A control parameter setting method for improving the voltage fault ride-through performance of photovoltaic clusters

Abstract

The present invention discloses a control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster, comprising constructing a fault set of voltage over-limit in a power grid at the sending end of a photovoltaic cluster, and determining key control parameters to be optimized according to the current command value expression of a photovoltaic inverter during the voltage over-limit process; taking the minimum deviation between the grid-connected point voltage of the photovoltaic cluster and a voltage reference value as the goal, constructing a voltage fault ride-through key control parameter optimization model of the photovoltaic inverter, and setting the operating range of the key control parameters to be optimized during the optimization process; using a genetic optimization algorithm to solve the voltage fault ride-through key control parameter optimization model of the photovoltaic inverter, and based on the model convergence condition, obtaining the inverter control parameter result for improving the voltage fault ride-through performance of the photovoltaic cluster, thereby improving the voltage fault ride-through capability of the photovoltaic inverter and ensuring the voltage support capability and system stability of the photovoltaic cluster under power grid fault conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter parameter optimization, and in particular to a control parameter setting method for improving voltage fault ride-through performance of a photovoltaic cluster. Background Art

[0002] With the continuous advancement of the global energy structure transformation, photovoltaic power generation has occupied an increasingly important position in renewable energy. The development of photovoltaic clusters and large-scale access to the power grid, especially long-distance transmission through ultra-high voltage direct current transmission channels, has become one of the important means to build a new power system.

[0003] However, as the core power electronic equipment in the photovoltaic power generation system, the photovoltaic inverter has relatively weak active support capabilities. Under abnormal conditions such as grid failure or DC receiving end commutation failure, the photovoltaic cluster is very likely to be disconnected from the grid due to voltage exceeding the limit, which will not only endanger the stable operation of the photovoltaic power generation system itself, but may also cause large-scale power shortages at the DC sending and receiving ends, thereby affecting the safety and stability of the entire power system.

[0004] Therefore, how to improve the voltage fault ride-through capability of photovoltaic clusters under fault conditions and ensure the stability of photovoltaic power generation systems has become an urgent problem to be solved in the current power system and photovoltaic power generation fields. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster. The method can construct a fault set of voltage over-limit in the power grid at the sending end of the photovoltaic cluster, and determine the key control parameters that need to be optimized and adjusted, that is, the parameters to be set, according to the current command value expression of the photovoltaic inverter during the voltage over-limit process; with the goal of minimizing the deviation between the grid-connected point voltage and the voltage reference value of the photovoltaic cluster, an optimization model for the key control parameters of the voltage fault ride-through of the photovoltaic inverter is constructed, and the operating range of the parameters to be set during the optimization process is set; a genetic optimization algorithm is used to solve the optimization model for the key control parameters of the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter results for improving the voltage fault ride-through performance of the photovoltaic cluster are obtained, so that by optimizing the key control parameters of the photovoltaic inverter, the voltage support capability and system stability of the photovoltaic cluster in the event of a grid fault can be improved, and the voltage fault ride-through capability of the photovoltaic inverter can be improved, so as to effectively ensure the stable power transmission of the "Shagohuang" photovoltaic cluster through the ultra-high voltage direct current transmission channel under different operating conditions.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] The present invention is a control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster, comprising:

[0008] Construct a fault set of voltage over-limit in the sending-end power grid containing a photovoltaic cluster, and determine the key control parameters to be optimized based on the current command value expression of the photovoltaic inverter during the voltage over-limit process;

[0009] The current command value expression of the photovoltaic inverter during the voltage over-limit process includes an active current command expression and a reactive current command expression during the low voltage ride through control process of the photovoltaic inverter, and an active current command expression and a reactive current command expression during the high voltage ride through control process of the photovoltaic inverter;

[0010] The active current command I in the low voltage ride through control process of the photovoltaic inverter d_LV and reactive current command I q_LV The expression is:

[0011]

[0012] In the formula, I d_LV Represents the active current command during the low voltage ride-through control process of the photovoltaic inverter; I q_LV It indicates the reactive current command during the low voltage ride-through control process of the photovoltaic inverter; min indicates the smaller value between the two; U indicates the photovoltaic cluster grid connection point voltage detected during the low voltage drop process; P refL Indicates the active power reference value of the photovoltaic cluster before the low voltage drops; i max Indicates the maximum current output by the photovoltaic inverter; k1 indicates the correction slope value of the photovoltaic inverter low voltage ride-through recovery process; k2 indicates the reactive current command value when the photovoltaic inverter low voltage is at the lowest value;

