A new energy power source fault control parameter identification method of network construction type control

Abstract

The application discloses a new energy power supply fault control parameter identification method of grid-connection type control, constructs a virtual internal electromotive force equation and a grid-connection type fault control mathematical model based on a virtual impedance, and further obtains a disturbance output function of a power control loop, an output current calculation formula and to-be-identified parameters; when a fault occurs, a time domain boundary is obtained according to the output current to distinguish internal loop control parameters and external loop control parameters; based on a transient time domain, an optimization algorithm is adopted to obtain internal loop control parameter values and low-voltage ride-through control parameter values according to the output current calculation formula; the low-voltage ride-through control parameter values are substituted into the virtual internal electromotive force equation to obtain virtual internal electromotive force values, based on a steady time domain, an optimization algorithm is adopted to obtain external loop control parameter values according to the disturbance output function of the power control loop, the new energy power supply fault control parameter of the grid-connection type control can be effectively identified, the problem that multiple solutions easily occur in unified identification of power external loop and internal loop parameter coupling is solved, the identification precision is high, and the application range is wide.

Description

Technical Field

[0001] This invention relates to the field of new energy power supply control system technology, and in particular to a method for identifying fault control parameters of new energy power supply in a grid-type control system. Background Technology

[0002] The installed capacity of new energy sources, primarily wind and solar power, is projected to exceed 50% by 2030. This new power system exhibits high energy consumption and high power density, posing a significant challenge to the safe and stable operation of the power grid. Grid-connected new energy power converters possess active support capabilities and exhibit "voltage source" characteristics, significantly improving the stability of the power grid. However, due to manufacturer confidentiality and a lack of corresponding theories for identifying control loop parameters, it is impossible to obtain the correct control parameters to establish an accurate grid-connected control model. Therefore, research on the identification of control parameters for grid-connected faults is urgently needed.

[0003] For the identification of control parameters in permanent magnet synchronous motors and traditional grid-connected new energy power sources, the problem of multiple identification results often arises due to the underranking of the system control equations caused by mathematical model reduction or rank enhancement. While injecting high-frequency or DC signals in the time and frequency domains has solved the problem of underranked equations and the inability to correctly identify some parameters, it adds extra equipment and is not suitable for complex field testing scenarios. Alternatively, the number of parameters to be identified can be reduced by setting low-sensitivity parameters to fixed values ​​based on empirical values ​​from engineering practice, and only high-sensitivity parameters can be identified through optimized algorithms. However, in grid-connected power outer loops, voltage and current inner loops, and low-throughput control loops are cascaded, with numerous fault control parameters and unclear coupling relationships, making the applicability of step-by-step identification algorithms uncertain.

[0004] The theories and algorithms for identifying control parameters of permanent magnet synchronous motors and traditional grid-connected new energy power sources cannot be directly applied to grid-connected control. Therefore, correlation analysis of grid-connected control parameters and identification algorithms for accurately identifying fault control parameters are urgently needed. Summary of the Invention

[0005] Type the invention description paragraph here.

[0006] In order to solve the problems in the prior art, the present invention provides a method for identifying fault control parameters of new energy power sources with grid-type control that can effectively identify fault control parameters. This method solves the problem that multiple solutions are easily generated when identifying the coupled and unified parameters of the power outer loop and inner loop. It provides a high-precision identification method for fault control parameters of new energy power sources with grid-type control.

[0007] To achieve the above-mentioned objectives, this invention provides a method for identifying fault control parameters of a new energy power source in a grid-based control system, comprising the following steps:

[0008] Obtain the virtual speed, virtual impedance, grid connection point voltage, output current, and filter circuit equations of the voltage drop generator;

[0009] A virtual internal potential equation is constructed based on the virtual impedance, the grid connection point voltage, and the output current.

[0010] A mathematical model for grid-type fault control is constructed based on the virtual speed of the voltage drop generator, the grid connection point voltage, the output current, and the virtual internal potential.

[0011] The disturbance output function and parameters to be identified for the power control loop are obtained based on the network-type fault control mathematical model; the parameters to be identified include the outer loop control parameters, the inner loop control parameters, and the low voltage ride-through control parameters.

[0012] The output current calculation formula is obtained based on the network-type fault control mathematical model and the filter circuit equation.

[0013] When a fault occurs, the time domain boundary is obtained based on the output current;

[0014] The identification region is obtained based on the time domain boundary, and the inner loop control parameters and the outer loop control parameters are distinguished: the identification region includes the transient time domain and the steady-state time domain;

[0015] Based on the transient time domain, an optimization algorithm is used to obtain the inner loop control parameter value and the low voltage ride-through control parameter value according to the output current calculation formula, the inner loop control parameter and the low voltage ride-through control parameter.

[0016] The virtual internal potential value is obtained based on the low voltage ride-through control parameter value and the virtual internal potential equation.

[0017] Based on the steady-state time domain, an optimization algorithm is used to obtain the outer loop control parameter values ​​according to the disturbance output function of the power control loop, the virtual internal potential value, and the outer loop control parameters.

[0018] Furthermore, the virtual internal potential equation is:

[0019] ;

[0020] In the formula, For virtual internal potential, For grid connection point shaft voltage, For grid connection point shaft voltage, For output shaft current, For output shaft current, This is the virtual impedance value. To generalize the virtual resistance value, All are generalized virtual reactance values.

