CN114865703B - High-pass characteristic parameter identification method for direct-drive fan inverter - Google Patents
High-pass characteristic parameter identification method for direct-drive fan inverter Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract
The invention discloses a method for identifying high-pass 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 actual measurement data; 2. identifying high voltage ride through parameters of the direct-drive fan inverter under N high voltage ride through operation 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 parameter of the grid-connected model of the direct-driven fan is set as a preliminary result, and selecting the parameter identification result with the smallest deviation as an optimal result. The invention 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-pass characteristic parameters of a direct-drive fan inverter.
Background
In a new energy power system, the direct-drive fan has the advantages of low noise, high efficiency, low maintenance cost and the like, and is widely applied to various fans. Therefore, accurate modeling of the direct-drive fan is also a basis for analyzing stable operation of the new energy power grid, wherein accuracy and feasibility of the whole model are directly influenced by accurate modeling of the direct-drive fan inverter model serving as a core component of the direct-drive fan. However, the most important accurate control parameters of the direct-drive fan inverter cannot be directly obtained, which can have serious influence on simulation of the direct-drive fan inverter and modeling analysis of grid-connected characteristics of the direct-drive fan station. Therefore, parameters of the direct-drive fan inverter are obtained through parameter identification calculation and research, so that the direct-drive fan inverter parameters with high identification precision and high accuracy are obtained, an accurate direct-drive fan model capable of reflecting the actual unit running condition is constructed, and the method is important in direct-drive fan running characteristic analysis and has great significance in safety and stability running capability analysis of a direct-drive fan grid-connected system.
Because the direct-driven fan control system is complex and can not be measured, the analysis and reproduction of the inside of the direct-driven fan can not be directly carried out, the research on obtaining accurate parameters of a new energy system model is mostly based on different system identification algorithms at present, and actual field stations or fan operation results of a test platform are utilized to obtain actual measurement data so as to carry out identification analysis on corresponding parameters. The method can be divided into a frequency domain identification method, a time domain identification method and an intelligent optimization algorithm, wherein the time domain data obtained by sampling the time domain data and the frequency domain data obtained by fast Fourier transform are respectively used for identifying model parameters, the intelligent optimization algorithm utilizes the global optimization characteristic of the optimization algorithm, the global optimization calculation can be automatically carried out only by providing corresponding sampling data, and finally, the model parameter target value with the highest fitness can be found, and common intelligent optimization algorithms comprise a genetic algorithm, a particle swarm algorithm, a gray wolf optimization algorithm, an ant colony algorithm and the like. The parameter identification methods based on the algorithms are widely applied in the current modeling field of new energy fans of the electric power system, but the existing research only focuses on double closed loop PI control parameters of the 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, only the reliability of a single identification result is verified, the reliability cannot be adapted to the complex working conditions in the actual operation engineering, and the achievement is difficult to apply to the actual project.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method for identifying the high-pass characteristic parameters of the direct-drive fan inverter so as to identify the high-pass characteristic parameters of the direct-drive fan inverter, thereby realizing accurate modeling of high-voltage ride-through control.
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 operation conditions of a direct-drive fan inverter, wherein parameters of each high-voltage ride through operation condition comprise: an active power output command, a voltage disturbance amplitude and duration during a fault;
step 1.2, collecting N groups of transient operation data corresponding to the direct-driven 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 an alternating-current 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 reactive power data of L sampling points in N groups of transient operation data as N groups of actual measurement 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 operation conditions to obtain N primary parameter identification results:
step 2.1, building a direct-driven fan grid-connected model, which comprises the following steps: the direct-drive fan, the variable-pitch controller, the machine side inverter controller, the direct-current bus capacitor, the grid side inverter controller, the filter and the power grid replaced by an ideal voltage source; let the network side inverter controller include: the high-voltage crossing judging module and the dynamic reactive power supporting module;
the high voltage ride through judgment module is used for detecting the voltage per unit value U of the grid-connected point T If the current is within the set threshold range, when the current is within the threshold range, the dynamic reactive power support module calculates the absorption reactive current per unit value delta I of the direct-drive fan inverter during high voltage ride through by using the formula (1) T Thereby according to the absorption 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 reactive current proportional coefficient of the direct-drive fan inverter is the high voltage ride through parameter; i R The rated current of the direct-drive fan inverter;
step 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:
