Phase modifier optimal configuration method based on short-circuit ratio improvement

Abstract

The invention relates to a phase modifier optimal configuration method based on short-circuit ratio improvement. The method comprises two stages: in the first stage, the position and the basic capacity of the phase modifier are optimally configured by taking the improvement of the short-circuit ratio of the wind power plant as a core constraint, and in the second stage, the configuration capacity of the phase modifier is corrected by taking the transient overvoltage safety as a key constraint, and an improved genetic simulated annealing algorithm is introduced for optimization solution. Taking the minimum configuration capacity of the phase modifier as a target, comprehensively considering the wind power plant, the overall short-circuit ratio, the short-circuit ratio balance degree constraint, the phase modifier configuration number constraint and the power flow constraint, and completing the first-stage phase modifier optimization configuration to improve the power grid strength of the wind power plant. And in combination with the optimal configuration result of the phase modifier in the first stage, constructing a wind power plant transient overvoltage suppression-based phase modifier capacity correction optimization model, solving the model by using an improved genetic simulated annealing algorithm, and determining the correction capacity of the phase modifier. The method is effective for improving the voltage supporting capacity of the wind power plant and suppressing transient overvoltage.

Description

Technical Field

[0001] This invention relates to the field of transient overvoltage suppression, and more specifically to a method for optimizing the configuration of synchronous condensers based on improving the short-circuit ratio. Background Technology

[0002] In recent years, with the large-scale integration of wind turbines into the power grid, the short-circuit ratio of new energy power plants at the sending-end power grid has decreased, and the voltage support capacity has weakened. On the other hand, when the sending-end power grid is faulty, the reactive voltage response of the wind turbine converter has a control time delay. When the fault is cleared, there will be a short-term reactive power surplus, which will cause transient overvoltage, affecting the safe and stable operation of the grid-connected units.

[0003] As rotating synchronizing elements, synchronous condensers exhibit strong reactive power throughput and excellent transient overvoltage suppression capabilities due to their electromagnetic transient instantaneous reactive power response characteristics during fault occurrence and clearing. Large centralized synchronous condensers are typically located in and near DC converter stations. However, due to their significant electrical distance from renewable energy sites, their ability to mitigate transient overvoltages is limited. Therefore, it is considered to configure distributed synchronous condensers (typically with a single unit capacity of 50Mvar) at renewable energy sites to enhance voltage support and transient overvoltage suppression capabilities. Because distributed synchronous condensers have high investment and maintenance costs, their connection locations and configuration capacities must be rationally optimized.

[0004] The short-circuit ratio is a crucial indicator for measuring voltage support capacity. Existing methods for improving the short-circuit ratio through perfecting the short-circuit ratio index and reactive power configuration are insufficient to specifically address the safety requirements of transient overvoltages after grid disturbances, resulting in issues such as unreasonable capacity configuration and inability to guarantee transient voltage safety. Research on the causes of grid transient overvoltages and the suppression effect of reactive power compensation configuration on transient overvoltages only focuses on optimizing synchronous condenser configurations for transient voltage safety, lacking effective integration with synchronous condenser configuration methods based on improving the short-circuit ratio index.

[0005] This invention addresses the comprehensive requirements of current power grid safety regulations regarding the short-circuit ratio level and transient overvoltage safety threshold of new energy power plants, thereby enhancing the transient voltage support capability of new energy power plants. Summary of the Invention

[0006] The purpose of this invention is to provide a method for optimizing the configuration of synchronous condensers based on improving the short-circuit ratio. This method optimizes the location and basic capacity of synchronous condensers by taking the improvement of the short-circuit ratio of the wind farm as the core constraint, and then corrects the configuration capacity of the synchronous condensers by taking transient overvoltage safety as the key constraint. Finally, an improved genetic simulated annealing algorithm is introduced to optimize the solution.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] Step 1: Considering the parameters of the sending-end grid components and the power flow operation status, establish a simulation model of the sending-end grid with a high proportion of wind power access.

