CN112234641A - Direct-current commutation failure blocking and preventing control method - Google Patents

Direct-current commutation failure blocking and preventing control method Download PDF

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CN112234641A
CN112234641A CN202010997688.0A CN202010997688A CN112234641A CN 112234641 A CN112234641 A CN 112234641A CN 202010997688 A CN202010997688 A CN 202010997688A CN 112234641 A CN112234641 A CN 112234641A
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commutation failure
fault
line voltage
time
sample
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CN112234641B (en
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张树卿
刘宁
宗炫君
朱亚楠
郭莉
邹盛
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Tsinghua University
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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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
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]

Abstract

The invention provides a direct current commutation failure blocking and preventing control method, which comprises the following steps: predicting the tracks of the bus voltage of the power grid inverter station in the fault period and the fault recovery stage by an online disturbed voltage transient track intelligent prediction model, and calculating to obtain the bus voltage effective value of the power grid inverter station; and calculating feasible regions and maximum values of the trigger angle instruction values without commutation failure based on the constraint calculation of the effective values of the commutation line voltages and the trigger angle instruction values so as to block and prevent secondary commutation failure. The method can quickly and accurately predict whether the secondary commutation failure occurs in the fault period within ms-level time, quickly and pertinently pre-judge the impending commutation failure in the system, reserve enough time margin and safety margin for subsequent control, realize quick prediction of short-term commutation phase voltage change, avoid the secondary commutation failure in the fault period, and realize a control strategy which can ensure timeliness, reliability and result constraint.

Description

Direct-current commutation failure blocking and preventing control method
Technical Field
The invention belongs to the field of power control, and particularly relates to a direct-current commutation failure blocking and preventing control method.
Background
The commutation failure is one of the faults with higher occurrence probability of the direct current transmission system. In the converter, the valve which is out of conduction can not restore the blocking capability within a period of time when the reverse voltage acts, or the phase change process is not completed during the reverse voltage, when the valve voltage changes to the positive direction, the phase of the valve which is out of conduction is changed to the original valve which is out of conduction, and the condition is called phase change failure.
Among many factors causing phase commutation failure, phase commutation voltage reduction and phase commutation change caused by short-circuit fault of an alternating current system can generate direct adverse effect on a phase commutation process, and are main factors causing the phase commutation failure.
It is widely believed in the current research that the first commutation failure after the disturbance occurs (lasting from 2/3 fundamental frequency cycles to 4/3 fundamental frequency cycles) cannot be avoided, and is usually recovered by itself. And the strategies such as preventive control and the like only aim at the secondary commutation failure of the subsequent fault.
The current commutation failure prejudgment criterion can be divided into two categories: based on commutation voltage, based on turn-off angle. The prejudgment based on the commutation voltage comprises a critical commutation voltage judgment method and a waveform transformation judgment method. The method comprises the following steps that 1, pre-judgment based on commutation voltage only considers the influence of external factors such as commutation voltage, and the characteristic change of the commutation voltage is taken as the basis of the pre-judgment, but the control characteristic of a direct current system is not fully considered; the judgment is only carried out aiming at the severity of the fault, but not aiming at the commutation failure risk judgment, so that the currently adopted commutation failure prevention module has a large amount of false start and missed start. 2. In the judgment based on the turn-off angle, the method of actually measuring the arc extinguishing angle signal is adopted, negative zero-crossing voltage is detected through a thyristor voltage detection board, the zero-crossing voltage signal is sent to valve base electronic equipment (VBE) to generate a valve current zero-crossing signal, and the valve current zero-crossing signal is compared with a phase-locked voltage zero-crossing signal to obtain the turn-off angle, but the scheme depends on the zero-crossing signal of current, the sensitivity is easy to be influenced, and the method does not belong to prediction in strict sense; the extinction angle prediction judgment method predicts the commutation failure phenomenon and calculates the commutation failure margin by comparing the relevant solid angle of interruption with the predicted turn-off angle of interruption, but the current calculation of the predicted turn-off angle is based on the prediction of direct current under a quasi-steady-state model, when the system is in a transient state, the calculation may have larger deviation, the prediction accuracy is limited, and the reference is difficult to provide for subsequent prevention and control.
The commutation failure is prevented from the source, and the optimization objects comprise hardware equipment, control strategies, network structures and the like depending on the device-system-level multi-layer multi-physical coupling coordination optimization. Among them, optimization measures based on control strategies have been widely studied for economy and convenience. According to the physical mechanism of the phase change process, the currently adopted optimization method is mainly based on the existing controller in direct current, and the phase change margin is increased by limiting direct current and increasing the advance trigger angle to relieve the risk of phase change failure. However, the existing control strategies mainly have the following problems:
1) only the commutation physical process is controlled qualitatively, so that the control result is sometimes conservative and sometimes aggressive, the commutation feasible domain boundary cannot be clearly defined accurately according to different transient conditions, and the control economy is more difficult to further improve on the basis;
2) the current control optimization is mainly based on the adjustment of parameters and structures by existing controls such as constant current, minimum extinction angle and the like, the original control characteristics of direct current can be changed, and the adaptability is insufficient under different transient conditions and operating conditions;
3) the parameter design mode lacks universality;
4) the control of a single physical factor in the phase conversion process is adopted, so that the optimization feasible area of the controller is limited.
The existing commutation failure prevention measures are difficult to reconcile the contradiction between the accurate and rapid suppression of commutation failure and the economy, and the potential of transient response and regulation and control characteristics of high-voltage direct-current transmission is not exploited.
Disclosure of Invention
In view of the above problems, the present invention provides a method for blocking and preventing dc commutation failure.
The direct current commutation failure blocking and preventing control method comprises the following steps:
uninterrupted acquisition of bus line voltage u of power grid inversion stationab(t)、ubc(t)、uca(t) when detecting the occurrence of an AC system fault, extracting the time t before said fault occurred1To the time t after the fault occurs3The bus line voltage u of the grid inverter stationab(t)、ubc(t)、uca(t), t represents time;
data extraction for the first time: extracting the time t before the fault occurs1To the time t after the fault occurs3The bus line voltage u of the grid inverter stationab(t)、ubc(t)、uca(t)；
Inputting the data obtained by the first data extraction into an online secondary commutation failure intelligent prediction model, judging whether secondary commutation failure occurs, and if the secondary commutation failure does not occur, ending the execution of the direct current commutation failure blocking and preventing control method; if the secondary commutation failure is judged to occur, performing secondary data extraction: extracting the fault occurrence time tstartTo the time t after the fault occurs3The bus line voltage u of the grid inverter stationab(t)、ubc(t)、uca(t)；
Inputting the data obtained by the second data extraction into an online disturbed voltage transient state track intelligent prediction model;
predicting the bus voltage of the power grid inverter station at [ t ] by the online disturbed voltage transient trajectory intelligent prediction model3，t2]And calculating to obtain [ t4，t2]The effective value of the bus voltage of the power grid inversion station is obtained;
based on the bus voltage effective value of the power grid inversion station and a trigger angle instruction value constraint formula, calculating to obtain [ t [ [ t ]4，t2]The condition satisfied by the trigger angle command value on which no commutation failure occurs is to inhibitCutting off and preventing the occurrence of the secondary commutation failure,
wherein, let tendIf it is the fault clearing time, the time t1、t2、t3And the time t of the fault occurrencestartAnd a fault clearing time tendThe relationship between (A) and (B) is:
[t1，tstart]is the fundamental frequency cycle prior to the occurrence of the fault;
[tend，t2]is 5 fundamental frequency cycles after the fault is over;
[tstart，t3]is the first fundamental period after the fault occurs.
