CN116991145B - Main control unit preparation test method and device applied to exciting current - Google Patents
Main control unit preparation test method and device applied to exciting current Download PDFInfo
- Publication number
- CN116991145B CN116991145B CN202311239269.0A CN202311239269A CN116991145B CN 116991145 B CN116991145 B CN 116991145B CN 202311239269 A CN202311239269 A CN 202311239269A CN 116991145 B CN116991145 B CN 116991145B
- Authority
- CN
- China
- Prior art keywords
- data
- electric signal
- signal
- target
- sampling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000002360 preparation method Methods 0.000 title claims abstract description 19
- 238000010998 test method Methods 0.000 title claims abstract description 9
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 156
- 238000005070 sampling Methods 0.000 claims abstract description 136
- 238000001914 filtration Methods 0.000 claims abstract description 100
- 238000012360 testing method Methods 0.000 claims abstract description 59
- 238000004140 cleaning Methods 0.000 claims abstract description 14
- 238000012216 screening Methods 0.000 claims abstract description 11
- 230000005284 excitation Effects 0.000 claims description 67
- 238000001228 spectrum Methods 0.000 claims description 32
- 238000012544 monitoring process Methods 0.000 claims description 26
- 238000000034 method Methods 0.000 claims description 25
- 230000009466 transformation Effects 0.000 claims description 12
- 230000001360 synchronised effect Effects 0.000 claims description 9
- 238000013507 mapping Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 230000003595 spectral effect Effects 0.000 claims description 6
- 229940088594 vitamin Drugs 0.000 claims description 6
- 229930003231 vitamin Natural products 0.000 claims description 6
- 235000013343 vitamin Nutrition 0.000 claims description 6
- 239000011782 vitamin Substances 0.000 claims description 6
- 150000003722 vitamin derivatives Chemical class 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000011156 evaluation Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000013473 artificial intelligence Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000005674 electromagnetic induction Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a production evaluation technology, and discloses a main control unit preparation test method applied to exciting current, which comprises the following steps: sequentially performing data sampling and data cleaning on the historical electric signal data to obtain standard electric signal data; performing characteristic filtering on the standard electric signal data to obtain a primary electric signal characteristic sequence, and performing characteristic clustering on the primary electric signal characteristic sequence to obtain an electric signal characteristic sequence; selecting a control algorithm corresponding to the target signal characteristics as a target control algorithm by using a preset characteristic distance algorithm; and screening target electric signal data corresponding to the target signal characteristics from the standard electric signal data, carrying out iterative updating on a target control algorithm to obtain a standard control algorithm, and establishing a main control unit of the target generator by utilizing all the standard control algorithms. The invention further provides a main control unit preparation testing device applied to exciting current. The invention can improve the flexibility of the main control unit of the generator in controlling exciting current.
Description
Technical Field
The invention relates to the technical field of production evaluation, in particular to a method and a device for preparing and testing a main control unit applied to exciting current.
Background
Along with the increase of electricity demand, the power generation equipment obtains the current generated when equipment such as a generator provides a working magnetic field by applying exciting current in more and more production lives, and the power generation equipment is equipment which generates electricity by utilizing the electromagnetic induction principle, so that the power generation equipment is used for generating more stable voltage and current output, and the main control unit is required to generate exciting current to control the power generation equipment.
In practical application, the excitation master control based on digital-analog sampling needs to perform a large amount of calculation tests in the sampling process, so that a large amount of calculation time can be generated, and the control algorithm can be delayed in switching, so that the flexibility in performing excitation current control of the generator is low.
Disclosure of Invention
The invention provides a method and a device for preparing and testing a main control unit applied to exciting current, and mainly aims to solve the problem of low flexibility in the process of controlling the exciting current of a generator.
In order to achieve the above object, the present invention provides a method for preparing and testing a main control unit applied to exciting current, comprising:
acquiring historical electric signal data of a target generator, and sequentially performing data sampling and data cleaning on the historical electric signal data to obtain standard electric signal data;
performing feature filtering on the standard electrical signal data to obtain a primary electrical signal feature sequence, performing feature clustering on the primary electrical signal feature sequence to obtain an electrical signal feature class set, and mapping the standard electrical signal data according to the electrical signal feature class set to obtain an electrical signal feature sequence;
selecting the electric signal characteristics in the electric signal characteristic sequence one by one as target signal characteristics, calculating characteristic distances between the target signal characteristics and each control algorithm in a preset control algorithm library by using a preset characteristic distance algorithm, and selecting a control algorithm corresponding to the target signal characteristics as a target control algorithm according to the characteristic distances;
screening target electric signal data corresponding to the target signal characteristics from the standard electric signal data, and calculating test exciting current of the target electric signal data by using the target control algorithm;
Synchronously sampling the test excitation current to obtain excitation sampling data, calculating a control error corresponding to the excitation sampling data, carrying out iterative updating on the target control algorithm according to the control error to obtain a standard control algorithm, and establishing a main control unit of the target generator by utilizing all the standard control algorithms, wherein the calculating the control error corresponding to the excitation sampling data comprises the following steps: performing maximum value sampling on the excitation sampling data to obtain a maximum value data set, performing minimum value sampling on the excitation sampling data to obtain a minimum value data set, and performing median sampling on the excitation sampling data to obtain a median data set; calculating a control error corresponding to the excitation sampling data according to the maximum value data set, the minimum value data set and the median data set by using the following control error algorithm:
wherein (1)>Means that the control error,/->Refers to->Data of->Means the total number of data of the maximum data set, and the total number of data of the maximum data set and the minimum data set and the median data set are identical,/respectively >Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->And->Is a preset challenge coefficient.
Optionally, the acquiring historical electrical signal data of the target generator includes:
monitoring the input electric signal data of the target generator to obtain monitored electric signal data;
block sampling is carried out on the monitoring electric signal data according to the time sequence, so as to obtain a monitoring signal;
judging whether the monitoring signal is equal to a preset synchronous signal or not;
if not, returning to the step of performing block sampling on the monitoring electric signal data according to the time sequence to obtain a monitoring signal;
if yes, taking the electric signal data except the synchronous signal in the monitoring electric signal data as initial electric signal data, and performing block sampling on the initial electric signal data according to a time sequence to obtain an initial signal;
judging whether the initial signal is equal to a preset interrupt signal or not;
If not, returning to the step of performing block sampling on the initial electric signal data according to the time sequence to obtain an initial signal;
if yes, taking the electric signal data except the initial signal in the initial electric signal data as historical electric signal data.