[0009] Step 2: Calculate the short-circuit ratio, overall short-circuit ratio, and balance of each renewable energy plant in the sending-end power grid under the feasible solution of power flow. Overvoltage is closely related to grid strength, and the current short-circuit ratio is one of the effective indicators for measuring grid strength. The short-circuit ratio of multiple renewable energy plants at any node i in the system (denoted as MRSCR) is... i ) can be represented as

[0010]

[0011] Wherein: S i S represents the short-circuit capacity of the new energy power station i at the grid-side connection point; REi and S REj The actual apparent power of new energy injected into the i-th and j-th new energy grid-connected bus nodes; Z eqji and Z eqii These are the self-impedance of new energy power station i and the mutual impedance between power stations i and j, respectively.

[0012] After a synchronous condenser is connected at point k in the system, the change in the short-circuit ratio of any wind farm node i in the system affects the mutual impedance Z. eqki Taking the partial derivative yields

[0013]

[0014] Among them: U Ni Rated voltage; Z eqki Z eqkj Z represents the equivalent mutual impedance between the new energy power stations i and j and the synchronous condenser access point k; eqkk The equivalent self-impedance of the synchronous modulator access point k; z k To adjust the equivalent parallel impedance of the camera to ground;

[0015] From the above equation, we can see that both the numerator and denominator are positive, therefore Z eqki The larger the value of Z, the larger the value of ΔMRSCRi. This is because points with lower MRSCR in the power grid are farther away from the grid electricalally. eqki The larger the MRSCR, the greater the increase in the short-circuit ratio of all new energy nodes, which is more beneficial to improving system stability.

[0016] Step 3: Select the node with the lowest short-circuit ratio at the wind farm in the power grid as the optimal location for configuring the synchronous condenser (SCE) at the sending end of the grid, and add one distributed SCE. As a synchronous rotating device, the SCE is electromagnetically coupled to the AC grid. Its spontaneous reactive power response without delay reflects the electrical characteristics of the synchronous grid itself. The connection of the SCE directly improves the short-circuit ratio of the grid, enhances the grid strength, and improves the voltage support capability during system faults. In particular, distributed SCEs, compared to traditional large SCEs, have advantages such as high integration, system stability, short construction period, low cost per unit capacity, and good regulation performance. They also possess superior transient, subtransient, and steady-state performance, which can significantly improve the capacity for transmitting new energy to other regions.

[0017] Step 4: Recalculate the short-circuit ratio, overall short-circuit ratio and balance of each new energy power station in the sending-end power grid.

[0018] Step 5: Determine whether the short-circuit ratio, overall short-circuit ratio, and balance of each new energy power station in the sending-end power grid meet the constraints. If the constraints are met, the first stage of optimizing the configuration of synchronous condensers to enhance the power grid strength is completed, and the second stage of adjusting the capacity of synchronous condensers is entered. Otherwise, return to step 3.

[0019] Step 6: Perform three-phase short-circuit fault detection on the grid-connected busbars of each wind farm in the sending-end power grid after the first-stage synchronous condenser configuration and connection. Perform transient time-domain simulation of the sending-end power grid and observe the voltage change curves of different wind turbine busbars under various fault scenarios.

[0020] Step 7: Determine whether the wind turbine voltage meets the transient overvoltage safety constraint under all fault scenarios. If it does, complete the synchronous condenser optimization configuration; otherwise, adjust the capacity based on the synchronous condenser configuration capacity correction model.

[0021] Step 8: Solve the model using the improved genetic simulated annealing algorithm to determine the synchronous condenser correction capacity, then return to Step 6. The improved genetic simulated annealing algorithm is used to solve the objective function. Genetic algorithms, as classic algorithms for optimization problems, have advantages such as convenient computation and fast convergence, but they still suffer from premature convergence and a tendency to get trapped in local optima. Simulated annealing, on the other hand, features probabilistic jumps and is a stochastic search algorithm that extends local search algorithms. Theoretically, it can avoid getting trapped in local optima. Solving multi-objective optimization problems using simulated annealing can probabilistically converge to the global optimum. To improve the genetic algorithm's tendency to get trapped in local optima and obtain a better mine water scheduling method, the improved genetic simulated annealing algorithm is proposed by combining simulated annealing with an adaptive genetic algorithm. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the optimized configuration of the synchronous condenser according to the present invention.

