Synchronous Condenser Capacity Configuration Method Based on Genetic Optimization Algorithm

Abstract

The present invention discloses a synchronous phase condenser capacity configuration method based on a genetic optimization algorithm, the steps of which are as follows: establishing a power system transient simulation model and a phase condenser capacity optimization configuration model in BPA and MatLab respectively, initializing parameters such as the number of individuals n, crossover rate c, mutation rate v, maximum number of iterations N, and convergence error ε, randomly generating an initial population, and starting iteration; BPA reads the phase condenser capacity to perform transient simulation and generate simulation data, MatLab reads the simulation data, normalizes the data, and calculates the fitness function of the current population; saving the individual with the smallest fitness function as SCo; performing selection, crossover, and mutation based on the genetic algorithm to produce a new generation of population, judging whether the convergence conditions are met, and if so, exiting the iteration; otherwise, continuing the iteration. This method realizes the automatic iteration and optimization solution of the synchronous phase condenser capacity configuration problem to improve system stability based on the genetic optimization algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of reactive power compensation optimization configuration of power systems, and in particular relates to a synchronous phase regulator capacity configuration method based on a genetic optimization algorithm. Background Art

[0002] In recent years, my country's power grid has become a complex hybrid system with AC and DC connections, and voltage stability issues in DC receiving grids have become increasingly prominent. To improve the transient voltage level of DC receiving grids, sufficient reactive power compensation devices are required. Synchronous condensers, as a representative device that provides dynamic reactive power, are ideal for rapidly providing reactive power compensation after an incident. Optimal configuration of synchronous condensers has become a key issue in ensuring voltage stability in DC receiving systems.

[0003] The existing synchronous condenser capacity optimization configuration method has problems such as tedious manual iteration, large amount of calculation, low calculation efficiency and low accuracy. Summary of the Invention

[0004] In order to overcome the problems existing in the above-mentioned prior art, the purpose of the present invention is to propose a synchronous phase condenser capacity configuration method based on a genetic optimization algorithm. This method uses BPA software for simulation to obtain the fitness function value. MatLab compares the fitness function value and continuously iterates and updates the individual to find the optimal individual that meets the constraints. It can efficiently solve the synchronous phase condenser capacity optimization configuration problem to improve system stability.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Step 1: Establish a power system transient simulation model including synchronous condensers in BPA software (power system analysis software);

[0007] Step 2: Establish a synchronous condenser capacity optimization configuration model in Maltab and determine the objective function and constraints. The objective function specifically includes the installation cost and safety index of the synchronous condenser, and the constraints specifically include the installation location of the synchronous condenser and the capacity upper limit Q. imax ;

[0008] Step 3: Take the synchronous condenser capacity of s reactive compensation points as s components of an individual, that is, the value of each individual is [Q1, Q2, ..., Q i ,…,Q s ], where Q i is the capacity of the i-th synchronous condenser of the individual; randomly generate an initial population consisting of n individuals, and set the number of iterations k = 1;

[0009] Step 4: Use the BPA software to perform simulations to obtain transient voltage drop data when a predicted fault occurs under the synchronous condenser capacity configuration scheme represented by each individual. Normalize the transient voltage drop data in MatLab and calculate the fitness function of each individual.

[0010] Step 5: Save the individual with the largest fitness function and record it as the optimal configuration capacity SCo of the condenser;

[0011] Step 6: Binary encode the individuals, perform selection, crossover, and mutation operations, and then restore the evolved individuals to decimal to generate a new population;

[0012] Step 7: Determine whether the number of iterations is greater than the maximum number of iterations. If not, set k = k + 1 and return to step 4 to continue iterating. If so, exit the loop.

[0013] Step 8: Output SCo at this time, which is the optimal configuration plan for the synchronous condenser capacity.

[0014] The fitness function described in step 4 is as follows:

[0015]

[0016] Where a1 represents the weight of the economic cost objective function, a2 represents the weight of the receiving system maximum voltage drop objective function, and a1 + a2 = 1; f1 represents the economic cost sub-objective function in the synchronous condenser capacity configuration scheme, and its value is calculated according to formula (2):

[0017]

[0018] Where C pu The cost of synchronous condenser per unit capacity;

[0019] f2 represents the maximum voltage drop index of the receiving system after the synchronous condenser is installed. The specific definition is as follows:

[0020]

[0021] Where q represents the total number of failures; p g represents the probability of occurrence of fault g; m is the total number of buses in the receiving system; V i,min represents the lowest voltage value of bus i when a fault occurs, which is obtained through time domain simulation; V i,0 Represents the steady-state voltage value of bus i.