Optionally, the sequentially performing data sampling and data cleaning on the historical electrical signal data to obtain standard electrical signal data includes:
performing data sampling on the historical electric signal data according to a preset sampling interval to obtain primary sampled electric signal data;
replacing missing signals and noise signals in the primary sampling electrical signal data by using preset occupied data to obtain secondary sampling electrical signal data;
selecting the occupied data in the sub-sampling electrical signal data one by one as target occupied data, and screening out target window filling data corresponding to the target occupied data from the sub-sampling electrical signal data by utilizing a preset filling window;
and performing convolution operation on the target window filling data to obtain convolution filling data, and replacing the target occupying data in the subsampled electric signal data by using the convolution filling data to obtain standard electric signal data.
Optionally, the performing feature filtering on the standard electrical signal data to obtain a primary electrical signal feature sequence includes:
performing convolution filtering on the standard electric signal data to obtain a rotary electric signal characteristic;
and performing multiple filtering on the rotary electric signal characteristics to obtain a primary electric signal characteristic sequence.
Optionally, the convoluting and filtering the standard electric signal data to obtain a gyration electric signal feature includes:
performing polynomial transformation on the standard electrical signal data to obtain a standard electrical signal polynomial;
splitting sub-items in the standard electric signal polynomials into odd-numbered electric signal polynomials and even-numbered electric signal polynomials according to the parity of sequence numbers;
performing point value transformation on the odd-numbered electric signal polynomials and the even-numbered electric signal polynomials to obtain electric signal point values;
and carrying out coefficient rotation transformation on the electric signal point value to obtain rotation electric signal characteristics.
Optionally, the multiple filtering the gyratory electrical signal feature to obtain a primary electrical signal feature sequence includes:
sequentially performing bit-wise modulo operation and square operation on the primary electric signal characteristic sequence to obtain a primary frame signal frequency spectrum;
Performing multiple triangular filtering on the primary frame signal spectrum to obtain an electric spectrum filtering group;
filtering each electric spectrum value in the electric spectrum filtering group by using the following electric filtering formula to obtain electric signal filtering data, and collecting all the electric signal filtering data into an electric signal filtering data group:
wherein (1)>Is the total number of terms of the set of electrical spectral filters,/->Means +.>The individual electrical signal filtering data, ">Refers to->Personal (S)>Is logarithmic sign>Representing the +.>-said primary frame power spectrum,/->Indicate->Personal (S)>Refers to the total number of primary frame power spectrums in the primary frame signal spectrum, +.>Refers to->The>The numerical value of the term;
performing cepstrum operation on each electric signal filtering data in the electric signal filtering data group by using the following secondary filtering formula to obtain electric signal filtering characteristics, and integrating all the electric signal filtering characteristics into a primary electric signal characteristic sequence:
wherein (1)>Refers to the +.about.in the characteristic sequence of the primary electric signal>-said electrical signal filtering characteristics,>refers to->Personal (S) >Refers to the total number of primary frame power spectrums in the primary frame signal spectrum, +.>Refers to->Personal (S)>Means +.>The individual electrical signal filtering data, ">Is the total number of terms of the set of electrical spectral filters.
Optionally, the performing feature clustering on the primary electrical signal feature sequence to obtain an electrical signal feature class set includes:
splitting the primary electric signal characteristic sequence into a plurality of primary signal characteristic groups, and randomly selecting initial center characteristics for each primary signal characteristic group;
calculating signal feature distances between all the electric signal filtering features and each initial center feature in the primary electric signal feature sequence;
and carrying out iterative updating on each primary signal feature group according to the signal feature distance to obtain electric signal feature classes, and collecting all the electric signal feature classes into an electric signal feature class set.
Optionally, the iteratively updating each primary signal feature set according to the signal feature distance to obtain an electrical signal feature class includes:
grouping all the electric signal filtering characteristics in the primary electric signal characteristic sequence according to the signal characteristic distance to obtain a plurality of secondary signal characteristic groups;
Calculating secondary center features of each secondary signal feature group, and calculating standard center distances of each secondary signal feature group according to the secondary center features;
and iteratively updating the secondary signal feature group by utilizing the standard center distance and a preset electric signal feature distance threshold value to obtain electric signal feature classes.
Optionally, the calculating, by using a preset feature distance algorithm, a feature distance between the target signal feature and each control algorithm in a preset control algorithm library includes:
selecting one control algorithm from a preset control algorithm library one by one as a target control algorithm, and calculating target algorithm characteristics corresponding to the target control algorithm by using a preset algorithm characteristic model;
calculating the feature distance between the target algorithm feature and the target signal feature by using the following feature distance algorithm:
wherein (1)>Means the characteristic distance,/->Means the total number of features in the target signal feature, and the total number of features of the target signal feature is equal to the total number of features of the target algorithm feature, +.>Refers to->Personal characteristics (I)>Is an inverse cosine function, +.>Refers to->Vitamin characteristics (I) >Means the total dimension of each of the target signal features, and the total dimension of each of the target signal features is equal to the total dimension of each of the target algorithm features, +.>Means +.>No. H of the individual features>Vitamin characteristics (I)>Means +.>No. H of the individual features>Dimensional characteristics.
In order to solve the above problems, the present invention also provides a main control unit preparation test device applied to exciting current, the device comprising:
the data processing module is used for acquiring historical electric signal data of the target generator, and sequentially carrying out data sampling and data cleaning on the historical electric signal data to obtain standard electric signal data;
the signal filtering module is used for carrying out characteristic filtering on the standard electric signal data to obtain a primary electric signal characteristic sequence, carrying out characteristic clustering on the primary electric signal characteristic sequence to obtain an electric signal characteristic class set, and mapping the standard electric signal data according to the electric signal characteristic class set to obtain an electric signal characteristic sequence;
the algorithm selection module is used for selecting the electric signal characteristics in the electric signal characteristic sequence one by one as target signal characteristics, calculating characteristic distances between the target signal characteristics and each control algorithm in a preset control algorithm library by utilizing a preset characteristic distance algorithm, and selecting a control algorithm corresponding to the target signal characteristics as a target control algorithm according to the characteristic distances;
The excitation test module is used for screening out target electric signal data corresponding to the target signal characteristics from the standard electric signal data, and calculating test excitation current of the target electric signal data by using the target control algorithm;
the error calculation module is used for synchronously sampling the test exciting current to obtain exciting sampling data, calculating a control error corresponding to the exciting sampling data, carrying out iterative updating on the target control algorithm according to the control error to obtain a standard control algorithm, and establishing a main control unit of the target generator by utilizing all the standard control algorithms, wherein the calculating the control error corresponding to the exciting sampling data comprises the following steps: performing maximum value sampling on the excitation sampling data to obtain a maximum value data set, performing minimum value sampling on the excitation sampling data to obtain a minimum value data set, and performing median sampling on the excitation sampling data to obtain a median data set; the minimum value data set and the median data set calculate a control error corresponding to the excitation sampling data:
wherein (1)>Means that the control error,/->Refers to- >Data of->Means the total number of data of the maximum data set, and the total number of data of the maximum data set and the minimum data set and the median data set are identical,/respectively>Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->And->Is a preset challenge coefficient.