[0023] Figure 2 This is a flowchart of the improved genetic simulated annealing algorithm of the present invention; Detailed Implementation

[0024] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0025] The camera adjustment method of the present invention is as follows: Figure 1 As shown, the process comprises two stages: the first stage optimizes the location and basic capacity of synchronous condensers with the core constraint of improving the short-circuit ratio of the wind farm; the second stage corrects the configuration capacity of the synchronous condensers with the key constraint of transient overvoltage safety, and introduces an improved genetic simulated annealing algorithm for optimization. The specific implementation steps are as follows:

[0026] The first stage is model building. Considering the model parameters of the sending-end power grid components and the power flow operation status, a simulation model of the sending-end power grid with a high proportion of wind power access is established.

[0027] The expression for calculating the short-circuit ratio, overall short-circuit ratio, and equilibrium degree of each renewable energy plant in the sending-end power grid under the feasible solution of power flow is as follows:

[0028] Short-circuit ratio:

[0029] Wherein: S i S represents the short-circuit capacity of the new energy power station i at the grid-side connection point; REi and S REj The actual apparent power of new energy injected into the i-th and j-th new energy grid-connected bus nodes; Z eqji and Z eqii These are the self-impedance of new energy power station i and the mutual impedance between power stations i and j, respectively.

[0030] Balance:

[0031] Among them: MRSCR max and MRSCR min These are the maximum and minimum values ​​of MRSCR for all wind and solar power plants. av The average value of the MRSCR of the wind farm in the sending-end power grid; MRSCR BD This represents the upper limit of short-circuit ratio balance.

[0032] Determine whether the short-circuit ratio and balance constraints are met. If the constraints are met, no synchronous condenser needs to be configured. If not, the node with the lowest short-circuit ratio of the wind farm in the power grid is taken as the optimal location for configuring the synchronous condenser at the sending end of the power grid, and one distributed synchronous condenser is added.

[0033] Recalculate the short-circuit ratio, overall short-circuit ratio, and balance of each new energy power station in the sending-end power grid.

[0034] Determine whether the short-circuit ratio, overall short-circuit ratio, and balance of each new energy power station in the sending-end power grid meet the constraints. If the constraints are met, the first stage of optimizing the configuration of synchronous condensers to enhance the power grid strength is completed, and the second stage of adjusting the capacity of synchronous condensers is entered. Otherwise, return to the node with the lowest short-circuit ratio of the wind farm in the power grid as the optimal position for configuring synchronous condensers in the sending-end power grid, and add one distributed synchronous condenser.

[0035] Three-phase short-circuit fault detection was performed on the grid-connected busbars of each wind farm in the sending-end power grid after the first phase of synchronous condenser configuration and connection. Transient time-domain simulation of the sending-end power grid was conducted to observe the busbar voltage change curves of different wind turbines under various fault scenarios.

[0036] Determine whether the wind turbine voltage meets the transient overvoltage safety constraints under all fault scenarios. The transient overvoltage level of the wind farm voltage should not exceed 1.3 pu within 0.5 seconds after the fault is cleared. If it meets the requirements, complete the optimized configuration of the synchronous condenser; otherwise, adjust the capacity based on the synchronous condenser configuration capacity correction model.

[0037] The improved genetic simulated annealing algorithm was used to solve the model, determine the synchronous condenser correction capacity, and return to re-evaluate whether the wind turbine voltage meets the transient overvoltage safety constraint under all fault scenarios.

[0038] Figure 2 The basic flow of the improved genetic simulated annealing algorithm is as follows:

[0039] (1) Set control parameters: population size L, initial temperature T0, cooling coefficient λ, and final temperature T. e Including the maximum number of generations, the number of generations is initialized to 0. The encoding length is set, and the population is initialized to obtain a series of chromosomes. The iterative method of the genetic algorithm is used for optimization and calculation. Each chromosome in the population contains n genes, which are used to represent the synchronous condenser capacity configured at 35kV of n different wind farms.

[0040] (2) Calculate the fitness value of each individual in the population, arrange them from largest to smallest, and calculate the average fitness value f of the population. avg and the optimal fitness value f max .

[0041] (3) Selection, crossover, and mutation. An adaptive crossover and mutation operator p is introduced. c and p m Perform crossover and mutation operations to obtain a new population.