[0022] In step 6, selection, crossover and mutation operations are performed. The selection principle is: select individuals to form a new population according to probability. The probability of the lth individual being selected is P l for:

[0023]

[0024] Where, F l is the fitness function of the lth individual, F sum is the sum of the fitness functions of all individuals;

[0025] The principle of crossover is: randomly pair the population, randomly select a crossover position for each paired individual and crossover according to the set crossover rate c;

[0026] The principle of mutation is: randomly select an individual, randomly select a position for the selected individual and mutate it according to the mutation rate v.

[0027] The method of the present invention establishes a power system transient simulation model in BPA software, sets a synchronous phase shifter capacity configuration program based on a genetic optimization algorithm in MatLab, and starts iterative calculation after initialization; the BPA software automatically modifies the power system model and synchronous phase shifter parameters according to the synchronous phase shifter capacity configuration plan, performs transient simulation calculations, and outputs the results to MatLab; MatLab is used to process and calculate data, and individuals are updated based on the genetic algorithm according to the fitness function, that is, the synchronous phase shifter capacity configuration plan is updated to complete an iterative process; when the maximum number of iterations is reached, the optimal solution for solving the optimization problem can be obtained. The synchronous phase shifter capacity configuration method based on the genetic optimization algorithm proposed by the present invention can combine MatLab with BPA, reduce the computational complexity of the complex power system modeling process and electromagnetic transient simulation, improve the computational efficiency of solving the optimization problem, and efficiently solve the phase shifter layout optimization problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the method of the present invention.

[0029] Figure 2 This is the example topology diagram of the improved IEEE-39 node system.

[0030] Specific implementation methods

[0031] The present invention is further illustrated below using a simulation example.

[0032] like Figure 1 As shown, the synchronous condenser capacity configuration method based on the genetic optimization algorithm proposed in the present invention has the following specific steps:

[0033] Step 1: Create a transient simulation model of the power system including synchronous condensers in BPA software. The system topology is as follows: Figure 2 As shown;

[0034] Step 2: Establish a synchronous condenser capacity optimization configuration model in Maltab and determine the objective function and constraints. The objective function specifically includes the installation cost and safety index of the synchronous condenser, and the constraints specifically include the installation location of the synchronous condenser and the capacity upper limit Q. ima =450MVA;

[0035] Step 3: The dimension of each individual is 3, that is, the value of each individual is [Q1, Q2, Q3]. Randomly generate an initial population of 20 individuals and set the number of iterations k = 1;

[0036] Step 4: Use the BPA software to perform simulations to obtain transient voltage drop data when a predicted fault occurs under the synchronous condenser capacity configuration scheme represented by each individual. Normalize the transient voltage drop data in MatLab and calculate the fitness function of each individual.

[0037] Step 5: Save the individual with the largest fitness function and record it as the optimal configuration capacity SCo of the condenser;

[0038] Step 6: Binary encode the individuals, perform selection, crossover, and mutation operations, where the crossover rate c is 0.5 and the mutation rate v is 0.01, and then restore the evolved individuals to decimal to generate a new population;

[0039] Step 7: Determine whether the number of iterations is greater than the maximum number of iterations N = 50. If not, set k = k + 1 and return to step 4 to continue iterating. If so, exit the loop.

[0040] Step 8: Output SCo at this time, which is the optimal configuration plan for the synchronous condenser capacity.

[0041] Simulation Example

[0042] In this invention, the simulation work is carried out on BPA software, and the improved IEEE-39 node system is selected as the test system. The system topology is as follows: Figure 2 As shown. After adjusting the power flow of the standard IEEE-39 busbar model, the improved 39-busbar system can be more clearly divided into the sending and receiving systems, with buses 39, 3, and 16 serving as the dividing line between the sending and receiving systems. To prevent line overload, a DC transmission line 1 with a rated power of 600 MW was constructed between busbars 19 and 6, and a DC transmission line 2 with a rated power of 950 MW was constructed between busbars 22 and 15. In the test system, all synchronous generators used fourth-order models, equipped with standard voltage regulators and linear speed regulators, and all load models used the universal load model.