Further, in the present invention,
the online secondary commutation failure intelligent prediction model is obtained by the following method, and comprises the following steps:
A. generating a multi-source training data set;
B. and on the basis of a supervised learning method, performing off-line training on the intelligent agent by using the multi-source training data set to construct the online secondary commutation failure intelligent prediction model.
Further, in the present invention,
the sources of the multi-source training dataset include: the method comprises the steps of simulating samples of an electromagnetic transient state and an electromechanical-electromagnetic transient state hybrid simulation of an alternating-current and direct-current power grid, and real-time measurement data obtained by measuring an alternating-current and direct-current power grid inverter station in real time through a power grid phasor measuring device and a power fault recorder;
the multi-source training data set comprises m samples, and the data of each sample records the time t before the fault occurs in the simulation sample and the real-time measurement data1To the time t after the fault occurs2The number of samples m is a positive integer greater than 1,
the physical quantity in the data of each sample includes: bus line voltage u of power grid inverter stationab(t)、ubc(t)、uca(t) and a grid inverter station arc-quenching angle gamma (t);
the simulation sample sets the following faults in the electromagnetic transient and electromechanical-electromagnetic transient hybrid simulation of the alternating current-direct current power grid:
the types of faults include: single-phase grounding short circuit, two-phase short circuit, three-phase short circuit and two-phase grounding short circuit on the AC system line;
the position where the fault occurs: selecting positions with different electrical distances from a bus of the power grid inversion station to set faults;
timing of the fault: setting fault angles to be different angles with intervals of theta degrees from 0 degrees to 180 degrees; wherein theta is more than 0 and less than 30;
the fault duration is: setting corresponding fault duration according to the actual relay protection action time of the simulated alternating current power grid;
the size of the fault: setting faults of different severity according to the scale of the simulated alternating current power grid.
Further, in the present invention,
carrying out the uninterrupted acquisition of the bus line voltage u of the power grid inverter stationab(t)、ubc(t)、ucaThe collection frequency in (t) is 2000 Hz-10000 Hz;
the number m of the samples satisfies that m is more than or equal to 100 and less than or equal to 10000.
Further, in the present invention,
the acquisition frequency is 5000 Hz;
the number m of the samples is 3000;
the fault duration is 0.1s, s representing seconds.
Further, in the present invention,
the step B comprises the following steps:
step b 1: extracting data characteristics;
step b 2: selecting a model, namely selecting a convolutional neural network method on a software platform with a machine learning function to train a prediction model;
step b 3: determining model input quantities hin and model output quantities hout;
step b 4: defining the architecture, parameters and training options of the convolutional neural network;
step b 5: splitting a sample to obtain a model training set and a model verification set;
step b 6: model training, namely training a prediction model by adopting the convolutional neural network and a model training set to obtain the online secondary commutation failure intelligent prediction model;
step b 7: the model verification is passed.
Further, in the present invention,
the step b1 includes:
for each sample data in the multi-source training data set, the following processing is carried out:
the bus line voltage u of the grid inverter station in the time period of the fundamental frequency cycle before the fault occursab(t)、ubc(t) and uca(t) Fourier transform to obtain an amplitude spectrum (U) of the line voltage of 1 × nab)1×n、(Ubc)1×n、(Uca)1×nAnd 1 xn line voltage phase spectrum
Figure BDA0002693169040000051
Figure BDA0002693169040000052
Wherein the content of the first and second substances,
(Uab)1×n＝[Uab0 Uab1 Uab2 ... Uabn-1]；
(Ubc)1×n＝[Ubc0 Ubc1 Ubc2 ... Ubcn-1]；
(Uca)1×n＝[Uca0 Uca1 Uca2 ... Ucan-1]；
Figure BDA0002693169040000061
Figure BDA0002693169040000062
Figure BDA0002693169040000063
0 is expressed as a direct current component after the Fourier transform, 1 is a fundamental frequency, 2 and 3 … n-1 are harmonic series, n-1 is the highest harmonic series after the Fourier transform, and n-1 harmonic is obtained by the Fourier transform; u shapeab0For said line voltage uab(t) amplitude of the direct current component, Uab1To Uabn-1Respectively constitute the line voltage uab(t) the magnitude of each frequency component,
Figure BDA0002693169040000064
for said line voltage uab(t) the phase angle of the direct current component,
Figure BDA0002693169040000065
to
Figure BDA0002693169040000066
To form the line voltage uab(t) the phase of each frequency component at the time origin; u shapebc0For said line voltage ubc(t) amplitude of the direct current component, Ubc1To Ubcn-1Respectively constitute the line voltage ubc(t) the magnitude of each frequency component,
Figure BDA0002693169040000067
for said line voltage ubc(t) the phase angle of the direct current component,
Figure BDA0002693169040000068
to
Figure BDA0002693169040000069
To form the line voltage ubc(t) the phase of each frequency component at the time origin; u shapeca0For said line voltage uca(t) amplitude of the direct current component, Uca1To Ucan-1Respectively constitute the line voltage uca(t) the magnitude of each frequency component,
Figure BDA00026931690400000610
for said line voltage uca(t) the phase angle of the direct current component,
Figure BDA00026931690400000611
to
Figure BDA00026931690400000612
To form the line voltage uca(t) the phase of each frequency component at the time origin;
creating a 6 x n feature matrix A using the amplitude and phase spectra6×n：
Figure BDA00026931690400000613
And U isab1、Ubc1、Uca1Respectively the fundamental frequency amplitude of the bus bar voltage of the power grid inverter station,
repeating the above calculation process on the bus line voltage of the power grid inverter station in the first fundamental frequency period after the fault occurs, and creating a characteristic matrix B of 6 multiplied by n6×n，
The feature matrix A6×nAnd the feature matrix B6×nSplicing, creating a 12 xn feature matrix C12×n：
Figure BDA0002693169040000071
Obtaining a corresponding data feature matrix C for each sample12×n，
For m sample data in the multi-source training data set, distributing fail or success labels to each sample according to the value of the extinction angle gamma (t) of the power grid inverter station from 1.5 fundamental frequency periods after the fault occurs to 5 fundamental frequency periods after the fault ends, wherein the distribution rule is that when the gamma (t) of a certain sample is [ t ], [ t ]4，t2]If a zero value occurs in it, the sample is marked as fail, i.e., what happensThe secondary commutation fails; otherwise, the sample is marked as success, namely the secondary commutation failure does not occur, and finally each sample has a corresponding label t4For the time instant t of 1.5 fundamental frequency cycles after the occurrence of the fault2For 5 fundamental frequency cycles after the end of the fault,
the data feature matrix C12×nAnd generating characteristic pictures as a picture pixel matrix, so that each sample has one corresponding characteristic picture, putting the characteristic pictures marked as fail of the sample into a folder named fail, putting the characteristic pictures marked as success of the sample into a folder named success, and putting the m characteristic pictures into the corresponding folders according to own labels.