2.2.1, defining the size of a particle swarm as K, and defining the serial number of any particle as K, wherein K is more than or equal to 1 and less than or equal to K;
defining and initializing the current iteration times, defining and initializing the maximum iteration times, defining and initializing the upper limit and the lower limit of the particle position, defining and initializing the upper limit of the particle speed, and defining and initializing the minimum fitness of algorithm termination;
initializing n=1;
2.2.2, setting parameters of each device in the direct-driven fan grid-connected model according to the nth high-voltage ride through operation 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 optimal position of the particle swarm is K gbest,n ；
Initializing an individual optimum position K of a kth particle pbest,n (k) And a global optimum position K gbest,n The corresponding adaptation values are all infinitesimal;
step 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 operation condition, and endowing each particle with random initial position and speed; initializing k=1;
step 2.2.4, assigning the kth particle of the current iteration to the reactive current scaling factor K HVRT Then, a direct-driven fan grid-connected model is operated, test data are obtained, wherein the test data of an ith sampling point obtained by the operation of a kth particle under the nth high-penetration working condition of the current iteration are recorded as Q text,n.k (i) Thereby making things convenient forCalculating 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 The weight of the average error and the weight of the maximum error are respectively; mean (), max () are the average function and the maximum function, respectively;
step 2.2.5, fitness f of the kth particle of the current iteration at the current position n,k Optimal position K with self individual pbest,n (k) The fitness of the particles is compared, and a position with large fitness is selected and assigned to the individual optimal position of the kth particle in the current iteration and used as the current position of the kth particle in the next iteration;
step 2.2.6 fitness f of the current position of the kth particle of the current iteration n,k With global optimum position K gbest,n The fitness of the iteration is compared, and a position with large 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 true, executing step 2.2.8; otherwise, returning to the step 2.2.4 to execute sequentially;
step 2.2.8, calculating the average aggregation distance mean under the nth high-penetration working condition of the current iteration according to the step (3) n And a 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 method 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 (), max () are an average function and a maximum function, respectively; q (Q) gbest,n (i) The test data corresponding to the global optimal position of the current iteration is obtained;
in the formula (4), a 1 、a 2 Two weight coefficients; delta 1 、Δ 2 Two judgment indexes are adopted;
step 2.2.9, judging the global optimal position K of the current iteration gbest,n If the corresponding fitness is greater than the minimum fitness of the algorithm termination, executing the step 2.2.10, otherwise, adding 1 to the iteration number, initializing k=1, returning to the step 2.2.4 until the maximum iteration number is reached, and executing the step 2.2.10;
step 2.2.10 Global best position K of the current iteration gbest,n As the preliminary identification result of the high voltage ride through parameter under the nth high voltage ride through operation condition, after n+1 is assigned to n, n is judged>Whether N is met or not, if so, the identification is finished, and preliminary identification results of voltage crossing parameters of the direct-drive fan inverter under N high-voltage crossing operation conditions are obtained; otherwise, returning to the step 2.2.2 to execute sequentially;
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 parameter of the grid-connected model of the direct-driven fan is set as a primary identification result, and selecting the primary identification result with the smallest deviation as an optimal result;
step 3.1, sequentially setting the high voltage ride through parameters of the direct-driven 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-pass crossing parameter of the direct-driven fan grid-connected model is set as an a-th primary identification result, the simulation data of an i-th sampling point obtained by running under an n-th high-voltage crossing working condition is recorded as Q S,a,n (i)，1≤n≤N，1≤a≤N；
Step 3.2, calculating the simulation data Q according to (5) S,a,n (i) Weighted average absolute deviation F of measured data under corresponding nth high voltage ride through working condition Q,a,n Thereby obtaining analog data Q S,a,n (i) Weighted average absolute deviation from measured data under N high voltage ride through conditions to obtain simulation data Q S,a,n (i) The sum of weighted average absolute deviations of the measured data under N high voltage ride through working conditions is further obtained, the sum of weighted average absolute deviations of the measured data under N high voltage ride through working conditions of N analog data is further obtained, and a preliminary identification result with the minimum sum of weighted average absolute deviations is selected from the sum of weighted average absolute deviations as an optimal identification result;
in formula (5), w A 、w B 、w C The weights of the time periods before, during and after the faults in the weighted average absolute deviation are respectively; k (K) Astart 、K Aend 、K Bstart 、K Bend 、K Cstart 、K Cend The sampling points are respectively a start sampling point and an end sampling point of a period before failure, a period during failure and a period after failure; q (Q) AM,n (i)、Q BM,n (i)、Q CM,n (i) The measured data of the ith sampling point under the nth high voltage ride through working condition in the pre-fault period, the fault period and the post-fault period are respectively obtained; q (Q) AS,a,n (i)、Q BS,a,n (i)、Q CS,a,n (i) And respectively setting the high-pass through parameters of the grid-connected model of the direct-driven fan as the a primary identification result, and operating under the n high-voltage through working condition to obtain simulation data of the i sampling points of the pre-fault period, the fault period and the post-fault period.
