Power system load model parameter identification method based on improved bat algorithm

Abstract

The invention discloses an electric power system load model parameter identification method based on an improved bat algorithm, and relates to the field of electric power systems and automation of the electric power systems. And fitness sharing and hybrid local search strategies are applied to the bat algorithm, so that the algorithm can jump out of a local optimal solution, and the problem of global optimization is solved. The method comprises the following steps of: firstly, traversing all solution spaces through chaotic mapping, initializing the solutions, applying a bat algorithm, calculating the fitness of each solution in the algorithm application process, sharing the fitness for the solutions near a peak value in the solution spaces, and after the algorithm runs for a certain number of cycles, determining the fitness of the solutions near the peak value in the solution spaces, wherein the more the solutions exist in a certain range, the lower the fitness is; and carrying out secondary search by using a simplex method. According to the method, the problem that a traditional bat algorithm is easily limited to a local optimal solution is solved, so that the load model parameter identification method has higher identification precision.

Description

Technical Field

[0001] The invention belongs to the field of power systems and automation thereof, and in particular relates to a method for identifying power system load model parameters based on an improved bat algorithm. Background Art

[0002] The formulation of power system operation control strategies mainly relies on simulation calculations, and the accuracy of simulation calculations depends on the model used. As one of the important components of the power system, the accuracy of the load model will significantly affect the accuracy of the simulation calculation results, resulting in radical or conservative overall results. Radical models will significantly reduce the ability of the power grid to prevent risks, leading to power outages, endangering the power grid and even public safety; conservative models will make it difficult to accurately calculate the power transmission limit of the power grid, which will limit power transmission and reduce the operating efficiency of the power system. Therefore, load modeling research is a fundamental issue in the operation and control of power systems, which has both important theoretical significance and significant practical value.

[0003] The prior art discloses a comprehensive load model structure that combines a static load model part and a dynamic load model part. For example, a comprehensive load model structure that connects a third-order induction motor model in parallel with a static load model. This load model has good performance in describing the response of the load when a power system fails, but it itself has many parameters and the model structure is relatively complex. In this case, it is difficult for the traditional parameter identification algorithm to simultaneously meet the requirements of both identification efficiency and identification accuracy. Therefore, it is of great significance to study and determine a new power system load model parameter identification algorithm, which is conducive to reducing parameter identification time, improving parameter identification efficiency, and effectively avoiding the problem of parameter identification falling into local optimality. Summary of the invention

[0004] In view of the problem that the traditional bat algorithm is easily limited to the local optimal solution, the present invention proposes a method for identifying the parameters of the power system load model based on the improved bat algorithm, so as to perform more efficient parameter identification of the power system load model. The technical solution of the present application is as follows:

[0005] A method for identifying parameters of a power system load model based on an improved bat algorithm, characterized by comprising:

[0006] Step 1: construct a comprehensive load model structure of a static load model parallel-connected three-order induction motor, wherein the load parameters in the load model structure include static load parameters, induction motor parameters, and motor load proportion;

[0007] Step 2: Construct a fitness function, which reflects the difference between the calculated active power and reactive power of the comprehensive load model and the active power and reactive power in the actual system under the same voltage excitation;

[0008] Step 3, using chaotic mapping to initialize the position of each bat in the bat population in the solution space of the parameters to be identified;

[0009] Step 4: Use the improved bat algorithm for iterative optimization, introduce shared fitness strategy and local search strategy to avoid falling into the local optimal solution.

[0010] Beneficial Effects

[0011] The present invention has made many improvements to the traditional bat algorithm, including using chaotic mapping for initialization, introducing fitness sharing strategy and local search strategy. The proposed method can enable the algorithm to traverse all solution spaces by introducing the chaotic mapping strategy in the initialization step, solving the problem that the initialization of the traditional bat algorithm has high randomness and uneven distribution, increasing the population diversity in the early stage of the algorithm operation, and being able to conduct extensive exploration in the search space; introducing a shared fitness strategy during the operation of the algorithm, sharing solutions within a certain range in the solution space and reducing the fitness, preventing the algorithm from converging to the local optimal solution too early, thereby improving the global search capability; when the algorithm iterates to a certain number of times, using a local search strategy, by increasing the search method to increase the probability of jumping out of the local optimal solution. Therefore, the proposed method can improve the efficiency of parameter identification, effectively avoid the problem of parameter identification falling into the local optimum, and has certain engineering application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of the power system load model parameter identification method based on the improved bat algorithm;

[0013] Figure 2 This is a comparison chart of the error curves between the improved bat algorithm and the traditional bat algorithm provided in an example. DETAILED DESCRIPTION

[0014] The present invention provides a method for identifying parameters of a load model of a power system based on an improved bat algorithm. To make the purpose, technical solution and effect of the present invention clearer, the specific implementation scheme of the present invention is described in detail below in conjunction with the accompanying drawings and examples. The specific examples described in the present invention are only used to explain the present invention and are not used to limit the present invention.