Further, in the present invention,
in step b3, the model input quantity hin is a feature picture of each sample, and the model output quantity hout is a label of the feature picture, that is, a name fail or success of a folder in which the feature picture is located.
Further, in the present invention,
in the step b5, 80% of the sample model input quantity hin and model output quantity hout are randomly extracted as a model training set, and the remaining 20% of the sample model input quantity hin and model output quantity hout are used as a model verification set.
Further, in the present invention,
in the step b7, verifying the online secondary commutation failure intelligent prediction model by using the model verification set in the step b 5: inputting the model input quantity hin of the sample into the trained online secondary commutation failure intelligent prediction model, comparing the predicted model output quantity pout with the real model output quantity hout of the sample, and calculating the probability that pout and hout are consistent, namely the accuracy and the rate of missing judgment, and when the accuracy is greater than 90% and the rate of missing judgment is about 5%, determining that the online secondary commutation failure intelligent prediction model passes the verification.
Further, in the present invention,
on the basis of the steps a and B,and intensively judging a sample which can cause the secondary commutation failure by using the multi-source training data, performing off-line training on the intelligent agent, and constructing an intelligent prediction model of the transient state trajectory of the on-line disturbed voltage, wherein the intelligent prediction model of the transient state trajectory of the on-line disturbed voltage is respectively constructed for the busbar voltage of the power grid inverter station, and u is setLV(t) any one of the grid inverter station busbar voltages, the offline training then comprising:
step c 1: extracting data characteristics;
step c 2: selecting a model, namely selecting a regression learning method on a software platform with a machine learning function to train a prediction model;
step c 3: determining a prediction factor and a prediction response of the online disturbed voltage transient trajectory intelligent prediction model;
step c 4: and c, training a model by using the prediction factor and the prediction response in the step c3, and verifying the accuracy of the prediction model by using a cross-validation method.
Further, in the present invention,
the step c1 includes:
aiming at each sample data in the multi-source training data set, carrying out fault detection on the line voltage u in the first fundamental frequency period after the fault occursLV(t) Fourier transforming the line voltage uLVThe amplitude and phase angle of the fundamental frequency and the 2, 3, 4 harmonics of (t) constitute the feature matrix D of each sample1×8，
Figure BDA0002693169040000091
Wherein, ULV1、ULV2、ULV3、ULV4Are respectively the line voltage uLV(t) fundamental frequency and amplitudes of the 2, 3, 4 harmonics;
Figure BDA0002693169040000092
are respectively the line voltage uLVFundamental frequency of (t) and phase of 2, 3, 4 th harmonic。
Further, in the present invention,
in the step c3, in which case,
setting the moment t of one fundamental frequency period after the fault occurs3To the time t of 5 fundamental frequency periods after the fault is finished2With a total of k sampling instants, at t3，t2]The predictor F in a time periodk*9Including the k sampling time instants and the feature matrix D of each sample extracted in the step c11×8Let Δ t be the sampling time interval of every two adjacent sampling instants in said k sampling instants, then
Figure BDA0002693169040000093
At [ t ]3，t2]The predicted response R over a period of timek*1For said line voltage uLV(t) at [ t3，t2]The values of the k sampling instants within the time period,
Figure BDA0002693169040000094
further, in the present invention,
when the AC system fault occurs, in a time period t4，t2]The trigger angle command value alpha of which the secondary commutation failure does not occurorder(t) a feasible region satisfying αorder(t)≤αorder_MAX(t)，
Wherein the firing angle command value alphaorder(t) maximum value at time t
Figure BDA0002693169040000101
arccos (X) is an inverse cosine function with respect to X, cos (y) is a cosine function with respect to y, XrFor the commutation reactance of a converter transformer on the inverting side, IdhIs the limit value of the direct current in the low-voltage current limiting unit,γminto a minimum extinction angle at which no commutation failure occurs,
URMS(t) bus line voltage u of grid inverter station at time tab(t)，ubc(t)，uca(t) minimum value of effective value, i.e.
Figure BDA0002693169040000106
URMS_AB(t)，URMS_BC(t)，URMS_CA(t) are time periods [ t ] respectively4，t2]The power grid inverter station bus line voltage u is obtained through predictionab1(t)，ubc1(t)，uca1(t) a significant value of
Figure BDA0002693169040000102
Figure BDA0002693169040000103
Figure BDA0002693169040000104
Wherein the content of the first and second substances,
Figure BDA0002693169040000105
f0 is the rated frequency of the grid, uab1(t)，ubc1(t)，uca1(t) is predicted by the online disturbed voltage transient trajectory intelligent prediction model.
According to the direct-current commutation failure blocking and preventing control method, prejudgment on commutation failure meets the requirements of rapidity and effectiveness, and whether secondary commutation failure occurs in a fault period can be rapidly and accurately predicted within ms-level time; the method can quickly, conservatively and pertinently pre-judge the impending commutation failure in the system, and reserve enough time margin and safety margin for subsequent control; the method can realize the rapid prediction of the change of the short-term phase-change voltage; under the condition that the performance of the phase-locked loop is ideal and the communication delay is not counted, the secondary commutation failure during the fault (caused by the fault of the alternating current system) can be effectively avoided; the control strategy implemented can guarantee timeliness, reliability and result constrainable.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating a DC commutation failure blocking and prevention control method according to an embodiment of the present invention;
FIG. 2 illustrates a multi-source training data set timeline according to an embodiment of the present invention;
fig. 3 illustrates a characteristic curve of a low voltage current limiting unit according to an embodiment of the present invention;
fig. 4 shows a flow chart of a dc commutation failure blocking and prevention control method according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a direct current commutation failure blocking and preventing control method based on system disturbed transient trajectory prediction, which is a method for predicting a direct current commutation transient boundary through machine learning to generate a secondary commutation failure blocking control strategy aiming at an electric power system.