Compared with the prior art, the invention has the beneficial effects that:
1. the measured data are obtained by running the direct-drive fan under different high-voltage ride through working conditions, and the influence of different active power output instructions, voltage amplitude values during faults and duration time on the identification result is considered, so that the parameter identification result can adapt to the complex working conditions in actual running.
2. The direct-driven fan grid-connected model adds the high-voltage ride-through judging and dynamic reactive power supporting module in the reactive current control of the direct-driven fan inverter, so that the direct-driven fan grid-connected model can absorb dynamic reactive current from a power grid to support the voltage recovery requirement 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 increased, and the high voltage crossing coefficient can be identified rapidly and accurately.
4. According to the invention, the weighted average absolute deviation calculation mode is used for extracting the optimal parameters from the plurality of groups of parameter identification preliminary results, and the accuracy of the optimal parameter identification results is compared and calculated according to the acquired optimal result verification data, so that the reliability of the parameter identification results is improved.
Drawings
FIG. 1 is a grid-connected topology of a direct drive fan of the present invention.
Detailed Description
In this embodiment, a method for identifying high pass 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 taking the transient operation data as measured data and optimal result verification data;
step 1.1, setting N high-voltage ride through operation conditions of a direct-drive fan inverter, wherein parameters of each high-voltage ride through operation condition comprise: an active power output command, a voltage disturbance amplitude and duration during a fault;
step 1.2, collecting N groups of transient operation data corresponding to the direct-driven 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 comprises: reactive power data, active power data, reactive current data, total current data and fundamental voltage data of grid voltage of the alternating-current side grid connection 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 reactive power data of L sampling points in N groups of transient operation data as N groups of actual measurement data, and taking the rest data of L sampling points in the N groups of transient operation 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 operation conditions to obtain N primary parameter identification results:
step 2.1, building a direct-driven fan grid-connected model, wherein specific elements are shown in fig. 1 and include: the direct-drive fan, the variable-pitch controller, the machine side inverter controller, the direct-current bus capacitor, the grid side inverter controller, the filter and the power grid replaced by an ideal voltage source; let the network side inverter controller include: the high-voltage crossing judging module and the dynamic reactive power supporting module;
the high voltage ride through judgment module is used for detecting the voltage per unit value U of the grid-connected point T If the current is within the set threshold range, when the current is within the threshold range, the dynamic reactive power support module calculates the absorption reactive current per unit value delta I of the direct-drive fan inverter during high-voltage crossing by using the formula (1) T Thereby according to the per unit value delta I of the absorption reactive current T Controlling a direct-drive fan grid-side inverter to output reactive current;
ΔI T ＝K HVRT ×(U T -1.1)×I R (1)
in formula (1); k (K) HVRT The reactive current proportional coefficient of the direct-drive fan inverter is the high voltage ride through parameter; i R The rated current of the direct-drive fan inverter;
step 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:
2.2.1, defining the size of a particle swarm as K, and defining the serial number of any particle as K, wherein K is more than or equal to 1 and less than or equal to K;
defining and initializing the current iteration times, defining and initializing the maximum iteration times, defining and initializing the upper limit and the lower limit of the particle position, defining and initializing the upper limit of the particle speed, and defining and initializing the minimum fitness of algorithm termination;
initializing n=1;
2.2.2, setting parameters of each device in the direct-driven fan grid-connected model according to the nth high-voltage ride through operation 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 optimal position of the particle swarm is K gbest,n ；
Initializing an individual optimum position K of a kth particle pbest,n (k) And a global optimum position K gbest,n The corresponding adaptation values are all infinitesimal;
step 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 operation condition, and endowing each particle with random initial position and speed; initializing k=1;
step 2.2.4, assigning the kth particle of the current iteration to the reactive current scaling factor K HVRT Then, a direct-driven fan grid-connected model is operated, test data are obtained, wherein the test data of an ith sampling point obtained by the operation of a kth particle under the nth high-penetration working condition of the current iteration are recorded as Q text,n.k (i) Thereby calculating 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 The weight of the average error and the weight of the maximum error are respectively; mean (), max () are the average function and the maximum function, respectively;
