CN114865703A - Method for identifying high-penetration characteristic parameters of direct-drive fan inverter - Google Patents
Method for identifying high-penetration characteristic parameters of direct-drive fan inverter Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a method for identifying high-penetration characteristic parameters of a direct-drive fan inverter, which comprises the following steps: 1. collecting transient operation data of the direct-drive fan inverter under N high-voltage ride-through working conditions and taking the transient operation data as measured data; 2. identifying high voltage ride through parameters of the direct-drive fan inverter under N types of high voltage ride through operating conditions to obtain N primary identification results: 3. and calculating the weighted average absolute deviation of the simulation data and the measured data obtained by operation when the high voltage ride through parameters of the direct-drive fan grid-connected model are set as preliminary results, and selecting the parameter identification result with the minimum deviation as the optimal result. The method can accurately identify the high voltage ride through control parameters, thereby accurately modeling the high voltage ride through control.
Description
Technical Field
The invention belongs to the technical field of power system analysis, and particularly relates to a method for identifying high-penetration characteristic parameters of a direct-drive fan inverter.
Background
In a new energy power system, the direct-drive fan is widely applied to various fans due to the advantages of low noise, high efficiency, low maintenance cost and the like. Therefore, accurate modeling of the direct-drive wind turbine is also the basis for analyzing stable operation of a new energy large power grid, wherein accuracy and feasibility of the whole model directly influenced by accurate modeling of a direct-drive wind turbine inverter model serving as a core component of the direct-drive wind turbine are achieved. However, the accurate control parameters of the most important direct-drive wind turbine inverter cannot be directly obtained, which will have a serious influence on the simulation of the direct-drive wind turbine inverter and the modeling analysis of the grid-connected characteristics of the direct-drive wind turbine station. Therefore, parameter identification calculation and research are carried out on the direct-drive fan inverter to obtain direct-drive fan inverter parameters with high identification precision and high accuracy, so that a direct-drive fan accurate model capable of reflecting the real unit operation condition is constructed, the method is the most important point in the operation characteristic analysis of the direct-drive fan, and the method has great significance in analyzing the safe and stable operation capacity of a direct-drive fan grid-connected system.
Because a direct-drive fan control system is complex and can not be measured, the interior of the direct-drive fan cannot be analyzed and reproduced directly, at present, research for obtaining accurate parameters of a new energy system model is based on different system identification algorithms, and actual measurement data is obtained by utilizing fan operation results of an actual field station or a test platform to identify and analyze corresponding parameters. The methods can be divided into a frequency domain identification method, a time domain identification method and an intelligent optimization algorithm, the former two methods respectively identify model parameters by sampling time domain data and frequency domain data obtained by fast Fourier transform, the intelligent optimization algorithm utilizes the global optimization characteristic of the optimization algorithm, the global optimization calculation can be automatically carried out by only providing corresponding sampling data, and finally a model parameter target value with the highest fitness is found, and common intelligent optimization algorithms comprise a genetic algorithm, a particle swarm algorithm, a wolf optimization algorithm, an ant colony algorithm and the like. The parameter identification method based on the algorithms is widely applied to the field of current new energy fan modeling of the power system, but the existing research only focuses on double closed loop PI control parameters of a direct drive fan inverter, the research on high voltage ride through characteristic parameters is still in a missing state, the current literature does not consider how to select the best result from a plurality of identification results under different working conditions, the reliability of a single identification result is only verified, the method cannot adapt to the complex working conditions in the actual operation engineering, and the result is difficult to apply to the actual project.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method for identifying the high-penetration characteristic parameters of the direct-drive fan inverter, so that the high-penetration characteristic parameters of the direct-drive fan inverter can be identified, and accurate modeling of high-voltage penetration control can be realized.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a high-penetration characteristic parameter identification method of a direct-drive fan inverter, which is characterized by comprising the following steps of:
step 1, collecting transient operation data of a direct-drive fan inverter under N high-voltage ride-through working conditions and taking the transient operation data as actual measurement data;
step 1.1, setting N high voltage ride through operating conditions of a direct drive fan inverter, wherein parameters of each high voltage ride through operating condition comprise: an active power output instruction, a voltage disturbance amplitude value during a fault period and a duration time;