[0015] See also Figure 1 , which is a schematic diagram of a method for identifying parameters of a power system load model based on an improved bat algorithm provided in an embodiment of the present application. The method for identifying parameters of a power system load model based on an improved bat algorithm includes steps 1 to 4.

[0016] Step 1: Construct a comprehensive load model structure of a static load model parallel-connected three-order induction motor, wherein the load parameters in the load model structure include static load parameters, induction motor parameters, and motor load proportion.

[0017] Common induction motor models in engineering can be classified according to the model order, including 1st order model, 3rd order model and 5th order model. As the model order increases, the complexity of the model continues to increase. In actual engineering applications, the simulation accuracy of the 1st order model is insufficient, and the simulation results are difficult to meet the engineering needs. The 5th order model expression is too complex and cannot meet the engineering needs in terms of calculation speed. Therefore, this application uses a 3rd order induction motor model.

[0018] The static load model part of the comprehensive load model structure is described by the following mathematical equation:

[0019]

[0020] Where P and Q represent the active power and reactive power consumed by the static part of the load model when the load node voltage is U, respectively; P 0 and Q 0 They represent the load nodes U 0 The active power and reactive power consumed by the static part of the load model when p , b p 、c p They represent the proportion of active power consumed by constant impedance load, constant current load, and constant power load, respectively, and the sum of the three is 1; a q , b q 、c q They respectively represent the proportion of reactive power consumed by constant impedance load, constant current load, and constant power load, and the sum of the three is 1.

[0021] The third-order induction motor model is described in the following form. The stator voltage equation of the third-order induction motor is:

[0022]

[0023] The rotor state equation is:

[0024]

[0025] The current equation is

[0026]

[0027] And there is

[0028] X=X s +X m

[0029] T m =(Aω 2 +Bω+C)T0

[0030] A+B+C=1

[0031]

[0032] Among them, U d and U q are the d-axis and q-axis components of the motor stator terminal voltage respectively; I d and I q are the d-axis and q-axis components of the stator current respectively; E' d and E' q are the d-axis and q-axis components of the transient potential of the motor respectively; T' d0 is the rotor circuit time constant when the stator is open; ω s is the synchronous speed of the system; s is the rotor slip of the motor; H is the inertia constant of the motor rotor; T m and T e are mechanical torque and electromagnetic torque respectively; T m0 is the mechanical torque of the motor in steady-state operation; A, B, C are mechanical torque coefficients; f * and f 0 * They are the current frequency per unit value and the system rated frequency per unit value respectively; X s is the stator reactance of the motor, X r is the rotor reactance, X m is the armature reaction reactance, R s is the stator resistance, R r is the rotor resistance, X' and X are the rotor locked-rotor reactance and rotor open-circuit reactance, respectively.

[0033] Step 2: Construct a fitness function, which reflects the difference between the calculated active power and reactive power of the comprehensive load model and the active power and reactive power in the actual system under the same voltage excitation;

[0034] The parameter identification problem of the comprehensive load model is transformed into an optimization problem. By continuously comparing the response of the load model under the existing parameters with the response in the actual system, a reasonable fitness function is constructed to characterize the difference between the two. The smaller the value of the fitness function, the more accurately this set of parameters can reflect the actual situation. The fitness function for the identification of the parameters of the power system comprehensive load model in this application is:

[0035]

[0036] Where n is the number of times the power grid receives disturbance, P sw , P loadw , Q sw, Q loadw They respectively represent the actual active power value of the load at the wth disturbance moment, the active power calculated value calculated by the comprehensive load model, the actual reactive power value of the load, and the reactive power calculated value calculated by the comprehensive load model.