According to the embodiment of the invention, the historical electric signal data of the target generator is acquired, the historical electric signal data is subjected to data sampling and data cleaning in sequence to obtain the standard electric signal data, the accuracy of an input signal of the target generator can be improved, the control accuracy of a main control unit and the accuracy of a test result are improved, the characteristic filtering is carried out on the standard electric signal data to obtain a primary electric signal characteristic sequence, the signal characteristic of the standard electric signal data can be extracted, the standard electric signal data is conveniently subjected to unified processing, the characteristic clustering is carried out on the primary electric signal characteristic sequence to obtain an electric signal characteristic class set, the standard electric signal data is mapped according to the electric signal characteristic class set to obtain an electric signal characteristic sequence, the standard electric signal data can be classified again, the control algorithm corresponding to different standard electric signal data can be conveniently selected for control according to the characteristic distance, the flexibility of main control switching is improved, the input signal of the generator can be controlled according to the characteristic distance to select the control algorithm corresponding to the target electric signal characteristic as the target control algorithm, the excitation current is obtained, and the flexibility of the main control unit of the generator is improved;
The target electric signal data corresponding to the target signal characteristics are screened out from the standard electric signal data, the target control algorithm is utilized to calculate the test exciting current of the target electric signal data, so that the generation test of exciting current can be realized, the subsequent test judgment is convenient, the control error corresponding to the exciting sampling data is calculated, the target control algorithm is iteratively updated according to the control error, the standard control algorithm is obtained, the main control unit of the target generator is established by utilizing all the standard control algorithms, and the flexibility of the test of the main control unit of the generator can be improved. Therefore, the method and the device for preparing and testing the main control unit applied to the exciting current can solve the problem of low flexibility in the process of controlling the exciting current of the generator.
Drawings
Fig. 1 is a schematic flow chart of a method for testing the preparation of a master control unit applied to exciting current according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating the generation of a gyrating electrical signal according to an embodiment of the present invention;
FIG. 3 is a flow chart of generating electrical signal characteristics according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a master control unit preparation test device applied to exciting current according to an embodiment of the present application;
the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a main control unit preparation test method applied to exciting current. The main control unit for exciting current preparation test method includes at least one of electronic devices, such as a server, a terminal, etc., which can be configured to execute the method provided by the embodiment of the application. In other words, the main control unit preparation test method applied to the exciting current may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for testing the preparation of a master control unit applied to exciting current according to an embodiment of the invention is shown. In this embodiment, the method for preparing and testing the master control unit applied to exciting current includes:
s1, acquiring historical electric signal data of a target generator, and sequentially performing data sampling and data cleaning on the historical electric signal data to obtain standard electric signal data.
In the embodiment of the present invention, the target generator refers to a generator that needs to perform excitation Current master control configuration, the excitation Current (Exciting Current) refers to a Current generated when an operating magnetic field is provided for an "electric device operating by using an electromagnetic induction principle" such as a generator, and the historical electrical signal data refers to an input electrical signal of the target generator, which may be a Current signal or a voltage signal arranged according to a certain rule.
In an embodiment of the present invention, the obtaining historical electrical signal data of the target generator includes:
monitoring the input electric signal data of the target generator to obtain monitored electric signal data;
block sampling is carried out on the monitoring electric signal data according to the time sequence, so as to obtain a monitoring signal;
Judging whether the monitoring signal is equal to a preset synchronous signal or not;
if not, returning to the step of performing block sampling on the monitoring electric signal data according to the time sequence to obtain a monitoring signal;
if yes, taking the electric signal data except the synchronous signal in the monitoring electric signal data as initial electric signal data, and performing block sampling on the initial electric signal data according to a time sequence to obtain an initial signal;
judging whether the initial signal is equal to a preset interrupt signal or not;
if not, returning to the step of performing block sampling on the initial electric signal data according to the time sequence to obtain an initial signal;
if yes, taking the electric signal data except the initial signal in the initial electric signal data as historical electric signal data.
In detail, the input electric signal refers to an input signal of the target generator, the block sampling is performed on the monitoring electric signal data according to a time sequence, the obtaining of the monitoring signal refers to sliding sampling on the monitoring electric signal data according to a preset time domain window, and a part of the monitoring electric signal data corresponding to the time domain window is used as the monitoring signal.
In detail, the synchronous signal is an electric signal which meets a specific rule and is used for prompting the generator to start synchronous operation, and the interrupt signal is an electric signal which meets a specific rule and is used for prompting the generator to end synchronous operation.
In the embodiment of the present invention, the sequentially performing data sampling and data cleaning on the historical electrical signal data to obtain standard electrical signal data includes:
performing data sampling on the historical electric signal data according to a preset sampling interval to obtain primary sampled electric signal data;
replacing missing signals and noise signals in the primary sampling electrical signal data by using preset occupied data to obtain secondary sampling electrical signal data;
selecting the occupied data in the sub-sampling electrical signal data one by one as target occupied data, and screening out target window filling data corresponding to the target occupied data from the sub-sampling electrical signal data by utilizing a preset filling window;
and performing convolution operation on the target window filling data to obtain convolution filling data, and replacing the target occupying data in the subsampled electric signal data by using the convolution filling data to obtain standard electric signal data.
In detail, the sampling interval is a preset fixed time interval, for example, 50ms, the missing signal refers to data missing due to sampling loss in the primary sampled electrical signal data, and the noise signal refers to abnormal data caused by sampling error in the primary sampled electrical signal data.
Specifically, the filling window is a time domain window with a fixed duration length, the target window filling data refers to part of the electrical signal data corresponding to the filling window, wherein the part of the electrical signal data in the sub-sampling electrical signal data takes the position of the target occupying data as a midpoint.
According to the embodiment of the invention, the historical electric signal data of the target generator is obtained, and the historical electric signal data is subjected to data sampling and data cleaning in sequence to obtain the standard electric signal data, so that the accuracy of the input signal of the target generator can be improved, and the control accuracy of the main control unit and the accuracy of the test result are further improved.
S2, performing feature filtering on the standard electric signal data to obtain a primary electric signal feature sequence, performing feature clustering on the primary electric signal feature sequence to obtain an electric signal feature class set, and mapping the standard electric signal data according to the electric signal feature class set to obtain an electric signal feature sequence.