step 1.2, collecting N groups of transient operation data corresponding to the direct-drive fan inverter under N high-voltage ride-through operation conditions, wherein each group of transient operation data comprises L sampling point data, and the ith sampling point data of any nth group of transient operation data is reactive power data of a grid-connected point at an alternating current side; i is more than or equal to 1 and less than or equal to L, N is more than or equal to 1 and less than or equal to N; l is the number of sampling points;
taking the reactive power data of L sampling points in the N groups of transient operation data as N groups of measured data;
let Q M,n (i) The measured data of the ith sampling point under the nth high voltage ride through operating condition is represented;
step 2, identifying high voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operating conditions to obtain N parameter identification preliminary results:
step 2.1, building a direct-drive fan grid-connected model, comprising the following steps: the system comprises a direct-drive fan, a variable pitch controller, a machine side inverter controller, a direct current bus capacitor, a grid side inverter controller, a filter and a power grid replaced by an ideal voltage source; let the grid-side inverter controller comprise: the high voltage ride through judgment module and the dynamic reactive power support module;
the high voltage ride through judgment module is used for detecting the per unit voltage value U of the grid-connected point T Whether or not it is within the range of the set threshold value,when the voltage is within the threshold value range, the dynamic reactive power support module calculates the per unit value delta I of the absorbed reactive current of the direct-drive fan inverter during the high voltage ride through by using the formula (1) T According to the absorbed reactive current per unit value delta I T Controlling the direct-drive fan grid-side inverter to output reactive current;
ΔI T ＝K HVRT ×(U T -1.1)×I R (1)
in the formula (1), K HVRT The direct-drive fan inverter is a reactive current proportionality coefficient, namely a high voltage ride through parameter, of the direct-drive fan inverter; i is R Rated current of the direct drive fan inverter;
2.2, based on N groups of measured data, identifying the high voltage ride through parameters by using an adaptive weight particle swarm optimization algorithm based on the aggregation distance to obtain N primary identification results:
step 2.2.1, defining the scale of the particle swarm to be K, defining the serial number of any particle to be K, wherein K is more than or equal to 1 and less than or equal to K;
defining and initializing current iteration times, defining and initializing maximum iteration times, defining and initializing upper and lower limits of particle positions, defining and initializing upper limits of particle speeds, and defining and initializing minimum fitness of algorithm termination;
initializing n-1;
2.2.2, setting parameters of each device in the direct-drive fan grid-connected model according to the nth high-voltage ride-through operating condition; let the kth particle represent the kth high voltage ride through parameter under the nth high voltage ride through operating condition;
defining the individual optimal position of the kth particle as K pbest,n (k) The global optimum position of the particle swarm is K gbest,n ；
Initializing an individual optimal position K of a kth particle pbest,n (k) And global optimum position K gbest,n The corresponding adaptive values are infinitesimal;
2.2.3, randomly generating a particle swarm of the current iteration, namely K high-voltage ride through parameters under the nth high-voltage ride through operating condition, and endowing each particle with a random initial position and speed; initializing k to 1;
step 2.2.4, assigning the kth particle of the current iteration to a reactive current proportionality coefficient K HVRT And then, operating a direct-drive fan grid-connected model and obtaining test data, wherein the test data of the ith sampling point obtained by the operation of the kth particle under the current iteration nth high-penetration working condition is recorded as Q text,n.k (i) So as to calculate the fitness f of the kth particle under the nth high voltage ride through operating condition of the current iteration by using the formula (2) n,k ：
In the formula (2), w 1 、w 2 Respectively the weight of the average error and the weight of the maximum error; mean (), max () are the mean function and the maximum function, respectively;
step 2.2.5, the fitness f of the kth particle of the current iteration at the current position is calculated n,k With self individual optimum position K pbest,n (k) The fitness of the k particle is compared, and a position with high fitness is selected and assigned to the individual optimal position of the k particle of the current iteration and is used as the current position of the k particle of the next iteration;
step 2.2.6, the fitness f of the current position of the kth particle of the current iteration is calculated n,k And global optimum position K gbest,n The fitness of the iteration is compared, and a position with high fitness is selected and assigned to the global optimal position of the current iteration;
step 2.2.7, after K +1 is assigned to K, judging whether K > K is true, if so, executing step 2.2.8; otherwise, returning to the step 2.2.4 for sequential execution;
step 2.2.8, calculating the average aggregation distance meand of the current iteration under the nth high-penetration working condition according to the formula (3) n And maximum aggregation distance maxd n And determining the weight w of the particle swarm algorithm under the nth high-penetration working condition of the current iteration according to the formula (4) n The system is used for updating the speed and the position of K particles under the nth high-penetration working condition of the current iteration;