[0042]

[0043]

[0044] Among them, f max f represents the maximum fitness value in the population. avg P represents the average fitness value in the population, f represents the fitness value among the parent individuals participating in the crossover, and P represents the fitness value among the parent individuals. cmax P represents the upper bound of the crossover probability. cmin This represents the lower bound of the crossover probability, 0 < P. cmin <P cmax <1. P mmax P represents the upper limit of the mutation probability. mmin This represents the lower bound of the mutation probability, 0 < P. mmin <P mmax <1.

[0045] (4) Perform simulated annealing on the new population. Let the objective function of the camera configuration capacity correction model be f(x). Calculate the objective function value f(j) for any optimal solution j of the problem. During the iterative process of solving the problem, the current solution j will be randomly perturbed.

[0046] (5) Utilize the perturbation process to calculate the new objective function value f(k), and accept the new solution according to the Metropolis criterion. The probability p is generally defined according to the Metropolis criterion, and K is the Boltzmann constant; T c Given the current temperature value, the Metropolis criterion formula is:

[0047]

[0048] (6) Slowly cool down and judge the temperature and number of iterations. If the number of iterations reaches the set value or the fitness of the best individual no longer changes significantly, convergence is achieved and a new population is output; if the judgment condition is not met, re-enter the random perturbation process and continue the annealing process.

[0049] (7) For a new population, if the termination condition is met, the optimal solution is output. If the termination condition is not met, the genetic algorithm returns to the fitness value calculation stage and the genetic simulated annealing algorithm is performed again.

Claims

1. A method for optimizing the configuration of synchronous condensers based on improving the short-circuit ratio, characterized in that, include: Step 1: Considering the parameters of the sending-end grid components and the power flow operation status, establish a simulation model of the sending-end grid with a high proportion of wind power access. Step 2: Calculate the short-circuit ratio, overall short-circuit ratio and balance degree of each new energy power station in the sending-end power grid under the feasible solution of power flow in the sending-end power grid. Step 3: Select the node with the lowest short-circuit ratio of the wind farm in the power grid as the optimal location for the synchronous condenser configuration of the sending-end power grid, and add 1 distributed synchronous condenser. Step 4: Recalculate the short-circuit ratio, overall short-circuit ratio and balance of each new energy power station in the sending-end power grid. Step 5: Determine whether the short-circuit ratio, overall short-circuit ratio, and balance of each new energy power station in the sending-end power grid meet the constraints. If the constraints are met, the first stage of optimizing the configuration of synchronous condensers to enhance the power grid strength is completed, and the second stage of adjusting the capacity of synchronous condensers is entered. Otherwise, return to step 3. Step 6: Perform three-phase short-circuit fault detection on the grid-connected busbars of each wind farm in the sending-end power grid after the first-stage synchronous condenser configuration and connection. Perform transient time-domain simulation of the sending-end power grid and observe the voltage change curves of different wind turbine busbars under various fault scenarios. Step 7: Determine whether the wind turbine voltage meets the transient overvoltage safety constraint under all fault scenarios. If it does, complete the synchronous condenser optimization configuration; otherwise, adjust the capacity based on the synchronous condenser configuration capacity correction model. Step 8: Solve the model using the improved genetic simulated annealing algorithm, determine the camera adjustment capacity, and return to step 6.

2. The method for optimizing the configuration of a synchronous condenser based on improving the short-circuit ratio according to claim 1, characterized in that, As synchronous rotating equipment, synchronous condensers are electromagnetically coupled to the AC power grid. Their spontaneous reactive power response without delay reflects the electrical characteristics of the synchronous power grid itself. The connection of synchronous condensers directly improves the short-circuit ratio of the power grid, enhances the system's grid strength, and improves the voltage support capability during system faults. In particular, distributed synchronous condensers, compared with traditional large-scale synchronous condensers, have advantages such as high integration, system stability, short construction period, low cost per unit capacity, and good regulation performance. They also have better transient, subtransient, and steady-state performance, which can significantly improve the capacity for transmitting new energy to other regions.