[0021] Furthermore, the network-based fault control mathematical model includes an outer-loop fault control model, an inner-loop fault control model, and a low-voltage ride-through control model; the outer-loop fault control model is as follows:

[0022] ;

[0023] The inner loop fault control model is as follows:

[0024] ,

[0025] ;

[0026] The low-voltage ride-through control model is as follows:

[0027] ;

[0028] In the formula, The actual value of active power of new energy power sources is used for grid-based control. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. The actual value of reactive power of new energy power sources is used for grid-based control. This serves as a reference value for the reactive power of new energy power sources in grid-connected control systems. The damping coefficient of the active outer loop is... The inertia coefficient of the active outer loop. This represents the actual virtual rotational speed of the voltage drop generator. This is the virtual speed rating of the voltage sag generator. This is a phase angle reference value. For time, This is the reactive voltage droop factor. For virtual internal potential, The voltage at the grid connection point. For grid connection point shaft voltage, For grid connection point shaft voltage, For grid connection point Shaft voltage reference value, For grid connection point Shaft voltage reference value, For output shaft current, For output shaft current, For output Shaft current reference value, For output Shaft current reference value, This is the proportional coefficient of the inner voltage loop. The integral coefficient of the inner voltage loop is denoted as . This is the proportionality coefficient of the inner current loop. The integral coefficient of the inner current loop is . For complex variables in the frequency domain, After voltage reduction Shaft voltage reference value, After voltage reduction Shaft voltage reference value, This is the equivalent capacitance value. This is the equivalent inductance value. To generalize the virtual resistance value, All are generalized virtual inductance values.

[0029] Furthermore, the disturbance output function of the power control loop is obtained based on the outer loop fault control model, and the output current calculation formula is obtained based on the inner loop fault control model and the filter circuit equation.

[0030] The disturbance output function of the power control loop is:

[0031] ;

[0032] The formula for calculating the output current is:

[0033] ;

[0034] In the formula, The total power factor is the step power factor. For output current, For output voltage, For time, It is an anti-pull operator. This is the equivalent resistance value.

[0035] Furthermore, the steps for distinguishing between the inner loop control parameters and the outer loop control parameters are as follows:

[0036] Running the network-type fault control mathematical model, the three-phase current envelope is extracted using the Hilbert transform; the Hilbert transform is:

[0037] ;

[0038] In the formula, For Hilbert transformation, Pi For time, This represents the time corresponding to the Hilbert transform;

[0039] The time domain boundary is obtained based on the current amplitude; the current amplitude is:

[0040] ;

[0041] In the formula, The current amplitude, The current envelope value is obtained through Hilbert transform. For the difference interval, The current threshold;

[0042] The identification region is obtained based on the time domain boundary;

[0043] The inner loop control parameters and the outer loop control parameters are distinguished based on the three-phase current envelope and the identification region.

[0044] Furthermore, before obtaining the inner loop control parameter values ​​and the low voltage ride-through control parameter values, the inner loop control parameters and the low voltage ride-through control parameters are distinguished by a trajectory sensitivity model;

[0045] The steps to distinguish between the inner loop control parameters and the low voltage ride-through control parameters are as follows:

[0046] An implicit function parameter set is constructed based on the inner loop control parameters and the low voltage ride-through control parameters; the implicit function parameter set is as follows:

[0047] ,

[0048] ;

[0049] In the formula, For implicit function parameter set, For the first One implicit function parameter, For the first One implicit function parameter, It is an implicit function. For the first One parameter to be identified;

[0050] An output current model is constructed based on the implicit function parameter set. The output current model is as follows:

[0051] ;

[0052] In the formula, For output current, The output current function, For the first The implicit function parameters corresponding to the parameters to be identified;

[0053] The trajectory sensitivity model is constructed based on the implicit function parameter set and the output current model; the trajectory sensitivity model is:

[0054] ;

[0055] In the formula, For the first The trajectory sensitivity of the parameter to be identified to the output current;

[0056] A set of random values ​​of the parameters to be identified is obtained within a preset numerical range, and a set of trajectory sensitivity values ​​is obtained by solving the trajectory sensitivity model based on the set of random values ​​of the parameters to be identified.

[0057] Obtain the trajectory sensitivity determination value;

[0058] The inner loop control parameters and the low voltage ride-through control parameters are distinguished based on the set of trajectory sensitivity values ​​and the trajectory sensitivity determination value. The inner loop control parameters include voltage inner loop control parameters and current inner loop control parameters.

[0059] The parameter to be identified corresponding to the trajectory sensitivity value being greater than the trajectory sensitivity determination value is the voltage inner loop control parameter or the low voltage ride-through control parameter.

[0060] The parameter to be identified when the trajectory sensitivity value is less than the trajectory sensitivity determination value is the current inner loop control parameter.

[0061] Further, the steps for obtaining the inner loop control parameter values ​​and the low voltage ride-through control parameter values ​​are as follows:

[0062] Within a preset value range, obtain random values ​​of current inner loop control parameters, a set of random values ​​of current inner loop control parameters, a set of random values ​​of voltage inner loop control parameters, and a set of random values ​​of low voltage ride-through control parameters.

[0063] An optimization algorithm is used to run the output current calculation formula based on the set of random values ​​of the current inner loop control parameters, the set of random values ​​of the voltage inner loop control parameters, and the set of random values ​​of the low voltage ride-through control parameters, so as to obtain the voltage inner loop control parameter values ​​and the low voltage ride-through control parameter values.

[0064] An optimization algorithm is used to run the output current calculation formula based on the set of the voltage inner loop control parameter values, the low voltage ride-through control parameter values, and the random values ​​of the current inner loop control parameters, so as to obtain the current inner loop control parameter values.