in the formula (3), mean () and max () are a mean function and a maximum function, respectively; q gbest,n (i) Test data corresponding to the global optimal position of the current iteration;
in the formula (4), a 1 、a 2 Two weight coefficients; delta 1 、Δ 2 Two judgment indexes are provided;
step 2.2.9, judging the global optimum position K of the current iteration gbest,n If the corresponding fitness is greater than the minimum fitness for stopping the algorithm, executing a step 2.2.10, otherwise, adding 1 to the iteration times, and returning to the step 2.2.4 after initializing k to 1 until the loop reaches the maximum iteration times, and executing the step 2.2.10;
step 2.2.10, the global optimum position K of the current iteration is calculated gbest,n As a preliminary identification result of the high voltage ride through parameter under the nth high voltage ride through operation condition, after assigning n +1 to n, judging n>Whether N is established or not, if so, indicating that the identification is finished, and obtaining a preliminary identification result of the voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operating conditions; otherwise, returning to the step 2.2.2 for sequential execution;
step 3, calculating the weighted average absolute deviation of the simulation data and the measured data obtained by operation when the high voltage ride through parameters of the direct-drive fan grid-connected model are set as the initial identification results, and selecting the initial identification result with the minimum deviation as the optimal result;
3.1, sequentially setting high voltage ride through parameters of a direct-drive fan grid-connected model as N primary identification results, and respectively operating under N high voltage ride through operating conditions to obtain N simulation data; when the high-voltage ride-through parameter of the direct-drive fan grid-connected model is set as the a-th primary identification result, the nth high-voltage ride-through is carried outThe simulation data of the ith sampling point obtained by operation under the working condition is recorded as Q S,a,n (i)，1≤n≤N，1≤a≤N；
Step 3.2, calculating simulation data Q according to formula (5) S,a,n (i) Weighted average absolute deviation F of measured data under the corresponding nth high voltage ride through working condition Q,a,n Thereby obtaining analog data Q S,a,n (i) The weighted average absolute deviation of the measured data under N high voltage ride through working conditions is obtained, and then the simulation data Q is obtained S,a,n (i) The weighted average absolute deviation sum of the actually measured data under the N high voltage ride through working conditions is obtained, the weighted average absolute deviation sum of the actually measured data of the N analog data under the N high voltage ride through working conditions is further obtained, and the primary identification result with the smallest weighted average absolute deviation sum is selected as the optimal identification result;
in the formula (5), w A 、w B 、w C Weights in the weighted average absolute deviation before, during and after the fault are respectively; k Astart 、K Aend 、K Bstart 、K Bend 、K Cstart 、K Cend Respectively as the start sampling point and the end sampling point of the period before, during and after the fault; q AM,n (i)、Q BM,n (i)、Q CM,n (i) Respectively measured data of the ith sampling point under the nth high voltage ride through working condition before, during and after the fault; q AS,a,n (i)、Q BS,a,n (i)、Q CS,a,n (i) The simulation data of the ith sampling point in the period before, during and after the fault are respectively obtained by operating under the nth high voltage ride through working condition when the high-pass-through parameter of the direct-drive fan grid-connected model is set as the a-th primary identification result.
Compared with the prior art, the invention has the beneficial effects that:
1. the measured data of the invention is obtained by the operation of the direct-drive fan under different high-voltage ride-through working conditions, and the influence of different active power output instructions, voltage amplitude values and duration time during the fault period on the identification result is considered, so that the parameter identification result can adapt to the complex working conditions in the actual operation.
2. According to the direct-drive fan grid-connected model, the high-voltage ride through judgment module and the dynamic reactive support module are added in the reactive current control of the direct-drive fan inverter, so that the direct-drive fan grid-connected model can meet the requirement that the dynamic reactive current can be absorbed from a power grid to support voltage recovery during the high-voltage ride through period, and the stable operation of a new energy power grid is maintained.
3. The parameter identification program of the invention uses the self-adaptive weight particle swarm optimization algorithm based on the aggregation distance, so that the program convergence speed is accelerated, and the high voltage ride through coefficient can be quickly and accurately identified.
4. The invention extracts the optimal parameters from a plurality of groups of parameter identification preliminary results by using a weighted average absolute deviation calculation mode, and compares and calculates the accuracy of the optimal result of parameter identification according to the acquired optimal result verification data, thereby improving the reliability of the parameter identification result.
