Method for optimal configuration of synchronous condenser capacity based on particle swarm algorithm

Abstract

The application discloses a synchronous phase modifier capacity optimization configuration method based on a particle swarm algorithm, and steps are as follows: a power system transient simulation model and a phase modifier capacity optimization configuration model are respectively established in DIgSILENT and MatLab, particle swarm data and parameters epsilon are initialized, and iteration is started; DIgSILENT reads a model parameter file to perform transient simulation, and generates simulation data; MatLab reads the simulation data, normalizes the data, calculates an objective function, determines individual optimal positions and a global optimal position of particles, compares a function value of the global optimal position of this time with a function value of the global optimal position of last time, if the difference is less than epsilon, iteration is exited, if not, iteration is continuously performed, and the speed and the position of the particles are updated until an optimal solution is obtained. The method is based on a particle swarm optimization algorithm, combines transient simulation of MatLab and DIgSILENT, and realizes automatic iteration and solution of a synchronous phase modifier capacity optimization problem considering constraints.

Description

Technical Field

[0001] This invention belongs to the technical field of synchronous condenser capacity optimization configuration methods, specifically relating to a synchronous condenser capacity optimization configuration method based on particle swarm optimization algorithm. Background Technology

[0002] With the rapid development of high-voltage direct current (HVDC) transmission projects in my country, the country's power grid has become increasingly complex, exhibiting a hybrid AC / DC configuration. The voltage stability of the DC receiving-end grid has become a growing concern. When a commutation failure occurs in the DC transmission system, if the AC system cannot provide sufficient dynamic reactive power, it can lead to continuous commutation failures, triggering DC blocking and causing a large-scale shift in system power flow, potentially resulting in widespread blackouts. To improve the transient voltage level of the DC receiving-end grid, sufficient reactive power compensation devices need to be installed. Synchronous synchronous condensers, as representative devices capable of providing dynamic reactive power, are ideal for rapidly providing reactive power compensation after an accident. Optimizing the configuration of synchronous condensers has become a crucial issue in ensuring the voltage stability of the DC receiving-end system.

[0003] Existing methods for optimizing the capacity of synchronous condensers suffer from problems such as tedious manual iteration, large computational load, low computational efficiency, and low accuracy. Summary of the Invention

[0004] To overcome the problems existing in the prior art, the present invention aims to propose a synchronous camera capacity optimization configuration method based on particle swarm optimization algorithm. This method uses DIgSLIENT software for simulation to obtain the objective function value, and MATLAB compares the objective function value to continuously update the particles and find the global optimal position of the particles. This method can efficiently solve the synchronous camera capacity optimization configuration problem with transient stability constraints.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for optimizing the capacity configuration of a synchronous camera based on particle swarm optimization algorithm, the specific steps of which are as follows:

[0007] Step 1: Establish a transient simulation model of the power system including a synchronous condenser in DIgSILENT software;

[0008] 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, operating cost and safety indicators of the synchronous condenser, and the constraints specifically include the installation location, capacity range, change step size and number of iterations of the synchronous condenser.

[0009] Step 3: Use DIgSLIENT software to perform simulation and obtain transient voltage drop data under the expected fault set;

[0010] Step 4: The synchronous condenser capacities of the n reactive power compensation points are respectively used as the n components of the particle position. Therefore, the dimension of the problem solved by the particle swarm optimization algorithm is n, where 0 ≤ Q. i ≤Q imax Initialization under constraints;

[0011] Step 5: Call the DIgSLIENT software for simulation, and normalize the transient voltage drop data obtained in Step 3 in MatLab. Calculate and compare the objective function values ​​to determine the optimal position of the individual particle and the global optimal position.

[0012] Step 6: Compare the function values ​​of the global optimal position in this iteration with those in the previous iteration. If the difference is less than ε, end the iteration; otherwise, return to step 5 to continue the iteration, updating the particle's position and velocity until the iteration termination condition is met.

[0013] Step 7: Output the particle position at this time, which is the optimal configuration scheme for the synchronous camera capacity.