[0065] Before obtaining the inner loop control parameter value and the low voltage ride-through control parameter value, it is determined whether the parameters to be identified are independent of each other; the step of determining whether the parameters to be identified are independent of each other is as follows:

[0066] Construct a Pearson correlation coefficient model. The Pearson correlation coefficient model is as follows:

[0067] ;

[0068] In the formula, The Pearson correlation coefficient is... , They are respectively The sensitivity of the parameters to be identified at each sampling time to the trajectory of the output current. , These represent the average trajectory sensitivity of the parameter to be identified to the output current. This is the sampling time sequence number. This represents the total number of sampling times.

[0069] The Pearson correlation coefficient model is run based on the set of trajectory sensitivity values ​​to obtain the Pearson correlation coefficient between any two parameters to be identified.

[0070] Obtain the Pearson correlation coefficient value;

[0071] If the Pearson correlation coefficient between two parameters to be identified is less than the Pearson correlation coefficient judgment value, then the two parameters to be identified are independent of each other.

[0072] Otherwise, the two parameters to be identified are not independent of each other.

[0073] Further, the steps for obtaining the outer loop control parameter values ​​are as follows:

[0074] Based on the steady-state time domain, a reference value for the power of the renewable energy source under grid-connected control is obtained; the reference value for the power of the renewable energy source under grid-connected control is:

[0075] ,

[0076] ;

[0077] In the formula, This is the rated power value. This is the per-unit value of the grid connection point voltage. This is the per-unit value for the voltage drop at the grid connection point. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. This serves as a reference value for the reactive power of new energy power sources in grid-connected control systems. Rated capacity of new energy power sources;

[0078] Within a preset numerical range, obtain a set of random values ​​for the outer loop control parameters;

[0079] An optimization algorithm is used to run the disturbance output function of the power control loop based on the reference power value of the grid-type control new energy source, the set of random values ​​of the outer loop control parameters, and the virtual internal potential value, so as to obtain the outer loop control parameter values.

[0080] Furthermore, the outer loop control parameters include the damping coefficient of the active outer loop, the inertia coefficient of the active outer loop, and the reactive voltage droop coefficient.

[0081] The inner loop control parameters include the proportional coefficient of the voltage inner loop, the integral coefficient of the voltage inner loop, the proportional coefficient of the current inner loop, and the integral coefficient of the current inner loop.

[0082] The low-voltage ride-through control parameters include intermediate parameters and parameters after voltage reduction. Shaft voltage reference value and voltage drop The shaft voltage reference value, the intermediate parameters include the generalized virtual resistance value, the generalized virtual reactance value, and the generalized virtual inductance value.

[0083] The beneficial effects of this invention are: the fault control parameter identification method for new energy power sources with grid-type control proposed in this invention does not require additional equipment, can effectively identify fault control parameters of new energy power sources with grid-type control, solves the problem of multiple solutions easily occurring in the unified identification of power outer loop and inner loop parameters, has high identification accuracy, wide application range, and is especially suitable for complex field test scenarios.

[0084] The fault control parameter identification method for new energy power sources with grid-type control provided by this invention can withstand noise with a signal-to-noise ratio of more than 30dB and has a certain degree of noise robustness. Attached Figure Description

[0085] Figure 1 This is a schematic diagram of the process of the present invention;

[0086] Figure 2 This is a schematic diagram illustrating the principle of the present invention;

[0087] Figure 3 This is a schematic diagram of the topology and control structure of the grid-type controlled new energy power transmission system in the simulation example of this invention;

[0088] Figure 4 The actual and fitted values ​​of active and reactive power of the grid-controlled new energy power source in the simulation example of this invention are shown.

[0089] Figure 5 The actual and fitted values ​​of the d-axis and q-axis current fault control parameters of the grid-controlled new energy power supply in the simulation example of this invention are shown. Detailed Implementation

[0090] To clearly illustrate the technical features of this solution, the following detailed implementation method will be used to explain the solution.

[0091] See Figure 1 , 2 This invention provides a method for identifying fault control parameters of a new energy power source in a grid-based control system, comprising the following steps:

[0092] Obtain the virtual speed, virtual impedance, grid connection point voltage, output current, and filter circuit equations of the voltage drop generator;

[0093] A virtual internal potential equation is constructed based on virtual impedance, grid connection point voltage, and output current.

[0094] A mathematical model for grid-type fault control is constructed based on the virtual speed of the voltage drop generator, the grid connection point voltage, the output current, and the virtual internal potential.

[0095] The disturbance output function and parameters to be identified for the power control loop are obtained based on the mathematical model of network-type fault control. The parameters to be identified include the outer loop control parameters, the inner loop control parameters, and the low voltage ride-through control parameters.

[0096] The formula for calculating the output current is obtained based on the mathematical model of network-type fault control and the filter circuit equation.

[0097] When a fault occurs, the time domain boundary is obtained based on the output current;

[0098] The identification region is obtained based on the time domain boundary, and the inner loop control parameters and outer loop control parameters are distinguished: the identification region includes the transient time domain and the steady-state time domain;

[0099] Based on the transient time domain, an optimization algorithm is used to obtain the values ​​of the inner loop control parameters and the low voltage ride-through control parameters according to the output current calculation formula, the inner loop control parameters, and the low voltage ride-through control parameters.

[0100] The virtual internal potential value is obtained based on the low voltage ride-through control parameter values ​​and the virtual internal potential equation.

[0101] Based on the steady-state time domain, an optimization algorithm is used to obtain the outer loop control parameter values ​​according to the disturbance output function of the power control loop, the virtual internal potential value, and the outer loop control parameters.

[0102] The intermediate variable virtual internal potential is calculated based on the reactive power of the virtual impedance. Specifically, the reactive power is calculated by introducing the virtual impedance voltage, and the virtual internal potential is obtained by the voltage, current and virtual impedance of the inverter output side. The intermediate variable virtual internal potential that cannot be directly measured is then calculated.