step 2.2.5, fitness f of the kth particle of the current iteration at the current position n,k Optimal position K with self individual pbest,n (k) Selecting the position with large fitness to assign to the individual optimal position of the kth particle of the current iteration and using the position as the kth particle of the next iterationA current location;
step 2.2.6 fitness f of the current position of the kth particle of the current iteration n,k With global optimum position K gbest,n The fitness of the iteration is compared, and a position with large 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 true, executing step 2.2.8; otherwise, returning to the step 2.2.4 to execute sequentially;
step 2.2.8, calculating the average aggregation distance mean under the nth high-penetration working condition of the current iteration according to the step (3) n And a 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 method 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 (), max () are an average function and a maximum function, respectively; q (Q) gbest,n (i) The test data corresponding to the global optimal position of the current iteration is obtained;
in the formula (4), a 1 、a 2 Two weight coefficients; delta 1 、Δ 2 Two judgment indexes are adopted;
step 2.2.9, judging the global optimal position K of the current iteration gbest,n If the corresponding fitness is greater than the minimum fitness of algorithm termination, executing the step 2.2.10, otherwise, adding 1 to the iteration number, initializing k=1, returning to the step 2.2.4 until the cycle reaches the maximum iteration number, and executing the step 2.2.10;
step 2.2.10 Global best position K of the current iteration gbest,n As a high voltage ride through parameter under the nth high voltage ride through operating conditionThe preliminary identification result of the number is that n+1 is assigned to n, and then n is judged>Whether N is met or not, if so, the identification is finished, and preliminary identification results of voltage crossing parameters of the direct-drive fan inverter under N high-voltage crossing operation conditions are obtained; otherwise, returning to the step 2.2.2 to execute sequentially;
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 parameter of the grid-connected model of the direct-driven fan is set as a primary identification result, and selecting the primary identification result with the smallest deviation as an optimal result;
step 3.1, sequentially setting the high voltage ride through parameters of the direct-driven 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-pass crossing parameter of the direct-driven fan grid-connected model is set as an a-th primary identification result, the simulation data of an i-th sampling point obtained by running under an n-th high-voltage crossing working condition is recorded as Q S,a,n (i)，1≤n≤N，1≤a≤N；
Step 3.2, calculating the simulation data Q according to (5) S,a,n (i) Weighted average absolute deviation F of measured data under corresponding nth high voltage ride through working condition Q,a,n Thereby obtaining analog data Q S,a,n (i) Weighted average absolute deviation from measured data under N high voltage ride through conditions to obtain simulation data Q S,a,n (i) The sum of weighted average absolute deviations of the measured data under N high voltage ride through working conditions is further obtained, the sum of weighted average absolute deviations of the measured data under N high voltage ride through working conditions of N analog data is further obtained, and a preliminary identification result with the minimum sum of weighted average absolute deviations is selected from the sum of weighted average absolute deviations as an optimal identification result;
in formula (5), w A 、w B 、w C The weights of the time periods before, during and after the faults in the weighted average absolute deviation are respectively; k (K) Astart 、K Aend 、K Bstart 、K Bend 、K Cstart 、K Cend The sampling points are respectively a start sampling point and an end sampling point of a period before failure, a period during failure and a period after failure; q (Q) AM,n (i)、Q BM,n (i)、Q CM,n (i) The measured data of the ith sampling point under the nth high voltage ride through working condition in the pre-fault period, the fault period and the post-fault period are respectively obtained; q (Q) AS,a,n (i)、Q BS,a,n (i)、Q CS,a,n (i) And respectively setting the high-pass through parameters of the grid-connected model of the direct-driven fan as the a primary identification result, and operating under the n high-voltage through working condition to obtain simulation data of the i sampling points of the pre-fault period, the fault period and the post-fault period.
And 4, calculating the weighted average absolute deviation of transient operation data obtained by operation and optimal result verification data when the high-voltage ride-through parameters of the grid-connected model of the direct-driven fan are set as optimal results, and verifying the accuracy of the obtained optimal parameter identification results.
Step 4.1, setting a high voltage ride through coefficient of a 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 operation data of the optimal direct-drive fan inverter under N high-voltage ride through operation conditions, and calculating weighted average absolute deviation between the transient operation data and the optimal result verification data recorded in the step 1.2, so that accuracy of the obtained optimal parameter identification result is verified.
