Method for optimizing and setting parameters of power system stabilizer

Abstract

The invention discloses a power system stabilizer parameter optimizing and setting method, which belongs to the technical field of power system operation and maintenance, and comprises the following steps of: establishing a power system stabilizer model for suppressing low-frequency oscillation of a power system, and establishing a parameter optimizing method with a maximum low-frequency oscillation damping ratio as a target. A synchronous generator power system stabilizer parameter optimization setting method based on swarm intelligence is designed, the optimal parameters of the power system stabilizer are obtained on the premise of suppressing low-frequency oscillation of the system, and safe operation of the system is guaranteed. 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 relates to a method for optimizing and setting parameters of a power system stabilizer. Background Art

[0002] The occurrence of low-frequency oscillations in the power system will threaten the safe and stable operation of the power grid. The parameter setting of the power system stabilizer directly affects the stability of the system's low-frequency oscillation small disturbance problem. Therefore, changing the structure of the power system stabilizer or adjusting the parameters of the power system stabilizer can improve the damping of low-frequency oscillations and thus suppress low-frequency oscillations. However, when using a power system stabilizer to suppress low-frequency oscillations, it is possible to deteriorate the damping of low-frequency oscillations. How to make the power system stabilizer improve the damping of frequency oscillations while ensuring the low-frequency oscillation suppression effect is an urgent problem to be solved. In summary, there is an urgent need in the prior art for a method for optimizing and adjusting the parameters of a synchronous generator power system stabilizer based on swarm intelligence, so as to effectively ensure the stability of the system with small disturbances. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for optimizing and setting parameters of a power system stabilizer to ensure the technical problem of small disturbance stability of the system.

[0004] The technical solution of the present invention is as follows: a method for optimizing and setting parameters of a power system stabilizer, comprising the following steps:

[0005] S1 constructs the transfer function of the power system stabilizer PSS optimization model applied to low-frequency oscillation suppression and determines the parameter K to be optimized STAB , T1 and T W ;

[0006] S2 uses the Hippo optimization algorithm to globally optimize the optimal problem and randomly generates individuals in the Hippo population, each of which represents a possible solution for an optimization parameter;

[0007] The S3 power system stabilizer PSS has the primary function of reducing low-frequency oscillations and optimizes the objective function when the maximum low-frequency oscillation damping ratio is used as the design parameter;

[0008] S4, under the premise of increasing damping, in order to effectively suppress low-frequency oscillation, consider the range of each parameter value and obtain the optimal parameter model;

[0009] S5 uses the Hippopotamus self-optimization algorithm to obtain the fitness value of the current parameter optimization model, and analyzes, compares and retains the current optimal fitness value; under the action of the Hippopotamus algorithm, by constantly searching for the optimal parameter combination, the optimal particle continuously evolves towards the optimal particle until the iteration of the Hippopotamus algorithm ends, the optimal fitness of the optimal parameters is obtained, and the optimization process of the algorithm is observed through its convergence curve.

[0010] Furthermore, the transfer function of the power system stabilizer PSS optimization model in step S1 is as follows:

[0011]

[0012] Among them, K STAB represents the gain; T1 represents the time constant of the leading link; T2 represents the time constant of the lagging link. W Represents the time constant of the DC isolation link.

[0013] Furthermore, the individual positions of the hippo population randomly generated in step S2 are as follows:

[0014]

[0015] in, represents the position of the male hippopotamus; Dhippe represents the position of the dominant hippopotamus (i.e., the hippopotamus with the best cost in the current iteration); The position of the female or immature hippopotamus in the herd; h1 is a number or vector randomly selected from the five scenarios; r1 and r2 are random numbers between 0 and 1.

[0016] Furthermore, the objective function optimized in step S3 is as follows:

[0017] J=min{ξ i,m ,i∈S,m=1,2,…,M}(5)

[0018] Among them, ξ i,m is the damping ratio of the i-th order oscillation mode under the m-th working state; S is the summary of all low-frequency vibration modes; M is the system operation mode to be considered; J is the minimum value of the damping ratio under different modes.

[0019] Furthermore, the parameter value range in step S4 is as follows:

[0020]

[0021] Among them, K STAB,k The variation range is [0.1, 100], T W,k The variation range is [0.01, 20], T 1,k The variation interval is [0.01, 1].

[0022] Furthermore, the optimal fitness of the hippo population in step S5 is as follows:

[0023]

[0024] Among them, F i is the objective function value.

[0025] The beneficial effects of the present invention are:

[0026] The present invention constructs a power system stabilizer model for suppressing low-frequency oscillations of the power system, establishes a parameter optimization method with the goal of maximizing the low-frequency oscillation damping ratio, and based on this, designs a synchronous generator power system stabilizer parameter optimization setting based on swarm intelligence. Under the premise of suppressing the low-frequency oscillation of the system, the optimal parameters of the power system stabilizer are obtained to ensure the safe operation of the system. Therefore, the method proposed in the present invention has good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of the work flow of the present invention.

[0028] Figure 2 This is a block diagram of the power system stabilizer of the present invention.

[0029] Figure 3 This is a curve diagram of the generator speed change of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] A method for optimizing and setting parameters of a power system stabilizer comprises the following steps:

[0033] Step 1: Construct the transfer function of the power system stabilizer (PSS) optimization model for low-frequency oscillation suppression and determine the parameter K to be optimized. STAB , T1 and T W ;

[0034] Step 2: The Hippopotamus Optimization Algorithm (HO) is a new meta-heuristic optimization algorithm inspired by the behavioral characteristics of hippos in water and on land. It globally optimizes the optimal problem by simulating the foraging, resting and social behaviors of hippos. When the hippo population is initialized, each individual represents a possible solution for the optimization parameter;

[0035] Step 3: PSS takes reducing low-frequency oscillation as its primary function, and takes the maximum low-frequency oscillation damping ratio as the objective function when designing parameters;

[0036] Step 4: Under the premise of increasing damping as much as possible, in order to effectively suppress low-frequency oscillation, the value range of each parameter is considered to obtain the optimal parameter model;

[0037] Step 5: Using the Hippo self-optimization algorithm, the fitness value of the current parameter optimization model is obtained, and the current optimal fitness value is analyzed, compared and retained; under the action of the Hippo algorithm, by constantly searching for the optimal parameter combination, the optimal particle is continuously evolved to the optimal particle until the iteration of the Hippo algorithm ends and the optimal value of the optimal parameter is obtained. Through its convergence curve, the optimization process of the algorithm can be seen;

[0038] The transfer function of the PSS optimization model in step 1 is as follows:

[0039]

[0040] Among them, K STAB represents the gain; T1 represents the time constant of the leading link; T2 represents the time constant of the lagging link. W Indicates the time constant of the DC isolation link;

[0041] The positions of the randomly generated hippopotamus individuals in step 2 are as follows:

[0042]

[0043] in, represents the position of the male hippopotamus; Dhippe represents the position of the dominant hippopotamus (i.e., the hippopotamus with the best cost in the current iteration); The position of the female or immature hippopotamus in the herd; h1 is a number or vector randomly selected from the five scenarios; r1 and r2 are random numbers between 0 and 1.

[0044] The objective function optimized in step 3 is as follows:

[0045] J=min{ξ i,m ,i∈S,m=1,2,…,M}(5)

[0046] Among them, ξ i,m is the damping ratio of the i-th order oscillation mode under the m-th working state; S is the summary of all low-frequency vibration modes; M is the system operation mode to be considered; J is the minimum value of the damping ratio under different modes.

[0047] The parameter value ranges in step 4 are as follows:

[0048]

[0049] Among them, K STAB,k The variation range is [0.1, 100], T W,k The variation range is [0.01, 20], T 1,k The variation interval is [0.01, 1].

[0050] The optimal fitness of the hippo population in step 5 is as follows:

[0051]

[0052] Among them, F i is the objective function value.

[0053] In summary, a low excitation limit setting method for the excitation regulator considering the dynamic characteristics of the new energy system is calculated.

[0054] Compared with the general power system stabilizer optimization, the system frequency oscillation damping ratio is significantly improved while taking into account the frequency oscillation parameter optimization, which proves the importance of power system stabilizer parameter setting in the present invention.

[0055] 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.

[0056] 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 optimizing and setting parameters of a power system stabilizer, characterized in that: The following steps are involved: S1 constructs the transfer function of the power system stabilizer optimization model applied to low-frequency oscillation suppression and determines the parameter K to be optimized STAB , T1 and T W ; S2 uses the Hippo optimization algorithm to globally optimize the optimal problem and randomly generates individuals of the Hippo population, each of which represents a possible solution for an optimization parameter; The S3 power system stabilizer has the primary function of reducing low-frequency oscillations and optimizes the objective function when the maximum low-frequency oscillation damping ratio is used as the design parameter; S4, under the premise of increasing damping, in order to effectively suppress low-frequency oscillation, considers the range of values ​​of each parameter and obtains the optimal parameter model; S5 uses the Hippopotamus self-optimization algorithm to obtain the fitness value of the current parameter optimization model, and analyzes, compares and retains the current optimal fitness value; under the action of the Hippopotamus algorithm, by constantly searching for the optimal parameter combination, the optimal particle continuously evolves towards the optimal particle until the iteration of the Hippopotamus algorithm ends, the optimal fitness of the optimal parameters is obtained, and the optimization process of the algorithm is observed through its convergence curve.

2. A method for optimizing and setting parameters of a power system stabilizer according to claim 1, characterized in that: The transfer function of the power system stabilizer optimization model in step S1 is as follows: Among them, K STAB represents gain; T1 represents the time constant of the leading link; T2 represents the time constant of the lagging link, T W Represents the time constant of the DC isolation link.

3. A method for optimizing and setting parameters of a power system stabilizer according to claim 1, characterized in that: The individual positions of the hippo population randomly generated in step S2 are as follows: in, represents the position of the male hippopotamus; Dhippe represents the position of the dominant hippopotamus (i.e., the hippopotamus with the best cost in the current iteration); The position of the female or immature hippopotamus in the herd; h1 is a number or vector randomly selected from the five scenarios; r1 and r2 are random numbers between 0 and 1.

4. A method for optimizing and setting parameters of a power system stabilizer according to claim 1, characterized in that: The objective function optimized in step S3 is as follows: J=min{ξ i,m ,i∈S,m=1,2,…,M}(5) Among them, ξ i,m is the damping ratio of the i-th order oscillation mode under the m-th working state; S is the summary of all low-frequency vibration modes; M is the system operation mode to be considered; J is the minimum value of the damping ratio under different modes.

5. A method for optimizing and setting parameters of a power system stabilizer according to claim 1, characterized in that: The parameter value range in step S4 is as follows: Among them, K STAB,k The variation range is [0.1, 100], T W,k The variation range is [0.01, 20], T 1,k The variation interval is [0.01, 1].

6. A method for optimizing and setting parameters of a power system stabilizer according to claim 1, characterized in that: The optimal fitness of the hippo population in step S5 is as follows: Among them, F i is the objective function value.

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