Referring to fig. 1, the dc commutation failure blocking and preventing control method of the present invention includes an offline portion and an online portion. The offline part is used to build two models: an online secondary commutation failure intelligent prediction model (which can be simply referred to as a secondary commutation failure intelligent prediction model) and an online disturbed voltage transient state trajectory intelligent prediction model. The online part is used for online generation of a control strategy to block and prevent occurrence of secondary commutation failure.
(1) Off-line part
The off-line part comprises the following steps:
step a: generating a multi-source training dataset
As can be seen from fig. 1, the sources of the multi-source training data set include: the method comprises the steps of simulating samples of an electromagnetic transient state and an electromechanical-electromagnetic transient state hybrid simulation of an alternating-current and direct-current power grid, and real-time measurement data of a power grid Phasor Measurement Unit (PMU) and a power failure wave recording device (TFR).
Referring to fig. 2, m sample data included in the multi-source training data set records the simulated simulation sample and the real-time measured data before the start of the fault (t)1Time of day) to 5 fundamental frequency cycles (t) after fault removal2Time) time length, m is a positive integer greater than 1, s represents seconds, the fundamental frequency period is a period corresponding to the rated frequency of the power system (namely, one period of the commutation voltage), and the rated frequency of the power system can be 50Hz or 60 Hz.
Wherein the content of the first and second substances,
tstartis the time of the fault occurrence;
[t1，tstart]is the fundamental frequency cycle before the fault;
tendis the time of fault clearance;
[tend，t2]is 5 fundamental frequency periods after the fault is over;
[tstart，t3]is the first fundamental frequency period after the fault occurs;
[tstart，t4]is 1.5 fundamental frequency cycles after the failure occurs.
The physical quantities in each sample data in the multi-source training dataset comprise: busbar voltage (line voltage for short) u of power grid inverter stationab(t)、ubc(t)、uca(t) and a power grid inverter station arc-quenching angle gamma (t), wherein the arc-quenching angle is used only when the intelligent prediction model for the secondary commutation failure is trained, and the arc-quenching angle is not extracted in other steps, wherein uab(t)、ubc(t)、ucaAnd (t) measuring voltages of an A phase and a B phase, a B phase and a C phase in three phases of a bus of the power grid inverter station and between the C phase and the A phase.
The real-time measurement data of the power grid PMU/TFR in the multi-source training data set come from the real-time measurement of the AC/DC power grid inverter station.
The simulation sample in the multi-source training data set is based on the following fault disturbance set in the electromagnetic transient state of the alternating current-direct current power grid and the electromechanical-electromagnetic transient hybrid simulation:
the type of failure: the fault of the alternating current line which may occur in the actual system such as a single-phase grounding short circuit, a two-phase short circuit, a three-phase short circuit and a two-phase grounding short circuit on the line of the alternating current system.
Location of failure occurrence: the method includes selecting a position where a line fault may occur in a simulated actual alternating current system, and generally selecting a position with different electrical distances from a power grid inversion station bus to set the fault.
Timing of the failure: setting fault angles as different angles with intervals of theta degrees from 0 degrees to 180 degrees, wherein theta is more than 0 and less than 30, and the smaller theta is, the more fault time sequences are covered;
duration of failure: setting corresponding fault duration according to the actual relay protection action time of the simulated alternating current power grid, wherein the fault duration is usually about 0.1 s;
size of the fault: and setting the fault severity from light to heavy according to the scale of the alternating current power grid.
Step b: referring to fig. 1, based on a supervised learning method, the intelligent agent is trained offline by using the multi-source training data set in step a, so as to construct an online secondary commutation failure intelligent prediction model. The training process mainly comprises the following steps:
step b 1: data feature extraction:
for each sample data in the multi-source training data set, the following processing is carried out: for the previous fundamental frequency cycle ([ t ] before the fault occurs1，tstart]) Line voltage u in the time period of (d)ab(t)、ubc(t) and uca(t) Fourier transform to obtain an amplitude spectrum (U) of the line voltage of 1 × nab)1×n、(Ubc)1×n、(Uca)1×nAnd 1 xn line voltage phase spectrum
Figure BDA0002693169040000141
Wherein the content of the first and second substances,
(Uab)1×n＝[Uab0 Uab1 Uab2 ... Uabn-1]；
(Ubc)1×n＝[Ubc0 Ubc1 Ubc2 ... Ubcn-1]；
(Uca)1×n＝[Uca0 Uca1 Uca2 ... Ucan-1]；
Figure BDA0002693169040000142
Figure BDA0002693169040000143
Figure BDA0002693169040000144
0 is expressed as direct current component after Fourier transform, 1 is fundamental frequency, 2 and 3 … n-1 are harmonic series, n-1 is the highest harmonic series after Fourier transform, the Fourier transform obtains n-1 harmonic, Uab0To a line voltage uab(t) amplitude of the direct current component, Uab1To Uabn-1Are respectively the component line voltage uab(t) the magnitude of each frequency component. In the same way as above, the first and second,
Figure BDA0002693169040000145
to a line voltage uab(t) the phase angle of the direct current component,
Figure BDA0002693169040000146
to
Figure BDA0002693169040000147
To form a line voltage uabThe phase that each frequency component of (t) has at the origin of time, and so on.
Using the amplitude and phase spectra described above, a 6 × n feature matrix A is created6×n：
Figure BDA0002693169040000148
Wherein, Uab1、Ubc1、Uca1The fundamental frequency amplitudes of the three line voltages, respectively.
For the first fundamental frequency cycle (i.e. [ t ]) after the fault occursstart，t3]) Repeating the calculation process on the bus line voltage of the internal grid inversion station to create a characteristic matrix B of 6 multiplied by n6×n：
Splicing the two matrixes up and down to create a 12 xn characteristic matrix C12×n：
Figure BDA0002693169040000151
Finally, each sample corresponds to a data characteristic matrix C12×n。
Aiming at m sample data in the multi-source training data set, the period from 1.5 fundamental frequency periods to the end of the fault (t)4，t2]) Assigning a fail or success label to each sample for the value of the extinction angle gamma (t) by the rule that when gamma (t) of a sample is [ t ]4，t2]If zero value appears in the sample, the sample is marked as fail, namely, the second commutation failure occurs; otherwise, the sample is marked as success, i.e., no secondary commutation failure occurs. Finally, each sample has a corresponding label.
The feature matrix C12×nAnd generating a characteristic picture with a format png as a picture pixel matrix, enabling each sample to have a corresponding characteristic picture, placing the characteristic picture of the sample marked as 'fail' into a folder named fail, and placing the characteristic picture of the sample marked as success into a folder named success. Thus, the m characteristic pictures are put into the corresponding folders according to the labels of the m characteristic pictures.