In the embodiment of the invention, the primary electric signal characteristic sequence is data for reflecting waveform characteristics of the standard electric signal data, and the electric signal characteristic class set is a set containing a plurality of electric signal characteristic classes, wherein each electric signal characteristic class represents the class of the plurality of primary electric signal characteristic sequences.
In the embodiment of the present invention, the performing feature filtering on the standard electrical signal data to obtain a primary electrical signal feature sequence includes:
performing convolution filtering on the standard electric signal data to obtain a rotary electric signal characteristic;
and performing multiple filtering on the rotary electric signal characteristics to obtain a primary electric signal characteristic sequence.
Specifically, the standard electric signal data is subjected to convolution filtering to obtain the characteristics of the rotary electric signal, so that the original electric signal data in a sequence form can be converted into a convolution form, and the subsequent extraction of the primary electric signal characteristic sequence is convenient.
In detail, referring to fig. 2, the convolution filtering the standard electrical signal data to obtain a gyrating electrical signal feature includes:
s21, performing polynomial transformation on the standard electrical signal data to obtain a standard electrical signal polynomial;
s22, dividing sub-items in the standard electric signal polynomial into an odd electric signal polynomial and an even electric signal polynomial according to the parity of the sequence numbers;
s23, performing point value transformation on the odd-numbered electric signal polynomials and the even-numbered electric signal polynomials to obtain electric signal point values;
s24, coefficient rotation transformation is carried out on the electric signal point values, and rotation electric signal characteristics are obtained.
Specifically, a fourier polynomial algorithm may be used to perform a polynomial transformation on the standard electrical signal data to obtain a standard electrical signal polynomial; the point value transformation can be carried out on the odd-numbered electric signal polynomials and the even-numbered electric signal polynomials by using a unit root algorithm to obtain electric signal point values; and carrying out coefficient rotation transformation on the electric signal point value by using an inverse discrete Fourier transform algorithm to obtain rotation electric signal characteristics.
Specifically, the multiple filtering of the gyration electric signal characteristic to obtain a primary electric signal characteristic sequence includes:
sequentially performing bit-wise modulo operation and square operation on the primary electric signal characteristic sequence to obtain a primary frame signal frequency spectrum;
performing multiple triangular filtering on the primary frame signal spectrum to obtain an electric spectrum filtering group;
filtering each electric spectrum value in the electric spectrum filtering group by using the following electric filtering formula to obtain electric signal filtering data, and collecting all the electric signal filtering data into an electric signal filtering data group:
wherein (1)>Is the total number of terms of the set of electrical spectral filters,/->Refers to whatThe>The individual electrical signal filtering data, " >Refers to->Personal (S)>Is logarithmic sign>Representing the +.>-said primary frame power spectrum,/->Indicate->Personal (S)>Refers to the total number of primary frame power spectrums in the primary frame signal spectrum, +.>Refers to->The>The numerical value of the term;
performing cepstrum operation on each electric signal filtering data in the electric signal filtering data group by using the following secondary filtering formula to obtain electric signal filtering characteristics, and integrating all the electric signal filtering characteristics into a primary electric signal characteristic sequence:
wherein (1)>Refers to the +.about.in the characteristic sequence of the primary electric signal>-said electrical signal filtering characteristics,>refers to->Personal (S)>Refers to the total number of primary frame power spectrums in the primary frame signal spectrum, +.>Refers to->Personal (S)>Means +.>The individual electrical signal filtering data, ">Is the total number of terms of the set of electrical spectral filters.
In detail, the electric filtering formula is utilized to filter each electric spectrum value in the electric spectrum filtering group to obtain electric signal filtering data, the amplitude-frequency characteristic of the electric signal can be enhanced, so that the electric signal characteristic can be conveniently extracted, and the secondary filtering formula is utilized to carry out cepstrum operation on each electric signal filtering data in the electric signal filtering data group to obtain electric signal filtering characteristics, so that the interference of the inter-frame signals in the electric signal can be reduced, and more accurate electric signal characteristics can be obtained.
In detail, the feature clustering is performed on the primary electric signal feature sequence to obtain an electric signal feature class set, which comprises the following steps:
splitting the primary electric signal characteristic sequence into a plurality of primary signal characteristic groups, and randomly selecting initial center characteristics for each primary signal characteristic group;
calculating signal feature distances between all the electric signal filtering features and each initial center feature in the primary electric signal feature sequence;
and carrying out iterative updating on each primary signal feature group according to the signal feature distance to obtain electric signal feature classes, and collecting all the electric signal feature classes into an electric signal feature class set.
In detail, splitting the primary electrical signal feature sequence into a plurality of primary signal feature groups refers to randomly distributing each electrical signal filtering feature in the primary electrical signal feature sequence into a plurality of primary signal feature groups, and randomly selecting an initial center feature for each primary signal feature group refers to randomly selecting one electrical signal filtering feature from each primary signal feature group as the initial center feature corresponding to the primary signal feature group.
Specifically, a euclidean distance algorithm may be used to calculate signal feature distances between all of the electrical signal filtering features in the primary electrical signal feature sequence and each of the initial center features.
In detail, referring to fig. 3, the iteratively updating the primary signal feature sets according to the signal feature distances to obtain electrical signal feature classes includes:
s31, grouping all electric signal filtering characteristics in the primary electric signal characteristic sequence according to the signal characteristic distance to obtain a plurality of secondary signal characteristic groups;
s32, calculating secondary center features of each secondary signal feature group, and calculating standard center distances of each secondary signal feature group according to the secondary center features;
and S33, iteratively updating the secondary signal feature group by utilizing the standard center distance and a preset electric signal feature distance threshold value to obtain electric signal feature classes.
In detail, the grouping the electric signal filtering features in the primary electric signal feature sequence according to the signal feature distance to obtain a plurality of secondary signal feature groups refers to adding each electric signal filtering feature in the primary electric signal feature sequence to a primary signal feature group corresponding to the minimum signal feature distance to obtain a secondary signal feature group.
Specifically, the calculating of the secondary center feature of each secondary signal feature group refers to calculating an electric signal filtering feature which is consistent with the signal feature distance between each electric signal filtering feature in the secondary signal feature group, and taking the electric signal filtering feature as a secondary center feature, and the calculating of the standard center distance of each secondary signal feature group according to the secondary center feature refers to calculating the average number of feature distances between each electric signal filtering feature and the secondary center feature in the secondary signal feature group.
In detail, the step of iteratively updating the secondary signal feature group by using the standard center distance and a preset feature distance threshold value, and the step of obtaining the electrical signal feature class refers to iteratively performing grouping update until the standard center distance is smaller than the preset electrical signal feature distance threshold value, and taking the secondary signal feature group at the moment as the electrical signal feature class.