[0103] Based on the relationship between the virtual internal potential across the virtual impedance and the inverter output voltage, the formula for calculating the reactive power delivered by the inverter can be obtained as follows:

[0104] ;

[0105] In the formula, Q is the reactive power delivered by the inverter, U is the inverter output voltage after voltage limiting, and θ is the voltage phase.

[0106] The formula for calculating reactive power using voltage and current in the dq-axis coordinate system is: ;

[0107] Therefore, the equation for the virtual internal potential can be obtained as follows:

[0108] ;

[0109] In the formula, For virtual internal potential, For grid connection point shaft voltage, For grid connection point shaft voltage, For output shaft current, For output shaft current, This is the virtual impedance value. To generalize the virtual resistance value, All are generalized virtual reactance values.

[0110] Furthermore, the mathematical model for grid-type fault control includes an outer-loop fault control model, an inner-loop fault control model, and a low-voltage ride-through control model. During normal operation, the output frequency of the grid-type converter is obtained based on the power difference between the actual active power output and the power reference value. The output voltage reference value is obtained through the power difference between the actual reactive power output and the reactive power reference value. During the fault period, to improve stability, the power loop is blocked, i.e., the active power difference and reactive power difference of the input power outer loop are set to 0, maintaining the voltage reference value as the virtual internal potential before the fault, and the voltage phase angle reference as the phase angle before the fault. The outer-loop fault control model is:

[0111] ;

[0112] The voltage and current inner loop, through two cascaded control loops, obtains a pulse width modulation signal via Parker inverse transform to control the inverter. Current and voltage feedforward decoupling control is typically employed to further improve dynamic response speed. The inner loop fault control model is as follows:

[0113] ,

[0114] ;

[0115] The low voltage ride-through control model is as follows:

[0116] ;

[0117] In the formula, The actual value of active power of new energy power sources is used for grid-based control. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. The actual value of reactive power of new energy power sources is used for grid-based control. This serves as a reference value for the reactive power of new energy power sources in grid-connected control systems. The damping coefficient of the active outer loop is... The inertia coefficient of the active outer loop. This represents the actual virtual rotational speed of the voltage drop generator. This is the virtual speed rating of the voltage sag generator. This is a phase angle reference value. For time, This is the reactive voltage droop factor. For virtual internal potential, The voltage at the grid connection point. For grid connection point shaft voltage, For grid connection point shaft voltage, For grid connection point Shaft voltage reference value, For grid connection point Shaft voltage reference value, For output shaft current, For output shaft current, For output Shaft current reference value, For output Shaft current reference value, This is the proportional coefficient of the inner voltage loop. The integral coefficient of the inner voltage loop is denoted as . This is the proportionality coefficient of the inner current loop. The integral coefficient of the inner current loop is . For complex variables in the frequency domain, After voltage reduction Shaft voltage reference value, After voltage reduction Shaft voltage reference value, This is the equivalent capacitance value. This is the equivalent inductance value. To generalize the virtual resistance value, All are generalized virtual inductance values.

[0118] The parameters to be identified can be obtained from the network-type fault control mathematical model, including:

[0119] The outer loop control parameters include the damping coefficient of the active outer loop. Inertia coefficient of the active outer loop and reactive voltage droop factor ;

[0120] Inner loop control parameters include the proportional coefficient of the voltage inner loop. (including the voltage inner loop) Shaft scaling factor Voltage inner loop Shaft scaling factor Integral coefficient of the inner voltage loop (including the voltage inner loop) integral coefficients of the axis Voltage inner loop integral coefficients of the axis ), the proportional coefficient of the inner current loop (including the inner current loop) Shaft scaling factor Inner current loop Shaft scaling factor ) and the integral coefficient of the inner current loop (including the inner current loop) integral coefficients of the axis Inner current loop integral coefficients of the axis );

[0121] Low voltage ride-through control parameters include intermediate parameters and voltage reduction parameters. Shaft voltage reference value and after voltage drop Shaft voltage reference value Intermediate parameters include the generalized virtual resistance value. Generalized virtual reactance value and generalized virtual inductance value .

[0122] Before identification, the transfer function matrix of the networked control is first constructed based on the mathematical model of networked fault control to study the correlation between the parameters to be identified; the transfer function matrix of the networked control is as follows:

[0123] ;

[0124] In the formula, for Phase current value, This is the proportional coefficient of the inner voltage loop. The integral coefficient of the inner voltage loop is denoted as . This is the proportionality coefficient of the inner current loop. The integral coefficient of the inner current loop is . For complex variables in the frequency domain, This is the equivalent inductance value. This is the equivalent resistance value. The damping coefficient of the active outer loop is... The inertia coefficient of the active outer loop. The actual value of active power of new energy power sources is used for grid-based control. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. Let be the transfer function of the inner voltage loop. for Phase voltage value, for Phase voltage value, for Phase voltage value;

[0125] As can be seen from the transfer function matrix of the network control, the parameters to be identified are related. The power outer loop, low voltage ride-through control loop and voltage and current inner loop of the network control are cascaded. Since the inner loop control bandwidth should be greater than the outer loop bandwidth, the transient response time of each control loop is different after the fault occurs. Therefore, the outer time domain boundary can be divided by the current waveform characteristics, and then the inner loop control parameters and the outer loop control parameters can be distinguished.