3. The method for optimizing the configuration of a synchronous condenser based on improving the short-circuit ratio according to claim 1, characterized in that, The degree of overvoltage is closely related to the strength of the power grid. Currently, the short-circuit ratio is one of the effective indicators for measuring the strength of the power grid. The short-circuit ratio of new energy multi-stations at any node i in the system (denoted as MRSCR) i ) can be represented as Wherein: S i S represents the short-circuit capacity of the new energy power station i at the grid-side connection point; REi and S REj The actual apparent power of new energy injected into the i-th and j-th new energy grid-connected bus nodes; Z eqji and Z eqii These are the self-impedance of new energy power station i and the mutual impedance between power stations i and j, respectively. After a synchronous condenser is connected at point k in the system, the change in the short-circuit ratio of any wind farm node i in the system affects the mutual impedance Z. eqki Taking the partial derivative yields Among them: U Ni Rated voltage; Z eqki Z eqkj Z represents the equivalent mutual impedance between the new energy power stations i and j and the synchronous condenser access point k; eqkk The equivalent self-impedance of the synchronous synchrotron access point k; z k To adjust the equivalent parallel impedance of the camera to ground; From the above equation, we can see that both the numerator and denominator are positive, therefore Z eqki The larger the value of Z, the larger the value of ΔMRSCRi. This is because points with lower MRSCR in the power grid are farther away from the grid electricalally. eqki The larger the value, the greater the increase in the short-circuit ratio of all new energy nodes when connecting a synchronous condenser at a point with a smaller MRSCR, which is more beneficial to improving system stability.

4. The method for optimizing the configuration of a synchronous condenser based on improving the short-circuit ratio according to claim 1, characterized in that, An improved genetic simulated annealing algorithm is used to solve the objective function. Genetic algorithms, as classic algorithms for optimization problems, have advantages such as convenient computation and fast convergence, but they still suffer from premature convergence and a tendency to get trapped in local optima. Simulated annealing, on the other hand, features probabilistic jumps and is a stochastic search algorithm that extends local search algorithms. Theoretically, it can avoid getting trapped in local optima. Using simulated annealing to solve multi-objective optimization problems can probabilistically converge to the global optimum. To improve the genetic algorithm's tendency to get trapped in local optima and obtain a better mine water scheduling method, an improved genetic simulated annealing algorithm is proposed by combining simulated annealing with an adaptive genetic algorithm. The specific algorithm steps are as follows: (1) Set control parameters: population size L, initial temperature T0, cooling coefficient λ, and final temperature T. e Including the maximum number of generations, the number of generations is initialized to 0. The encoding length is set, and the population is initialized to obtain a series of chromosomes. The iterative method of the genetic algorithm is used for optimization and calculation. Each chromosome in the population contains n genes, which are used to represent the synchronous condenser capacity configured at 35kV of n different wind farms. (2) Calculate the fitness value of each individual in the population, arrange them from largest to smallest, and calculate the average fitness value f of the population. avg and the optimal fitness value f max . (3) Selection, crossover, and mutation. An adaptive crossover and mutation operator p is introduced. c and p m Perform crossover and mutation operations to obtain a new population. Among them, f max f represents the maximum fitness value in the population. avg P represents the average fitness value in the population, f represents the fitness value among the parent individuals participating in the crossover, and P represents the fitness value among the parent individuals. cmax P represents the upper bound of the crossover probability. cmin This represents the lower bound of the crossover probability, 0 < P. cmin <P cmax <1. P mmax P represents the upper limit of the mutation probability. mmin This represents the lower bound of the mutation probability, 0 < P. mmin <P mmax <1. (4) Perform simulated annealing on the new population. Let the objective function of the camera configuration capacity correction model be f(x). Calculate the objective function value f(j) for any optimal solution j of the problem. During the iterative process of solving the problem, the current solution j will be randomly perturbed. (5) Utilize the perturbation process to calculate the new objective function value f(k), and accept the new solution according to the Metropolis criterion. The probability p is generally defined according to the Metropolis criterion, and K is the Boltzmann constant; T c Given the current temperature value, the Metropolis criterion formula is: (6) Slowly cool down and judge the temperature and number of iterations. If the number of iterations reaches the set value or the fitness of the best individual no longer changes significantly, convergence is achieved and a new population is output; if the judgment condition is not met, re-enter the random perturbation process and continue the annealing process. (7) For a new population, if the termination condition is met, the optimal solution is output. If the termination condition is not met, the genetic algorithm returns to the fitness value calculation stage and the genetic simulated annealing algorithm is performed again.

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