[0013] The active current command I in the high voltage ride through control process of the photovoltaic inverter d_HV and reactive current command I q_HV The expression is:

[0014]

[0015] In the formula, I d_HV Represents the active current command during the high voltage ride-through control process of the photovoltaic inverter; I q_HV It represents the reactive current command in the high-voltage ride-through control process of the photovoltaic inverter; min represents the smaller value between the two; U represents the photovoltaic cluster grid-connected point voltage detected during the overvoltage process; a represents the sudden rise amplitude of the grid voltage; P refH Represents the active power reference value of the photovoltaic cluster before overvoltage; i maxIndicates the maximum current output by the photovoltaic inverter; k3 indicates the reactive current command value of the photovoltaic inverter high-voltage ride-through when the detected grid-connected point voltage U∈(1,1.1]); k4 indicates the correction value of the reactive current command value of the photovoltaic inverter high-voltage ride-through when the detected grid-connected point voltage U∈(1.1,∞);

[0016] According to the expressions of active current and reactive current instructions in the high voltage ride through and low voltage ride through control processes of the photovoltaic inverter, the key control parameters to be optimized are determined, and the key control parameters to be optimized include: the correction slope value k1 of the low voltage ride through recovery process of the photovoltaic inverter, the reactive current instruction value k2 when the low voltage of the photovoltaic inverter is at the lowest value, the reactive current instruction value k3 of the high voltage ride through of the photovoltaic inverter when the detected grid connection point voltage U∈(1,1.1], and the correction value k4 of the reactive current instruction value of the high voltage ride through of the photovoltaic inverter when the detected grid connection point voltage U∈(1.1,∞);

[0017] With the goal of minimizing the deviation between the grid-connected point voltage of the photovoltaic cluster and the voltage reference value, a voltage fault ride-through key control parameter optimization model of the photovoltaic inverter is constructed, and the operating range of the key control parameters to be optimized during the optimization process is set;

[0018] A genetic optimization algorithm is used to solve the optimization model of key control parameters of the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter results that improve the voltage fault ride-through performance of the photovoltaic cluster are obtained.

[0019] A further improvement of the present invention is that: the construction of the fault set of voltage over-limit in the sending-end power grid containing photovoltaic clusters includes two scenarios, namely, a scenario in which a fault occurs in the sending-end power grid containing a high proportion of photovoltaic clusters, causing the voltage of the local photovoltaic inverter to drop, and a scenario in which the receiving-end power grid commutation failure causes an overvoltage in the sending-end power grid;

[0020] When constructing the scenario where a fault occurs in the sending-end power grid containing a high proportion of photovoltaic clusters and causes the local photovoltaic inverter voltage to drop, a fault scan is performed on each line in the sending-end power grid containing a high proportion of photovoltaic clusters to construct a scenario set Ω of the local photovoltaic inverter voltage drop. L , the scenario set of the local PV inverter voltage drop Ω L The expression is:

[0021] Ω L ={σ1,σ2,σ3,...,σ i ,...,σ N}

[0022] In the formula, Ω L Represents the scenario set of local PV inverter voltage drop; σ iIndicates that a fault occurs in the i-th sending-end power grid; N indicates the total number of sending-end power grids with faults determined after scanning.

[0023] A further improvement of the present invention is that when constructing the scenario where the receiving-end power grid commutation failure causes the sending-end power grid overvoltage, different faults are set in the receiving-end power grid, resulting in the receiving-end power grid commutation failure, and the sending-end power grid overvoltage scenario containing a high proportion of photovoltaic clusters occurs, and a scene set Ω of the sending-end power grid overvoltage containing a high proportion of photovoltaic clusters is constructed. c , the scenario set of overvoltage of the sending-end grid containing a high proportion of photovoltaic clusters Ω c The expression is:

[0024] Ω c ={μ1,μ2,μ3,...,μ j ,...,μ M}

[0025] In the formula, Ω c represents the set of scenarios of overvoltage in the sending-end grid with a high proportion of photovoltaic clusters; μ j It indicates that the jth fault set in the receiving-end power grid causes the receiving-end power grid to fail in commutation, resulting in an overvoltage scenario in the sending-end power grid containing a high proportion of photovoltaic clusters; M indicates the total number of receiving-end power grids with faults determined after scanning.