Drawings
FIG. 1 is a direct-drive fan grid-connected topological diagram.
Detailed Description
In this embodiment, a method for identifying high penetration characteristic parameters of a direct drive fan inverter is performed according to the following steps:
step 1, collecting transient operation data of a direct drive fan inverter under N high voltage ride through working conditions and using the transient operation data as actual measurement data and optimal result verification data;
step 1.1, setting N high voltage ride through operating conditions of a direct drive fan inverter, wherein parameters of each high voltage ride through operating condition comprise: an active power output instruction, a voltage disturbance amplitude value during a fault period and a duration time;
step 1.2, collecting N groups of transient state operation data corresponding to the direct drive fan inverter under N high voltage ride through operation conditions, wherein each group of transient state operation data comprises L sampling point data, and the ith sampling point data of any nth group of transient state operation data comprises: reactive power data, active power data, reactive current data, total current data and fundamental voltage data of grid voltage of the AC side grid-connected point; i is more than or equal to 1 and less than or equal to L, N is more than or equal to 1 and less than or equal to N; l is the number of sampling points;
taking the reactive power data of L sampling points in the N groups of transient operating data as N groups of actually measured data, and taking the rest data of the L sampling points in the N groups of transient operating data as optimal result verification data;
let Q M,n (i) The measured data of the ith sampling point under the nth high voltage ride through operating condition is represented;
step 2, identifying high voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operating conditions to obtain N parameter identification preliminary results:
step 2.1, building a direct-drive fan grid-connected model, wherein specific elements are shown in figure 1 and comprise the following steps: the system comprises a direct-drive fan, a variable pitch controller, a machine side inverter controller, a direct current bus capacitor, a grid side inverter controller, a filter and a power grid replaced by an ideal voltage source; let the net side inverter controller include: the high voltage ride through judgment module and the dynamic reactive power support module;
the high voltage ride-through judgment module is used for detecting the per unit voltage value U of the grid-connected point T Whether the voltage is within the set threshold range or not, and when the voltage is within the threshold range, the dynamic reactive power support module calculates the per unit value delta I of the absorbed reactive current of the direct-drive fan inverter in the high voltage ride-through period by using the formula (1) T So as to obtain a per unit value of absorbed reactive current Δ I T Controlling a direct-drive fan network side inverter to output reactive current;
ΔI T ＝K HVRT ×(U T -1.1)×I R (1)
in the formula (1); k HVRT The direct-drive fan inverter is a reactive current proportionality coefficient, namely a high voltage ride through parameter, of the direct-drive fan inverter; i is R Rated current of the direct drive fan inverter;
2.2, based on N groups of measured data, identifying the high voltage ride through parameters by using an adaptive weight particle swarm optimization algorithm based on the aggregation distance to obtain N primary identification results:
step 2.2.1, defining the scale of the particle swarm to be K, defining the serial number of any particle to be K, wherein K is more than or equal to 1 and less than or equal to K;
defining and initializing current iteration times, defining and initializing maximum iteration times, defining and initializing upper and lower limits of particle positions, defining and initializing upper limits of particle speeds, and defining and initializing minimum fitness of algorithm termination;
initializing n-1;
2.2.2, setting parameters of each device in the direct-drive fan grid-connected model according to the nth high-voltage ride-through operating condition; let the kth particle represent the kth high voltage ride through parameter under the nth high voltage ride through operating condition;
defining the individual optimal position of the kth particle as K pbest,n (k) The global optimum position of the particle swarm is K gbest,n ；
Initializing an individual optimal position K of a kth particle pbest,n (k) And global optimum position K gbest,n The corresponding adaptive values are infinitesimal;
2.2.3, randomly generating a particle swarm of the current iteration, namely K high-voltage ride through parameters under the nth high-voltage ride through operating condition, and endowing each particle with a random initial position and speed; initializing k to 1;
step 2.2.4, assigning the kth particle of the current iteration to a reactive current proportionality coefficient K HVRT And then, operating a direct-drive fan grid-connected model and obtaining test data, wherein the test data of the ith sampling point obtained by the operation of the kth particle under the current iteration nth high-penetration working condition is recorded as Q text,n.k (i) So as to calculate the fitness f of the kth particle under the nth high voltage ride through operating condition of the current iteration by using the formula (2) n,k ：
In the formula (2), w 1 、w 2 Respectively the weight of the average error and the weight of the maximum error; mean (), max () are mean functions and max, respectivelyA large value function;
