionally, a first objective function is obtained based on the relationship between blade opening and turbine efficiency or turbine output under different guide vane openings; a second objective function is obtained based on the relationship between blade opening and unit stability under different guide vane openings; a multi-objective genetic algorithm based on non-dominated sorting is used to obtain the blade opening that minimizes both the first and second objective functions, resulting in the turbine coordination curve. Specifically, the fitness function of an individual is obtained by combining the first and second objective functions, and non-dominated quicksort and crowding calculation are performed to obtain an initial population; mutation and crossover operations are used to generate a new generation population; the parent and offspring populations are merged, and the objective function value is recalculated, and the next generation of individuals is obtained through non-dominated quicksort and crowding calculation; it is determined whether the maximum number of generations has been reached, and if so, the optimal solution set is output.

[0042] In this embodiment of the invention, if the optimized head is greater than the critical discharge head, a multi-objective optimization algorithm is used, with the highest turbine efficiency as the first objective and optimal unit stability as the second objective, to obtain the turbine cooperative relationship curve by considering the relationship between the blade opening and turbine efficiency and unit stability under different guide vane openings.

[0043] When the optimal head H is greater than the minimum discard head H q The turbine coordination curve was obtained using a multi-objective optimization algorithm that maximizes turbine efficiency and optimizes unit stability. The main steps are as follows:

[0044] 1) Algorithm initialization settings: Set the parameters for the multi-objective genetic algorithm, including: population size N, number of iterations Gnum, crossover algorithm distribution index δ1, mutation algorithm distribution index δ2, and number of decision variables N. j Set the objective function dimension Vm to 1, and the upper and lower bounds of the decision variables to 2. Based on experience, this value is set as the initial blade opening. ±10%.

[0045] Setting a multi-objective objective function: The first objective function for turbine efficiency is set as follows:

[0046]

[0047]

[0048] The second objective function for the operational stability of the water turbine is defined as follows:

[0049]

[0050]

[0051] 2) Generate initial individual: using the current blade opening. As an initial individual, the objective function is calculated.

[0052] 3) A multi-objective genetic algorithm based on non-dominated sorting is used to solve the problem: Since there is a conflict between maximizing turbine efficiency and optimizing unit stability, a single-objective optimization algorithm cannot reconcile the contradiction between these two objectives. Therefore, this embodiment introduces a multi-objective genetic algorithm (NSGA-Ⅱ) based on a fast non-dominated sorting algorithm to optimize the turbine efficiency index while considering unit stability. The algorithm optimization process is as follows:

[0053] Step 11: Algorithm Initialization. Set the algorithm parameters including population size N, total number of iterations T, number of objective functions M, decision vector dimension D, and the upper and lower bounds of the decision variables.

[0054] Step 12: Randomly initialize individual locations.

[0055] Step 13: Calculate the first objective function Obj1 for turbine efficiency and the second objective function Obj2 for turbine operation stability based on the individual positions.

[0056] Step 14: Calculate the fitness function value of individuals, perform non-dominated quicksort and crowding calculation to obtain the initial population.

[0057] The fitness function is the objective function, i.e.:

[0058]

[0059] According to the definition of non-dominated, for a multi-objective optimization problem, F(x) = (F1(x), F2(x), ..., F... m (x)) T m is the number of individuals in the objective function, and x is the number of individuals in the population. i ,x j If for all q = 1, 2, ..., m, there exists F q (xi )≤F q (x j If X is the same as X, then X is called X. i Non-dominant to X j , that is, X i Superior to X j If for all q = 1, 2, ..., m, there exists F q (x i )>F q (x j If X is the same as X, then X is called X. i Dominated by X j .

[0060] The non-dominated quicksort method is as follows:

[0061] (1)Suppose i=1;

[0062] (2) For all j = 1, 2, ..., N and j ≠ i, compare individual X according to the above definition. i With individual X j The relationship of dominance and non-domination between them;

[0063] (3) If there is no individual X j Superior to X i Then X i Marked as a non-dominant individual;

[0064] (4) Let i = i + 1, go to step (2) until all non-dominated individuals are found;

[0065] (5) The set of non-dominated individuals obtained through the above steps is the first level of non-dominated layer of the population. Then, these marked non-dominated individuals are ignored (i.e., these individuals are not compared in the next round), and steps (1)-(4) are followed again to obtain the second level of non-dominated layer. This process continues until the entire population is stratified.