[0014] The method of this invention establishes a power system transient simulation model in DIgSILENT software, sets up a synchronous condenser capacity optimization program in MATLAB based on the particle swarm optimization algorithm, and starts iterative calculation after initialization. DIgSILENT software automatically modifies the power system model and synchronous condenser parameters according to the synchronous condenser capacity configuration scheme, executes transient simulation calculations, and outputs the results to MATLAB. MATLAB is used for data processing and calculation, updating the individual optimal and global optimal positions of particles through the objective function value, i.e., updating the synchronous condenser capacity configuration scheme, completing one iteration process. When the iteration termination condition is met or the maximum number of iterations is reached, the optimal solution to the optimization problem can be obtained. Through the synchronous condenser capacity optimization configuration method based on the particle swarm optimization algorithm proposed in this invention, a particle swarm optimization algorithm adapted to the optimization problem can be written in MATLAB and combined with the professional power system transient simulation calculation in DIgSILENT software. This reduces the complex power system modeling process and the computational load of electromagnetic transient simulation, improves the computational efficiency of solving the optimization problem, and can efficiently solve the synchronous condenser placement optimization problem. The synchronous condenser capacity optimization configuration scheme obtained by the method of the present invention effectively improves the transient voltage stability level of the DC receiving-end power grid. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention.

[0016] Figure 2 This is a computational topology diagram of the improved IEEE-39 node system. Specific implementation methods

[0017] The invention will be further illustrated below using simulation examples.

[0018] like Figure 1 As shown, the specific steps of the synchronous condenser capacity optimization configuration method based on particle swarm optimization proposed in this invention are as follows:

[0019] Step 1: Establish a transient simulation model of the power system including a synchronous condenser in the DIgSILENT software. The system topology is as follows: Figure 2 As shown;

[0020] 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 economic and safety indicators of the synchronous condenser, and the constraints specifically include the installation location of the synchronous condenser, the capacity range, the change step size, and the number of iterations.

[0021] Step 3: Use DIgSLIENT software to perform simulation and obtain transient voltage drop data under the expected fault set;

[0022] Step 4: The synchronous condenser capacities of the three reactive power compensation points are respectively used as the three components of the particle position. Therefore, the dimension of the problem solved by the particle swarm optimization algorithm is 3, where 0 ≤ Q. i Initialize under the constraint of ≤450MVar;

[0023] Step 5: Call the DIgSLIENT software for simulation, and normalize the transient voltage drop data obtained in Step 3 in MatLab. Calculate and compare the objective function values ​​to determine the optimal position of the individual particle and the global optimal position.

[0024] Step 6: Compare the function values ​​of the global optimal position in this iteration with those in the previous iteration. If the difference is less than ε, end the iteration; otherwise, return to step 5 to continue the iteration, updating the particle's position and velocity until the iteration termination condition is met.

[0025] Step 7: Output the particle position at this time, which is the optimal configuration scheme for the synchronous camera capacity.

[0026] Simulation Examples

[0027] In this invention, the simulation was performed using DIgSILENT software, and an improved IEEE-39 node system was selected as the test system. The system topology is as follows: Figure 2As shown, after adjusting the power flow of the standard IEEE-39 node model, the improved 39-node system can be clearly divided into a sending-end system and a receiving-end system, with buses 39, 3, and 16 serving as the dividing lines between the sending-end and receiving-end systems. Furthermore, to avoid line overload, a DC transmission line with a rated power of 600MW was constructed between buses 19 and 6; and a DC transmission line with a rated power of 950MW was constructed between buses 22 and 15. In the test system, all synchronous generators adopted a fourth-order model, equipped with standard voltage regulators and linear speed governors, and all load models used a general load model.

[0028] To verify the effectiveness of the method of the present invention, the synchronous conversion camera capacity was optimized and tested in an improved IEEE-39 system.

[0029] To account for typical system faults, fault locations were selected at 50% of lines 13-14, 10-11, and 7-8, respectively. The fault type was a three-phase short circuit, and the fault duration was 0.1 seconds. These three faults represent fault scenarios occurring in three different areas of the receiving-end power grid. The DC distances between the receiving-end power grid nodes were obtained based on the system impedance matrix. Based on the DC distance parameters and the reactive power compensation-voltage sensitivity index, three nodes were selected as the installation nodes for the synchronous condenser: bus 4, bus 14, and bus 6.