[0043] In order to verify the effectiveness of the method of the present invention, the capacity optimization configuration of synchronous condenser is carried out in the test case of the improved IEEE-39 system.

[0044] To account for typical system faults, the fault locations were selected at 50% of the line length on lines 13-14, 10-11, and 7-8, respectively. The fault type was a three-phase short circuit, and the fault duration was 0.1s. These three faults represent faults occurring in different areas of the receiving grid. The DC distances between the receiving grid nodes were obtained based on the system impedance matrix. Three nodes were selected as synchronous condenser installation locations based on the DC distance parameters and the reactive power compensation-voltage sensitivity metric: bus 4, bus 14, and bus 6.

[0045] Using the objective function of formula (1) and the genetic optimization algorithm proposed in this invention, the optimal allocation scheme obtained by calculation is shown in Table 1:

[0046] Table 1 Comparison of the effects of synchronous condensers with a total capacity of 830MVA

[0047]

Claims

1. A method for synchronous condenser capacity configuration based on a genetic optimization algorithm, characterized by: The specific steps are as follows: Step 1: Establish a power system transient simulation model including synchronous condensers in BPA software; Step 2: Establish a synchronous condenser capacity optimization configuration model in Maltab and determine the objective function and constraints. The objective function specifically includes the installation cost and safety index of the synchronous condenser, and the constraints specifically include the installation location of the synchronous condenser and the capacity upper limit Q. imax ; Step 3: Take the synchronous condenser capacity of s reactive compensation points as s components of an individual, that is, the value of each individual is [Q1, Q2, ..., Q i ,…,Q s ], where Q i is the capacity of the i-th synchronous condenser of the individual; randomly generate an initial population consisting of n individuals, and set the number of iterations k = 1; Step 4: Use the BPA software to perform simulations to obtain transient voltage drop data when a predicted fault occurs under the synchronous condenser capacity configuration scheme represented by each individual. Normalize the transient voltage drop data in MatLab and calculate the fitness function of each individual. Step 5: Save the individual with the largest fitness function and record it as the optimal configuration capacity SCo of the condenser; Step 6: Binary encode the individuals, perform selection, crossover, and mutation operations, and then restore the evolved individuals to decimal to generate a new population; Step 7: Determine whether the number of iterations is greater than the maximum number of iterations. If not, set k = k + 1 and return to step 4 to continue iterating. If so, exit the loop. Step 8: Output SCo at this time, which is the optimal configuration plan for the synchronous condenser capacity.

2. The method for synchronous condenser capacity configuration based on genetic optimization algorithm according to claim 1, characterized in that: The fitness function described in step 4 is as follows: Where a1 represents the weight of the economic cost objective function, a2 represents the weight of the receiving system maximum voltage drop objective function, and a1 + a2 = 1; f1 represents the economic cost sub-objective function in the synchronous condenser capacity configuration scheme, and its value is calculated according to formula (2): Where C pu The cost of synchronous condenser per unit capacity; f2 represents the maximum voltage drop index of the receiving system after the synchronous condenser is installed. The specific definition is as follows: Where q represents the total number of failures; p g represents the probability of occurrence of fault g; m is the total number of buses in the receiving system; V i,min represents the lowest voltage value of bus i when a fault occurs, which is obtained through time domain simulation; V i,0 Represents the steady-state voltage value of bus i.

3. The method for synchronous condenser capacity configuration based on genetic optimization algorithm according to claim 1, characterized in that: In step 6, selection, crossover and mutation operations are performed. The selection principle is: select individuals to form a new population according to probability. The probability of the lth individual being selected is P l for: Where, F l is the fitness function of the lth individual, F sum is the sum of the fitness functions of all individuals; The principle of crossover is: randomly pair the population, randomly select a crossover position for each paired individual and crossover according to the set crossover rate c; The principle of mutation is: randomly select an individual, randomly select a position for the selected individual and mutate it according to the mutation rate v.

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