[0066] (6) For each individual X i Let there be two parameters n. i and S i n i To dominate individual X in the population i The number of individual solutions, S i For individual X i The set of individual solutions governed.

[0067] (7) Find all n in the population i Individuals with a value of 0 are stored in set Z1.

[0068] (8) For each individual X in the current set Z1 j Examine the set of individuals S it governs jSet S j Each individual X in k n k Subtract 1, that is, the dominant individual X k The number of solutions is reduced by 1 (because the dominant individual X) k Individual X j If n has already been stored in the current set Z1), then k If -1 = 0, then individual X will be... k Store it in another set H.

[0069] (9) Take Z1 as the first-level non-dominated individual set. The individuals in Z1 are optimal, as they only dominate individuals and are not dominated by any other individuals. Assign the same non-dominated order i to the individuals in this set. rank =1, and then continue to perform the above classification operation on H and assign the corresponding non-dominated order until all individuals are classified.

[0070] In this embodiment of the invention, the calculation of the crowding coefficient requires sorting the population according to the ascending order of the objective function values ​​(i.e., if the first level of non-dominated layer is obtained, it is sorted according to the objective function values, and then the crowding coefficient is calculated). Therefore, for each objective function, the boundary solution (the solution with the maximum and minimum values) is assigned the value of infinite distance. All other intermediate solutions are assigned the value equal to the normalized absolute difference between the function values ​​of two adjacent solutions. The calculation method is the same for other objective functions. All crowding coefficient values ​​are calculated by summing the distance values ​​of each individual for each objective, and each objective function is normalized before calculating the crowding coefficient. The steps for calculating the crowding coefficient for each layer of the population are as follows:

[0071] (1) Set the congestion level of each point to 0;

[0072] (2) For each objective function F i (x), based on this objective function, the population is sorted, and the crowding degree between the two individuals at the boundary is infinite, i.e., o d =l d =∞;

[0073] (3) Calculate the crowding level for other individuals:

[0074]

[0075] Among them, i d Represents individual X i Crowding at the point Represents individual X i+1 The j-th objective function value at point , Represents individual X i-1 The objective function value of the j-th point.

[0076] After the preceding fast non-dominated sorting and crowding calculation, each individual X in the population... i Both have two attributes: the non-dominated order i determined by the non-dominated sort. rank and crowding level i d .

[0077] Based on these two attributes, a crowding comparison operator can be defined: Individual X i With another individual X j For comparison, if any one of the following conditions is true, then individual X... i Victory.

[0078] ①If individual X i The non-dominated layer is superior to individual X j The non-dominated layer, i rank <j rank

[0079] ②If they have the same rank, and individual X i Compared to individual X j There is a larger crowding distance, i rank =j rank , and i d >j d .

[0080] Step 15: Merge the parent and offspring populations, recalculate the objective function value according to steps 12-14, and then calculate the next generation of individuals by sorting and crowding.

[0081] Step 16: Generate a new generation population using mutation and crossover operations. First, randomly pair up individuals in the population; then, randomly set the crossover points; finally, exchange some genes between the paired chromosomes. Mutation operation: Use basic position mutation to perform the mutation operation. The specific process is as follows: first, determine the gene mutation location for each individual; then, invert the original gene value at the mutation point according to a certain probability.

[0082] Step 17: Determine if the maximum number of generations has been reached. If it has, the algorithm terminates and the optimal solution set is output; otherwise, proceed to step 15.

[0083] When the optimal head H is less than the minimum discard head H q The turbine coordination curve was obtained using a multi-objective optimization algorithm that maximizes turbine output and optimizes unit stability. The main steps are as follows:

[0084] 1) Algorithm initialization settings: Set the parameters for the multi-objective genetic algorithm, including: population size N, number of iterations Gnum, crossover algorithm distribution index δ1, mutation algorithm distribution index δ2, and number of decision variables N.j Set the objective function dimension Vm to 1, and the upper and lower bounds of the decision variables to 2. Based on experience, this value is set as the initial blade opening. ±10%.

[0085] Set the objective function for multiple objectives: The first objective function for turbine output is set as follows:

[0086]

[0087]

[0088] The second objective function for the operational stability of the water turbine is defined as follows:

[0089]

[0090]

[0091] 2) Generate initial individual: using the current blade opening. As an initial individual, the objective function is calculated.

[0092] 3) A multi-objective genetic algorithm based on non-dominated sorting is used to solve the problem: Since there is a conflict between maximizing turbine output and optimizing unit stability, a single-objective optimization algorithm cannot reconcile this conflict. Therefore, a multi-objective genetic algorithm (NSGA-II) based on fast non-dominated sorting is introduced to optimize the turbine output index while considering unit stability. The algorithm optimization process is as follows:

[0093] Step 21: Algorithm Initialization. Set the algorithm parameters including population size N, total number of iterations T, number of objective functions M, decision vector dimension D, and the upper and lower bounds for the optimization of decision variables.

[0094] Step 22: Randomly initialize individual locations.

[0095] Step 23: Calculate the first objective function Obj1 for turbine efficiency and the second objective function Obj2 for turbine operation stability based on the individual positions.

[0096] Step 24: Calculate the fitness function value of individuals, perform non-dominated quicksort and crowding calculation to obtain the initial population.

[0097] The specific methods and steps are the same as described above, and will not be repeated here.

[0098] Step 25: Merge the parent and offspring populations, recalculate the objective function value according to steps 22-24, and then calculate the next generation of individuals by sorting and crowding.

[0099] Step 26: Generate a new generation population using mutation and crossover operations. First, randomly pair up individuals in the population; then, randomly set the crossover points; finally, exchange some genes between the paired chromosomes. Mutation operation: Use basic position mutation to perform the mutation operation. The specific process is as follows: first, determine the gene mutation location for each individual; then, invert the original gene value at the mutation point according to a certain probability.

[0100] Step 27: Determine if the maximum number of generations has been reached. If it has, the algorithm terminates and the optimal solution set is output; otherwise, proceed to step 25.

[0101] Thus, the turbine-hydropower cooperative relationship curve under a certain cooperative optimized head is obtained.

[0102] Repeatedly obtain turbine-hydropower cooperative relationship curves under different cooperative optimization heads, and summarize the head H. <H q And the head H>H q The optimized turbine-hydrodynamic relationship curve is used to obtain the turbine-hydrodynamic relationship curve under full head.

[0103] The multi-objective optimization method for determining the coordination relationship of bulb turbine units in this invention uses the critical head at the time of water curtailment at the hydropower station as the boundary. When the unit head is greater than the critical head, a multi-objective optimization algorithm that maximizes turbine efficiency and optimizes unit stability is used to determine the coordination relationship. When the unit head is less than the critical head, a multi-objective optimization algorithm that maximizes turbine output and optimizes unit stability is used to obtain the turbine coordination relationship curve, which better adapts to the operating characteristics of bulb turbine units. Under high head conditions, turbine operating efficiency can be improved, increasing the water utilization rate of the hydropower station; under low head conditions, turbine power generation flow can be increased, reducing water curtailment at the hydropower station, increasing power generation, and improving the overall power generation efficiency of the hydropower station.

[0104] In summary, the multi-objective optimization method for determining the coordination relationship of bulb turbine units in this embodiment of the invention acquires field test data, including: optimized head, turbine efficiency, turbine output, and unit stability data under different guide vane openings and blade openings; analyzes the field test data to obtain the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings; based on the comparison results between the optimized head and the critical discharge head, and the relationship between blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, the method takes the highest turbine efficiency or the largest turbine output as the first objective and the optimal unit stability as the second objective, and uses a multi-objective optimization algorithm to obtain the turbine coordination relationship curve. This method can accurately obtain the turbine coordination relationship curve, better adapt to the operating characteristics of bulb turbine units, and is conducive to improving the comprehensive power generation efficiency of hydropower stations.

[0105] The foregoing has described specific embodiments of the present invention. In some cases, the actions or steps described in the embodiments of the present invention may be performed in a different order than that shown in the embodiments and the desired results may still be achieved. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] Based on the same concept, embodiments of the present invention also provide a multi-objective optimized device for determining the coordination relationship of a bulb-type turbine generator set. Applied to servers. (See appendix) Figure 2 As shown, the determination of the cooperative relationship of the bulb turbine generator set through multi-objective optimization includes: a data acquisition unit, a data analysis unit, and a cooperative relationship acquisition unit. Among them,

[0107] The data acquisition unit is used to acquire field test data, which includes: optimized head, turbine efficiency, turbine output and unit stability data under different guide vane opening and blade opening.

[0108] The data analysis unit is used to analyze the field test data and obtain the relationship between the blade opening and the turbine efficiency, turbine output and unit stability under different guide vane openings.

[0109] The coordinating relationship acquisition unit is used to obtain the turbine coordinating relationship curve based on the comparison results of the coordinating optimized head and the critical discharge head, as well as the relationship between the blade opening and turbine efficiency, turbine output and unit stability under different guide vane openings. The first objective is to achieve the highest turbine efficiency or the largest turbine output, and the second objective is to achieve the best unit stability.