[0030] Using the objective function of equation (1) and the particle swarm optimization algorithm proposed in this invention, the optimal allocation scheme obtained is shown in Table 1.

[0031] Table 1 Comparison of Synchronous Condensation Capacity at a Total Capacity of 830MVA (Table 1: Performance Comparison of Synchronous Condensation Units)

[0032]

Claims

1. A method for optimal configuration of synchronous condenser capacity based on particle swarm optimization algorithm, characterized in that: The specific steps are as follows: Step 1: Establish a power system transient simulation model containing synchronous compensators in DIgSILENT software; Step 2: Establish a synchronous compensator capacity optimization configuration model in Maltab, determine the objective function and constraint conditions, the objective function specifically includes the installation cost, operation cost and safety index of the synchronous compensator, and the constraint conditions specifically include the installation location, capacity range, step length and convergence deviation ε of the compensator; Step 3: Call the DIgSLIENT software for simulation to obtain the transient voltage drop data under the set of expected faults; Step 4: The n components of the particle position are respectively the capacities of the n synchronous compensators, i.e. the dimension of the problem solved by the particle swarm optimization algorithm is n, and the particle position is initialized under the constraints of 0≤Q i ≤Q imax , where Q i is the configuration capacity of the i-th synchronous compensator, and Q imax is the upper limit of the capacity of the synchronous compensator; Step 5: Call the DIgSLIENT software for simulation, and normalize the transient voltage drop data obtained in step 3 in MatLab, calculate and compare the objective function values, and determine the individual optimal position and global optimal position of the particle; Step 6: Compare the function values of the global optimal position of this iteration with those of the global optimal position of the last iteration, if the difference is less than ε, end the iteration, otherwise return to step 5 to continue the iteration, update the position and speed of the particle until the iteration end condition is met; Step 7: Output the particle position at this time, which is the optimal configuration scheme of the synchronous compensator capacity.

2. The method of claim 1, wherein the method is characterized by: In step 5, DIgSILENT software is used for simulation, and the transient voltage drop data obtained in step 3 is normalized in MatLab to calculate the objective function value, and the objective function is as follows: min F=c1f1+c2f2 (1) In the formula, c1 represents the weight value of the economic cost objective function, c2 represents the weight value of the maximum voltage drop of the receiving end system objective function, and c1+c2=1; f1 represents the economic cost sub-objective function in the synchronous compensator capacity configuration scheme, and its value is calculated according to formula (2); wherein, wherein k represents the installation point of all synchronous condensers; C install is the installation cost of the synchronous condenser; C pu is the cost of the unit capacity synchronous condenser; Q i is the capacity of the synchronous condenser at node i; EVLis the solution of the economic cost; EVL max and EVL min are the economic cost of the maximum / minimum capacity condenser configuration scheme, respectively; f2 represents the maximum voltage drop index of the receiving end system after installing the synchronous compensator, which is defined as follows: In the formula, is the maximum voltage drop index of the receiving end power grid; the index n represents the total number of faults; p g represents the occurrence probability of fault g; m is the total number of busbars of the receiving end system; V i,min respectively represent the minimum voltage of busbar i when a fault occurs, which is obtained through time domain simulation; V i,0 represents the steady-state voltage value of busbar i; TVF max and TVF min are respectively the maximum value and the minimum value of the maximum voltage drop index of the receiving end power grid under different faults.

3. The method of claim 1, wherein the method is characterized by: In step 6, the position and speed of the particle are updated using the particle swarm optimization algorithm in each iteration process of MatLab and DIgSILENT interaction until the iteration end condition is met and the loop is exited; the formula for updating the individual optimal position and speed of the particle by the particle swarm optimization algorithm is as follows: x i (t+1) = x i (t) + v i (t+1) (5) where v i (t) is the velocity of particle i at time t, v i (t+1) is the velocity of particle i at time t+1, x i (t+1) is the position of particle i at time t+1, x i (t) is the position of particle i at time t, ω is the inertia weight, c1, c2 are acceleration constants, r1, r2 are acceleration factors, x gbest are the individual historical best and global historical best, respectively.

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