[0026] A further improvement of the present invention is that: the voltage fault ride-through key control parameter optimization model of the photovoltaic inverter is constructed with the goal of minimizing the deviation between the grid-connected point voltage of the photovoltaic cluster and the voltage reference value, and the expression of the objective function corresponding to the voltage fault ride-through key control parameter optimization model of the photovoltaic inverter is:

[0027]

[0028] In the formula, min means taking the minimum value operation; t0 means the moment when the voltage at the photovoltaic inverter grid-connected point exceeds the limit; t r Indicates the time when the grid-connected voltage of the photovoltaic inverter recovers to the rated voltage after the transient process; U PV Indicates the grid-connected point voltage of the photovoltaic inverter; U PVN Indicates the rated voltage of the photovoltaic inverter grid connection point;

[0029] min OF represents the objective function.

[0030] A further improvement of the present invention is that: the setting of the operating range of the key control parameter to be optimized during the optimization process specifically includes: setting upper and lower limits for the key control parameter to be optimized:

[0031] N lower ≤x pi ≤Nupper

[0032] In the formula, x pi Indicates the key control parameter to be optimized; N lower Indicates the lower limit of the key control parameter to be optimized; N upper Indicates the upper limit of the key control parameter to be optimized.

[0033] A further improvement of the present invention is that before the genetic optimization algorithm is used to solve the optimization model of the key control parameters of the voltage fault crossing of the photovoltaic inverter, two key fault scenarios are designed on the electromechanical transient simulation platform to analyze the dynamic response of the high-proportion photovoltaic cluster access to the power grid. The first scenario is to introduce a fault in the sending-end power grid, resulting in a voltage drop in the local photovoltaic inverter; the second scenario is to set different types of faults in the receiving-end power grid, causing the receiving-end power grid to fail in phase switching, resulting in an overvoltage in the sending-end power grid to which the high-proportion photovoltaic cluster is connected;

[0034] During the simulation process, dynamic simulation data is generated by the electromechanical transient simulation platform, and the dynamic simulation data includes dynamic response data of grid-connected point voltage parameters and key control parameters of the photovoltaic inverter, and the dynamic simulation data is input into the voltage fault crossing key control parameter optimization model of the photovoltaic inverter for solution.

[0035] A further improvement of the present invention is that: the genetic optimization algorithm is used to solve the optimization model of the key control parameters of the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter results that improve the voltage fault ride-through performance of the photovoltaic cluster are obtained, the key control parameters output in each iteration are assigned to the control parameters of the target wind turbine group in the electromechanical transient simulation platform, the fluctuation amplitude of the photovoltaic inverter grid connection point voltage under all working conditions in the typical fault set of the sending-end power grid containing a high proportion of photovoltaic clusters is recorded, and compared with the fluctuation amplitude recorded in the previous time, to determine the key control parameters of the next iteration;

[0036] When the model convergence condition corresponding to the genetic optimization algorithm is met, the current key control parameter is the optimal solution of the key control parameter of the photovoltaic inverter voltage ride-through, and the inverter control parameter result that improves the voltage fault ride-through performance of the photovoltaic cluster is obtained.

[0037] The beneficial effects of the present invention are as follows: the present invention proposes a control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster. By optimizing the key control parameters of the photovoltaic inverter, the voltage support capability and system stability of the photovoltaic cluster in the event of a power grid failure can be improved, and the voltage fault ride-through capability of the photovoltaic inverter can be improved, effectively ensuring the stable power transmission of the "Shagohuang" photovoltaic cluster through the ultra-high voltage direct current transmission channel under different operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of the steps of a method for setting inverter control parameters to improve the voltage fault ride-through performance of a photovoltaic cluster provided by an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the topological structure of a high-ratio photovoltaic cluster power transmission system via direct current transmission provided by an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of simulation comparison before and after optimization of key control parameters in a simulation example provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0042] See also Figure 1 A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster in this embodiment includes:

[0043] Construct a fault set of voltage over-limit in the sending-end power grid containing a photovoltaic cluster, and determine the key control parameters to be optimized based on the current command value expression of the photovoltaic inverter during the voltage over-limit process;

[0044] With the goal of minimizing the deviation between the grid-connected point voltage of the photovoltaic cluster and the voltage reference value, a voltage fault ride-through key control parameter optimization model of the photovoltaic inverter is constructed, and the operating range of the key control parameters to be optimized during the optimization process is set;

[0045] A genetic optimization algorithm is used to solve the optimization model of key control parameters of the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter results that improve the voltage fault ride-through performance of the photovoltaic cluster are obtained.