step 2.2.5, the fitness f of the kth particle of the current iteration at the current position is calculated n,k With self individual optimum position K pbest,n (k) The fitness of the k particle is compared, and a position with high fitness is selected and assigned to the individual optimal position of the k particle of the current iteration and is used as the current position of the k particle of the next iteration;
step 2.2.6, the fitness f of the current position of the kth particle of the current iteration is calculated n,k And global optimum position K gbest,n The fitness of the iteration is compared, and a position with high fitness is selected and assigned to the global optimal position of the current iteration;
step 2.2.7, after K +1 is assigned to K, judging whether K > K is true, if so, executing step 2.2.8; otherwise, returning to the step 2.2.4 for sequential execution;
step 2.2.8, calculating the average aggregation distance meand of the current iteration under the nth high-penetration working condition according to the formula (3) n And maximum aggregation distance maxd n And determining the weight w of the particle swarm algorithm under the nth high-penetration working condition of the current iteration according to the formula (4) n The system is used for updating the speed and the position of K particles under the nth high-penetration working condition of the current iteration;
in the formula (3), mean () and max () are a mean function and a maximum function, respectively; q gbest,n (i) Test data corresponding to the global optimal position of the current iteration;
in the formula (4), a 1 、a 2 Two weight coefficients; delta 1 、Δ 2 Two judgment indexes are provided;
step 2.2.9, judging the global optimum position K of the current iteration gbest,n Whether the corresponding fitness is greater than the minimum fitness for algorithm terminationIf yes, executing step 2.2.10, otherwise, adding 1 to the iteration number, and after the initialization k is equal to 1, returning to step 2.2.4 until the loop reaches the maximum iteration number, and executing step 2.2.10;
step 2.2.10, the global optimum position K of the current iteration gbest,n As a preliminary identification result of the high voltage ride through parameter under the nth high voltage ride through operation condition, after assigning n +1 to n, judging n>Whether N is established or not, if so, indicating that the identification is finished, and obtaining a primary identification result of the voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operating conditions; otherwise, returning to the step 2.2.2 for sequential execution;
step 3, calculating the weighted average absolute deviation of the simulation data and the measured data obtained by operation when the high voltage ride through parameters of the direct-drive fan grid-connected model are set as the initial identification results, and selecting the initial identification result with the minimum deviation as the optimal result;
3.1, sequentially setting high voltage ride through parameters of a direct-drive fan grid-connected model as N primary identification results, and respectively operating under N high voltage ride through operating conditions to obtain N simulation data; when the high-voltage ride-through parameter of the direct-drive fan grid-connected model is set as the a-th primary identification result, simulation data of the ith sampling point obtained by operation under the nth high-voltage ride-through working condition is recorded as Q S,a,n (i)，1≤n≤N，1≤a≤N；
Step 3.2, calculating simulation data Q according to formula (5) S,a,n (i) Weighted average absolute deviation F of measured data under the corresponding nth high voltage ride through working condition Q,a,n Thereby obtaining analog data Q S,a,n (i) The weighted average absolute deviation of the measured data under N high voltage ride through working conditions is obtained, and then the simulation data Q are obtained S,a,n (i) The weighted average absolute deviation sum of the actually measured data under the N high voltage ride through working conditions is obtained, the weighted average absolute deviation sum of the actually measured data of the N analog data under the N high voltage ride through working conditions is further obtained, and the primary identification result with the smallest weighted average absolute deviation sum is selected as the optimal identification result;
in the formula (5), w A 、w B 、w C Weights in the weighted average absolute deviation before, during and after the fault are respectively; k Astart 、K Aend 、K Bstart 、K Bend 、K Cstart 、K Cend Respectively as the start sampling point and the end sampling point of the period before, during and after the fault; q AM,n (i)、Q BM,n (i)、Q CM,n (i) Respectively are measured data of the ith sampling point under the nth high voltage ride through working condition before, during and after the fault; q AS,a,n (i)、Q BS,a,n (i)、Q CS,a,n (i) The simulation data of the ith sampling point in the period before, during and after the fault are respectively obtained by operating under the nth high voltage ride through working condition when the high-pass-through parameter of the direct-drive fan grid-connected model is set as the a-th primary identification result.
And 4, calculating the weighted average absolute deviation of transient operation data and optimal result verification data obtained by operation when the direct-drive fan grid-connected model high voltage ride through parameters are set to be optimal results, and verifying the accuracy of the obtained optimal parameter identification result.