[0110] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of the present invention, the functions of each module can be implemented in one or more software and / or hardware.

[0111] The apparatus of the above embodiments is applied to the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0112] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any of the above embodiments.

[0113] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the method described in any of the above embodiments.

[0114] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 301, a memory 302, an input / output interface 303, a communication interface 304, and a bus 305. The processor 301, memory 302, input / output interface 303, and communication interface 304 are interconnected internally via the bus 305.

[0115] The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0116] The memory 302 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 302 can store the operating system and other applications. When the technical solution provided by the method embodiment of the present invention is implemented by software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301.

[0117] Input / output interface 303 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0118] Communication interface 304 is used to connect a communication module (not shown in the figure) to enable communication and interaction between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0119] Bus 305 includes a pathway for transmitting information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304).

[0120] It should be noted that although the above-described device only shows the processor 301, memory 302, input / output interface 303, communication interface 304, and bus 305, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of the present invention, and does not necessarily include all the components shown in the figures.

[0121] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of brevity.

[0122] This application is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of all embodiments. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this invention should be included within the protection scope of this disclosure.

Claims

1. A method for determining the cooperative relationship of a bulb turbine generator unit through multi-objective optimization, characterized in that, The method for determining the coordination relationship of the bulb-type turbine unit includes: Acquire field test data, including: optimized head, turbine efficiency, turbine output and unit stability data under different guide vane opening and blade opening; The field test data were analyzed to obtain the relationship between the blade opening and turbine efficiency, turbine output and unit stability under different guide vane openings. Based on the comparison results of the optimized head and the critical discharge head, and the relationship between the blade opening and turbine efficiency, turbine output and unit stability under different guide vane openings, the turbine efficiency or turbine output is taken as the first objective and the unit stability is taken as the second objective. A multi-objective optimization algorithm is used to obtain the turbine cooperative relationship curve. Based on the comparison results between the optimized head and the critical discharge head, and the relationships between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, a multi-objective optimization algorithm is used to obtain the turbine cooperative relationship curve, with the highest turbine efficiency or maximum turbine output as the first objective and optimal unit stability as the second objective. This includes: If the synergistic optimization head is greater than the critical discharge head, then based on the relationship between the blade opening and the turbine efficiency and unit stability under different guide vane openings, a multi-objective optimization algorithm with the highest turbine efficiency as the first objective and the optimal unit stability as the second objective is adopted to obtain the blade opening that satisfies the first objective and the second objective under different guide vane openings, and the turbine synergistic relationship curve is obtained. If the synergistic optimization head is less than the critical discharge head, then based on the relationship between the blade opening and the turbine output and unit stability under different guide vane openings, a multi-objective optimization algorithm with the turbine output as the first objective and the unit stability as the second objective is adopted to obtain the blade opening that satisfies the first objective and the second objective under different guide vane openings, and the turbine synergistic relationship curve is obtained. The analysis of the field test data to obtain the relationship between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings includes: The field test data are grouped according to the guide vane opening, with different groups corresponding to different guide vane openings; Generate a blade opening matrix based on the blade opening in each group; Based on the turbine efficiency, turbine output, and water guide bearing vibration value (which serves as unit stability data) under different guide vane opening and blade opening, the turbine efficiency matrix, turbine output matrix, and water guide bearing vibration matrix are generated respectively. The relationship between the blade opening matrix, the turbine efficiency matrix, the turbine output matrix, and the water guide bearing vibration matrix is ​​obtained. The process of obtaining the relationship between the blade opening, turbine efficiency, turbine output, and water bearing vibration value based on the blade opening matrix, turbine efficiency matrix, turbine output matrix, and water bearing vibration matrix includes: The least squares method is used to fit a quadratic function relationship between the blade opening and the turbine efficiency based on the blade opening matrix and the turbine efficiency matrix. A linear function relationship between the blade opening and the turbine output is fitted based on the blade opening matrix and the turbine output matrix; Based on the blade opening matrix and the water bearing vibration matrix, a linear function relationship between the blade opening and the water bearing vibration is fitted.