[0046] In the DC transmission system containing a high proportion of photovoltaic clusters, a voltage limit fault set in the power grid at the sending end of the photovoltaic cluster is constructed. The topology of the DC transmission system containing a high proportion of photovoltaic clusters is as follows: Figure 2 As shown. Among them, P acr and Q acr P is the active power and reactive power output by conventional units in the sending-end power grid containing a high proportion of photovoltaic clusters; acPV and Q acPV is the active power and reactive power output by the photovoltaic cluster in the sending-end power grid containing a high proportion of photovoltaic clusters; U r is the AC / DC bus voltage at the sending end; Q CF The reactive power output by the reactive power compensation device configured in the rectifier station of the DC transmission system; P dc and Q dc P is the active power and reactive power transmitted by the DC transmission line in the AC / DC hybrid transmission system; ac and Q ac are the transmitted active power and reactive power of the AC transmission line in the AC / DC hybrid transmission system; j is the phase difference between the active power and the reactive power in the apparent power. The construction of the fault set of voltage over-limit in the sending-end power grid containing photovoltaic clusters includes two scenarios, namely, the scenario in which a fault occurs in the sending-end power grid containing a high proportion of photovoltaic clusters, causing the local photovoltaic inverter voltage to drop, and the scenario in which the receiving-end power grid fails to commutate, causing the sending-end power grid to overvoltage. The sending-end power grid with a photovoltaic cluster installed capacity of more than 50% can be considered as a sending-end power grid containing a high proportion of photovoltaic clusters;

[0047] When constructing the scenario where a fault occurs in the sending-end power grid containing a high proportion of photovoltaic clusters and causes the local photovoltaic inverter voltage to drop, a fault scan is performed on each line in the sending-end power grid containing a high proportion of photovoltaic clusters to construct a scenario set Ω of the local photovoltaic inverter voltage drop. L , the scenario set of the local PV inverter voltage drop Ω L The expression is:

[0048] Ω L ={σ1,σ2,σ3,...,σ i ,...,σ N}

[0049] In the formula, Ω L Represents the scenario set of local PV inverter voltage drop; σ i Indicates that a fault occurs in the i-th sending-end power grid; N indicates the total number of sending-end power grids with faults determined after scanning.

[0050] When constructing the scenario where the receiving-end grid commutation failure causes the sending-end grid overvoltage, different faults are set in the receiving-end grid, resulting in the receiving-end grid commutation failure, and the sending-end grid overvoltage scenario with a high proportion of photovoltaic clusters appears, and the sending-end grid overvoltage scenario set Ω with a high proportion of photovoltaic clusters is constructed. c , the scenario set of overvoltage of the sending-end grid containing a high proportion of photovoltaic clusters Ω c The expression is:

[0051] Ω c ={μ1,μ2,μ3,...,μ j ,...,μ M}

[0052] In the formula, Ω c represents the set of scenarios of overvoltage in the sending-end grid with a high proportion of photovoltaic clusters; μ j Indicates that the jth fault set in the receiving-end power grid causes the receiving-end power grid to fail to commutate, resulting in an overvoltage scenario in the sending-end power grid containing a high proportion of photovoltaic clusters; M represents the total number of receiving-end power grids that have failed after scanning. The current command value expression of the photovoltaic inverter during the voltage over-limit process includes the active current command expression and the reactive current command expression during the low-voltage ride-through control process of the photovoltaic inverter, and the active current command expression and the reactive current command expression during the high-voltage ride-through control process of the photovoltaic inverter. The active current command I during the low-voltage ride-through control process of the photovoltaic inverter d_LV and reactive current command I q_LV The expression is:

[0053]

[0054] In the formula, I d_LV Represents the active current command during the low voltage ride-through control process of the photovoltaic inverter; I q_LV It indicates the reactive current command during the low voltage ride-through control process of the photovoltaic inverter; min indicates the smaller value between the two; U indicates the photovoltaic cluster grid connection point voltage detected during the low voltage drop process; P refL Indicates the active power reference value of the photovoltaic cluster before the low voltage drops; i max It indicates the maximum current output by the photovoltaic inverter; k1 indicates the correction slope value of the low voltage ride-through recovery process of the photovoltaic inverter; k2 indicates the reactive current command value when the low voltage of the photovoltaic inverter is at the lowest value.