Step 4.1, setting a high voltage ride through coefficient of the direct-drive fan grid-connected model as an optimal result, and establishing an optimal direct-drive fan grid-connected model;
and 4.2, collecting transient state operation data of the optimal direct-drive fan inverter under N high-voltage ride-through operation working conditions, and calculating weighted average absolute deviation of the transient state operation data and the optimal result verification data recorded in the step 1.2, so that the accuracy of the obtained optimal parameter identification result is verified.
Example (b):
1. the high voltage ride through operating conditions of 6 groups of direct drive fan inverters are set according to the step 1.1 and are shown in table 1.
TABLE 1 operating conditions
| P/pu | U/pu | Duration/s | |
| Working condition 1 | 0.2 | 1.2 | 10 |
| Working condition 2 | 0.2 | 1.3 | 0.5 |
| Working condition 3 | 0.4 | 1.2 | 10 |
| Working condition 4 | 0.4 | 1.3 | 0.5 |
| Working condition 5 | 0.8 | 1.2 | 10 |
| Working condition 6 | 0.8 | 1.3 | 0.5 |
2. According to the step 1.2, reactive power data, active power data, reactive current data, total current data and fundamental voltage data of grid voltage of an alternating-current side grid-connected point of the alternating-current side grid-connected point under 6 groups of working conditions are collected; taking reactive power data in 6 groups of transient operation data as 6 groups of measured data, and taking the rest data in 6 groups of transient operation data as optimal result verification data;
3. according to the step 2.1, a direct-drive fan grid-connected model required for identification is built on the Matlab simulation platform, specific elements are shown in FIG. 1, the direct-drive fan grid-connected model comprises a direct-drive fan, a variable pitch controller, a machine side inverter controller, a direct-current bus capacitor, a grid side inverter controller, a filter and a power grid replaced by an ideal voltage source, and internal parameters are assigned according to a table 2.
TABLE 2 model parameters
| Rated voltage of fan | 110V | Frequency of the grid | 50Hz |
| DC bus voltage | 350V | Network voltage | 190.53V |
| DC bus capacitor | 1.7mF | Filter inductance reactance | 3mH |
| Upper limit of bus voltage | 1.1p.u. | Fan capacity | 2MW |
| Rated speed of fan | 25rad/s | Maximum current of GSC | 1.1p.u. |
| Number of pole pairs of synchronous machine | 4 | Synchronous machine inductor | 2mH |
| Rated wind speed | 12m/s | Moment of inertia of synchronous machine | 0.5kg.m 2 |
5. And (3) identifying the 6 groups of measured data according to the step 2.2 to obtain 6 initial identification results shown in the table 3.
TABLE 3 preliminary identification results
| Results/parameters | High voltage ride through coefficientK_HVRT |
| Results 1 | 50.5247 |
| Results 2 | 49.5269 |
| Results 3 | 49.9783 |
| Results 4 | 49.7399 |
| Results 5 | 50.2063 |
| Results 6 | 49.9784 |
6. Sequentially setting the high voltage ride through parameters of the direct-drive fan grid-connected model as 6 initial identification results according to the step 3.1, and respectively operating under 6 high voltage ride through operating conditions, thereby obtaining 6 simulation data; calculating the weighted average absolute deviation of the simulated data and the corresponding measured data under the high voltage ride through working condition according to the step 3.2, wherein w A 、w B 、w C Taking 0.1, 0.6 and 0.3 respectively, and calculating results are shown in the following table 4; the initial recognition result with the smallest sum of weighted average absolute deviations is selected as the best recognition result, i.e., result 3, i.e., K _ HVRT 49.9783.
Table 46 sets of parameter identification results reactive power weighted average absolute deviation (unit:% pu) under 6 sets of working conditions
7. The procedure followed in step 4.2 resulted in 6 sets of electrical data, the weighted mean absolute deviation from the best results validation data recorded in step 1.2 was calculated and the results are shown in table 5 below.