Step b 2: model selection
And selecting a Convolutional Neural Network (CNN) method on a software platform containing a machine learning function to train the prediction model.
Step b 3: determining model input quantities and model output quantities
The model input hin is the feature picture of each sample in step b1, and the model output hout is the label of the feature picture, i.e. the name (fail or success) of the folder in which the picture is located.
Step b 4: defining architecture, parameters and training options for CNN networks
Step b 5: sample splitting
And randomly extracting 80% of the model input quantity and the model output quantity of the sample as a model training set, and remaining 20% of the model input quantity and the model output quantity of the sample as a model verification set.
Step b 6: model training
And (c) training the prediction model by using the model training set in the step b5 and the CNN network built in the step b4 to obtain an online secondary commutation failure intelligent prediction model, wherein the online secondary commutation failure intelligent prediction model can be programmed by self, and parameters of the CNN network can be defined by self, or the existing CNN network of the platform can be used.
Step b 7: model validation pass
Verifying the online secondary commutation failure intelligent prediction model by using the model verification set in the step b 5: inputting the model input quantity hin of the sample into the trained online secondary commutation failure intelligent prediction model, comparing the predicted model output quantity pout with the real model output quantity hout of the sample, and calculating the probability (namely the accuracy) that pout and hout are consistent and the rate of missing judgment, and when the accuracy is greater than 90% and the rate of missing judgment is about 5%, determining that the online secondary commutation failure intelligent prediction model passes the verification. The miss-judging rate is the percentage of cases which actually have secondary commutation failure but are judged not to have commutation failure. The values of the accuracy rate and the misjudgment rate are different for different alternating current power grids and different model training sets and model verification sets. And if the accuracy rate or the missing judgment rate does not reach the set target, increasing the number of samples and retraining.
Step c: on the basis of a supervised learning method, on the basis of the steps a and b, samples which can cause secondary commutation failure are judged in a concentrated mode by utilizing multi-source training data, an intelligent body is trained off line, and an online disturbed voltage transient state track intelligent prediction model is constructed. The method comprises the steps of respectively constructing an online disturbed voltage transient state track intelligent prediction model aiming at three line voltages of a bus of a power grid inversion station, and using the bus line voltage u of the power grid inversion stationab(t) taking the training process of the prediction model as an example, the training process mainly comprises the following steps:
step c 1: data feature extraction
Setting u for each sample data in multi-source training data setLV(t) is any one of the bus-bar voltages of the grid inversion station, and the first fundamental frequency period after the fault occurs ([ t)start，t3]) Bus line voltage u of internal grid inversion stationLV(t) Fourier transforming the resultantThe amplitude and phase angle of the line voltage fundamental frequency and the 2, 3 and 4 harmonics form a characteristic matrix D of each sample1×8。
Figure BDA0002693169040000171
Wherein U isLV1、ULV2、ULV3、ULV4Are respectively the component line voltage uLV(t) fundamental frequency and amplitudes of the 2, 3, 4 harmonics;
Figure BDA0002693169040000172
are respectively the component line voltage uLVThe fundamental frequency of (t) and the phases of the 2, 3, 4 harmonics.
Step c 2: model selection
And (3) selecting a regression learning method on a software platform containing a machine learning function to train the prediction model.
Step c 3: determining a predictor and a predicted response of a predictive model
Predictor F of a prediction modelk*9Including one fundamental frequency period after the fault occurs to 5 fundamental frequency periods after the fault ends ([ t ]3，t2]) K sampling time instants and the feature matrix D of each sample extracted in step c11×8Wherein k is [ t ]3，t2]The number of internal samples, i.e. the number of samples of the signal that are extracted from the continuous signal and constitute a discrete signal per second, the magnitude of the value k depends on the sampling frequency f set in the simulation model or on the real-time measurement instrument. Predictor Fk*9Satisfy the requirement of
Figure BDA0002693169040000173
Wherein, Δ t is a sampling time interval, and the value of Δ t is determined according to the simulation platform or PMU/TFR real-time measurement sampling frequency.
Predicted response R of a prediction modelk*1For line voltage u of power grid inverter stationLV(t) at [ t3，t2]The values of k sample points within the time period.
Figure BDA0002693169040000181
Step c 4: model training and cross validation
Using the predictor F in step c3k*9And predicted response Rk*1And training the model, and verifying the accuracy of the prediction model by using a cross-validation method.
Step c 5: if the accuracy of the prediction model does not meet the requirement, repeating the steps c1 to c4, training and obtaining the busbar voltage u of the grid inverter stationbc(t) and uca(t) intelligent prediction model of transient trajectory of online disturbed voltage.
(2) On-line part
The system is started in an online part after disturbance occurs, and the online part comprises the following steps:
step d 1: referring to fig. 1, the ([ t ] is obtained by real-time measurement or sampling of, for example, grid PMU/TFR1，t3]) Line voltage u of power grid inverter station bus in time periodab(t)、ubc(t) and uca(t), because the sampled line voltage is mainly used for Fourier transform to extract signal characteristics, the sampling frequency can be lower, 2000 Hz-10000 Hz, preferably 5000 Hz;
step d 2: referring to FIG. 1, the measured value of the line voltage in step d1 is input to the 'prediction model' created in step b to obtain [ t4，t2]Judging whether secondary commutation failure occurs in the time period, and if the secondary commutation failure occurs, entering the step d 3;
step d 3: the bus line voltage [ t ] in the step d1start，t3]Inputting the measured value in the time period into the online disturbed voltage transient state track intelligent prediction model established in the step c, and instantly predicting to obtain the bus voltage of the power grid inverter station in the future time period [ t ]3，t2]Internal voltage uab1(t)，ubc1(t)，uca1(t)，
Step d 4: by using stepsThe power grid inverter station bus line voltage u predicted in step d3ab1(t)，ubc1(t)，uca1(t) calculating [ t4，t2]Bus voltage effective value U of power grid inversion station corresponding to timeRMS_AB(t)，URMS_BC(t)，URMS_CA(t)；
Step d 5: referring to fig. 1, the [ t ] is obtained by on-line instant solution according to the constraint conditions of commutation failure blocking and preventive control strategy4，t2]And the trigger angle instruction value in the time period can be in a feasible range and a maximum value, and is sent to an execution unit controlled by a direct current inverter of an alternating current-direct current power grid, so that control parameters are adjusted on line, and the occurrence of subsequent secondary commutation failure is avoided.
Wherein, the commutation failure blocking and preventing control strategy needs to consider the following 3 constraint conditions:
constraint 1:
due to the quantitative relationship between commutation voltage, dc current (i.e. inverter dc side current), firing angle and arc-quenching angle:
Figure BDA0002693169040000191
wherein id(alpha) and id(α + μ) is the DC current at the start and end of commutation, XrFor the commutation reactance, U, of a converter transformer on the inverting sideLThe effective value of the bus voltage of the power grid inversion station is shown, alpha is a trigger angle, and gamma is an arc extinguishing angle.