[0055] The active current command I in the high voltage ride through control process of the photovoltaic inverter d_HV and reactive current command I q_HV The expression is:

[0056]

[0057] In the formula, I d_HV Represents the active current command during the high voltage ride-through control process of the photovoltaic inverter; I q_HV It represents the reactive current command in the high-voltage ride-through control process of the photovoltaic inverter; min represents the smaller value between the two; U represents the photovoltaic cluster grid-connected point voltage detected during the overvoltage process; a represents the sudden rise amplitude of the grid voltage; P refH Represents the active power reference value of the photovoltaic cluster before overvoltage; i max It represents the maximum current output by the photovoltaic inverter; k3 represents the reactive current command value of the photovoltaic inverter high-voltage ride-through when the detected grid-connected point voltage U∈(1,1.1]); k4 represents the correction value of the reactive current command value of the photovoltaic inverter high-voltage ride-through when the detected grid-connected point voltage U∈(1.1,∞).

[0058] According to the expressions of active current and reactive current instructions in the high-voltage ride-through and low-voltage ride-through control processes of the photovoltaic inverter, the key control parameters to be optimized are determined, and the key control parameters to be optimized include: the correction slope value k1 of the low-voltage ride-through recovery process of the photovoltaic inverter, the reactive current instruction value k2 when the low voltage of the photovoltaic inverter is at the lowest value, the reactive current instruction value k3 of the high-voltage ride-through of the photovoltaic inverter when the detected grid-connected point voltage U∈(1,1.1], and the correction amount k4 of the reactive current instruction value of the high-voltage ride-through of the photovoltaic inverter when the detected grid-connected point voltage U∈(1.1,∞).

[0059] The goal is to minimize the deviation between the grid-connected point voltage of the photovoltaic cluster and the voltage reference value, and to construct a key control parameter optimization model for voltage fault ride-through of the photovoltaic inverter. The expression of the objective function corresponding to the key control parameter optimization model for voltage fault ride-through of the photovoltaic inverter is:

[0060]

[0061] In the formula, min means taking the minimum value operation; t0 means the moment when the voltage at the photovoltaic inverter grid-connected point exceeds the limit; t r Indicates the time when the grid-connected voltage of the photovoltaic inverter recovers to the rated voltage after the transient process; U PV Indicates the grid-connected point voltage of the photovoltaic inverter; U PVN represents the rated voltage of the PV inverter grid-connected point; min OF represents the objective function.

[0062] The setting of the operating range of the key control parameters to be optimized during the optimization process specifically includes: setting upper and lower limits for the key control parameters to be optimized, so as to ensure that the optimization process is carried out within a reasonable range. The setting of such upper and lower limits can not only limit the value range of the decision variables, but also ensure the feasibility and stability of the optimization results. The expression is:

[0063] N lower ≤x pi ≤N upper

[0064] In the formula, x pi Indicates the key control parameter to be optimized; N lower Indicates the lower limit of the key control parameter to be optimized; N upper Indicates the upper limit of the key control parameter to be optimized.

[0065] Before solving the optimization model of key control parameters of the voltage fault ride-through of the photovoltaic inverter using the genetic optimization algorithm, two key fault scenarios are designed on the electromechanical transient simulation platform to analyze the dynamic response of a high-proportion photovoltaic cluster connected to the power grid. The first scenario is to introduce a fault in the sending-end power grid, causing a voltage drop in the local photovoltaic inverter; the second scenario is to set different types of faults in the receiving-end power grid, causing the receiving-end power grid to fail in phase switching, resulting in an overvoltage in the sending-end power grid to which a high-proportion photovoltaic cluster is connected;

[0066] During the simulation process, dynamic simulation data is generated through the electromechanical transient simulation platform, and the dynamic simulation data includes dynamic response data of grid-connected point voltage parameters and key control parameters of photovoltaic inverters. The dynamic simulation data is input into the voltage fault ride-through key control parameter optimization model of the photovoltaic inverter for further solution and analysis to obtain more accurate optimization results.