Examples:
1. the high voltage ride through operating conditions of the 6 sets of direct drive fan inverters are set according to step 1.1 as 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. Collecting reactive power data, active power data, reactive current data, total current data and fundamental wave voltage data of grid voltage of an alternating current side grid connection point under 6 groups of working conditions according to the step 1.2; taking the reactive power data in the 6 sets of transient operation data as 6 sets of actual measurement data, and taking the rest data in the 6 sets of transient operation data as optimal result verification data;
3. according to the step 2.1, a grid-connected model of the direct-drive fan required for identification is built on a Matlab simulation platform, specific elements are shown in fig. 1, the 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 are included, and the internal parameters of the direct-drive fan are assigned according to the table 2.
Table 2 model parameters
| Rated voltage of fan | 110V | Grid frequency | 50Hz |
| DC bus voltage | 350V | Grid voltage | 190.53V |
| DC bus capacitor | 1.7mF | Filtering inductance reactance | 3mH |
| Bus voltage upper limit | 1.1p.u. | Fan capacity | 2MW |
| Rated rotation speed of fan | 25rad/s | Maximum current of GSC | 1.1p.u. |
| Pole pair number 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 primary identification results shown in the table 3.
TABLE 3 preliminary identification results
| Results \parameters | High voltage ride-through coefficient K_HVRT |
| Result 1 | 50.5247 |
| Result 2 | 49.5269 |
| Result 3 | 49.9783 |
| Result 4 | 49.7399 |
| Result 5 | 50.2063 |
| Result 6 | 49.9784 |
6. Setting the high voltage ride through parameters of the direct-driven fan grid-connected model to 6 primary identification results according to the step 3.1 in sequence, and respectively operating under 6 high voltage ride through operating conditions so as to obtain 6 simulation data; calculating the weighted average absolute deviation of the analog 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 the results as shown in the following table 4; the preliminary 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 4 6 sets of parameter identification results are reactive power weighted average absolute deviation (unit:% pu) under 6 sets of conditions
7. The procedure was followed as per step 4.2 and 6 sets of electrical data were obtained, the weighted average absolute deviation from the best results verification data recorded in step 1.2 was calculated, and the calculation results are shown in table 5 below.
Table 5 weighted average deviation (unit:% pu) under optimum result parameters
| Working condition/electrical parameter | 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 modeling rules of the NBT 31066-2015 wind turbine generator system electrical simulation model, errors are in an allowable range, and the accuracy of the identification result is verified.
Claims (1)
1. The high-penetration characteristic parameter identification method of the direct-drive fan inverter 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 operation conditions of a direct-drive fan inverter, wherein parameters of each high-voltage ride through operation condition comprise: an active power output command, a voltage disturbance amplitude and duration during a fault;
step 1.2, collecting N groups of transient operation data corresponding to the direct-driven 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 an alternating-current 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 reactive power data of L sampling points in N groups of transient operation data as N groups of actual measurement 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 operation conditions to obtain N primary parameter identification results:
step 2.1, building a direct-driven fan grid-connected model, which comprises the following steps: the direct-drive fan, the variable-pitch controller, the machine side inverter controller, the direct-current bus capacitor, the grid side inverter controller, the filter and the power grid replaced by an ideal voltage source; let the network side inverter controller include: the high-voltage crossing judging module and the dynamic reactive power supporting module;
the high voltage ride through judgment module is used for detecting the voltage per unit value U of the grid-connected point T If the current is within the set threshold range, when the current is within the threshold range, the dynamic reactive power support module calculates the absorption reactive current per unit value delta I of the direct-drive fan inverter during high voltage ride through by using the formula (1) T Thereby according to the absorption 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 reactive current proportional coefficient of the direct-drive fan inverter is the high voltage ride through parameter; i R Rated current for direct-drive fan inverter；
Step 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:
2.2.1, defining the size of a particle swarm as K, and defining the serial number of any particle as K, wherein K is more than or equal to 1 and less than or equal to K;
defining and initializing the current iteration times, defining and initializing the maximum iteration times, defining and initializing the upper limit and the lower limit of the particle position, defining and initializing the upper limit of the particle speed, and defining and initializing the minimum fitness of algorithm termination;
initializing n=1;
2.2.2, setting parameters of each device in the direct-driven fan grid-connected model according to the nth high-voltage ride through operation 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 optimal position of the particle swarm is K gbest,n ；