When the system has phase change failure, the effective values of the direct current and the bus voltage of the power grid inversion station are not constant any more, and the phase change time is very short, so that i is enabled to bed(α + μ) is approximately equal to id(α)，ULTaking the power grid inverter station bus line voltage u at the time t calculated by prediction in the step d4ab1(t)，ubc1(t)，uca1(t) minimum value of effective value, i.e.
URMS(t)＝min[URMS_AB(t)，URMS_BC(t)，URMS_CA(t)]，
Figure BDA0002693169040000192
Figure BDA0002693169040000193
Figure BDA0002693169040000201
Wherein the content of the first and second substances,
Figure BDA0002693169040000202
f0 is the rated frequency of the grid, specifically 50Hz or 60Hz, and T is one period of the line voltage in the grid.
Then, a constraint equation when commutation fails is obtained:
Figure BDA0002693169040000203
where α isorderAnd (t) is a trigger angle command value.
Constraint 2:
the constraint conditions of the extinction angle when no commutation failure occurs are as follows:
γ≥γmin (3)，
wherein, γminThe minimum extinction angle is the minimum extinction angle at which no commutation failure occurs, and the minimum extinction angle is the minimum time to satisfy the restoration blocking capability of the converter valve. If the extinction angle is smaller than the value, the converter valve does not have enough time to recover the blocking capability, and phase commutation failure can occur.
Constraint 3:
referring to fig. 3, the constraint is obtained according to the relationship between the dc voltage and the dc current in the low voltage current limiting unit (VDCOL):
Figure BDA0002693169040000204
wherein id、UdIs a direct current, Idl、Idh、Udl、UdhThe upper limit value and the lower limit value of the direct current (namely, the direct current side current of the inverter) and the direct constant voltage (namely, the direct current side voltage of the inverter) are respectively. Obtaining i from the constraint (2)dThe value range is as follows:
Idl≤id≤Idh (5)，
substituting the formulas (3) and (5) into the formula (2) to obtain the value range of the extinction angle instruction value without commutation failure:
Figure BDA0002693169040000211
it can be seen that at time t, αorder(t) in the case of γ ═ γmin，id＝IdhThe maximum value is then taken, which is:
Figure BDA0002693169040000212
referring to fig. 1, in actual work, the time for prejudging the secondary commutation failure and the time for setting the feasible domain of the trigger angle instruction value are both negligible, the transmission switching delay of the trigger angle instruction value is within 0.01 second, [ t ] t4，t2]Trigger angle instruction value in [ t ] in time period3，t4]The value in a feasible domain where secondary commutation failure does not occur can be switched within time, and the requirement of preventing and controlling timeliness of the secondary commutation failure during the failure period is met.
Referring to fig. 4, fig. 4 shows an embodiment of basic steps executed when the dc commutation failure (or simply commutation failure) blocking and preventing control method of the present invention is adopted, including:
when detecting that the AC system fault occurs, recording the time between the previous fundamental frequency period of the fault occurrence and the next fundamental frequency period of the fault occurrenceLine voltage u ofab(t)、ubc(t)、uca(t)
Data extraction for the first time: extracting the line voltage u from the fundamental frequency period before the fault occurs to the fundamental frequency period after the fault occursab(t)、ubc(t)、uca(t)；
Inputting data obtained by first data extraction into an online secondary commutation failure intelligent prediction model, judging whether secondary commutation failure occurs, and if the secondary commutation failure does not occur, ending the execution of the method;
and if the second commutation failure occurs, performing second data extraction: extracting the line voltage u from the fault to the period of the fundamental frequency after the faultab(t)、ubc(t)、uca(t)；
Inputting data obtained by the second data extraction into an online disturbed voltage transient state trajectory intelligent prediction model:
calculating to obtain a line voltage effective value by an online disturbed voltage transient state track intelligent prediction model;
and calculating based on the constraint of the line voltage effective value and the trigger angle instruction value to obtain a condition, namely a maximum value and a feasible region of the trigger angle instruction value, which are met by the trigger angle instruction value without commutation failure under the symmetric fault and the asymmetric fault so as to block and prevent the occurrence of secondary commutation failure, wherein the trigger angle instruction value is input into an inverter control unit, and the value of the trigger angle is adjusted in time so as to avoid the secondary commutation failure.
The embodiment can show that: the method solves the problems that the prior commutation failure prejudging method only considers the disturbance severity degree and neglects the influence of the characteristics of a direct current control system on the commutation failure state, the control parameter design is lack of design universality depending on experience, and the starting response speed is influenced by a characteristic extraction method and the disturbance characteristics, and the judging performance is different under different transient conditions.
The invention also solves the following problems of the current existing control strategy:
1) only the commutation physical process is controlled qualitatively, so that the control result is sometimes conservative and sometimes aggressive, the commutation feasible domain boundary cannot be clearly defined accurately according to different transient conditions, and the control economy is more difficult to further improve on the basis;
2) the current control optimization is mainly based on the adjustment of parameters and structures by existing controls such as constant current, minimum extinction angle and the like, the original control characteristics of direct current can be changed, and the adaptability is insufficient under different transient conditions and operating conditions;
3) the parameter design mode lacks universality;
4) under the existing improvement scheme aiming at a single control strategy, the control feasible region is limited, and the phase commutation failure suppression effect cannot be ensured under the serious fault.