[0126] The steps to distinguish between inner-loop control parameters and outer-loop control parameters are as follows:

[0127] The mathematical model for network-based fault control is run, and the three-phase current envelopes are extracted using the Hilbert transform. The Hilbert transform is:

[0128] ;

[0129] In the formula, For Hilbert transformation, Pi For time, This represents the time corresponding to the Hilbert transform;

[0130] The time domain boundary is obtained based on the current amplitude; the current amplitude is:

[0131] ;

[0132] In the formula, The current amplitude, The current envelope value is obtained through Hilbert transform. For the difference interval, The current threshold;

[0133] The identification region is obtained based on the temporal domain boundary;

[0134] The inner loop control parameters and outer loop control parameters are distinguished based on the three-phase current envelope and the identification region.

[0135] Before obtaining the inner loop control parameter values ​​and the low voltage ride-through control parameter values, the inner loop control parameters and the low voltage ride-through control parameters are distinguished by the trajectory sensitivity model.

[0136] The steps to distinguish between inner-loop control parameters and low-voltage ride-through control parameters are as follows:

[0137] An implicit function parameter set is constructed based on the inner loop control parameters and the low voltage ride-through control parameters; the implicit parameter set is as follows:

[0138] ,

[0139] ;

[0140] In the formula, For implicit function parameter set, For the first One implicit function parameter, For the first One implicit function parameter, It is an implicit function. For the first One parameter to be identified;

[0141] The output current model is constructed based on the implicit function parameter set. The output current model is as follows:

[0142] ;

[0143] In the formula, For output current, The output current function, For the first The implicit function parameters corresponding to the parameters to be identified;

[0144] A trajectory sensitivity model is constructed based on the implicit function parameter set and the output current model; the trajectory sensitivity model is as follows:

[0145] ;

[0146] In the formula, For the first The trajectory sensitivity of the parameter to be identified to the output current;

[0147] Within a preset numerical range, obtain a set of random values ​​for the parameters to be identified, and solve the trajectory sensitivity model based on the set of random values ​​for the parameters to be identified to obtain a set of trajectory sensitivity values.

[0148] The trajectory sensitivity determination value is obtained. In this invention, the trajectory sensitivity determination value is set to 0.5. If the trajectory sensitivity value is less than ten times that of other parameters, it is considered a low sensitivity parameter.

[0149] The inner-loop control parameters and low-voltage ride-through control parameters are distinguished based on the set of trajectory sensitivity values ​​and the trajectory sensitivity judgment value. The inner-loop control parameters include voltage inner-loop control parameters and current inner-loop control parameters.

[0150] The parameters to be identified corresponding to a trajectory sensitivity value greater than the trajectory sensitivity judgment value are either voltage inner loop control parameters or low voltage ride-through control parameters. That is, both voltage inner loop control parameters and low voltage ride-through control parameters are high trajectory sensitivity parameters.

[0151] The parameter to be identified when the trajectory sensitivity value is less than the trajectory sensitivity judgment value is the current inner loop control parameter, that is, the current inner loop control parameter is a low trajectory sensitivity parameter.

[0152] Before obtaining the inner-loop control parameter values ​​and the low-voltage ride-through control parameter values, a Pearson correlation coefficient model is constructed to investigate whether the parameters to be identified are independent of each other. The Pearson correlation coefficient model is as follows:

[0153] ;

[0154] In the formula, The Pearson correlation coefficient is... , They are respectively The sensitivity of the parameters to be identified at each sampling time to the trajectory of the output current. , These represent the average trajectory sensitivity of the parameter to be identified to the output current. This is the sampling time sequence number. This represents the total number of sampling times.

[0155] Since the trajectory sensitivity of the associated parameters is linearly correlated, the degree of linear correlation between the parameters to be identified can be analyzed by Pearson correlation analysis. The Pearson correlation coefficient ranges from -1 to 1. The closer the absolute value is to 1, the more significant the linear correlation between the parameters to be identified. The closer it is to 0, the weaker the linear correlation between the parameters to be identified. A value |r| greater than 0.8 indicates a strong correlation.

[0156] The Pearson correlation coefficient model is run based on the set of trajectory sensitivity values ​​to obtain the Pearson correlation coefficient between any two parameters to be identified.

[0157] Obtain the Pearson correlation coefficient value;

[0158] If the Pearson correlation coefficient between two parameters to be identified is less than the Pearson correlation coefficient judgment value, then the two parameters to be identified are independent of each other.

[0159] Otherwise, the two parameters to be identified are not independent of each other.

[0160] In this invention, the Pearson correlation coefficient is set to 0.8. Experiments have shown that the Pearson correlation coefficient between any two parameters to be identified in the inner loop control parameters and the low voltage ride-through control parameters is within 0.8, that is, each parameter is an independent parameter and there is no strong correlation.

[0161] The output current calculation formula is obtained based on the inner-loop fault control model and the filter circuit equation; the filter circuit equation is:

[0162] ;

[0163] In the formula, This is the filter resistance value. All are filter inductance values;

[0164] The formula for calculating the output current is:

[0165] ;

[0166] In the formula, The total power factor is the step power factor. For output current, For output voltage, For time, It is an anti-pull operator. This is the equivalent resistance value.

[0167] The steps to obtain the inner loop control parameter values ​​and the low voltage ride-through control parameter values ​​are as follows:

[0168] When any two parameters to be identified in the inner loop control parameters and the low voltage ride-through control parameters are independent of each other:

[0169] The steps to obtain the inner loop control parameter values ​​and the low voltage ride-through control parameter values ​​are as follows:

[0170] Within a preset value range, obtain random values ​​of current inner loop control parameters, a set of random values ​​of current inner loop control parameters, a set of random values ​​of voltage inner loop control parameters, and a set of random values ​​of low voltage ride-through control parameters; the set of random values ​​of low voltage ride-through control parameters is a set of random values ​​of intermediate parameters, which are respectively a set of random values ​​of generalized virtual resistance and a set of random values ​​of generalized virtual inductance.