2. The method according to claim 1, characterized in that, The primary objective is to achieve the highest turbine efficiency or maximum turbine output, while the secondary objective is to optimize unit stability. A multi-objective optimization algorithm is used to obtain the turbine coordination curve, including: The first objective function is obtained based on the relationship between the blade opening and the turbine efficiency or turbine output under different guide vane openings. The second objective function is obtained based on the relationship between blade opening and unit stability under different guide vane openings. Based on the first objective function and the second objective function, a multi-objective genetic algorithm based on non-dominated sorting algorithm is used to obtain the blade opening that minimizes the first objective function and the second objective function, and the turbine coordination relationship curve is obtained.

3. The method according to claim 2, characterized in that, The step of obtaining the blade opening that minimizes both the first and second objective functions using a multi-objective genetic algorithm based on a non-dominated sorting algorithm, and thus obtaining the turbine coordination curve, includes: The fitness function of an individual is obtained by combining the first objective function and the second objective function, and non-dominated quicksort and crowding calculation are performed to obtain the initial population; By employing mutation and crossover operations, a new generation of populations can be generated; The parent and offspring populations are merged, the objective function value is recalculated, and the next generation of individuals is obtained through non-dominated quicksort and crowding calculation. Determine if the maximum number of generations has been reached; if so, output the optimal solution set.

4. The method according to claim 1, characterized in that, The method for determining the cooperative relationship of bulb turbine generator units through multi-objective optimization also includes: By repeatedly obtaining the turbine-hydropower cooperative relationship curves under different cooperative optimization heads, the turbine-hydropower cooperative relationship curves under full head are obtained.

5. A multi-objective optimization device for determining the coordination relationship of a bulb turbine generator unit, characterized in that, The device includes: The data acquisition unit is used to acquire field test data, which includes: optimized head, turbine efficiency, turbine output and unit stability data under different guide vane opening and blade opening. The data analysis unit is used to analyze the field test data and obtain the relationship between the blade opening and the turbine efficiency, turbine output and unit stability under different guide vane openings. The cooperative relationship acquisition unit is used to obtain the turbine cooperative relationship curve based on the comparison results between the cooperative optimized head and the critical discharge head, as well as the relationship between the blade opening and turbine efficiency, turbine output and unit stability under different guide vane openings. The first objective is to achieve the highest turbine efficiency or the largest turbine output, and the second objective is to achieve the best unit stability. Based on the comparison results between the optimized head and the critical discharge head, and the relationships between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings, a multi-objective optimization algorithm is used to obtain the turbine cooperative relationship curve, with the highest turbine efficiency or maximum turbine output as the first objective and optimal unit stability as the second objective. This includes: If the synergistic optimization head is greater than the critical discharge head, then based on the relationship between the blade opening and the turbine efficiency and unit stability under different guide vane openings, a multi-objective optimization algorithm with the highest turbine efficiency as the first objective and the optimal unit stability as the second objective is adopted to obtain the blade opening that satisfies the first objective and the second objective under different guide vane openings, and the turbine synergistic relationship curve is obtained. If the synergistic optimization head is less than the critical discharge head, then based on the relationship between the blade opening and the turbine output and unit stability under different guide vane openings, a multi-objective optimization algorithm with the turbine output as the first objective and the unit stability as the second objective is adopted to obtain the blade opening that satisfies the first objective and the second objective under different guide vane openings, and the turbine synergistic relationship curve is obtained. The analysis of the field test data to obtain the relationship between the blade opening and turbine efficiency, turbine output, and unit stability under different guide vane openings includes: The field test data are grouped according to the guide vane opening, with different groups corresponding to different guide vane openings; Generate a blade opening matrix based on the blade opening in each group; Based on the turbine efficiency, turbine output, and water guide bearing vibration value (which serves as unit stability data) under different guide vane opening and blade opening, the turbine efficiency matrix, turbine output matrix, and water guide bearing vibration matrix are generated respectively. The relationship between the blade opening matrix, the turbine efficiency matrix, the turbine output matrix, and the water guide bearing vibration matrix is ​​obtained. The process of obtaining the relationship between the blade opening, turbine efficiency, turbine output, and water bearing vibration value based on the blade opening matrix, turbine efficiency matrix, turbine output matrix, and water bearing vibration matrix includes: The least squares method is used to fit a quadratic function relationship between the blade opening and the turbine efficiency based on the blade opening matrix and the turbine efficiency matrix. A linear function relationship between the blade opening and the turbine output is fitted based on the blade opening matrix and the turbine output matrix; Based on the blade opening matrix and the water bearing vibration matrix, a linear function relationship between the blade opening and the water bearing vibration is fitted.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-4.

7. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the method as described in any one of claims 1-4.

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