The establishment of the commutation control strategy needs to adopt the combination of various control strategies according to the physical mechanism of the commutation process to realize accurate and economic control on commutation failure. The invention combines the constant current control of the rectifying side and the trigger angle control of the inverting side which are common in the disturbance period, and fully excavates the control feasible region of the trigger angle of the inverting side. In the calculation of the trigger angle, the commutation voltage value is a real-time predicted value in disturbance, so that the trigger angle is calculated more accurately.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (14)

1. A direct current commutation failure blocking and prevention control method is characterized by comprising the following steps:
uninterrupted acquisition of bus line voltage u of power grid inversion stationab(t)、ubc(t)、uca(t) when detecting the occurrence of an AC system fault, extracting the time t before said fault occurred1To the time t after the fault occurs3The bus line voltage u of the grid inverter stationab(t)、ubc(t)、uca(t), when t representsA (c) is added;
data extraction for the first time: extracting the time t before the fault occurs1To the time t after the fault occurs3The bus line voltage u of the grid inverter stationab(t)、ubc(t)、uca(t)；
Inputting the data obtained by the first data extraction into an online secondary commutation failure intelligent prediction model, judging whether secondary commutation failure occurs, and if the secondary commutation failure does not occur, ending the execution of the direct current commutation failure blocking and preventing control method; if the secondary commutation failure is judged to occur, performing secondary data extraction: extracting the fault occurrence time tstartTo the time t after the fault occurs3The bus line voltage u of the grid inverter stationab(t)、ubc(t)、uca(t)；
Inputting the data obtained by the second data extraction into an online disturbed voltage transient state track intelligent prediction model;
predicting the bus voltage of the power grid inverter station at [ t ] by the online disturbed voltage transient trajectory intelligent prediction model3，t2]And calculating to obtain [ t4，t2]The effective value of the bus voltage of the power grid inversion station is obtained;
based on the bus voltage effective value of the power grid inversion station and a trigger angle instruction value constraint formula, calculating to obtain [ t [ [ t ]4，t2]The condition satisfied by the trigger angle command value for which no commutation failure occurs is satisfied to block and prevent the occurrence of the secondary commutation failure,
wherein, let tendIf it is the fault clearing time, the time t1、t2、t3And the time t of the fault occurrencestartAnd a fault clearing time tendThe relationship between (A) and (B) is:
[t1，tstart]is the fundamental frequency cycle prior to the occurrence of the fault;
[tend，t2]is 5 fundamental frequency cycles after the fault is over;
[tstart，t3]is as followsThe first fundamental period after the occurrence of the fault.
2. The method according to claim 1, wherein the DC commutation failure blocking and preventing control method,
the online secondary commutation failure intelligent prediction model is obtained by the following method, and comprises the following steps:
A. generating a multi-source training data set;
B. and on the basis of a supervised learning method, performing off-line training on the intelligent agent by using the multi-source training data set to construct the online secondary commutation failure intelligent prediction model.
3. The method according to claim 2, wherein the DC commutation failure blocking and preventing control method,
the sources of the multi-source training dataset include: the method comprises the steps of simulating samples of an electromagnetic transient state and an electromechanical-electromagnetic transient state hybrid simulation of an alternating-current and direct-current power grid, and real-time measurement data obtained by measuring an alternating-current and direct-current power grid inverter station in real time through a power grid phasor measuring device and a power fault recorder;
the multi-source training data set comprises m samples, and the data of each sample records the time t before the fault occurs in the simulation sample and the real-time measurement data1To the time t after the fault occurs2The number of samples m is a positive integer greater than 1,
the physical quantity in the data of each sample includes: bus line voltage u of power grid inverter stationab(t)、ubc(t)、uca(t) and a grid inverter station arc-quenching angle gamma (t);
the simulation sample sets the following faults in the electromagnetic transient and electromechanical-electromagnetic transient hybrid simulation of the alternating current-direct current power grid:
the types of faults include: single-phase grounding short circuit, two-phase short circuit, three-phase short circuit and two-phase grounding short circuit on the AC system line;
the position where the fault occurs: selecting positions with different electrical distances from a bus of the power grid inversion station to set faults;
timing of the fault: setting fault angles to be different angles with intervals of theta degrees from 0 degrees to 180 degrees; wherein theta is more than 0 and less than 30;
the fault duration is: setting corresponding fault duration according to the actual relay protection action time of the simulated alternating current power grid;
the size of the fault: setting faults of different severity according to the scale of the simulated alternating current power grid.
4. The DC commutation failure blocking and preventing control method according to claim 3,
carrying out the uninterrupted acquisition of the bus line voltage u of the power grid inverter stationab(t)、ubc(t)、ucaThe collection frequency in (t) is 2000 Hz-10000 Hz;
the number m of the samples satisfies that m is more than or equal to 100 and less than or equal to 10000.
5. The DC commutation failure blocking and preventing control method according to claim 4,
the acquisition frequency is 5000 Hz;
the number m of the samples is 3000;
the fault duration is 0.1s, s representing seconds.
6. The DC commutation failure blocking and preventing control method according to claim 3,
the step B comprises the following steps:
step b 1: extracting data characteristics;
step b 2: selecting a model, namely selecting a convolutional neural network method on a software platform with a machine learning function to train a prediction model;
step b 3: determining model input quantities hin and model output quantities hout;
step b 4: defining the architecture, parameters and training options of the convolutional neural network;
step b 5: splitting a sample to obtain a model training set and a model verification set;
step b 6: model training, namely training a prediction model by adopting the convolutional neural network and a model training set to obtain the online secondary commutation failure intelligent prediction model;
step b 7: the model verification is passed.
7. The method according to claim 6, wherein the DC commutation failure blocking and preventing control method,
the step b1 includes:
for each sample data in the multi-source training data set, the following processing is carried out:
the bus line voltage u of the grid inverter station in the time period of the fundamental frequency cycle before the fault occursab(t)、ubc(t) and uca(t) Fourier transform to obtain an amplitude spectrum (U) of the line voltage of 1 × nab)1×n、(Ubc)1×n、(Uca)1×nAnd 1 xn line voltage phase spectrum
Figure FDA0002693169030000041
Figure FDA0002693169030000042
Wherein the content of the first and second substances,
(Uab)1×n＝[Uab0 Uab1 Uab2 … Uabn-1]；
(Ubc)1×n＝[Ubc0 Ubc1 Ubc2 ... Ubcn-1]；
(Uca)1×n＝[Uca0 Uca1 Uca2 ... Ucan-1]；
Figure FDA0002693169030000043
Figure FDA0002693169030000044
Figure FDA0002693169030000045
0 is expressed as a direct current component after the Fourier transform, 1 is a fundamental frequency, 2 and 3 … n-1 are harmonic series, n-1 is the highest harmonic series after the Fourier transform, and n-1 harmonic is obtained by the Fourier transform; u shapeab0For said line voltage uab(t) amplitude of the direct current component, Uab1To Uabn-1Respectively constitute the line voltage uab(t) the magnitude of each frequency component,
Figure FDA0002693169030000046
for said line voltage uab(t) the phase angle of the direct current component,
Figure FDA0002693169030000047
to
Figure FDA0002693169030000048
To form the line voltage uab(t) the phase of each frequency component at the time origin; u shapebc0For said line voltage ubc(t) amplitude of the direct current component, Ubc1To Ubcn-1Respectively constitute the line voltage ubc(t) the magnitude of each frequency component,
Figure FDA0002693169030000049
for said line voltage ubc(t) the phase angle of the direct current component,
Figure FDA00026931690300000410
to
Figure FDA00026931690300000411
Into a groupTo said line voltage ubc(t) the phase of each frequency component at the time origin; u shapeca0For said line voltage uca(t) amplitude of the direct current component, Uca1To Ucan-1Respectively constitute the line voltage uca(t) the magnitude of each frequency component,
Figure FDA00026931690300000412
for said line voltage uca(t) the phase angle of the direct current component,
Figure FDA00026931690300000413
to
Figure FDA00026931690300000414
To form the line voltage uca(t) the phase of each frequency component at the time origin;
creating a 6 x n feature matrix A using the amplitude and phase spectra6×n：
Figure FDA0002693169030000051
And U isab1、Ubc1、Uca1Respectively the fundamental frequency amplitude of the bus bar voltage of the power grid inverter station,
repeating the above calculation process on the bus line voltage of the power grid inverter station in the first fundamental frequency period after the fault occurs, and creating a characteristic matrix B of 6 multiplied by n6×n，
The feature matrix A6×nAnd the feature matrix B6×nSplicing, creating a 12 xn feature matrix C12×n:
Figure FDA0002693169030000052
Obtaining a corresponding data feature matrix C for each sample12×n，
For m sample data in the multi-source training data set, distributing fail or success labels to each sample according to the value of the extinction angle gamma (t) of the power grid inverter station from 1.5 fundamental frequency periods after the fault occurs to 5 fundamental frequency periods after the fault ends, wherein the distribution rule is that when the gamma (t) of a certain sample is [ t ], [ t ]4，t2]If zero value appears in the sample, the sample is marked as fail, namely the secondary commutation failure occurs; otherwise, the sample is marked as success, namely the secondary commutation failure does not occur, and finally each sample has a corresponding label t4For the time instant t of 1.5 fundamental frequency cycles after the occurrence of the fault2For 5 fundamental frequency cycles after the end of the fault,
the data feature matrix C12×nAnd generating characteristic pictures as a picture pixel matrix, so that each sample has one corresponding characteristic picture, putting the characteristic pictures marked as fail of the sample into a folder named fail, putting the characteristic pictures marked as success of the sample into a folder named success, and putting the m characteristic pictures into the corresponding folders according to own labels.