[0171] An optimization algorithm is employed to run the output current calculation formula based on the sets of random values ​​for the current inner loop control parameters, the voltage inner loop control parameters, and the low-voltage ride-through control parameters. This process yields the voltage inner loop control parameter values ​​and the low-voltage ride-through control parameter values. The low-voltage ride-through control parameter value directly obtained in this step using the optimization algorithm is the generalized virtual resistance value. and generalized virtual inductance value Substituting the generalized virtual resistance and generalized virtual inductance values ​​into the low-voltage ride-through control model yields the voltage reduction result. Shaft voltage reference value and after voltage drop Shaft voltage reference value ;

[0172] An optimization algorithm is employed, based on the generalized virtual resistance value in the voltage inner loop control parameter value and the low voltage ride-through control parameter value. and generalized virtual inductance value The set of random values ​​of the current inner loop control parameters is used to run the output current calculation formula to obtain the current inner loop control parameter values.

[0173] Specifically, the formula for calculating the output current includes: Shaft output current calculation formula and Shaft output current calculation formula, The formula for calculating shaft output current is:

[0174] ;

[0175] When using an optimization algorithm to calculate the inner-loop control parameters, based on The formula for calculating shaft output current yields the voltage inner loop. Shaft scaling factor Voltage inner loop integral coefficients of the axis Inner current loop Shaft scaling factor and the inner loop of current integral coefficients of the axis ;

[0176] The formula for calculating shaft output current is:

[0177]

[0178] When using an optimization algorithm to calculate the inner-loop control parameters, based on The formula for calculating shaft output current yields the voltage inner loop. Shaft scaling factor Voltage inner loop integral coefficients of the axis Inner current loop Shaft scaling factor and the inner loop of current integral coefficients of the axis .

[0179] In addition, a generalized virtual inductance value is obtained. Afterwards, according to Calculate the generalized virtual reactance value ; Generalize the virtual resistance value and generalized virtual reactance value Obtain the virtual internal potential value by performing the virtual internal potential equation;

[0180] The disturbance output function of the power control loop is obtained based on the outer loop fault control model; the disturbance output function of the power control loop is:

[0181] ;

[0182] The steps to obtain the outer loop control parameter values ​​are as follows:

[0183] Based on the steady-state time domain, obtain the reference value of renewable energy power for grid-connected control; to ensure the transient stability of grid-connected control during faults, the active power reference value should be reduced during faults. Typically, the active power reference value is set to decrease with the degree of voltage drop at the grid connection point. The adjusted power reference value is: The reference value of renewable energy power for grid-connected control is:

[0184] ,

[0185] ;

[0186] In the formula, This is the rated power value. This is the per-unit value of the grid connection point voltage. This is the per-unit value for the voltage drop at the grid connection point. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. This serves as a reference value for the reactive power of new energy power sources in grid-connected control systems. Rated capacity of new energy power sources;

[0187] Within a preset numerical range, obtain a set of random values ​​for the outer loop control parameters;

[0188] An optimization algorithm is used to obtain the outer loop control parameter values ​​based on the power reference value of the new energy power source in the network-type control, the set of random values ​​of the outer loop control parameters, and the disturbance output function of the virtual internal potential value of the operating power control loop.

[0189] The set of random values ​​of inner-loop control parameters (including the set of random values ​​of voltage inner-loop control parameters and the set of random values ​​of current inner-loop control parameters), the set of random values ​​of low-voltage ride-through control parameters, and the set of random values ​​of outer-loop control parameters obtained during the process of obtaining the parameters to be identified are collectively referred to as the set of random values ​​of the parameters to be identified. The set of random values ​​of the parameters to be identified is as follows:

[0190] ;

[0191] In the formula, The set of random values ​​for the parameter to be identified. ~ 1~ A random value for the parameter to be identified. for The number of random values ​​of the parameters to be identified in the middle;

[0192] In some preferred embodiments, the optimization error in the optimization algorithm is:

[0193] ;

[0194] In the formula, To optimize the error, for The sequence number of the random value of the parameter to be identified. for The Middle A random value for the parameter to be identified. for The Middle The output value corresponding to each random value of the parameter to be identified is the value to be optimized. for The fitted values ​​of the corresponding parameters to be identified;

[0195] The purpose of the optimization algorithm is to reduce the optimization error. The minimum optimization algorithm is an existing technology and will not be elaborated here.

[0196] Furthermore, the preset numerical range is:

[0197] .

[0198] Simulation example:

[0199] Build such on PSCAD / EMTDC Figure 3The network-based fault control mathematical model shown is used as a simulation model to identify various fault control parameters, i.e., the parameters to be identified. The new energy power supply capacity for network-based control is 0.5MW, the grid short-circuit ratio (SCR) is 2, and a three-phase symmetrical fault with different voltage drops is set on the 35kV bus at 0s, with a fault duration of 0.1s. The accuracy of the algorithm's identification is described by calculating the average error of the identification results for each parameter to be identified 10 times.

[0200] like Figure 4 , 5 The figure shows the simulation data and the output waveform of the identification results. According to the method for identifying fault control parameters of new energy power sources with grid-type control in this invention, the grid-type fault control parameters are identified, and after time-domain decoupling, the output waveforms are...