TABLE 5 weighted mean deviation (unit:% pu) under optimal outcome parameters
| Working conditions/electrical parameters | Active power P | Reactive power Q | Reactive current Iq | Grid-connected voltage U | Current I |
| Working condition 1 | 0.0063 | 0.0004 | 0.0001 | 0.0011 | 0.0040 |
| Working condition 2 | 0.0080 | 0.0110 | 0.0081 | 0.0060 | 0.0210 |
| Working condition 3 | 0.0025 | 0.0051 | 0.0040 | 0.0013 | 0.0044 |
| Working condition 4 | 0.0002 | 0.0035 | 0.0031 | 0.0019 | 0.0068 |
| Working condition 5 | 0.0033 | 0.0003 | 0.0004 | 0.0001 | 0.0004 |
| Working condition 6 | 0.0028 | 0.0117 | 0.0076 | 0.0047 | 0.0166 |
8. According to the NBT 31066-2015 wind turbine generator electrical simulation model modeling guide rule, the error is within an allowable range, and the accuracy of the identification result is verified.
Claims (1)
1. A high-penetration characteristic parameter identification method of a direct-drive fan inverter is characterized by comprising the following steps:
step 1, collecting transient operation data of a direct-drive fan inverter under N high-voltage ride-through working conditions and taking the transient operation data as actual measurement data;
step 1.1, setting N high voltage ride through operating conditions of a direct drive fan inverter, wherein parameters of each high voltage ride through operating condition comprise: an active power output instruction, a voltage disturbance amplitude value during a fault period and a duration time;
step 1.2, collecting N groups of transient operation data corresponding to the direct-drive fan inverter under N high-voltage ride-through operation conditions, wherein each group of transient operation data comprises L sampling point data, and the ith sampling point data of any nth group of transient operation data is reactive power data of a grid-connected point at an alternating current side; i is more than or equal to 1 and less than or equal to L, N is more than or equal to 1 and less than or equal to N; l is the number of sampling points;
taking the reactive power data of L sampling points in the N groups of transient operation data as N groups of measured data;
let Q M,n (i) The measured data of the ith sampling point under the nth high voltage ride through operating condition is represented;
step 2, identifying high voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operating conditions to obtain N parameter identification preliminary results:
step 2.1, building a direct-drive fan grid-connected model, comprising the following steps: the system comprises a direct-drive fan, a variable pitch controller, a machine side inverter controller, a direct current bus capacitor, a grid side inverter controller, a filter and a power grid replaced by an ideal voltage source; let the grid-side inverter controller comprise: the high voltage ride through judgment module and the dynamic reactive power support module;
the high voltage ride through judgment module is used for detecting the per unit voltage value U of the grid-connected point T Whether the voltage is within the set threshold range or not, and when the voltage is within the threshold range, the dynamic reactive power support module calculates the per unit value delta I of the absorbed reactive current of the direct-drive fan inverter during the high-voltage ride-through by using the formula (1) T According to the absorbed reactive current per unit value delta I T Controlling the direct driveThe wind turbine grid side inverter outputs reactive current;
ΔI T ＝K HVRT ×(U T -1.1)×I R (1)
in the formula (1), K HVRT The direct-drive fan inverter is a reactive current proportionality coefficient, namely a high voltage ride through parameter, of the direct-drive fan inverter; i is R Rated current of the direct drive fan inverter;
2.2, based on N groups of measured data, identifying the high voltage ride through parameters by using an adaptive weight particle swarm optimization algorithm based on the aggregation distance to obtain N primary identification results:
step 2.2.1, defining the scale of the particle swarm to be K, defining the serial number of any particle to be K, wherein K is more than or equal to 1 and less than or equal to K;
defining and initializing current iteration times, defining and initializing maximum iteration times, defining and initializing upper and lower limits of particle positions, defining and initializing upper limits of particle speeds, and defining and initializing minimum fitness of algorithm termination;
initializing n-1;
2.2.2, setting parameters of each device in the direct-drive fan grid-connected model according to the nth high-voltage ride-through operating condition; let the kth particle represent the kth high voltage ride through parameter under the nth high voltage ride through operating condition;
defining the individual optimal position of the kth particle as K pbest,n (k) The global optimum position of the particle swarm is K gbest,n ；
Initializing an individual optimal position K of a kth particle pbest,n (k) And global optimum position K gbest,n The corresponding adaptive values are infinitesimal;
2.2.3, randomly generating a particle swarm of the current iteration, namely K high-voltage ride through parameters under the nth high-voltage ride through operating condition, and endowing each particle with a random initial position and speed; initializing k to 1;
step 2.2.4, assigning the kth particle of the current iteration to a reactive current proportionality coefficient K HVRT And then, operating a direct-drive fan grid-connected model and obtaining test data, wherein the current iteration is obtained by operating the kth particle under the nth high-penetration working conditionThe test data at the ith sample point is recorded as Q text,n.k (i) So as to calculate the fitness f of the kth particle under the nth high voltage ride through operating condition of the current iteration by using the formula (2) n,k ：