8. The method according to claim 7, wherein the DC commutation failure blocking and preventing control method,
in step b3, the model input quantity hin is a feature picture of each sample, and the model output quantity hout is a label of the feature picture, that is, a name fail or success of a folder in which the feature picture is located.
9. The method according to claim 7, wherein the DC commutation failure blocking and preventing control method,
in the step b5, 80% of the sample model input quantity hin and model output quantity hout are randomly extracted as a model training set, and the remaining 20% of the sample model input quantity hin and model output quantity hout are used as a model verification set.
10. The method according to claim 7, wherein the DC commutation failure blocking and preventing control method,
in the step b7, verifying the online secondary commutation failure intelligent prediction model by using the model verification set in the step b 5: inputting the model input quantity hin of the sample into the trained online secondary commutation failure intelligent prediction model, comparing the predicted model output quantity pout with the real model output quantity hout of the sample, and calculating the probability that pout and hout are consistent, namely the accuracy and the rate of missing judgment, and when the accuracy is greater than 90% and the rate of missing judgment is about 5%, determining that the online secondary commutation failure intelligent prediction model passes the verification.
11. The method for blocking and preventing DC commutation failure according to any one of claims 2-10,
on the basis of the steps A and B, the multi-source training data are utilized to centrally judge samples which can cause the secondary commutation failure, the intelligent agent is trained off line, and an intelligent prediction model of the transient state track of the on-line disturbed voltage is constructed, wherein the intelligent prediction model of the transient state track of the on-line disturbed voltage is respectively constructed for the bus voltage of the power grid inverter station, and u is setLV(t) any one of the grid inverter station busbar voltages, the offline training then comprising:
step c 1: extracting data characteristics;
step c 2: selecting a model, namely selecting a regression learning method on a software platform with a machine learning function to train a prediction model;
step c 3: determining a prediction factor and a prediction response of the online disturbed voltage transient trajectory intelligent prediction model;
step c 4: and c, training a model by using the prediction factor and the prediction response in the step c3, and verifying the accuracy of the prediction model by using a cross-validation method.
12. The method according to claim 11, wherein the DC commutation failure blocking and preventing control method,
the step c1 includes:
aiming at each sample data in the multi-source training data set, carrying out fault detection on the line voltage u in the first fundamental frequency period after the fault occursLV(t) Fourier transforming the line voltage uLVThe amplitude and phase angle of the fundamental frequency and the 2, 3, 4 harmonics of (t) constitute the feature matrix D of each sample1×8，
Figure FDA0002693169030000071
Wherein, ULV1、ULV2、ULV3、ULV4Are respectively the line voltage uLV(t) fundamental frequency and amplitudes of the 2, 3, 4 harmonics;
Figure FDA0002693169030000072
are respectively the line voltage uLVThe fundamental frequency of (t) and the phases of the 2, 3, 4 harmonics.
13. The method according to claim 12, wherein the DC commutation failure blocking and preventing control method,
in the step c3, in which case,
setting the moment t of one fundamental frequency period after the fault occurs3To the time t of 5 fundamental frequency periods after the fault is finished2With a total of k sampling instants, at t3，t2]The predictor F in a time periodk*9Including the k sampling time instants and the feature matrix D of each sample extracted in the step c11×8Let Δ t be the sampling time interval of every two adjacent sampling instants in said k sampling instants, then
Figure FDA0002693169030000081
At [ t ]3，t2]The predicted response R over a period of timek*1For said line voltage uLV(t) at [ t3，t2]The values of the k sampling instants within the time period,
Figure FDA0002693169030000082
14. the method for blocking and preventing DC commutation failure according to any one of claims 1-10,12 and 13,
when the AC system fault occurs, in a time period t4，t2]The trigger angle command value alpha of which the secondary commutation failure does not occurorder(t) a feasible region satisfying αorder(t)≤αorder_MAX(t),
Wherein the firing angle command value alphaorder(t) maximum value at time t
Figure FDA0002693169030000083
arccos (X) is an inverse cosine function with respect to X, cos (y) is a cosine function with respect to y, XrFor the commutation reactance of a converter transformer on the inverting side, IdhIs the limit value of DC current in the low-voltage current-limiting unit, gammaminTo a minimum extinction angle at which no commutation failure occurs,
URMS(t) bus line voltage u of grid inverter station at time tab(t)，ubc(t)，uca(t) minimum value of effective value, i.e.
Figure FDA0002693169030000095
URMS_AB(t)，URMS_BC(t)，URMS_CA(t) are time periods [ t ] respectively4，t2]Internal channelThe power grid inverter station bus line voltage u obtained through over predictionab1(t)，ubc1(t)，uca1(t) a significant value of
Figure FDA0002693169030000091
Figure FDA0002693169030000092
Figure FDA0002693169030000093
Wherein the content of the first and second substances,
Figure FDA0002693169030000094
f0 is the rated frequency of the grid, uab1(t)，ubc1(t)，uca1(t) is predicted by the online disturbed voltage transient trajectory intelligent prediction model.
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