[0201] Table 1. Identification results and mean error of 10 attempts

[0202]

[0203] The average error between the outer loop control parameters independently identified by the optimization algorithm and the high-sensitivity low-voltage ride-through control parameters, voltage inner loop parameters and low-sensitivity current inner loop parameters identified step by step by the optimization algorithm and the true values ​​is within 5% (see Table 1). The relative error is small, which can realize the correct identification of network-type fault control parameters.

[0204] Specifically, regarding the appendix Figure 3 The current limiting method was used to set noise interference with signal-to-noise ratios of 40, 30, and 20 dB, respectively, to consider the feasibility of the proposed identification algorithm in practical engineering applications.

[0205] Table 2 Identification error at different signal-to-noise ratios

[0206]

[0207] As shown in Table 2, when the signal-to-noise ratio (SNR) is 40dB and 30dB respectively, the identification error obtained by the algorithm is within 5%, which can accurately identify the fault control parameters. However, when the SNR is 20dB, the identification errors of the voltage inner loop proportional coefficient and the current inner loop both exceed 10%. Therefore, the identification method proposed in this invention can withstand noise with an SNR of 30dB or higher and has a certain degree of noise robustness.

[0208] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0209] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0210] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying fault control parameters of a new energy power source using grid-based control, characterized in that, Includes the following steps: Obtain the virtual speed, virtual impedance, grid connection point voltage, output current, and filter circuit equations of the voltage drop generator; A virtual internal potential equation is constructed based on the virtual impedance, the grid connection point voltage, and the output current. A mathematical model for grid-type fault control is constructed based on the virtual speed of the voltage drop generator, the grid connection point voltage, the output current, and the virtual internal potential. The disturbance output function and parameters to be identified for the power control loop are obtained based on the network-type fault control mathematical model; the parameters to be identified include the outer loop control parameters, the inner loop control parameters, and the low voltage ride-through control parameters. The output current calculation formula is obtained based on the network-type fault control mathematical model and the filter circuit equation. When a fault occurs, the time domain boundary is obtained based on the output current; The identification region is obtained based on the time domain boundary, and the inner loop control parameters and the outer loop control parameters are distinguished: the identification region includes the transient time domain and the steady-state time domain; Based on the transient time domain, an optimization algorithm is used to obtain the inner loop control parameter value and the low voltage ride-through control parameter value according to the output current calculation formula, the inner loop control parameter and the low voltage ride-through control parameter. The virtual internal potential value is obtained based on the low voltage ride-through control parameter value and the virtual internal potential equation. Based on the steady-state time domain, an optimization algorithm is used to obtain the outer loop control parameter values ​​according to the disturbance output function of the power control loop, the virtual internal potential value, and the outer loop control parameters.

2. The method for identifying fault control parameters of new energy power sources with grid-type control according to claim 1, characterized in that, The virtual internal potential equation is: ； In the formula, For virtual internal potential, For grid connection point shaft voltage, For grid connection point shaft voltage, For output shaft current, For output shaft current, This is the virtual impedance value. To generalize the virtual resistance value, All are generalized virtual reactance values.

3. The method for identifying fault control parameters of new energy power sources with grid-type control according to claim 2, characterized in that, The network-based fault control mathematical model includes an outer-loop fault control model, an inner-loop fault control model, and a low-voltage ride-through control model; the outer-loop fault control model is as follows: ； The inner loop fault control model is as follows: ， ； The low-voltage ride-through control model is as follows: ； In the formula, The actual value of active power of new energy power sources is used for grid-based control. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. The actual value of reactive power of new energy power sources is used for grid-based control. This serves as a reference value for the reactive power of new energy power sources in grid-connected control systems. The damping coefficient of the active outer loop is... The inertia coefficient of the active outer loop. This represents the actual virtual rotational speed of the voltage drop generator. This is the virtual speed rating of the voltage drop generator. This is a phase angle reference value. For time, This is the reactive voltage droop factor. For virtual internal potential, The voltage at the grid connection point. For grid connection point shaft voltage, For grid connection point shaft voltage, For grid connection point Shaft voltage reference value, For grid connection point Shaft voltage reference value, For output shaft current, For output shaft current, For output Shaft current reference value, For output Shaft current reference value, This is the proportional coefficient of the inner voltage loop. The integral coefficient of the inner voltage loop is . This is the proportionality coefficient of the inner current loop. The integral coefficient of the inner current loop is . For complex variables in the frequency domain, After voltage reduction Shaft voltage reference value, After voltage reduction Shaft voltage reference value, This is the equivalent capacitance value. This is the equivalent inductance value. To generalize the virtual resistance value, All are generalized virtual inductance values.

4. The method for identifying fault control parameters of new energy power sources with grid-type control according to claim 3, characterized in that, The disturbance output function of the power control loop is obtained based on the outer loop fault control model, and the output current calculation formula is obtained based on the inner loop fault control model and the filter circuit equation. The disturbance output function of the power control loop is: ； The formula for calculating the output current is: ； In the formula, The total power factor is the step power factor. For output current, For output voltage, For time, It is an anti-pull operator. This is the equivalent resistance value.

5. The method for identifying fault control parameters of new energy power sources with grid-based control according to claim 1, characterized in that, The steps to distinguish between the inner loop control parameters and the outer loop control parameters are as follows: Running the network-type fault control mathematical model, the three-phase current envelope is extracted using the Hilbert transform; the Hilbert transform is: ； In the formula, For Hilbert transformation, Pi For time, This represents the time corresponding to the Hilbert transform; The time domain boundary is obtained based on the current amplitude; the current amplitude is: ； In the formula, The current amplitude, The current envelope value is obtained through Hilbert transform. For the difference interval, The current threshold; The identification region is obtained based on the time domain boundary; The inner loop control parameters and the outer loop control parameters are distinguished based on the three-phase current envelope and the identification region.

