Generator saturation coefficient calculation method based on directional expansion crossover genetic algorithm

Abstract

The invention provides a directional expansion cross genetic algorithm-based generator saturation coefficient calculation method, which comprises the following steps of: 1, obtaining a group of generator no-load test data through a generator no-load characteristic test; 2, defining a saturation function; and step 3, based on the generator no-load test data obtained in the step 1 and the saturation function established in the step 2, solving a generator saturation coefficient in the saturation function by using a genetic algorithm based on directional expansion crossover, so that the variance of the fitted function and the original data reaches the minimum value. According to the method, the no-load curve of the generator is fitted through the directional expansion cross genetic algorithm, so that the purpose of calculating the saturation coefficient is achieved, errors caused by manual identification are avoided, and the method can adapt to test data containing interference. The method is suitable for generator saturation coefficient calculation based on the generator no-load characteristic curve.

Description

Technical Field

[0001] The invention relates to a method for calculating a saturation coefficient of a generator no-load curve, in particular to a method for calculating a saturation coefficient of a generator based on a directional expansion crossover genetic algorithm. Background Art

[0002] In the parameter calculation of large generator sets, the calculation of generator saturation coefficients a, b and n is a key parameter, which is defined as follows:

[0003] I f =aU+b(U) n

[0004] Among them I f It is the per-unit value of the excitation current measured after the generator no-load characteristic test, and U is the per-unit value of the terminal voltage measured after the generator no-load characteristic test.

[0005] This parameter directly affects the dynamic characteristics of the generator and is one of the key parameters of the generator. Accurately calculating the saturation coefficient from the generator no-load curve is the key to accurately calculating the generator parameters.

[0006] The calculation method widely used at present only uses the air gap line and no-load saturation curve, and calculates the excitation current value at 1 times and 1.2 times the rated voltage value respectively:

[0007]

[0008] Among them I fB ,I fJ is the excitation current value at the generator air gap line corresponding to 1 and 1.2 times the rated voltage value, I f0 ,I fK It is the excitation current value corresponding to 1 times and 1.2 times the rated voltage value on the generator no-load curve.

[0009] From the above formula, we can know that the saturation coefficient is determined by the generator no-load characteristic test curve. However, in actual field tests, the test results of the generator no-load curve are affected by various interference factors in the environment and often contain certain disturbances. f0 , I fK The numerical requirements are high, and relying on manual experience for identification may result in large errors, and it is difficult to ensure the consistency of different interpretation results. Summary of the invention

[0010] The present invention provides a method for calculating the saturation coefficient of a generator based on a directional extended crossover genetic algorithm. The method is aimed at a no-load curve obtained in a no-load characteristic test. The saturation coefficient can be calculated in the presence of interference data, thereby achieving accurate interpretation of the test data and providing an accurate basis for calculating the parameters of the generator excitation system.

[0011] A method for calculating a saturation coefficient of a generator based on a directed extended crossover genetic algorithm comprises the following steps:

[0012] Step 1: Get a set of generator no-load test data through the generator no-load characteristic test:

[0013] X={I i ,U i},i=1,2,…,m; (1)

[0014] Among them U i is the measured excitation voltage value, I i is the measured generator excitation current value, m is a positive integer greater than or equal to 2;

[0015] Step 2: Define the following saturation function:

[0016] f(U)=aU+b(U) n (2)

[0017] Where a, b and n are the generator saturation coefficients;

[0018] Step 3: Based on the generator no-load test data obtained in step 1 and the saturation function established in step 2, a, b and n are solved using a genetic algorithm based on directional expansion crossover, so that the variance of the fitted function and the original data is:

[0019]

[0020] Reached minimum value.

[0021] Furthermore, the genetic algorithm based on directional extended crossover comprises the following steps:

[0022] 1) Generate the first generation population, the number of which is n;

[0023] 2) Crossover n individuals to generate new m individuals, where the crossover uses the following directional expansion crossover algorithm, so that each crossover generates a new group with a greater distribution probability in the direction of the original group:

[0024] Z mi =Z i +K dir ·(Z j -Z i )+S·|Zj -Z| i

[0025] Where S is a random number between 0 and 1, K dir is the direction coefficient:

[0026]

[0027] Z i , Z j For individuals used for crossover:

[0028] Z i =(a i ,b i ,n i ), Z j =(a j ,b j ,n j );

[0029] 3) Merge the individuals in steps 1) and 2) into a new generation group, with the number of groups being m+n;

[0030] 4) Calculate the objective function according to formula (2) and formula (3):

[0031]

[0032] 5) According to the objective function, sort the individuals from small to large and select the first n individuals;

[0033] 6) If the objective function does not decrease after 20 iterations, end the calculation and jump to step 7), otherwise jump to step 2);

[0034] 7) Take the a, b and n values ​​of the first individual as the calculated values.