[0037] Step 3, using chaotic mapping to initialize the position of each bat in the bat population in the solution space of the parameters to be identified;

[0038] The traditional bat algorithm is initialized randomly, and it is impossible to traverse the solution space completely. This initialization significantly causes the traditional bat algorithm to fall into the local optimal solution. In terms of solution set initialization, this application uses Logistic chaos mapping to traverse the overall solution space:

[0039] x t ′=ux t (1-x t )

[0040] Wherein, u is the control parameter of the chaotic mapping, and its value range is (0,4]. The larger the value, the higher the chaos. In this application, let u = 4. In this state, the system is in a completely chaotic state, so that the initialization positions have more diversity and are evenly distributed in the solution space, which is conducive to improving the diversity of the population.

[0041] Step 4: Use the improved bat algorithm for iterative optimization, introduce shared fitness strategy and local search strategy to avoid falling into the local optimal solution.

[0042] (1) Update the speed, loudness, ultrasonic frequency, and pulse frequency of each bat:

[0043]

[0044] Where t is the iteration order; f t+1 、f min 、f max represent the ultrasonic frequency, the minimum value of the ultrasonic frequency, and the maximum value of the ultrasonic frequency in the t+1th iteration order respectively; v t and v t+1 They represent the speed of the bat in the tth iteration and the speed of the bat in the t+1th iteration respectively; t and A represents the current position of the bat in the t-th iteration order and the optimal position of all bats in the t-th iteration order respectively; t and A 0 Respectively represent the loudness and initial loudness in the t-th iteration order; α and γ are certain random values, α∈(0,1); r t+1 and r tThey represent the pulse frequency of the bat in the t+1th iteration order and the pulse frequency of the bat in the tth iteration order respectively;

[0045] (2) Based on the relationship between the random value and the loudness, update the possible location of the bat in the next iteration order:

[0046]

[0047] Among them, ε is a certain random value;

[0048] (3) According to the fitness sharing strategy, the solutions near the peak in the solution space share fitness. The more solutions there are within a certain range, the lower their fitness value. Its mathematical expression is as follows:

[0049]

[0050] Among them, d(i,j) represents the Euclidean distance between the i-th solution and the j-th solution, L is the number of variables in the solution space, and are the upper and lower bounds of the solution respectively; s(d(i,j)) is the shared function between individual i and individual j, σ share represents the shared radius, α is a constant; c i represents the number of solution systems that share fitness; f' m Represents x i The shared fitness value of

[0051] (4) Calculate the fitness function value of the new position where the bat may move to. If the fitness function value at the new position is less than the fitness function value at the old position and the random number is greater than the current loudness, the position of the bat is updated; otherwise, the original position remains unchanged.

[0052] (5) Determine whether the current iteration order is an integer multiple of 10. If so, perform a local search using the simplex method to obtain a new position and update the bat position;

[0053] (6) Set the maximum number of iterations, repeat (1) to (5) until the number of iterations is equal to the maximum number of iterations, and output a set of load parameters with the minimum fitness function value at this time.

[0054] See also Figure 2 , is a comparison diagram of the error curves of the method of the present application and the traditional bat algorithm in an example, and the error function is:

[0055]

[0056] Among them, the traditional bat algorithm converges slowly and is easily limited to the local optimal solution; the algorithm proposed in this application introduces chaotic mapping for initialization, so the error is large in the first iteration of the algorithm, but the error decreases rapidly with the increase in the number of iterations. And due to the introduction of fitness sharing strategy and hybrid local search strategy, different from the traditional bat algorithm, the improved bat algorithm can quickly jump out after falling into the local optimal solution during the parameter identification process. In the first 100 iterations, the improved bat algorithm jumped out of the local optimal solution by introducing the local search strategy. The final errors of the two algorithms are shown in the following table.

[0057] Table 1 Error comparison of different algorithms

[0058]

[0059] It can be seen that the improved bat algorithm is superior to the traditional bat algorithm and genetic algorithm in terms of calculation accuracy.

[0060] It can be seen from the above scheme that the power system load model parameter identification method based on the improved bat algorithm provided in this application adopts a static load model in parallel with a third-order induction motor load model structure, and uses an improved bat algorithm to identify the parameters of the load model. Compared with the traditional bat algorithm, it has higher calculation speed and calculation accuracy.

[0061] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for identifying parameters of a power system load model based on an improved bat algorithm, characterized in that: include: Step 1: construct a comprehensive load model structure of a static load model parallel-connected three-order induction motor, wherein the load parameters in the load model structure include static load parameters, induction motor parameters, and motor load proportion; Step 2: Construct a fitness function, which reflects the difference between the calculated active power and reactive power of the comprehensive load model and the active power and reactive power in the actual system under the same voltage excitation; Step 3, using chaotic mapping to initialize the position of each bat in the bat population in the solution space of the parameters to be identified; Step 4: Use the improved bat algorithm for iterative optimization, introduce shared fitness strategy and local search strategy to avoid falling into the local optimal solution.