[0067] The genetic optimization algorithm is used to solve the optimization model of the key control parameters of the voltage fault ride-through of the photovoltaic inverter, and the final convergence condition of the genetic algorithm in the optimization process is set. Based on the model convergence condition, the inverter control parameter results that improve the voltage fault ride-through performance of the photovoltaic cluster are obtained, and the new key control parameters obtained in each iteration are assigned to the control parameters of the target wind turbine group in the electromechanical transient simulation platform. The fluctuation amplitude of the photovoltaic inverter grid connection point voltage under all working conditions in the typical fault set of the sending-end power grid containing a high proportion of photovoltaic clusters is recorded, and compared with the fluctuation amplitude recorded in the previous time, to determine the key control parameters for the next iteration;

[0068] When the model convergence condition corresponding to the genetic optimization algorithm is met, the current key control parameter is the optimal solution of the key control parameter of the photovoltaic inverter voltage ride-through, and the inverter control parameter result that improves the voltage fault ride-through performance of the photovoltaic cluster is obtained.

[0069] The present invention will be further described below in conjunction with specific embodiments:

[0070] In order to verify the effectiveness of the method proposed in this paper, a simulation model of a high-proportion photovoltaic cluster through a DC power transmission system was constructed using the electromechanical transient simulation platform PSD-BPA. The model simulates the sending-end power grid of a high-proportion photovoltaic cluster, in which each photovoltaic power station is connected to the ±800kV rectifier-side converter station through a high-voltage line and provides power to the receiving-end power grid through a DC line. The simulation calculation uses the second-generation new energy electromechanical model, and the DC model part uses the two-terminal DC control system model recommended by CIGRE.

[0071] When constructing the voltage over-limit fault set in the sending-end power grid containing a photovoltaic cluster, the following simulation scenario was set according to the actual project operation status: at the 2nd second, a three-phase short-circuit fault occurred at the node of the sending-end power grid, causing the local photovoltaic inverter voltage to drop significantly, and the voltage drop continued to occur during the fault recovery process, eventually leading to voltage instability. For the scenario where the commutation failure in the receiving-end power grid caused the overvoltage of the photovoltaic cluster sending-end power grid, the following fault scenario was specifically set: the first commutation failure occurred at the 2nd second, the second commutation failure occurred at 2.14 seconds, and the third commutation failure occurred at 2.28 seconds. Due to the continuous commutation failure, the DC system was finally locked at 2.4 seconds. The simulation results show that in the process of continuous commutation failure and lockout, the DC current of the DC system, the commutation bus voltage of the rectifier station, and the turn-off angle of the inverter station fluctuated significantly. Among them, the overvoltage phenomenon at the commutation bus was the most serious, with a peak value of 1.267pu, indicating that the sending-end power grid faced a serious voltage over-limit problem.

[0072] Based on the key control parameter optimization method proposed in the present invention, the key control parameters in the low voltage ride-through process are optimized. The initial values ​​of all parameters are used as the starting values ​​of the simulated annealing algorithm, and the minimum deviation between the overvoltage value and the rated voltage in the transient process of the photovoltaic cluster grid-connected point voltage is used as the objective function. After 200 iterations, the optimization results of each key parameter are obtained. Taking the overvoltage scenario of the photovoltaic cluster sending-end grid caused by the phase change failure of the receiving-end grid as an example, the voltage operation curves of the photovoltaic inverter grid-connected point before and after optimization are compared. Figure 3 The peak voltage of the optimized photovoltaic inverter grid-connected point is reduced from 1.267pu to 1.182pu, and the overall fluctuation amplitude of the wind turbine terminal voltage is also reduced to a certain extent.

[0073] Table 1 Optimization results of voltage ride-through control parameters after 200 iterations

[0074]

[0075] Through the introduction of the above embodiments, the present invention can construct a fault set of voltage over-limit in the power grid at the sending end of the photovoltaic cluster through a control parameter setting method for improving the voltage fault ride-through performance of the photovoltaic cluster, and determine the key control parameters that need to be optimized and adjusted, that is, the parameters to be adjusted, according to the current command value expression of the photovoltaic inverter during the voltage over-limit process; with the goal of minimizing the deviation between the grid-connected point voltage and the voltage reference value of the photovoltaic cluster, a key control parameter optimization model for the voltage fault ride-through of the photovoltaic inverter is constructed, and the operating range of the parameters to be adjusted during the optimization process is set; a genetic optimization algorithm is used to solve the key control parameter optimization model for the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter result for improving the voltage fault ride-through performance of the photovoltaic cluster is obtained, so that by optimizing the key control parameters of the photovoltaic inverter, the voltage support capability and system stability of the photovoltaic cluster in the case of power grid faults can be improved, and the voltage fault ride-through capability of the photovoltaic inverter can be improved, and the stable power transmission of the "Shagohuang" photovoltaic cluster through the ultra-high voltage direct current transmission channel can be effectively guaranteed under different operating conditions.