In the formula (2), w 1 、w 2 Respectively the weight of the average error and the weight of the maximum error; mean (), max () are the mean function and the maximum function, respectively;
step 2.2.5, the fitness f of the kth particle of the current iteration at the current position is calculated n,k With self individual optimum position K pbest,n (k) The fitness of the k particle is compared, and a position with high fitness is selected and assigned to the individual optimal position of the k particle of the current iteration and is used as the current position of the k particle of the next iteration;
step 2.2.6, the fitness f of the current position of the kth particle of the current iteration is calculated n,k And global optimum position K gbest,n The fitness of the iteration is compared, and a position with high fitness is selected and assigned to the global optimal position of the current iteration;
step 2.2.7, after K +1 is assigned to K, judging whether K > K is true, if so, executing step 2.2.8; otherwise, returning to the step 2.2.4 for sequential execution;
step 2.2.8, calculating the average aggregation distance meand of the current iteration under the nth high-penetration working condition according to the formula (3) n And maximum aggregation distance maxd n And determining the weight w of the particle swarm algorithm under the nth high-penetration working condition of the current iteration according to the formula (4) n The system is used for updating the speed and the position of K particles under the nth high-penetration working condition of the current iteration;
in the formula (3), mean () and max () are a mean function and a maximum function, respectively; q gbest,n (i) Test data corresponding to the global optimal position of the current iteration;
in the formula (4), a 1 、a 2 Two weight coefficients; delta 1 、Δ 2 Two judgment indexes are provided;
step 2.2.9, judging the global optimum position K of the current iteration gbest,n If the corresponding fitness is greater than the minimum fitness for stopping the algorithm, executing a step 2.2.10, otherwise, adding 1 to the iteration times, and returning to the step 2.2.4 after initializing k to 1 until the loop reaches the maximum iteration times, and executing the step 2.2.10;
step 2.2.10, the global optimum position K of the current iteration gbest,n As a preliminary identification result of the high voltage ride through parameter under the nth high voltage ride through operation condition, after assigning n +1 to n, judging n>Whether N is established or not, if so, indicating that the identification is finished, and obtaining a primary identification result of the voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operating conditions; otherwise, returning to the step 2.2.2 for sequential execution;
step 3, calculating the weighted average absolute deviation of the simulation data and the measured data obtained by operation when the high voltage ride through parameters of the direct-drive fan grid-connected model are set as the initial identification results, and selecting the initial identification result with the minimum deviation as the optimal result;
3.1, sequentially setting high voltage ride through parameters of a direct-drive fan grid-connected model as N primary identification results, and respectively operating under N high voltage ride through operating conditions to obtain N simulation data; when the high-voltage ride-through parameter of the direct-drive fan grid-connected model is set as the a-th primary identification result, simulation data of the ith sampling point obtained by operation under the nth high-voltage ride-through working condition is recorded as Q S,a,n (i)，1≤n≤N，1≤a≤N；
Step 3.2, calculating simulation data Q according to formula (5) S,a,n (i) Corresponding nth high voltage ride through conditionWeighted average absolute deviation F of lower measured data Q,a,n Thereby obtaining analog data Q S,a,n (i) The weighted average absolute deviation of the measured data under N high voltage ride through working conditions is obtained, and then the simulation data Q is obtained S,a,n (i) The weighted average absolute deviation sum of the actually measured data under the N high voltage ride through working conditions is obtained, the weighted average absolute deviation sum of the actually measured data of the N analog data under the N high voltage ride through working conditions is further obtained, and the primary identification result with the smallest weighted average absolute deviation sum is selected as the optimal identification result;
in the formula (5), w A 、w B 、w C Weights in the weighted average absolute deviation before, during and after the fault are respectively; k Astart 、K Aend 、K Bstart 、K Bend 、K Cstart 、K Cend Respectively as the start sampling point and the end sampling point of the period before, during and after the fault; q AM,n (i)、Q BM,n (i)、Q CM,n (i) Respectively are measured data of the ith sampling point under the nth high voltage ride through working condition before, during and after the fault; q AS,a,n (i)、Q BS,a,n (i)、Q CS,a,n (i) The simulation data of the ith sampling point in the period before, during and after the fault are respectively obtained by operating under the nth high voltage ride through working condition when the high-pass-through parameter of the direct-drive fan grid-connected model is set as the a-th primary identification result.
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