Specifically, the mapping the standard electrical signal data according to the electrical signal feature class set to obtain an electrical signal feature sequence refers to mapping each electrical signal feature class in the electrical signal feature class set to the standard electrical signal data to obtain a sequence composed of electrical signal feature classes arranged in time sequence, and taking the sequence as an electrical signal feature sequence.
In the embodiment of the invention, the primary electric signal characteristic sequence is obtained by carrying out characteristic filtering on the standard electric signal data, the signal characteristics of the standard electric signal data can be extracted, unified processing is convenient for the standard electric signal data, the electric signal characteristic class set is obtained by carrying out characteristic clustering on the primary electric signal characteristic sequence, the standard electric signal data is mapped according to the electric signal characteristic class set, the electric signal characteristic sequence is obtained, the standard electric signal data can be classified again, the corresponding control algorithm is conveniently selected and controlled according to different standard electric signal data, and the flexibility of main control switching is improved.
S3, selecting the electric signal characteristics in the electric signal characteristic sequence one by one as target signal characteristics, calculating characteristic distances between the target signal characteristics and each control algorithm in a preset control algorithm library by using a preset characteristic distance algorithm, and selecting a control algorithm corresponding to the target signal characteristics as a target control algorithm according to the characteristic distances.
In the embodiment of the invention, the control algorithm library is a preset database containing corresponding signal characteristics of a plurality of exciting current control algorithms, wherein the exciting current control algorithms can be control algorithms such as a PID algorithm, an ICPID algorithm, a PID+PSS algorithm, a MNEC algorithm and the like.
In the embodiment of the present invention, the calculating, by using a preset feature distance algorithm, a feature distance between the target signal feature and each control algorithm in a preset control algorithm library includes:
selecting one control algorithm from a preset control algorithm library one by one as a target control algorithm, and calculating target algorithm characteristics corresponding to the target control algorithm by using a preset algorithm characteristic model;
calculating the feature distance between the target algorithm feature and the target signal feature by using the following feature distance algorithm:
wherein (1)>Refers to the featuresDistance (L)>Means the total number of features in the target signal feature, and the total number of features of the target signal feature is equal to the total number of features of the target algorithm feature, +.>Refers to->Personal characteristics (I)>Is an inverse cosine function, +.>Refers to->Vitamin characteristics (I)>Means the total dimension of each of the target signal features, and the total dimension of each of the target signal features is equal to the total dimension of each of the target algorithm features, +.>Means +.>No. H of the individual features>Vitamin characteristics (I)>Means +.>No. H of the individual features >Dimensional characteristics.
In detail, the feature distance between the target algorithm feature and the target signal feature is calculated by using the feature distance algorithm, so that the number of features and dimensions can be conveniently compared, and the characterization of the feature distance is improved.
In detail, the algorithm feature model is a pre-trained neural network model for mapping between a control algorithm and electrical signal features, and the algorithm feature model can be a decision tree model; the step of selecting the control algorithm corresponding to the target signal feature according to the feature distance as a target control algorithm refers to taking the control algorithm corresponding to the target algorithm feature with the nearest feature distance as a target control algorithm.
In the embodiment of the invention, the control algorithm corresponding to the target signal characteristic is selected as the target control algorithm according to the characteristic distance, and the input signal of the generator can be controlled and debugged by selecting the proper control algorithm according to the characteristic distance, so that the exciting current is obtained, and the flexibility of the main control unit of the generator is improved.
And S4, screening out target electric signal data corresponding to the target signal characteristics from the standard electric signal data, and calculating the test exciting current of the target electric signal data by using the target control algorithm.
In the embodiment of the present invention, the step of screening the target electrical signal data corresponding to the target signal feature from the standard electrical signal data refers to taking a time domain period corresponding to the target signal feature as a target time domain period, and screening data corresponding to the target time domain period from the standard electrical signal data as target electrical signal data.
In detail, the calculating the test excitation current of the target electrical signal data by using the target control algorithm means calculating an excitation control trigger angle of the target electrical signal data by using the target control algorithm, and calculating the excitation current of the target electrical signal data as the test excitation current according to the excitation control trigger angle.
In the embodiment of the invention, the target electric signal data corresponding to the target signal characteristics are screened from the standard electric signal data, and the target control algorithm is utilized to calculate the test exciting current of the target electric signal data, so that the generation test of the exciting current can be realized, and the subsequent test judgment is convenient.
S5, synchronously sampling the test excitation current to obtain excitation sampling data, calculating a control error corresponding to the excitation sampling data, carrying out iterative updating on the target control algorithm according to the control error to obtain a standard control algorithm, and establishing a main control unit of the target generator by using all the standard control algorithms.
In the embodiment of the present invention, the excitation sampling data refers to excitation current data after sampling, and the method for sampling the test excitation current synchronously to obtain excitation sampling data is consistent with the steps of sequentially performing data sampling and data cleaning on the historical electrical signal data in the step S1 to obtain standard electrical signal data, which is not described herein again.
In the embodiment of the present invention, the calculating the control error corresponding to the excitation sampling data includes:
performing maximum value sampling on the excitation sampling data to obtain a maximum value data set, performing minimum value sampling on the excitation sampling data to obtain a minimum value data set, and performing median sampling on the excitation sampling data to obtain a median data set;
calculating a control error corresponding to the excitation sampling data according to the maximum value data set, the minimum value data set and the median data set by using the following control error algorithm:
wherein (1)>Means that the control error,/->Refers to->Data of->Means the total number of data of the maximum data set, and the total number of data of the maximum data set and the minimum data set and the median data set are identical,/respectively >Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->And->Is a preset challenge coefficient.
Specifically, by calculating the control error corresponding to the excitation sampling data according to the maximum value data set, the minimum value data set and the median data set by using the control error algorithm, the accuracy of calculating the control error can be improved, and the test data can be better represented.
In detail, performing maximum sampling on the excitation sampling data to obtain a maximum data set, namely dividing the excitation sampling data into a plurality of sampling periods according to waveforms, and converging the maximum value of each sampling period into the maximum data set; the step of carrying out minimum value sampling on the excitation sampling data to obtain a maximum value data set refers to collecting the minimum value of each sampling period into the maximum value data set; and carrying out maximum sampling on the excitation sampling data to obtain a maximum data set, namely collecting the median value of each sampling period into a median data set.
In the embodiment of the invention, the flexibility of the main control unit test of the generator can be improved by calculating the control error corresponding to the excitation sampling data, carrying out iterative updating on the target control algorithm according to the control error to obtain the standard control algorithm, and establishing the main control unit of the target generator by utilizing all the standard control algorithms.