6. The method for identifying fault control parameters of new energy power sources with grid-type control according to claim 4, characterized in that, Before obtaining the inner loop control parameter values ​​and the low voltage ride-through control parameter values, the inner loop control parameters and the low voltage ride-through control parameters are distinguished by a trajectory sensitivity model; The steps to distinguish between the inner loop control parameters and the low voltage ride-through control parameters are as follows: An implicit function parameter set is constructed based on the inner loop control parameters and the low voltage ride-through control parameters; the implicit function parameter set is as follows: ， ； In the formula, For implicit function parameter set, For the first One implicit function parameter, For the first One implicit function parameter, It is an implicit function. For the first One parameter to be identified; An output current model is constructed based on the implicit function parameter set. The output current model is as follows: ； In the formula, For output current, The output current function, For the first The implicit function parameters corresponding to the parameters to be identified; The trajectory sensitivity model is constructed based on the implicit function parameter set and the output current model; the trajectory sensitivity model is: ； In the formula, For the first The trajectory sensitivity of the parameter to be identified to the output current; A set of random values ​​of the parameters to be identified is obtained within a preset numerical range, and a set of trajectory sensitivity values ​​is obtained by solving the trajectory sensitivity model based on the set of random values ​​of the parameters to be identified. Obtain the trajectory sensitivity determination value; The inner loop control parameters and the low voltage ride-through control parameters are distinguished based on the set of trajectory sensitivity values ​​and the trajectory sensitivity determination value. The inner loop control parameters include voltage inner loop control parameters and current inner loop control parameters. The parameter to be identified corresponding to the trajectory sensitivity value being greater than the trajectory sensitivity determination value is the voltage inner loop control parameter or the low voltage ride-through control parameter. The parameter to be identified when the trajectory sensitivity value is less than the trajectory sensitivity determination value is the current inner loop control parameter.

7. The method for identifying fault control parameters of new energy power sources with grid-based control according to claim 6, characterized in that, The steps for obtaining the inner loop control parameter values ​​and the low voltage ride-through control parameter values ​​are as follows: Within a preset value range, obtain random values ​​of current inner loop control parameters, a set of random values ​​of current inner loop control parameters, a set of random values ​​of voltage inner loop control parameters, and a set of random values ​​of low voltage ride-through control parameters. An optimization algorithm is used to run the output current calculation formula based on the set of random values ​​of the current inner loop control parameters, the set of random values ​​of the voltage inner loop control parameters, and the set of random values ​​of the low voltage ride-through control parameters, so as to obtain the voltage inner loop control parameter values ​​and the low voltage ride-through control parameter values. An optimization algorithm is used to run the output current calculation formula based on the set of the voltage inner loop control parameter values, the low voltage ride-through control parameter values, and the random values ​​of the current inner loop control parameters, so as to obtain the current inner loop control parameter values.

8. The method for identifying fault control parameters of new energy power sources with grid-based control according to claim 7, characterized in that, Before obtaining the inner loop control parameter value and the low voltage ride-through control parameter value, it is determined whether the parameters to be identified are independent of each other; the step of determining whether the parameters to be identified are independent of each other is as follows: Construct a Pearson correlation coefficient model. The Pearson correlation coefficient model is as follows: ； In the formula, The Pearson correlation coefficient is... , They are respectively The sensitivity of the parameters to be identified at each sampling time to the trajectory of the output current. , These represent the average trajectory sensitivity of the parameter to be identified to the output current. This is the sampling time sequence number. This represents the total number of sampling times. The Pearson correlation coefficient model is run based on the set of trajectory sensitivity values ​​to obtain the Pearson correlation coefficient between any two parameters to be identified. Obtain the Pearson correlation coefficient value; If the Pearson correlation coefficient between two parameters to be identified is less than the Pearson correlation coefficient judgment value, then the two parameters to be identified are independent of each other. Otherwise, the two parameters to be identified are not independent of each other.

9. The method for identifying fault control parameters of new energy power sources with grid-type control according to claim 4, characterized in that, The steps to obtain the outer loop control parameter values ​​are as follows: Based on the steady-state time domain, a reference value for the power of the renewable energy source under grid-connected control is obtained; the reference value for the power of the renewable energy source under grid-connected control is: ， ； In the formula, This is the rated power value. This is the per-unit value of the grid connection point voltage. This is the per-unit value for the voltage drop at the grid connection point. This serves as a reference value for the active power of renewable energy sources in grid-connected control systems. This serves as a reference value for the reactive power of new energy power sources in grid-connected control systems. Rated capacity of new energy power sources; Within a preset numerical range, obtain a set of random values ​​for the outer loop control parameters; An optimization algorithm is used to run the disturbance output function of the power control loop based on the reference power value of the grid-type control new energy source, the set of random values ​​of the outer loop control parameters, and the virtual internal potential value, so as to obtain the outer loop control parameter values.

10. The method for identifying fault control parameters of new energy power sources with grid-based control according to claim 1, characterized in that, The outer loop control parameters include the damping coefficient of the active outer loop, the inertia coefficient of the active outer loop, and the reactive voltage droop coefficient. The inner loop control parameters include the proportional coefficient of the voltage inner loop, the integral coefficient of the voltage inner loop, the proportional coefficient of the current inner loop, and the integral coefficient of the current inner loop. The low-voltage ride-through control parameters include intermediate parameters and parameters after voltage reduction. Shaft voltage reference value and voltage drop The shaft voltage reference value, the intermediate parameters include the generalized virtual resistance value, the generalized virtual reactance value, and the generalized virtual inductance value.

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