[0076] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have the same meaning as in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined similarly here.

[0077] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster, characterized in that: include: Construct a fault set of voltage over-limit in the sending-end power grid containing photovoltaic clusters, and determine the key control parameters to be optimized based on the current command value expression of the photovoltaic inverter during the voltage over-limit process; The current command value expression of the photovoltaic inverter during the voltage over-limit process includes: the active current command expression and the reactive current command expression during the low voltage ride through control process of the photovoltaic inverter, and the active current command expression and the reactive current command expression during the high voltage ride through control process of the photovoltaic inverter; The active current command I in the low voltage ride through control process of the photovoltaic inverter d_LV and reactive current command I q_LV The expression is: In the formula, I d_LV Represents the active current command during the low voltage ride-through control process of the photovoltaic inverter; I q_LV It indicates the reactive current command during the low voltage ride-through control process of the photovoltaic inverter; min indicates the smaller value between the two; U indicates the photovoltaic cluster grid connection point voltage detected during the low voltage drop process; P refL Indicates the active power reference value of the photovoltaic cluster before the low voltage drops; i max Indicates the maximum current output by the photovoltaic inverter; k1 indicates the correction slope value of the photovoltaic inverter low voltage ride-through recovery process; k2 indicates the reactive current command value when the photovoltaic inverter low voltage is at the lowest value; The active current command I in the high voltage ride through control process of the photovoltaic inverter d_HV and reactive current command I q_HV The expression is: In the formula, I d_HV Represents the active current command during the high voltage ride-through control process of the photovoltaic inverter; I q_HV It represents the reactive current command in the high-voltage ride-through control process of the photovoltaic inverter; min represents the smaller value between the two; U represents the photovoltaic cluster grid-connected point voltage detected during the overvoltage process; a represents the sudden rise amplitude of the grid voltage; P refH Represents the active power reference value of the photovoltaic cluster before overvoltage; i max Indicates the maximum current output by the photovoltaic inverter; k3 indicates the reactive current command value of the photovoltaic inverter high-voltage ride-through when the detected grid-connected point voltage U∈(1,1.1]; k4 indicates the correction value of the reactive current command value of the photovoltaic inverter high-voltage ride-through when the detected grid-connected point voltage U∈(1.1,∞); According to the expressions of active current and reactive current instructions in the high voltage ride through and low voltage ride through control processes of the photovoltaic inverter, the key control parameters to be optimized are determined, and the key control parameters to be optimized include: the correction slope value k1 of the low voltage ride through recovery process of the photovoltaic inverter, the reactive current instruction value k2 when the low voltage of the photovoltaic inverter is at the lowest value, the reactive current instruction value k3 of the high voltage ride through of the photovoltaic inverter when the detected grid connection point voltage U∈(1,1.1], and the correction amount k4 of the reactive current instruction value of the high voltage ride through of the photovoltaic inverter when the detected grid connection point voltage U∈(1.1,∞); With the goal of minimizing the deviation between the grid-connected point voltage of the photovoltaic cluster and the voltage reference value, a voltage fault ride-through key control parameter optimization model of the photovoltaic inverter is constructed, and the operating range of the key control parameters to be optimized during the optimization process is set; A genetic optimization algorithm is used to solve the optimization model of key control parameters of the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter results that improve the voltage fault ride-through performance of the photovoltaic cluster are obtained.

2. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster according to claim 1, characterized in that: The construction of the fault set of voltage over-limit in the sending-end power grid containing photovoltaic clusters includes two scenarios, namely, a scenario in which a fault occurs in the sending-end power grid containing a high proportion of photovoltaic clusters, causing the voltage of the local photovoltaic inverter to drop, and a scenario in which the receiving-end power grid commutation failure causes overvoltage in the sending-end power grid. The sending-end power grid containing a high proportion of photovoltaic clusters is a sending-end power grid in which the installed capacity of photovoltaic clusters accounts for more than 50%; When constructing the scenario where a fault occurs in the sending-end power grid containing a high proportion of photovoltaic clusters and causes the local photovoltaic inverter voltage to drop, a fault scan is performed on each line in the sending-end power grid containing a high proportion of photovoltaic clusters to construct a scenario set Ω of the local photovoltaic inverter voltage drop. L , the scenario set of the local PV inverter voltage drop Ω L The expression is: Oh L ={σ1,σ2,σ3,...,σ i ,...,s N } In the formula, Ω L Represents the scenario set of local PV inverter voltage drop; σ i Indicates that a fault occurs in the i-th sending-end power grid; N indicates the total number of sending-end power grids with faults determined after scanning.

3. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster according to claim 2, characterized in that: When constructing the scenario where the receiving-end grid commutation failure causes the sending-end grid overvoltage, different faults are set in the receiving-end grid, resulting in the receiving-end grid commutation failure, and the sending-end grid overvoltage scenario with a high proportion of photovoltaic clusters appears, and the scenario set Ω of the sending-end grid overvoltage with a high proportion of photovoltaic clusters is constructed. c , the scenario set of overvoltage of the sending-end grid containing a high proportion of photovoltaic clusters Ω c The expression is: Oh c ={μ1,μ2,μ3,…,μ j ,…,m M } In the formula, Ω c represents the set of scenarios of overvoltage in the sending-end grid with a high proportion of photovoltaic clusters; μ j It indicates that the jth fault set in the receiving-end power grid causes the receiving-end power grid to fail in commutation, resulting in an overvoltage scenario in the sending-end power grid containing a high proportion of photovoltaic clusters; M indicates the total number of receiving-end power grids with faults determined after scanning.

4. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster according to claim 1, characterized in that: The goal is to minimize the deviation between the grid-connected point voltage of the photovoltaic cluster and the voltage reference value, and to construct a key control parameter optimization model for voltage fault ride-through of the photovoltaic inverter. The expression of the objective function corresponding to the key control parameter optimization model for voltage fault ride-through of the photovoltaic inverter is: In the formula, min means taking the minimum value operation; t0 means the moment when the voltage at the photovoltaic inverter grid-connected point exceeds the limit; t r Indicates the time when the grid-connected voltage of the photovoltaic inverter recovers to the rated voltage after the transient process; U PV Indicates the grid-connected point voltage of the photovoltaic inverter; U PVN represents the rated voltage of the PV inverter grid-connected point; min OF represents the objective function.

5. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster according to claim 1, characterized in that: The setting of the operating range of the key control parameter to be optimized during the optimization process specifically includes: setting upper and lower limits for the key control parameter to be optimized: N lower ≤x pi ≤N upper In the formula, x pi Indicates the key control parameter to be optimized; N lower Indicates the lower limit of the key control parameter to be optimized; N upper Indicates the upper limit of the key control parameter to be optimized.

6. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster according to claim 3, characterized in that: Before solving the optimization model of key control parameters of the voltage fault ride-through of the photovoltaic inverter using the genetic optimization algorithm, two key fault scenarios are designed on the electromechanical transient simulation platform to analyze the dynamic response of a high-proportion photovoltaic cluster connected to the power grid. The first scenario is to introduce a fault in the sending-end power grid, causing a voltage drop in the local photovoltaic inverter; the second scenario is to set different types of faults in the receiving-end power grid, causing the receiving-end power grid to fail in phase switching, resulting in an overvoltage in the sending-end power grid to which a high-proportion photovoltaic cluster is connected; During the simulation process, dynamic simulation data is generated by the electromechanical transient simulation platform, and the dynamic simulation data includes dynamic response data of grid-connected point voltage parameters and key control parameters of the photovoltaic inverter, and the dynamic simulation data is input into the voltage fault crossing key control parameter optimization model of the photovoltaic inverter for solution.

7. A control parameter setting method for improving the voltage fault ride-through performance of a photovoltaic cluster according to claim 6, characterized in that: The genetic optimization algorithm is used to solve the optimization model of the key control parameters of the voltage fault ride-through of the photovoltaic inverter, and based on the model convergence condition, the inverter control parameter results that improve the voltage fault ride-through performance of the photovoltaic cluster are obtained, and the key control parameters output in each iteration are assigned to the control parameters of the target wind turbine in the electromechanical transient simulation platform, and the fluctuation amplitude of the photovoltaic inverter grid connection point voltage under all working conditions in the typical fault set of the sending-end power grid containing a high proportion of photovoltaic clusters is recorded, and compared with the fluctuation amplitude recorded in the previous time, to determine the key control parameters of the next iteration; When the model convergence condition corresponding to the genetic optimization algorithm is met, the current key control parameter is the optimal solution of the key control parameter of the photovoltaic inverter voltage ride-through, and the inverter control parameter result that improves the voltage fault ride-through performance of the photovoltaic cluster is obtained.

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