According to the embodiment of the invention, the historical electric signal data of the target generator is acquired, the historical electric signal data is subjected to data sampling and data cleaning in sequence to obtain the standard electric signal data, the accuracy of an input signal of the target generator can be improved, the control accuracy of a main control unit and the accuracy of a test result are improved, the characteristic filtering is carried out on the standard electric signal data to obtain a primary electric signal characteristic sequence, the signal characteristic of the standard electric signal data can be extracted, the standard electric signal data is conveniently subjected to unified processing, the characteristic clustering is carried out on the primary electric signal characteristic sequence to obtain an electric signal characteristic class set, the standard electric signal data is mapped according to the electric signal characteristic class set to obtain an electric signal characteristic sequence, the standard electric signal data can be classified again, the control algorithm corresponding to different standard electric signal data can be conveniently selected for control according to the characteristic distance, the flexibility of main control switching is improved, the input signal of the generator can be controlled according to the characteristic distance to select the control algorithm corresponding to the target electric signal characteristic as the target control algorithm, the excitation current is obtained, and the flexibility of the main control unit of the generator is improved;
The target electric signal data corresponding to the target signal characteristics are screened out from the standard electric signal data, the target control algorithm is utilized to calculate the test exciting current of the target electric signal data, so that the generation test of exciting current can be realized, the subsequent test judgment is convenient, the control error corresponding to the exciting sampling data is calculated, the target control algorithm is iteratively updated according to the control error, the standard control algorithm is obtained, the main control unit of the target generator is established by utilizing all the standard control algorithms, and the flexibility of the test of the main control unit of the generator can be improved. Therefore, the main control unit preparation test method applied to exciting current can solve the problem of low flexibility in the process of controlling the exciting current of the generator.
Fig. 4 is a functional block diagram of a master control unit preparation test device applied to exciting current according to an embodiment of the present invention.
The main control unit preparation test device 100 applied to exciting current can be installed in electronic equipment. The master control unit preparation test device 100 applied to exciting current may include a data processing module 101, a signal filtering module 102, an algorithm selecting module 103, an exciting test module 104 and an error calculating module 105 according to the implemented functions. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data processing module 101 is configured to obtain historical electrical signal data of a target generator, and sequentially perform data sampling and data cleaning on the historical electrical signal data to obtain standard electrical signal data;
the signal filtering module 102 is configured to perform feature filtering on the standard electrical signal data to obtain a primary electrical signal feature sequence, perform feature clustering on the primary electrical signal feature sequence to obtain an electrical signal feature class set, and map the standard electrical signal data according to the electrical signal feature class set to obtain an electrical signal feature sequence;
the algorithm selection module 103 is configured to select electrical signal features in the electrical signal feature sequence one by one as target signal features, calculate feature distances between the target signal features and each control algorithm in a preset control algorithm library by using a preset feature distance algorithm, and select a control algorithm corresponding to the target signal features as a target control algorithm according to the feature distances;
the excitation test module 104 is configured to screen out target electrical signal data corresponding to the target signal feature from the standard electrical signal data, and calculate a test excitation current of the target electrical signal data by using the target control algorithm;
The error calculation module 105 is configured to synchronously sample the test excitation current to obtain excitation sampling data, calculate a control error corresponding to the excitation sampling data, iteratively update the target control algorithm according to the control error to obtain a standard control algorithm, and establish a master control unit of the target generator by using all the standard control algorithms, where the calculating the control error corresponding to the excitation sampling data includes: performing maximum value sampling on the excitation sampling data to obtain a maximum value data set, performing minimum value sampling on the excitation sampling data to obtain a minimum value data set, and performing median sampling on the excitation sampling data to obtain a median data set; calculating a control error corresponding to the excitation sampling data according to the maximum value data set, the minimum value data set and the median data set by using the following control error algorithm:
wherein (1)>Means that the control error,/->Refers to->Data of->Means the total number of data of the maximum data set, and the total number of data of the maximum data set and the minimum data set and the median data set are identical,/respectively >Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->And->Is a preset challenge coefficient.
In detail, each module in the main control unit preparation test device 100 for exciting current in the embodiment of the present invention adopts the same technical means as the main control unit preparation test method for exciting current in fig. 1 to 3, and can produce the same technical effects, which are not repeated here.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or means as set forth in the system embodiments may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. The method for preparing and testing the main control unit applied to exciting current is characterized by comprising the following steps of:
s1: acquiring historical electric signal data of a target generator, and sequentially performing data sampling and data cleaning on the historical electric signal data to obtain standard electric signal data;
s2: performing feature filtering on the standard electrical signal data to obtain a primary electrical signal feature sequence, performing feature clustering on the primary electrical signal feature sequence to obtain an electrical signal feature class set, and mapping the standard electrical signal data according to the electrical signal feature class set to obtain an electrical signal feature sequence;
S3: selecting the electric signal characteristics in the electric signal characteristic sequence one by one as target signal characteristics, calculating characteristic distances between the target signal characteristics and each control algorithm in a preset control algorithm library by using a preset characteristic distance algorithm, and selecting a control algorithm corresponding to the target signal characteristics as a target control algorithm according to the characteristic distances;
s4: screening target electric signal data corresponding to the target signal characteristics from the standard electric signal data, and calculating test exciting current of the target electric signal data by using the target control algorithm;
s5: synchronously sampling the test excitation current to obtain excitation sampling data, calculating a control error corresponding to the excitation sampling data, carrying out iterative updating on the target control algorithm according to the control error to obtain a standard control algorithm, and establishing a main control unit of the target generator by utilizing all the standard control algorithms, wherein the calculating the control error corresponding to the excitation sampling data comprises the following steps:
s51: performing maximum value sampling on the excitation sampling data to obtain a maximum value data set, performing minimum value sampling on the excitation sampling data to obtain a minimum value data set, and performing median sampling on the excitation sampling data to obtain a median data set;
S52: calculating a control error corresponding to the excitation sampling data according to the maximum value data set, the minimum value data set and the median data set by using the following control error algorithm:
wherein,means that the control error,/->Refers to->Data of->Means the total number of data of the maximum data set, and the total number of data of the maximum data set and the minimum data set and the median data set are identical,/respectively>Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->And->Is a preset countermeasure coefficient;
the calculating the feature distance between the target signal feature and each control algorithm in the preset control algorithm library by using a preset feature distance algorithm comprises the following steps:
selecting one control algorithm from a preset control algorithm library one by one as a target control algorithm, and calculating target algorithm characteristics corresponding to the target control algorithm by using a preset algorithm characteristic model;
Calculating the feature distance between the target algorithm feature and the target signal feature by using the following feature distance algorithm:
wherein,means the characteristic distance,/->Means the total number of features in the target signal feature, and the total number of features of the target signal feature is equal to the total number of features of the target algorithm feature, +.>Refers to->Personal characteristics (I)>Is an inverse cosine function, +.>Refers to->Vitamin characteristics (I)>Means the total dimension of each of the target signal features, and the total dimension of each of the target signal features is equal to the total dimension of each of the target algorithm features, +.>Means +.>No. H of the individual features>Vitamin characteristics (I)>Means +.>No. H of the individual features>Dimensional characteristics.