[0035] Beneficial effects of the present invention:

[0036] 1. It has more accurate and consistent calculation results than conventional algorithms, avoiding the problem of different manual interpretation results;

[0037] 2. Based on the principle of genetic algorithm, the mean square error of the fitting curve is the smallest;

[0038] 3. Using an algorithm based on directed extended crossover genetics, the offspring produced by the crossover have a greater distribution probability in the direction of the objective function descent and have a faster convergence speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of test data adopted by the present invention;

[0040] Figure 2It is a schematic diagram of the iteration of the objective function calculated by the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific examples of the present invention.

[0042] The embodiment of the present invention provides a method for calculating a saturation coefficient of a generator based on a directional extended crossover genetic algorithm, comprising the following steps:

[0043] A synchronous generator was tested for no-load characteristics. The no-load test data of the generator were measured as shown in Table 1. The test data curve is shown in Figure 1 shown.

[0044] Table 1

[0045]

[0046]

[0047] Construct the following saturation function:

[0048] f(U)=aU+b(U) n

[0049] Where a, b and n are the generator saturation coefficients.

[0050] According to the directed expansion crossover genetic algorithm, 200 first-generation individuals are randomly generated to start iterative calculation. After the iteration, the minimum objective function value of each generation of individuals is converged as shown in Table 2:

[0051] Table 2

[0052]

[0053]

[0054] The objective function convergence diagram is as follows Figure 2 After the calculation converges, the individual with the smallest objective function is selected, and the values ​​of each item are as follows:

[0055] Table 3

[0056]

[0057]

[0058] The saturation function identified is:

[0059] f(U)=0.9985U+0.232(U) 7.4212 (6)

[0060] For comparison, the a, b, n and SSE obtained by the conventional algorithm are shown in Table 4:

[0061] Table 4

[0062] project Algorithm of the present invention Conventional Algorithm a 0.9985 1 b 0.2327 0.197755 n 7.4212 6.244039 SSE 0.0014145 0.008669

[0063] It can be seen that the algorithm of the present invention has a better effect because it takes the direction of the objective function descent into consideration when crossing.

[0064] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for calculating the saturation coefficient of a generator based on a directed extended crossover genetic algorithm, characterized in that: The steps include: Step 1: Get a set of generator no-load test data through the generator no-load characteristic test: X={I i ,U i },i=1,2,…,m; (1) Among them U i is the measured excitation voltage value, I i is the measured generator excitation current value, m is a positive integer greater than or equal to 2; Step 2: Define the following saturation function: f(U)=aU+b(U) n (2) Where a, b and n are the generator saturation coefficients; Step 3: Based on the generator no-load test data obtained in step 1 and the saturation function established in step 2, a, b and n are solved using a genetic algorithm based on directional expansion crossover, so that the variance of the fitted function and the original data is: Reached minimum value.

2. The method for calculating the generator saturation coefficient based on the directed extended crossover genetic algorithm according to claim 1, characterized in that: The genetic algorithm based on directional extended crossover comprises the following steps: 1) Generate the first generation population, the number of which is n; 2) Crossover n individuals to generate new m individuals, where the crossover uses the following directional expansion crossover algorithm, so that each crossover generates a new group with a greater distribution probability in the direction of the original group: WITH mi =Z i +K dir ·(WITH j -WITH i )+S |Z j -Z| i Where S is a random number between 0 and 1, K dir is the direction coefficient: Z i , Z j For individuals used for crossover: Z i =(a i ,b i ,n i ),Z j =(a j ,b j ,n j ); 3) Merge the individuals in steps 1) and 2) into a new generation group, with the number of groups being m+n; 4) Calculate the objective function according to formula (2) and formula (3): 5) According to the objective function, sort the individuals from small to large and select the first n individuals; 6) If the objective function does not decrease after 20 iterations, end the calculation and jump to step 7), otherwise jump to step 2); 7) Take the a, b and n values ​​of the first individual as the calculated values.

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