Initializing an individual optimum position K of a kth particle pbest,n (k) And a global optimum position K gbest,n The corresponding adaptation values are all infinitesimal;
step 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 operation condition, and endowing each particle with random initial position and speed; initializing k=1;
step 2.2.4, assigning the kth particle of the current iteration to the reactive current scaling factor K HVRT Then, a direct-driven fan grid-connected model is operated, test data are obtained, wherein the test data of an ith sampling point obtained by the operation of a kth particle under the nth high-penetration working condition of the current iteration are recorded as Q text,n.k (i) Thereby calculating 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 The weight of the average error and the weight of the maximum error are respectively; mean (), max () are the average function and the maximum function, respectively;
step 2.2.5, fitness f of the kth particle of the current iteration at the current position n,k Optimal position K with self individual pbest,n (k) The fitness of the particles is compared, and a position with large fitness is selected and assigned to the individual optimal position of the kth particle in the current iteration and used as the current position of the kth particle in the next iteration;
step 2.2.6 fitness f of the current position of the kth particle of the current iteration n,k With global optimum position K gbest,n The fitness of the iteration is compared, and a position with large 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 true, executing step 2.2.8; otherwise, returning to the step 2.2.4 to execute sequentially;
step 2.2.8, calculating the average aggregation distance mean under the nth high-penetration working condition of the current iteration according to the step (3) n And a 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 method 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 (), max () are an average function and a maximum function, respectively; q (Q) gbest,n (i) The test data corresponding to the global optimal position of the current iteration is obtained;
in the formula (4), a 1 、a 2 Two weight coefficients; delta 1 、Δ 2 Two judgment indexes are adopted;
step 2.2.9, judging the global optimal position K of the current iteration gbest,n If the corresponding fitness is greater than the minimum fitness of the algorithm termination, executing the step 2.2.10, otherwise, adding 1 to the iteration number, initializing k=1, returning to the step 2.2.4 until the maximum iteration number is reached, and executing the step 2.2.10;
step 2.2.10 Global best position K of the current iteration gbest,n As the preliminary identification result of the high voltage ride through parameter under the nth high voltage ride through operation condition, after n+1 is assigned to n, n is judged>Whether N is met or not, if so, the identification is finished, and preliminary identification results of voltage crossing parameters of the direct-drive fan inverter under N high-voltage crossing operation conditions are obtained; otherwise, returning to the step 2.2.2 to execute sequentially;
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 parameter of the grid-connected model of the direct-driven fan is set as a primary identification result, and selecting the primary identification result with the smallest deviation as an optimal result;
step 3.1, sequentially setting the high voltage ride through parameters of the direct-driven 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-pass crossing parameter of the direct-driven fan grid-connected model is set as an a-th primary identification result, the simulation data of an i-th sampling point obtained by running under an n-th high-voltage crossing working condition is recorded as Q S,a,n (i)，1≤n≤N，1≤a≤N；
Step 3.2, calculating the simulation data Q according to (5) S,a,n (i) Weighted average absolute deviation F of measured data under corresponding nth high voltage ride through working condition Q,a,n Thereby obtaining analog data Q S,a,n (i) Weighted average absolute deviation from measured data under N high voltage ride through conditions to obtain simulation data Q S,a,n (i) The sum of weighted average absolute deviation of the measured data under N high voltage ride through conditions is further obtained to obtain N analog data under N high voltagesThe sum of weighted average absolute deviation of the measured data under the passing working condition is selected, and a preliminary identification result with the smallest sum of weighted average absolute deviation is selected as an optimal identification result;
in formula (5), w A 、w B 、w C The weights of the time periods before, during and after the faults in the weighted average absolute deviation are respectively; k (K) Astart 、K Aend 、K Bstart 、K Bend 、K Cstart 、K Cend The sampling points are respectively a start sampling point and an end sampling point of a period before failure, a period during failure and a period after failure; q (Q) AM,n (i)、Q BM,n (i)、Q CM,n (i) The measured data of the ith sampling point under the nth high voltage ride through working condition in the pre-fault period, the fault period and the post-fault period are respectively obtained; q (Q) AS,a,n (i)、Q BS,a,n (i)、Q CS,a,n (i) And respectively setting the high-pass through parameters of the grid-connected model of the direct-driven fan as the a primary identification result, and operating under the n high-voltage through working condition to obtain simulation data of the i sampling points of the pre-fault period, the fault period and the post-fault period.
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