2. The method for preparing a test for a main control unit applied to exciting current according to claim 1, wherein the step of obtaining historical electrical signal data of a target generator comprises:
monitoring the input electric signal data of the target generator to obtain monitored electric signal data;
block sampling is carried out on the monitoring electric signal data according to the time sequence, so as to obtain a monitoring signal;
Judging whether the monitoring signal is equal to a preset synchronous signal or not;
if not, returning to the step of performing block sampling on the monitoring electric signal data according to the time sequence to obtain a monitoring signal;
if yes, taking the electric signal data except the synchronous signal in the monitoring electric signal data as initial electric signal data, and performing block sampling on the initial electric signal data according to a time sequence to obtain an initial signal;
judging whether the initial signal is equal to a preset interrupt signal or not;
if not, returning to the step of performing block sampling on the initial electric signal data according to the time sequence to obtain an initial signal;
if yes, taking the electric signal data except the initial signal in the initial electric signal data as historical electric signal data.
3. The method for preparing and testing the main control unit applied to exciting current according to claim 1, wherein the step of sequentially performing data sampling and data cleaning on the historical electrical signal data to obtain standard electrical signal data comprises the following steps:
performing data sampling on the historical electric signal data according to a preset sampling interval to obtain primary sampled electric signal data;
replacing missing signals and noise signals in the primary sampling electrical signal data by using preset occupied data to obtain secondary sampling electrical signal data;
Selecting the occupied data in the sub-sampling electrical signal data one by one as target occupied data, and screening out target window filling data corresponding to the target occupied data from the sub-sampling electrical signal data by utilizing a preset filling window;
and performing convolution operation on the target window filling data to obtain convolution filling data, and replacing the target occupying data in the subsampled electric signal data by using the convolution filling data to obtain standard electric signal data.
4. The method for preparing and testing the main control unit applied to exciting current according to claim 1, wherein the step of performing characteristic filtering on the standard electrical signal data to obtain a primary electrical signal characteristic sequence comprises the following steps:
performing convolution filtering on the standard electric signal data to obtain a rotary electric signal characteristic;
and performing multiple filtering on the rotary electric signal characteristics to obtain a primary electric signal characteristic sequence.
5. The method for preparing and testing the main control unit applied to exciting current according to claim 4, wherein the step of convoluting and filtering the standard electric signal data to obtain the characteristics of the rotary electric signal comprises the following steps:
performing polynomial transformation on the standard electrical signal data to obtain a standard electrical signal polynomial;
Splitting sub-items in the standard electric signal polynomials into odd-numbered electric signal polynomials and even-numbered electric signal polynomials according to the parity of sequence numbers;
performing point value transformation on the odd-numbered electric signal polynomials and the even-numbered electric signal polynomials to obtain electric signal point values;
and carrying out coefficient rotation transformation on the electric signal point value to obtain rotation electric signal characteristics.
6. The method for preparing and testing the main control unit applied to exciting current according to claim 5, wherein the step of performing multiple filtering on the characteristics of the rotary electric signal to obtain a primary electric signal characteristic sequence comprises the steps of:
sequentially performing bit-wise modulo operation and square operation on the primary electric signal characteristic sequence to obtain a primary frame signal frequency spectrum;
performing multiple triangular filtering on the primary frame signal spectrum to obtain an electric spectrum filtering group;
filtering each electric spectrum value in the electric spectrum filtering group by using the following electric filtering formula to obtain electric signal filtering data, and collecting all the electric signal filtering data into an electric signal filtering data group:
wherein,is the total number of terms of the set of electrical spectral filters,/->Means +. >The individual electrical signal filtering data, ">Refers to->Personal (S)>Is logarithmic sign>Representing the +.>A primary frame power spectrum,/->Indicate->Personal (S)>Refers to the total number of primary frame power spectrums in the primary frame signal spectrum, +.>Refers to->The>The numerical value of the term;
performing cepstrum operation on each electric signal filtering data in the electric signal filtering data group by using the following secondary filtering formula to obtain electric signal filtering characteristics, and integrating all the electric signal filtering characteristics into a primary electric signal characteristic sequence:
wherein,refers to the +.about.in the characteristic sequence of the primary electric signal>-said electrical signal filtering characteristics,>refers to->Personal (S)>Refers to the total number of primary frame power spectrums in the primary frame signal spectrum, +.>Refers to->Personal (S)>Means +.>The individual electrical signal filtering data, ">Is the total number of terms of the set of electrical spectral filters.
7. The method for preparing and testing the main control unit applied to exciting current according to claim 6, wherein the step of performing feature clustering on the primary electrical signal feature sequence to obtain an electrical signal feature class set comprises the following steps:
Splitting the primary electric signal characteristic sequence into a plurality of primary signal characteristic groups, and randomly selecting initial center characteristics for each primary signal characteristic group;
calculating signal feature distances between all the electric signal filtering features and each initial center feature in the primary electric signal feature sequence;
and carrying out iterative updating on each primary signal feature group according to the signal feature distance to obtain electric signal feature classes, and collecting all the electric signal feature classes into an electric signal feature class set.
8. The method for preparing and testing the main control unit for exciting current according to claim 7, wherein the step of iteratively updating each primary signal feature group according to the signal feature distance to obtain an electrical signal feature class comprises the steps of:
grouping all the electric signal filtering characteristics in the primary electric signal characteristic sequence according to the signal characteristic distance to obtain a plurality of secondary signal characteristic groups;
calculating secondary center features of each secondary signal feature group, and calculating standard center distances of each secondary signal feature group according to the secondary center features;
And iteratively updating the secondary signal feature group by utilizing the standard center distance and a preset electric signal feature distance threshold value to obtain electric signal feature classes.