2. The method for identifying power system load model parameters based on the improved bat algorithm according to claim 1 is characterized in that: The step 1 further includes the following contents: The static load model part of the comprehensive load model structure is described by the following mathematical equation: Where P and Q represent the active power and reactive power consumed by the static part of the load model when the load node voltage is U; P0 and Q0 represent the active power and reactive power consumed by the static part of the load model when the load node is U0; a p 、b p 、c p They represent the proportion of active power consumed by constant impedance load, constant current load, and constant power load, respectively, and the sum of the three is 1; a q 、b q 、c q They represent the proportion of reactive power consumed by constant impedance load, constant current load, and constant power load respectively, and the sum of the three is 1; The third-order induction motor model is described in the following form: The stator voltage equation of the third-order induction motor is: The rotor state equation is: The current equation is And there is Among them, U d and U q are the d-axis and q-axis components of the motor stator terminal voltage respectively; I d and I q are the d-axis and q-axis components of the stator current respectively; E' d and E' q are the d-axis and q-axis components of the transient potential of the motor respectively; T' d0 is the rotor circuit time constant when the stator is open; ω s is the synchronous speed of the system; s is the rotor slip of the motor; H is the inertia constant of the motor rotor; T m and T e are mechanical torque and electromagnetic torque respectively; T m0 is the mechanical torque of the motor in steady-state operation; A, B, C are mechanical torque coefficients; f * and f0 * They are the current frequency per unit value and the system rated frequency per unit value respectively; X s is the stator reactance of the motor, X r is the rotor reactance, X m is the armature reaction reactance, R s is the stator resistance, R r is the rotor resistance, X' and X are the rotor locked-rotor reactance and rotor open-circuit reactance, respectively.

3. The method for identifying power system load model parameters based on the improved bat algorithm according to claim 1 is characterized in that: The step 2 further includes the following contents: The fitness function of the power system comprehensive load model parameter identification is: Among them, n is the number of times the power grid is disturbed, P sw , P loadw , Q sw , Q loadw They respectively represent the actual active power value of the load at the wth disturbance moment, the active power calculated value calculated by the comprehensive load model, the actual reactive power value of the load, and the reactive power calculated value calculated by the comprehensive load model.

4. The method for identifying power system load model parameters based on the improved bat algorithm according to claim 1 is characterized in that: The step 3 further includes the following contents: using Logistic chaotic mapping to initialize the position x of each bat in the bat population t ′=ux t (1-x t ), random number u∈[0,4], x t is the randomly initialized position, x t ′ is the position of each bat after chaotic mapping.

5. The method for identifying power system load model parameters based on an improved bat algorithm according to claim 1, characterized in that: The step 4 further includes the following contents: Step 402, updating the speed, loudness, ultrasonic frequency and pulse frequency of each bat; Step 403, updating the possible position of the bat in the next iteration order according to the relationship between the random value and the loudness; Step 404, sharing fitness with solutions near the peak in the solution space according to the fitness sharing strategy, the more solutions there are within a certain range, the lower the fitness value; Step 405, calculating the fitness function value of the new position to which the bat may move, if the fitness function value at the new position is less than the fitness function value at the old position, and the random number is greater than the current loudness, then the position of the bat is updated, otherwise the original position remains unchanged; Step 406, determining whether the current iteration order is an integer multiple of 10, if so, performing a local search using the simplex method to obtain a new position, and updating the bat position; Step 407, set the maximum number of iterations, repeat steps 402 to 406 until the number of iterations is equal to the maximum number of iterations, and output a set of load parameters with the minimum fitness function value at this time.

6. The method for identifying parameters of a power system load model based on an improved bat algorithm according to claim 5, characterized in that: The step 404 further includes the following contents: According to the fitness sharing strategy, the solutions near the peak in the solution space share fitness. The more solutions there are within a certain range, the lower their fitness value. Its mathematical expression is as follows: Where L is the number of variables in the solution space, and are the upper and lower bounds of the solution respectively; s(d(i,j)) is the shared function between individual i and individual j, σ share represents the shared radius, α is a constant; f m ' indicates x i The shared fitness value of .

7. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

8. An electronic device, characterized in that: It comprises a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method described in any one of claims 1 to 6 when executed.

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