Excitation system accurate modeling parameter identification optimization method

Abstract

The invention discloses an excitation system accurate modeling parameter identification optimization method, which belongs to the technical field of power system operation and maintenance, and comprises the following steps of: dividing an iteration process of a cystoid colony algorithm into two stages, and adjusting a basis for updating positions of cystoid individuals in the iteration process by utilizing a random selection method; the optimal cystoid individual position is replaced by the random cystoid individual position, the problem that an algorithm is converged to a local optimal solution in a traditional method is avoided, the search capability and flexibility of the algorithm are improved, and therefore the accuracy and calculation speed of parameter identification of the generator excitation system are improved. Therefore, the method provided by the invention has good practical application value.

Description

Technical Field

[0001] The invention belongs to the technical field of power system operation and maintenance, and specifically is a method for accurately modeling parameters of an excitation system and optimizing the same. Background Art

[0002] The accurate model and parameters of the generator excitation system are crucial for the dynamic simulation and analysis of power systems. Different excitation system models and parameters will produce different results for the operation and control of power systems. The excitation system model and its parameters based on the original manufacturer's information may not be accurate enough after a few years of operation. The excitation system of the synchronous generator plays an important role in voltage regulation and reactive power distribution, and is a key link in ensuring the safe and stable operation of the power system. Since detailed generator models are inevitably required in the online calculation and analysis of the current power system, and the traditional excitation parameter identification method cannot guarantee the accuracy of the algorithm and the convergence speed, it affects the efficiency of the excitation system parameter identification and is no longer suitable for application in modern power systems. Therefore, it is urgent to develop a faster and more accurate generator excitation system parameter identification algorithm for use in power system simulation. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for accurately modeling parameter identification and optimization of a synchronous generator excitation system based on an improved sac worm swarm algorithm, which is used to solve the problems of inaccurate generator excitation model parameters and slow identification speed in power system simulation calculations.

[0004] The technical solution of the present invention is as follows: a method for accurately modeling parameters of a synchronous generator excitation system, comprising the following steps:

[0005] S1 selects the excitation system model that is closest to the actual generator excitation system model structure as the identification model and constructs the step disturbance signal x i , in x i The excitation system is disturbed by random noise. After the excitation system is disturbed by a step, the excitation voltage of the generator in the actual model and the identification model is recorded as the output data set y i and Construct error function e;

[0006] S2 identifies the parameters of the generator excitation system according to the improved sacworm swarm algorithm. For any parameter T of the generator excitation system p Perform identification and generate random cystic insect swarms;

[0007] S3 establishes an evaluation function of the individual position fitness in the cystic worm swarm according to the error function e in S1;

[0008] S4 randomly selects the current individual position of the cysticercus, and updates the group position according to the randomly selected position;

[0009] S5 calculates the best individual position in the sac-like group population according to the food source, and further continues to update the individual position in the population based on the best individual position;

[0010] S6 adjusts the positions of the cysticercus individuals that are beyond the boundary in the given search space;

[0011] S7 calculates the fitness value of the individual cysticercus after the position update according to the fitness evaluation function of the cysticercus swarm individuals in S3, determines whether the termination condition is met, and returns the optimal solution obtained so far.

[0012] Furthermore, the error function of the generator excitation system parameter identification in step S1 is as follows:

[0013]

[0014] Among them, y i and are the generator excitation voltage output in the actual model and the identification model respectively; e is the error function; N is the number of sampling points.

[0015] Furthermore, the positions of the individuals of the cysticercus population randomly generated in step S2 are as follows:

[0016]

[0017] in, is the position of the individual in the population; rand is a random number ranging from (0,1); and Where are the upper and lower limits of the individual positions of the population.

[0018] Furthermore, in step S3, the fitness function is constructed based on the error function:

[0019] f=C max -e(3)

[0020] Among them, C max is the fitness setting value; e is the error function in step 1.

[0021] Furthermore, in step S4, the group position is updated according to the randomly selected cysticercus position:

[0022]

[0023] in, is the position of an individual randomly selected from the current population.

[0024] Furthermore, in step S5, the group position is updated according to the best cysticercus position:

[0025]

[0026] in, is the best individual position; c1 is a random number in the range of (0,1). The best position can be calculated by the food source position as shown below:

[0027]

[0028] Among them, SF is the food source; A is a random vector, which is defined as follows:

[0029]

[0030] Among them, c2, c3 are random numbers in the range of (0, 1); VT min and VT max To reflect the minimum and maximum speeds used to create social interactions.

[0031] The beneficial effects of the present invention are:

[0032] The present invention divides the iterative process of the sacworm swarm algorithm into two stages, uses a random selection method to adjust the basis for updating the position in the iterative process, and uses random sacworm individual positions to replace the best sacworm individual positions, thereby avoiding the problem of the algorithm converging to a local optimal solution in the traditional method, improving the search ability and flexibility of the algorithm, thereby improving the accuracy and calculation speed of the generator excitation system parameter identification. Therefore, the method proposed by the present invention has good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of the work flow of the present invention.

[0034] Figure 2 This is a transfer function block diagram of the excitation parameter model to be identified in the present invention.

[0035] Figure 3 Input step signal diagram for the present invention.

[0036] Figure 4 This is a comparison chart of the output results of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that in the description of the present invention, the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", "clockwise", "counterclockwise", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction.

[0038] In the present invention, unless otherwise clearly specified and limited, the terms "set", "install", "connect", "connect" and the like should be understood in a broad sense. For example, "fixed" can be a fixed connection, a detachable connection, or an integral connection; the connection can be a mechanical connection or an electrical connection; the connection can be a direct connection or an indirect connection through an intermediate medium, and can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] The present invention divides the iterative process of the sac swarm algorithm into two stages and uses a random selection method to adjust the basis for updating the position during the iterative process, thereby effectively improving the accuracy of the method for identifying the excitation parameters of the generator. The calculation process includes the following steps:

[0040] S1: Select the excitation system model that is closest to the actual generator excitation system model structure as the identification model, and construct the step disturbance signal x i , since the system measurement data obtained by sampling in practical applications usually contains certain noise, therefore, in the above x i The random noise interference is added to the model to simulate the actual system operation environment. After the excitation system is subjected to step disturbance, the excitation voltage of the generator in the actual model and the identification model is recorded as the output data set y i and Construct error function e;

[0041] S2: The Enhanced Tunicate Swarm Algorithm (ETSA) simulates the group behavior of deep-sea tunicates in the process of gathering towards the target food source. The tunicates look for food sources and move according to the food sources, which is the optimal behavior. Therefore, with the help of this algorithm, the parameters of the generator excitation system can be identified. For any parameter T of the generator excitation system, p Perform identification and generate random cystic insect swarms;

[0042] S3: establishing an individual position fitness evaluation function in the cystic worm swarm according to the error function in step 1;

[0043] S4: In order to make the cystic swarm individuals obtain a larger search space during the iteration process, the traditional cystic worm swarm algorithm is improved. First, the current cystic worm individual position is randomly selected, and the group position is updated according to the randomly selected position;

[0044] S5: Calculate the best individual position in the sac population according to the food source, and further update the individual position in the population based on the best individual position;

[0045] S6: In a given search space, adjust the position of the cysticercus individuals that are beyond the boundary;

[0046] S7: Calculate the fitness value of the individual cysticercus after the position update according to the individual fitness evaluation function of the cysticercus swarm in step 3, determine whether the termination condition is met, and return the optimal solution obtained so far.

[0047] The error function of the generator excitation system parameter identification in S1 is as follows:

[0048]

[0049] Among them, y i and are the generator excitation voltage output in the actual model and the identification model respectively; e is the error function; N is the number of sampling points.

[0050] The positions of the randomly generated cystidial worm population individuals in S2 are as follows:

[0051]

[0052] in, is the position of the individual in the population; rand is a random number ranging from (0,1); and Where are the upper and lower limits of the individual positions of the population.

[0053] In S3, the fitness function is constructed based on the error function:

[0054] f=C max -e(3)

[0055] Among them, C max is the fitness setting value; e is the error function in step 1.

[0056] In S4, the position of the colony is updated according to the randomly selected cysticercus position:

[0057]

[0058] in, is the position of an individual randomly selected from the current population.

[0059] In S5, the group position is updated according to the best cyst position:

[0060]

[0061] in, is the best individual position; c1 is a random number between (0,1). The best position can be calculated by the food source position as shown below:

[0062]

[0063] Among them, SF is the food source; A is a random vector, which is defined as follows:

[0064]

[0065] Among them, c2, c3 are random numbers in the range of (0, 1); VT min and VT max To reflect the minimum and maximum speeds for creating social interactions, the present invention considers 1 and 4 respectively.

[0066] In summary, the generator excitation system parameter identification method based on the improved sacworm swarm algorithm is calculated.

[0067] The present invention is further described in detail below in conjunction with embodiments:

[0068] like Figure 2 The excitation system model is shown in Figure 1. The identification model is built in Simulink, and the following is added to the actual excitation system model and the identification model signal input terminal: Figure 3 The step signal shown is used to collect the output terminal voltage, and white noise with a signal-to-noise ratio of 10dB is added to the output data using MATLAB commands to simulate the actual operation.

[0069] The proposed generator excitation parameter identification method based on the improved sacworm swarm algorithm is used for parameter identification, and the initial parameters are set. The number of iterations is 30. The identification is performed 100 times and the average value is taken to obtain the parameters of the identification model as shown in Table 1. The identification parameters are substituted into the identification model for verification, and the output of the actual system is compared with the output of the identification model. The results are shown in Figure 4 It can be seen that after 30 iterations, the output of the identification system is consistent with the actual system output, indicating that the identification results of each parameter of the identification system are accurate at this time, and the parameters can be used to model the system and perform subsequent analysis of system characteristics.

[0070] Table 1 Parameter identification results

[0071]

[0072] The identification time of each parameter is statistically analyzed, and the results are shown in Table 2. From the analysis of Table 2, it can be found that the proposed method has a fast calculation speed, can identify the excitation parameters online, and meet the requirements of online analysis of the power system, which further demonstrates the effectiveness and superiority of the proposed method in practical engineering applications.

[0073] Table 2 Identification time of each parameter of the proposed method

[0074]

[0075] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement and improvement made by any person skilled in the art within the technical scope disclosed by the present invention and within the spirit and principle of the present invention shall be covered within the protection scope of the present invention. At the same time, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0076] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

Claims

1. A method for accurate modeling parameter identification and optimization of an excitation system, characterized in that: The following steps are involved: S1 builds the excitation system model and the step disturbance signal x i , in x i The excitation system is disturbed by random noise. After the excitation system is disturbed by a step, the excitation voltage of the generator in the actual model and the identification model is recorded as the output data set y i and Construct error function e; S2 identifies the parameters of the generator excitation system according to the improved sacworm swarm algorithm. For any parameter T of the generator excitation system p Perform identification and generate random cystic insect swarms; S3 establishes an evaluation function of the individual position fitness in the cystic worm swarm according to the error function e in S1; S4 randomly selects the current individual position of the cysticercus, and updates the group position according to the randomly selected position; S5 calculates the best individual position in the sac-like group population according to the food source, and further continues to update the individual position in the population based on the best individual position; S6 adjusts the positions of the cysticercus individuals that are beyond the boundary in the given search space; S7 calculates the fitness value of the individual cysticercus after the position update according to the fitness evaluation function of the cysticercus swarm individuals in S3, determines whether the termination condition is met, and returns the optimal solution obtained so far.

2. The method for accurate modeling parameter identification and optimization of an excitation system according to claim 1, characterized in that: The error function of the generator excitation system parameter identification in step S1 is as follows: Among them, y i and are the generator excitation voltage output in the actual model and the identification model respectively; e is the error function; N is the number of sampling points.

3. The method for accurate modeling parameter identification and optimization of an excitation system according to claim 1, characterized in that: The positions of the individuals of the cysticercus population randomly generated in step S2 are as follows: in, is the position of the individual in the population; rand is a random number ranging from (0,1); and Where are the upper and lower limits of the individual positions of the population.

4. The method for accurate modeling parameter identification and optimization of an excitation system according to claim 1, characterized in that: In step S3, the fitness function is constructed based on the error function: f=C max -e(3) Among them, C max is the fitness setting value; e is the error function in step 1.

5. The method for accurate modeling parameter identification and optimization of an excitation system according to claim 1, characterized in that: In step S4, the colony position is updated according to the randomly selected cysticercus position: in, is the position of an individual randomly selected from the current population.

6. The method for accurate modeling parameter identification and optimization of an excitation system according to claim 1, characterized in that: In step S5, the colony position is updated according to the best cysticercus position: in, is the best individual position; c1 is a random number in the range of (0,1). The best position can be calculated by the food source position as shown below: Among them, SF is the food source; A is a random vector, which is defined as follows: Among them, c2, c3 are random numbers in the range of (0, 1); VT min and VT max To reflect the minimum and maximum speeds used to create social interactions.

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