9. A master control unit preparation test device applied to exciting current for realizing the master control unit preparation test method applied to exciting current according to any one of claims 1 to 7, characterized in that the device comprises:
the data processing module is used for acquiring historical electric signal data of the target generator, and sequentially carrying out data sampling and data cleaning on the historical electric signal data to obtain standard electric signal data;
the signal filtering module is used for carrying out characteristic filtering on the standard electric signal data to obtain a primary electric signal characteristic sequence, carrying out characteristic clustering on the primary electric signal characteristic sequence to obtain an electric signal characteristic class set, and mapping the standard electric signal data according to the electric signal characteristic class set to obtain an electric signal characteristic sequence;
the algorithm selection module is used for selecting the electric signal characteristics in the electric signal characteristic sequence one by one as target signal characteristics, calculating characteristic distances between the target signal characteristics and each control algorithm in a preset control algorithm library by utilizing a preset characteristic distance algorithm, and selecting a control algorithm corresponding to the target signal characteristics as a target control algorithm according to the characteristic distances;
The excitation test module is used for screening out target electric signal data corresponding to the target signal characteristics from the standard electric signal data, and calculating test excitation current of the target electric signal data by using the target control algorithm;
the error calculation module is used for synchronously sampling the test exciting current to obtain exciting sampling data, calculating a control error corresponding to the exciting sampling data, carrying out iterative updating on the target control algorithm according to the control error to obtain a standard control algorithm, and establishing a main control unit of the target generator by utilizing all the standard control algorithms, wherein the calculating the control error corresponding to the exciting sampling data comprises the following steps: performing maximum value sampling on the excitation sampling data to obtain a maximum value data set, performing minimum value sampling on the excitation sampling data to obtain a minimum value data set, and performing median sampling on the excitation sampling data to obtain a median data set; calculating a control error corresponding to the excitation sampling data according to the maximum value data set, the minimum value data set and the median data set by using the following control error algorithm:
Wherein (1)>Means that the control error,/->Refers to->Data of->Means the total number of data of the maximum data set, and the total number of data of the maximum data set and the minimum data set and the median data set are identical,/respectively>Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->Means +.>Data of->Mean value of said maximum data set,/->And->Is a preset challenge coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311239269.0A CN116991145B (en) | 2023-09-25 | 2023-09-25 | Main control unit preparation test method and device applied to exciting current |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311239269.0A CN116991145B (en) | 2023-09-25 | 2023-09-25 | Main control unit preparation test method and device applied to exciting current |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116991145A CN116991145A (en) | 2023-11-03 |
| CN116991145B true CN116991145B (en) | 2023-12-15 |
Family
ID=88523455
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311239269.0A Active CN116991145B (en) | 2023-09-25 | 2023-09-25 | Main control unit preparation test method and device applied to exciting current |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116991145B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107765210A (en) * | 2017-10-19 | 2018-03-06 | 中国矿业大学 | A kind of integrated moving target alignment system of software definition |
| CN115221915A (en) * | 2022-06-02 | 2022-10-21 | 深圳市锐能微科技有限公司 | Target electric signal detection method and device, terminal equipment and storage medium |
| CN115795329A (en) * | 2023-02-06 | 2023-03-14 | 佰聆数据股份有限公司 | Power utilization abnormal behavior analysis method and device based on big data grid |
| CN116699400A (en) * | 2023-06-14 | 2023-09-05 | 广州珠江天然气发电有限公司 | Generator rotor short-circuit fault monitoring system, method and readable storage medium |
| CN116757078A (en) * | 2023-06-16 | 2023-09-15 | 国能神华九江发电有限责任公司 | Method and system for measuring flow velocity of pulverized coal based on acting force |
-
2023
- 2023-09-25 CN CN202311239269.0A patent/CN116991145B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107765210A (en) * | 2017-10-19 | 2018-03-06 | 中国矿业大学 | A kind of integrated moving target alignment system of software definition |
| CN115221915A (en) * | 2022-06-02 | 2022-10-21 | 深圳市锐能微科技有限公司 | Target electric signal detection method and device, terminal equipment and storage medium |
| CN115795329A (en) * | 2023-02-06 | 2023-03-14 | 佰聆数据股份有限公司 | Power utilization abnormal behavior analysis method and device based on big data grid |
| CN116699400A (en) * | 2023-06-14 | 2023-09-05 | 广州珠江天然气发电有限公司 | Generator rotor short-circuit fault monitoring system, method and readable storage medium |
| CN116757078A (en) * | 2023-06-16 | 2023-09-15 | 国能神华九江发电有限责任公司 | Method and system for measuring flow velocity of pulverized coal based on acting force |
Non-Patent Citations (2)
| Title |
|---|
| 电力系统动态等值中励磁系统参数聚合方法对比研究;闻丹银等;《河海大学学报（ 自然科学版）》;第40卷(第3期);全文 * |
| 电流负反馈的局起始励磁系统在核电站的应用;张云林等;《电机技术》(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116991145A (en) | 2023-11-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Guo et al. | Deep-learning-based fault classification using Hilbert–Huang transform and convolutional neural network in power distribution systems | |
| de Souza et al. | Data compression in smart distribution systems via singular value decomposition | |
| CN107909118B (en) | Power distribution network working condition wave recording classification method based on deep neural network | |
| CN110059845B (en) | Metering device clock error trend prediction method based on time sequence evolution gene model | |
| CN108154223B (en) | Power distribution network working condition wave recording classification method based on network topology and long time sequence information | |
| CN111563077B (en) | Power grid voltage data missing filling method, system, terminal and storage medium | |
| WO2010103354A1 (en) | Time domain electromagnetic interference monitoring method and system | |
| CN115795329B (en) | Power utilization abnormal behavior analysis method and device based on big data grid | |
| CN112750051A (en) | Random forest algorithm-based phase sequence identification method and device for transformer area and terminal equipment | |
| CN111027197B (en) | Power equipment simulation analysis system and method | |
| Kapisch et al. | Spectral variation-based signal compression technique for gapless power quality waveform recording in smart grids | |
| CN113949069B (en) | Method and system for determining transient voltage stability of high-proportion new energy power system | |
| CN115759365A (en) | Photovoltaic power generation power prediction method and related equipment | |
| CN116991145B (en) | Main control unit preparation test method and device applied to exciting current | |
| CN113608023A (en) | Sampling frequency self-adaptive harmonic detection method and system | |
| CN108307267B (en) | Random sampling system based on target information distribution mode | |
| CN108833156B (en) | Evaluation method and system for simulation performance index of power communication network | |
| CN116127447A (en) | Virtual power plant false data injection attack detection method, device, terminal and medium | |
| CN115902557A (en) | Switch cabinet fault diagnosis processing method and device and nonvolatile storage medium | |
| CN115795360A (en) | Cable fault detection method based on artificial neural network | |
| CN106618552A (en) | Detection model acquisition method and device | |
| Zhang et al. | Workload forecasting with hybrid stochastic configuration networks in clouds | |
| CN117538760B (en) | Lead-acid storage battery parameter identification method and related device | |
| Grebović et al. | The principles of a new line surge arrester's transient current measurement system | |
| CN113536589B (en) | Electromagnetic transient modeling method and system for alternating current power grid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |