CN116611741A - Construction method and system of service quality index system based on wind power equipment - Google Patents
Construction method and system of service quality index system based on wind power equipment Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to the field of quality evaluation, and discloses a service quality index system construction method based on wind power equipment, which comprises the following steps: extracting basic performance factors and multidimensional factors of wind power equipment to be evaluated, and constructing multidimensional association relations between the basic performance factors and the multidimensional factors; preprocessing historical state data of wind power equipment to be evaluated to obtain historical preprocessing data, and constructing a performance degradation target model according to the multidimensional factors and the historical preprocessing data; according to the performance degradation target model, calculating the failure time of the wind power equipment to be evaluated, analyzing the multidimensional quality index corresponding to the multidimensional factor, and constructing a multidimensional relation model between the multidimensional factor and the multidimensional quality index in the failure time; and constructing a service quality index system of the wind power equipment to be evaluated according to the multidimensional association relation, the multidimensional relation model and the performance degradation target model. The method can improve the accuracy of service quality assessment of the wind power equipment.
Description
Technical Field
The invention relates to the field of quality assessment, in particular to a method for constructing a service quality index system based on wind power equipment.
Background
Because the service period of the wind power generation equipment is long, the service environment is bad. Therefore, how to improve the running quality level of the wind power equipment under a long service period, reduce running faults and reduce the electricity cost of the whole service period of the wind power equipment is a focus of attention of the industry.
However, at present, a unified basic evaluation index of the service quality of the wind power equipment is lacking in the industry, so that a wind power enterprise cannot quantitatively analyze and compare the product quality problem under the same standard, and the accuracy of the service quality evaluation of the wind power equipment is low.
Disclosure of Invention
The invention provides a construction method of a service quality index system based on wind power equipment, which mainly aims to improve the accuracy of service quality assessment of the wind power equipment.
In order to achieve the above purpose, the method for constructing the service quality index system based on the wind power equipment provided by the invention comprises the following steps:
identifying key constituent units and service scenes of wind power equipment to be evaluated, extracting basic performance factors of the wind power equipment to be evaluated according to the service scenes, extracting physical sign level factors, overall performance factors and unit performance factors of the key constituent units of the wind power equipment to be evaluated according to the basic performance factors, and constructing a first association relationship, a second association relationship and a third association relationship among the basic performance factors, the physical sign level factors, the overall performance factors and the unit performance factors respectively;
Acquiring historical state data of the wind power equipment to be evaluated, preprocessing the historical state data according to the physical sign level factors, the overall machine performance factors and the unit performance factors to obtain historical preprocessing data, constructing a performance degradation initial model of the key constituent unit according to the unit performance factors, and determining a performance degradation target model of the key constituent unit based on the performance degradation initial model and the historical preprocessing data;
calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, analyzing the physical sign quality index, the whole machine quality index and the unit quality index corresponding to the physical sign level factor, the whole machine performance factor and the unit performance factor, and constructing a first relation model between the physical sign level factor and the physical sign quality index, a second relation model between the whole machine performance factor and the whole machine quality index and a third relation model between the unit performance factor and the unit quality index in the failure time;
and constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model.
Optionally, extracting the physical sign level factor, the overall performance factor and the unit performance factor of the key constituent unit of the wind power equipment to be evaluated according to the basic performance factor includes:
inquiring factor abnormal data corresponding to the basic performance factors from a preset factor abnormal state database, generating a basic factor abnormal set according to the factor abnormal data, and constructing a performance factor item set according to the basic factor abnormal set;
calculating the item set support degree of the performance factor item set, and determining a factor frequent item set of the basic factor abnormal set when the item set support degree is not smaller than a preset item set support degree threshold;
constructing an association rule of the factor frequent item set, splitting the factor frequent item set into a first association set and a second association set according to the association rule, calculating rule support degree corresponding to the association rule according to the first association set and the second association set, and determining a rule factor item set of the factor frequent item set when the item set support degree is not less than a preset rule support degree threshold;
calculating the rule confidence coefficient of the rule factor item set, and determining the associated factor item set of the factor frequent item set when the rule confidence coefficient is not smaller than a preset rule confidence coefficient threshold;
Analyzing the causal relation between the first association set and the second association set corresponding to the association factor item set, and determining target factor item sets in the first association set and the second association set according to the causal relation;
and identifying factor performance characteristics of the target factor item set, and determining factor categories corresponding to the target factor item set according to the factor performance characteristics, wherein the factor categories comprise the sign level factors, the overall machine performance factors and the unit performance factors.
Optionally, the calculating, according to the first association set and the second association set, a rule support degree corresponding to the association rule includes:
acquiring the factor frequent item set corresponding to the first association set, acquiring the basic factor abnormal set corresponding to the factor frequent item set, and calculating the total record number of the basic factor abnormal set;
and calculating rule support corresponding to the association rule according to the total record number by using the following formula:
；
wherein ,representing rule support, ->Representing association rules between the first association set and the second association set,/>Representing a first set of associations->Representing a second set of associations- >Representing support count function, +_>Representing the aggregate symbol>Indicating the total number of records.
Optionally, the calculating the rule confidence of the rule factor item set includes:
acquiring the first association set and the second association set of the rule factor item set;
calculating the rule confidence of the rule factor item set according to the first association set and the second association set by using the following formula:
；
wherein ,representing rule confidence, ++>Representing association rules between the first association set and the second association set,/>Representing a first set of associations->Representing a second set of associations->Representing support count function, +_>Representing the aggregate and symbol.
Optionally, preprocessing the historical state data according to the sign level factor, the overall performance factor and the unit performance factor to obtain historical preprocessed data, including:
performing data cleaning on the historical state data according to the physical sign level factors, the overall machine performance factors and the unit performance factors to obtain cleaning data;
performing correlation redundancy check on the cleaning data to obtain a redundancy check value, and determining redundancy data in the cleaning data according to the redundancy check value;
Removing the redundant data from the cleaning data to obtain redundancy-removed data;
and carrying out standardization processing on the redundancy removal data to obtain standardization data, and determining the historical preprocessing data according to the standardization data.
Optionally, the performing a correlation redundancy check on the cleaning data to obtain a redundancy check value includes:
identifying attribute factors corresponding to the cleaning data, selecting two groups of check data corresponding to different attribute factors from the cleaning data, and constructing a cross-linked list of the check data;
identifying cells of the cross-linked list, calculating the observation frequency of the cells, calculating the row edge distribution and the column edge distribution of the cross-linked list, and calculating the expected frequency of the cells according to the edge distribution and the column edge distribution;
according to the observed frequency and the expected frequency, calculating a check chi-square value of the check data by using the following formula:
；
wherein ,representing the check card square value,/->Indicating the frequency of observation->Indicating the desired frequency->Representing the number of rows of the cross-column table, +.>Representing the number of columns of the cross-column list, +.>Indicates the number of lines, < > >Representing the number of columns;
and determining the redundancy check value according to the check card square value.
Optionally, the constructing the performance degradation initial model of the key constituent unit according to the unit performance factor includes:
the initial model of performance degradation of the key constituent units is constructed using the following formula:
；
wherein ,represents the key constituent unit->Performance degradation initial model of individual unit performance factors, < ->Indicate->Initial value of individual unit performance factor,/->Represents the +.o->The rate of increase factor of the respective facilitation mechanisms,represents the +.o->Degradation rate factor of individual inhibition mechanisms, +.>Indicate->Enhancement of the power law parameters of the individual facilitation mechanisms, +.>Indicate->Degenerate power law parameters of individual suppression mechanisms, +.>Sequence number representing unit performance factor,/->Sequence number representing facilitation mechanism,/->Sequence number representing inhibition mechanism, < >>Indicates the number of facilitation mechanisms, +.>Indicating the number of inhibition mechanisms.
Optionally, the calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model includes:
identifying a key constituent unit and a unit performance factor corresponding to the performance degradation target model, and calculating the factor failure time of the unit performance factor of the key constituent unit when the current value corresponding to the unit performance factor of the key constituent unit is not smaller than the preset failure threshold by utilizing a preset failure threshold;
Calculating the minimum time value of the factor failure time, and determining the unit failure time of the key constituent unit according to the minimum time value;
and calculating a unit minimum value of the unit failure time, and determining the failure time of the wind power equipment to be evaluated according to the unit minimum value.
Optionally, the constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model includes:
analyzing the hierarchical structure relationship among the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model, and constructing an initial hierarchical structure diagram of the wind power equipment to be evaluated according to the hierarchical structure relationship;
calculating the hierarchy weight between adjacent layers of the initial hierarchy chart, and constructing a target hierarchy chart of the wind power equipment to be evaluated according to the hierarchy weight and the initial hierarchy chart;
And determining a service quality index system of the wind power equipment to be evaluated according to the target hierarchical structure diagram.
In order to solve the problems, the invention also provides a service quality index system construction system based on wind power equipment, which comprises:
the multi-dimensional factor extraction module is used for identifying key constituent units and service scenes of the wind power equipment to be evaluated, extracting basic performance factors of the wind power equipment to be evaluated according to the service scenes, extracting physical sign level factors, whole machine performance factors and unit performance factors of the key constituent units of the wind power equipment to be evaluated according to the basic performance factors, and constructing a first association relationship, a second association relationship and a third association relationship among the basic performance factors, the physical sign level factors, the whole machine performance factors and the unit performance factors respectively;
the degradation model construction module is used for acquiring historical state data of the wind power equipment to be evaluated, preprocessing the historical state data according to the physical sign level factors, the overall performance factors and the unit performance factors to obtain historical preprocessing data, constructing a performance degradation initial model of the key constituent unit according to the unit performance factors, and determining a performance degradation target model of the key constituent unit based on the performance degradation initial model and the historical preprocessing data;
The factor index relation construction module is used for calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, analyzing the physical sign level factors, the physical sign quality indexes, the physical machine quality indexes and the unit quality indexes corresponding to the whole machine performance factors and the unit performance factors, and constructing a first relation model between the physical sign level factors and the physical sign quality indexes, a second relation model between the whole machine performance factors and the whole machine quality indexes and a third relation model between the unit performance factors and the unit quality indexes in the failure time;
the quality index system construction module is used for constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model.
It can be seen that, in the embodiment of the present invention, by identifying key constituent units and service scenarios of a wind power device to be evaluated, a key subsystem or a component of the wind power device to be evaluated and an application scenario can be obtained as a precondition for subsequent operations, according to the service scenarios, basic performance factors of the wind power device to be evaluated are extracted to determine basic factors for evaluating performance of the wind power device to be evaluated, so as to provide basis for subsequent extraction of physical sign level factors, overall performance factors and unit performance factors, and according to the basic performance factors, the physical sign level factors, overall performance factors and unit performance factors of the key constituent units of the wind power device to be evaluated are extracted to obtain more comprehensive multidimensional features characterizing the wind power device to be evaluated: the two integral features and one local feature, and the first association relation, the second association relation and the third association relation among the basic performance factors, the physical performance factors, the overall machine performance factors and the unit performance factors respectively are constructed, so that factors affecting the quality of equipment can be analyzed in a multi-dimensional manner from the physical performance perspective, and the relation between the quality of equipment and the influencing factors is more explanatory; secondly, according to the embodiment of the invention, basic data can be provided for further modeling and analysis by acquiring the historical state data of the wind power equipment to be evaluated, standard and clean historical preprocessing data can be obtained by preprocessing the state data according to the physical sign level factor, the overall performance factor and the unit performance factor, so that the accuracy of subsequent data analysis is improved, and the degradation condition of a subsystem or a component of the wind power equipment can be actually simulated by constructing the performance degradation initial model of the key constituent unit according to the unit performance factor, and the performance degradation target model of the key constituent unit can be determined to reflect the operation condition of the equipment more truly based on the performance degradation initial model and the historical preprocessing data so as to provide powerful support for the subsequent establishment of a more accurate service quality index system; further, according to the embodiment of the invention, the effective operation period of the wind power equipment to be evaluated can be determined by calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, so that the method has more practical significance, and the physical sign quality indexes, the whole machine quality indexes and the unit quality indexes corresponding to the physical sign level factors, the whole machine performance factors and the unit performance factors can be used for measuring and evaluating the service quality of the wind power equipment to be evaluated from three aspects of basic physical signs, whole and part in the follow-up process, so that the accuracy of evaluating the service quality is improved, a first relation model between the physical sign level factors and the physical sign quality indexes, a second relation model between the whole machine performance factors and the whole machine quality indexes and a third relation model between the unit performance factors and the unit quality indexes are constructed, a system structure causal relation is built from factors to indexes and part to whole in a multi-dimension mode, and the service quality of the wind power equipment to be evaluated can be evaluated according to the first relation, the second relation, the third relation, the first relation model, the third relation model and the third relation model can be used for realizing the accuracy of evaluating the service quality of the wind power equipment to be evaluated, and the accuracy of evaluating the service quality is realized. Therefore, the construction method of the service quality index system based on the wind power equipment can improve accuracy of service quality assessment of the wind power equipment.
Drawings
FIG. 1 is a schematic flow chart of a method for constructing a service quality index system based on wind power equipment according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
S1, identifying key constituent units and service scenes of wind power equipment to be evaluated, extracting basic performance factors of the wind power equipment to be evaluated according to the service scenes, extracting physical sign level factors, overall performance factors and unit performance factors of the key constituent units of the wind power equipment to be evaluated according to the basic performance factors, and constructing a first association relationship, a second association relationship and a third association relationship among the basic performance factors, the physical sign level factors, the overall performance factors and the unit performance factors respectively.
According to the embodiment of the invention, the key subsystem or the component and the application scene of the wind power equipment to be evaluated can be obtained by identifying the key constituent units and the service scene of the wind power equipment to be evaluated, so that the key subsystem or the component and the application scene of the wind power equipment to be evaluated can be used as the premise of subsequent operation. The wind power equipment to be evaluated refers to a device which is to be evaluated and used for drawing wind energy through wind turbine blades and converting mechanical energy into electric energy. The key constituent units refer to key subsystems or components constituting the wind power plant to be evaluated, and can be used as separately studied plant modules. The business scenario refers to an application environment for describing products or services which may be needed and associated by a user in due course, such as wind power equipment manufacturers, government regulatory authorities, third party authorities and the like.
As an optional embodiment of the invention, the identification of the key constituent units and the service scenes of the wind power equipment to be evaluated can be realized by analyzing the structural characteristics and the application scenes of the wind power equipment to be evaluated.
Furthermore, according to the embodiment of the invention, the basic factors for evaluating the performance of the wind power equipment to be evaluated can be determined by extracting the basic performance factors of the wind power equipment to be evaluated according to the service scene, so that basis is provided for the subsequent extraction of the physical sign level factors, the overall performance factors and the unit performance factors. The basic performance factors refer to basic factors of physical, chemical or technical characteristics of the wind power equipment, such as blade wing profiles, blade lengths, blade areas, blade torsion angles, blade numbers, wind wheel diameter wind wheel swept areas, wind wheel cone angles, wind wheel elevation angles, wind wheel solidity and the like of the wind power equipment, which are suitable for requirements of users.
Further, as an alternative embodiment of the present invention, the extracting the basic performance factor of the wind power plant to be evaluated according to the service scenario may be performed by identifying a component structure of the wind power plant to be evaluated and analyzing a performance characteristic of the component structure.
Further, according to the embodiment of the invention, the physical sign level factor, the overall performance factor and the unit performance factor of the key constituent unit of the wind power equipment to be evaluated are extracted according to the basic performance factor, so that the more comprehensive multidimensional characteristic of the wind power equipment to be evaluated can be obtained: two global features and one local feature. The physical sign level factor refers to basic constitution factors for judging the running state level indication of the wind power equipment, such as a cooling mode of a motor, the quality of power supply voltage, the load of the motor, the type of a motor stator bearing and the like. The whole machine performance factor refers to a constituent factor influencing the macroscopic state of wind power equipment, such as rated power, rotating speed, torque and the like of a motor. The unit performance factor refers to a component factor affecting the local state of a subsystem or a component of the wind power equipment, such as threshold voltage of a transistor, leakage capacity of a capacitor, abrasion strength and the like.
Further, as an optional embodiment of the present invention, the extracting, according to the basic performance factor, the physical sign level factor, the overall performance factor, and the unit performance factor of the key constituent unit of the wind power equipment to be evaluated includes: inquiring factor abnormal data corresponding to the basic performance factors from a preset factor abnormal state database, generating a basic factor abnormal set according to the factor abnormal data, and constructing a performance factor item set according to the basic factor abnormal set; calculating the item set support degree of the performance factor item set, and determining a factor frequent item set of the basic factor abnormal set when the item set support degree is not smaller than a preset item set support degree threshold; constructing an association rule of the factor frequent item set, splitting the factor frequent item set into a first association set and a second association set according to the association rule, calculating rule support degree corresponding to the association rule according to the first association set and the second association set, and determining a rule factor item set of the factor frequent item set when the item set support degree is not less than a preset rule support degree threshold; calculating the rule confidence coefficient of the rule factor item set, and determining the associated factor item set of the factor frequent item set when the rule confidence coefficient is not smaller than a preset rule confidence coefficient threshold; analyzing the causal relation between the first association set and the second association set corresponding to the association factor item set, and determining target factor item sets in the first association set and the second association set according to the causal relation; and identifying factor performance characteristics of the target factor item set, and determining factor categories corresponding to the target factor item set according to the factor performance characteristics, wherein the factor categories comprise the sign level factors, the overall machine performance factors and the unit performance factors.
The number of the performance factor items in the performance factor item set is not less than two. The second set of associations is typically set to have only one performance factor item, which is initially defaulted to a factor performance factor set, which is adjustable according to subsequently calculated parameter values. The preset item set support threshold, the preset rule support threshold and the preset rule confidence threshold are all critical values for judging subsequent parameters, and can be set according to application scenes.
Optionally, the calculating the item set support of the performance factor item set includes:
acquiring the basic factor abnormal set corresponding to the performance factor item set, and respectively calculating the factor record number of the performance factor item set in the basic factor abnormal set and all record numbers of the basic factor abnormal set;
and calculating the item set support degree of the performance factor item set according to the factor record number and the total record number by using the following formula:
；
wherein ,item set support representing performance factor item set, < ->Representing the number of factor recordings,/->Indicating the total number of records.
Optionally, the calculating, according to the first association set and the second association set, a rule support degree corresponding to the association rule includes:
Acquiring the factor frequent item set corresponding to the first association set, acquiring the basic factor abnormal set corresponding to the factor frequent item set, and calculating the total record number of the basic factor abnormal set;
and calculating rule support corresponding to the association rule according to the total record number by using the following formula:
；
wherein ,representing rule support, ->Representing association rules between the first association set and the second association set,/>Representing a first set of associations->Representing a second set of associations->Representing support count function, +_>Representing the aggregate symbol>Indicating the total number of records.
Optionally, the calculating the rule confidence of the rule factor item set includes:
acquiring the first association set and the second association set of the rule factor item set;
calculating the rule confidence of the rule factor item set according to the first association set and the second association set by using the following formula:
；
wherein ,representing rule confidence, ++>Representing association rules between the first association set and the second association set,/>Representing a first set of associations->Representing a second set of associations->Representing support count function, +_>Representing the aggregate and symbol.
Further, according to the embodiment of the invention, the factors influencing the quality of the equipment can be analyzed in a multidimensional manner from the physical performance perspective by constructing the first association relationship, the second association relationship and the third association relationship among the basic performance factors, the physical level factors, the overall machine performance factors and the unit performance factors, so that the relationship between the quality of the equipment and the influencing factors is more explanatory.
Further, as an optional embodiment of the present invention, the constructing the first, second and third association relationships between the basic performance factor and the sign level factor, the overall performance factor and the unit performance factor, respectively, includes: referring to the implementation step of extracting the physical sign level factor, the overall machine performance factor and the unit performance factor of the key constituent unit of the wind power equipment to be evaluated according to the basic performance factor, extracting a self-variation factor item set and the target factor item set in the associated factor item set, wherein the target factor item set comprises the physical sign level factor, the overall machine performance factor and the unit performance factor; and analyzing factor association relations between the self-changing factor item set and the target factor item set, and respectively constructing a first association relation, a second association relation and a third association relation between the basic performance factor and the physical sign level factor, the overall performance factor and the unit performance factor when the target factor item set is the physical sign level factor, the overall performance factor and the unit performance factor.
S2, acquiring historical state data of the wind power equipment to be evaluated, preprocessing the historical state data according to the physical sign level factors, the overall performance factors and the unit performance factors to obtain historical preprocessing data, constructing a performance degradation initial model of the key constituent unit according to the unit performance factors, and determining a performance degradation target model of the key constituent unit based on the performance degradation initial model and the historical preprocessing data.
According to the embodiment of the invention, the historical state data of the wind power equipment to be evaluated can be obtained to provide basic data for further modeling and analysis, and the historical state data can be obtained from a multisource database such as a SCADA system, a CMS system and wind power experiment tests.
Further, according to the embodiment of the invention, standard and clean historical preprocessing data can be obtained by preprocessing the state data according to the physical sign level factor, the overall machine performance factor and the unit performance factor, so that the accuracy of subsequent data analysis is improved.
Further, as an optional embodiment of the present invention, the preprocessing the historical state data according to the sign level factor, the overall performance factor and the unit performance factor to obtain historical preprocessed data includes: performing data cleaning on the historical state data according to the physical sign level factors, the overall machine performance factors and the unit performance factors to obtain cleaning data; performing correlation redundancy check on the cleaning data to obtain a redundancy check value, and determining redundancy data in the cleaning data according to the redundancy check value; removing the redundant data from the cleaning data to obtain redundancy-removed data; and carrying out standardization processing on the redundancy removal data to obtain standardization data, and determining the historical preprocessing data according to the standardization data.
Alternatively, the cleansing data may be obtained by performing deletion value deletion or population, outlier deletion, correction of inconsistent data, or the like on the history state data.
Optionally, the performing a correlation redundancy check on the cleaning data to obtain a redundancy check value includes:
identifying attribute factors corresponding to the cleaning data, selecting two groups of check data corresponding to different attribute factors from the cleaning data, and constructing a cross-linked list of the check data;
identifying cells of the cross-linked list, calculating the observation frequency of the cells, calculating the row edge distribution and the column edge distribution of the cross-linked list, and calculating the expected frequency of the cells according to the edge distribution and the column edge distribution;
according to the observed frequency and the expected frequency, calculating a check chi-square value of the check data by using the following formula:
；
wherein ,representing the check card square value,/->Indicating the frequency of observation->Indicating the desired frequency->Representing the number of rows of the cross-column table, +.>Representing the number of columns of the cross-column list, +.>Indicates the number of lines, < >>Representing the number of columns;
and determining the redundancy check value according to the check card square value.
Further, according to the embodiment of the invention, the degradation condition of the subsystem or the component of the wind power equipment can be actually simulated by constructing the performance degradation initial model of the key constituent unit according to the unit performance factors.
Further, as an optional embodiment of the present invention, the constructing the performance degradation initial model of the key constituent unit according to the unit performance factor includes:
the initial model of performance degradation of the key constituent units is constructed using the following formula:
；
wherein ,represents the key constituent unit->Performance degradation initial model of individual unit performance factors, < ->Indicate->Initial value of individual unit performance factor,/->Represents the +.o->The rate of increase factor of the respective facilitation mechanisms,represents the +.o->Degradation rate factor of individual inhibition mechanisms, +.>Indicate->Enhancement of the power law parameters of the individual facilitation mechanisms, +.>Indicate->Degenerate power law parameters of individual suppression mechanisms, +.>Sequence number representing unit performance factor,/->Sequence number representing facilitation mechanism,/->Sequence number representing inhibition mechanism, < >>Indicates the number of facilitation mechanisms, +.>Indicating the number of inhibition mechanisms.
Further, the embodiment of the invention determines that the performance degradation target model of the key constituent unit can reflect the running condition of the equipment more truly based on the performance degradation initial model and the historical preprocessing data so as to provide powerful support for the subsequent establishment of a more accurate service quality index system.
Further, as an optional embodiment of the present invention, the determining the performance degradation target model of the key constituent unit based on the performance degradation initial model and the historical preprocessing data includes: identifying unit performance factors of the key constituent units, and constructing a degradation equation of the key constituent units with respect to the unit performance factors based on the performance degradation initial model and the historical preprocessing data; and calculating a parameter solution of the degradation equation, determining an optimal parameter solution in the parameter solutions by using a least square method, and determining a performance degradation target model of the key constituent unit according to the optimal parameter solution.
S3, calculating failure time of the wind power equipment to be evaluated according to the performance degradation target model, analyzing the physical sign level factor, the whole machine performance factor and physical sign quality indexes, the whole machine quality indexes and the unit quality indexes corresponding to the unit performance factor, and constructing a first relation model between the physical sign level factor and the physical sign quality indexes, a second relation model between the whole machine performance factor and the whole machine quality indexes and a third relation model between the unit performance factor and the unit quality indexes in the failure time.
According to the embodiment of the invention, the effective operation period of the wind power equipment to be evaluated can be determined by calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, so that the method has more practical significance.
Further, as an optional embodiment of the present invention, the calculating, according to the performance degradation target model, a failure time of the wind power plant to be evaluated includes: identifying a key constituent unit and a unit performance factor corresponding to the performance degradation target model, and calculating the factor failure time of the unit performance factor of the key constituent unit when the current value corresponding to the unit performance factor of the key constituent unit is not smaller than the preset failure threshold by utilizing a preset failure threshold; calculating the minimum time value of the factor failure time, and determining the unit failure time of the key constituent unit according to the minimum time value; and calculating a unit minimum value of the unit failure time, and determining the failure time of the wind power equipment to be evaluated according to the unit minimum value.
Further, according to the embodiment of the invention, the quality indexes of the physical sign, the whole machine performance factor and the unit performance factor can be used for measuring and evaluating the service quality of the wind power equipment to be evaluated from three aspects of basic physical sign, whole and part in the follow-up process so as to improve the accuracy of evaluating the service quality. The physical sign quality index, the overall machine quality index and the unit quality index can be obtained by performing factor analysis on the physical sign level factor, the overall machine performance factor and the unit performance factor.
Further, the embodiment of the invention constructs the system structure causal relationship from factors to indexes and from local to whole in a multi-dimensional way by constructing a first relationship model between the physical sign level factors and the physical sign quality indexes, a second relationship model between the whole machine performance factors and the whole machine quality indexes and a third relationship model between the unit performance factors and the unit quality indexes.
Further, as an optional embodiment of the present invention, the constructing a first relationship model between the sign level factor and the sign quality index includes: acquiring the sign factor data and the sign quality data of the sign level factors and the sign quality indexes from a historical service database preset by the wind power equipment to be evaluated; constructing an index factor regression equation according to the sign factor data and the sign quality data by using a preset index factor regression model; and calculating a regression parameter solution of the index factor regression equation, and determining a first relation model between the sign level factor and the sign quality index according to the regression parameter solution and the index factor regression model. The historical service database refers to a set of basic data such as current running state data and environment state data of wind power equipment to be evaluated, historical data before service and in a service period and the like. The index factor regression model is a fundamental mode of characterizing the relationship between an index and a factor, and can be determined according to expert assessment methods in the industry.
Optionally, the implementation principle of the second relationship model between the overall performance factor and the overall quality index and the third relationship model between the unit performance factor and the unit quality index is the same as the implementation principle of the first relationship model between the sign level factor and the sign quality index, and will not be described herein.
S4, constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model.
According to the embodiment of the invention, the problem intention can be finally realized by constructing the service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model, so that the service quality of the wind power equipment to be evaluated can be more comprehensively and accurately evaluated.
Further, as an optional embodiment of the present invention, the constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model, and the performance degradation target model includes: analyzing the hierarchical structure relationship among the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model, and constructing an initial hierarchical structure diagram of the wind power equipment to be evaluated according to the hierarchical structure relationship; calculating the hierarchy weight between adjacent layers of the initial hierarchy chart, and constructing a target hierarchy chart of the wind power equipment to be evaluated according to the hierarchy weight and the initial hierarchy chart; and determining a service quality index system of the wind power equipment to be evaluated according to the target hierarchical structure diagram.
It can be seen that, in the embodiment of the present invention, by identifying key constituent units and service scenarios of a wind power device to be evaluated, a key subsystem or a component of the wind power device to be evaluated and an application scenario can be obtained as a precondition for subsequent operations, according to the service scenarios, basic performance factors of the wind power device to be evaluated are extracted to determine basic factors for evaluating performance of the wind power device to be evaluated, so as to provide basis for subsequent extraction of physical sign level factors, overall performance factors and unit performance factors, and according to the basic performance factors, the physical sign level factors, overall performance factors and unit performance factors of the key constituent units of the wind power device to be evaluated are extracted to obtain more comprehensive multidimensional features characterizing the wind power device to be evaluated: the two integral features and one local feature, and the first association relation, the second association relation and the third association relation among the basic performance factors, the physical performance factors, the overall machine performance factors and the unit performance factors respectively are constructed, so that factors affecting the quality of equipment can be analyzed in a multi-dimensional manner from the physical performance perspective, and the relation between the quality of equipment and the influencing factors is more explanatory; secondly, according to the embodiment of the invention, basic data can be provided for further modeling and analysis by acquiring the historical state data of the wind power equipment to be evaluated, standard and clean historical preprocessing data can be obtained by preprocessing the state data according to the physical sign level factor, the overall performance factor and the unit performance factor, so that the accuracy of subsequent data analysis is improved, and the degradation condition of a subsystem or a component of the wind power equipment can be actually simulated by constructing the performance degradation initial model of the key constituent unit according to the unit performance factor, and the performance degradation target model of the key constituent unit can be determined to reflect the operation condition of the equipment more truly based on the performance degradation initial model and the historical preprocessing data so as to provide powerful support for the subsequent establishment of a more accurate service quality index system; further, according to the embodiment of the invention, the effective operation period of the wind power equipment to be evaluated can be determined by calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, so that the method has more practical significance, and the physical sign quality indexes, the whole machine quality indexes and the unit quality indexes corresponding to the physical sign level factors, the whole machine performance factors and the unit performance factors can be used for measuring and evaluating the service quality of the wind power equipment to be evaluated from three aspects of basic physical signs, whole and part in the follow-up process, so that the accuracy of evaluating the service quality is improved, a first relation model between the physical sign level factors and the physical sign quality indexes, a second relation model between the whole machine performance factors and the whole machine quality indexes and a third relation model between the unit performance factors and the unit quality indexes are constructed, a system structure causal relation is built from factors to indexes and part to whole in a multi-dimension mode, and the service quality of the wind power equipment to be evaluated can be evaluated according to the first relation, the second relation, the third relation, the first relation model, the third relation model and the third relation model can be used for realizing the accuracy of evaluating the service quality of the wind power equipment to be evaluated, and the accuracy of evaluating the service quality is realized. Therefore, the construction method of the service quality index system based on the wind power equipment can improve accuracy of service quality assessment of the wind power equipment.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention 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 invention 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 invention 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. The terms 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 (10)
1. A service quality index system construction method based on wind power equipment is characterized by comprising the following steps:
identifying key constituent units and service scenes of wind power equipment to be evaluated, extracting basic performance factors of the wind power equipment to be evaluated according to the service scenes, extracting physical sign level factors, overall performance factors and unit performance factors of the key constituent units of the wind power equipment to be evaluated according to the basic performance factors, and constructing a first association relationship, a second association relationship and a third association relationship among the basic performance factors, the physical sign level factors, the overall performance factors and the unit performance factors respectively;
acquiring historical state data of the wind power equipment to be evaluated, preprocessing the historical state data according to the physical sign level factors, the overall machine performance factors and the unit performance factors to obtain historical preprocessing data, constructing a performance degradation initial model of the key constituent unit according to the unit performance factors, and determining a performance degradation target model of the key constituent unit based on the performance degradation initial model and the historical preprocessing data;
Calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, analyzing the physical sign quality index, the whole machine quality index and the unit quality index corresponding to the physical sign level factor, the whole machine performance factor and the unit performance factor, and constructing a first relation model between the physical sign level factor and the physical sign quality index, a second relation model between the whole machine performance factor and the whole machine quality index and a third relation model between the unit performance factor and the unit quality index in the failure time;
and constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model.
2. The method for constructing a service quality index system based on wind power equipment according to claim 1, wherein the extracting the physical sign level factor, the overall machine performance factor and the unit performance factor of the key constituent units of the wind power equipment to be evaluated according to the basic performance factor comprises:
Inquiring factor abnormal data corresponding to the basic performance factors from a preset factor abnormal state database, generating a basic factor abnormal set according to the factor abnormal data, and constructing a performance factor item set according to the basic factor abnormal set;
calculating the item set support degree of the performance factor item set, and determining a factor frequent item set of the basic factor abnormal set when the item set support degree is not smaller than a preset item set support degree threshold;
constructing an association rule of the factor frequent item set, splitting the factor frequent item set into a first association set and a second association set according to the association rule, calculating rule support degree corresponding to the association rule according to the first association set and the second association set, and determining a rule factor item set of the factor frequent item set when the item set support degree is not less than a preset rule support degree threshold;
calculating the rule confidence coefficient of the rule factor item set, and determining the associated factor item set of the factor frequent item set when the rule confidence coefficient is not smaller than a preset rule confidence coefficient threshold;
analyzing the causal relation between the first association set and the second association set corresponding to the association factor item set, and determining target factor item sets in the first association set and the second association set according to the causal relation;
And identifying factor performance characteristics of the target factor item set, and determining factor categories corresponding to the target factor item set according to the factor performance characteristics, wherein the factor categories comprise the sign level factors, the overall machine performance factors and the unit performance factors.
3. The method for constructing a service quality index system based on wind power equipment according to claim 2, wherein the calculating the rule support corresponding to the association rule according to the first association set and the second association set comprises:
acquiring the factor frequent item set corresponding to the first association set, acquiring the basic factor abnormal set corresponding to the factor frequent item set, and calculating the total record number of the basic factor abnormal set;
and calculating rule support corresponding to the association rule according to the total record number by using the following formula:
；
wherein ,representing rule support, ->Representing association rules between the first association set and the second association set,/>Representing a first set of associations->Representing a second set of associations->Representing support count function, +_>Representing the aggregate symbol>Indicating the total number of records.
4. The method for constructing a service quality index system based on wind power equipment according to claim 2, wherein the calculating the rule confidence of the rule factor item set comprises:
Acquiring the first association set and the second association set of the rule factor item set;
calculating the rule confidence of the rule factor item set according to the first association set and the second association set by using the following formula:
；
wherein ,representing rule confidence, ++>Representing association rules between the first association set and the second association set,/>Representing a first set of associations->Representing a second set of associations->Representing support count function, +_>Representing the aggregate and symbol.
5. The method for constructing a service quality index system based on wind power equipment according to claim 1, wherein preprocessing the historical state data according to the sign level factor, the overall machine performance factor and the unit performance factor to obtain historical preprocessing data comprises:
performing data cleaning on the historical state data according to the physical sign level factors, the overall machine performance factors and the unit performance factors to obtain cleaning data;
performing correlation redundancy check on the cleaning data to obtain a redundancy check value, and determining redundancy data in the cleaning data according to the redundancy check value;
removing the redundant data from the cleaning data to obtain redundancy-removed data;
And carrying out standardization processing on the redundancy removal data to obtain standardization data, and determining the historical preprocessing data according to the standardization data.
6. The method for constructing a service quality index system based on wind power equipment according to claim 5, wherein the performing a correlation redundancy check on the cleaning data to obtain a redundancy check value comprises:
identifying attribute factors corresponding to the cleaning data, selecting two groups of check data corresponding to different attribute factors from the cleaning data, and constructing a cross-linked list of the check data;
identifying cells of the cross-linked list, calculating the observation frequency of the cells, calculating the row edge distribution and the column edge distribution of the cross-linked list, and calculating the expected frequency of the cells according to the edge distribution and the column edge distribution;
according to the observed frequency and the expected frequency, calculating a check chi-square value of the check data by using the following formula:
；
wherein ,representing the check card square value,/->Indicating the frequency of observation->Indicating the desired frequency->Representing the number of rows of the cross-linked list,representing the number of columns of the cross-column list, +.>Indicates the number of lines, < > >Representing the number of columns;
and determining the redundancy check value according to the check card square value.
7. The method for constructing a service quality index system based on wind power equipment according to claim 1, wherein the constructing the initial model of performance degradation of the key constituent unit according to the unit performance factor comprises:
the initial model of performance degradation of the key constituent units is constructed using the following formula:
；
wherein ,represents the key constituent unit->Performance degradation initial model of individual unit performance factors, < ->Indicate->Initial value of individual unit performance factor,/->Represents the +.o->An improvement rate factor of the individual promotion mechanisms, +.>Represents the +.o->Degradation rate factor of individual inhibition mechanisms, +.>Indicate->Enhancement of the power law parameters of the individual facilitation mechanisms, +.>Indicate->Degenerate power law parameters of individual suppression mechanisms, +.>Sequence number representing unit performance factor,/->Sequence number representing facilitation mechanism,/->Sequence number representing inhibition mechanism, < >>Indicates the number of facilitation mechanisms, +.>Indicating the number of inhibition mechanisms.
8. The method for constructing a service quality index system based on wind power equipment according to claim 1, wherein the calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model comprises:
Identifying a key constituent unit and a unit performance factor corresponding to the performance degradation target model, and calculating the factor failure time of the unit performance factor of the key constituent unit when the current value corresponding to the unit performance factor of the key constituent unit is not smaller than the preset failure threshold by utilizing a preset failure threshold;
calculating the minimum time value of the factor failure time, and determining the unit failure time of the key constituent unit according to the minimum time value;
and calculating a unit minimum value of the unit failure time, and determining the failure time of the wind power equipment to be evaluated according to the unit minimum value.
9. The method for constructing a service quality index system based on wind power equipment according to claim 1, wherein the constructing the service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model comprises:
analyzing the hierarchical structure relationship among the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model, and constructing an initial hierarchical structure diagram of the wind power equipment to be evaluated according to the hierarchical structure relationship;
Calculating the hierarchy weight between adjacent layers of the initial hierarchy chart, and constructing a target hierarchy chart of the wind power equipment to be evaluated according to the hierarchy weight and the initial hierarchy chart;
and determining a service quality index system of the wind power equipment to be evaluated according to the target hierarchical structure diagram.
10. A service quality index system construction system based on wind power equipment is characterized in that the system comprises:
the multi-dimensional factor extraction module is used for identifying key constituent units and service scenes of the wind power equipment to be evaluated, extracting basic performance factors of the wind power equipment to be evaluated according to the service scenes, extracting physical sign level factors, whole machine performance factors and unit performance factors of the key constituent units of the wind power equipment to be evaluated according to the basic performance factors, and constructing a first association relationship, a second association relationship and a third association relationship among the basic performance factors, the physical sign level factors, the whole machine performance factors and the unit performance factors respectively;
the degradation model construction module is used for acquiring historical state data of the wind power equipment to be evaluated, preprocessing the historical state data according to the physical sign level factors, the overall performance factors and the unit performance factors to obtain historical preprocessing data, constructing a performance degradation initial model of the key constituent unit according to the unit performance factors, and determining a performance degradation target model of the key constituent unit based on the performance degradation initial model and the historical preprocessing data;
The factor index relation construction module is used for calculating the failure time of the wind power equipment to be evaluated according to the performance degradation target model, analyzing the physical sign level factors, the physical sign quality indexes, the physical machine quality indexes and the unit quality indexes corresponding to the whole machine performance factors and the unit performance factors, and constructing a first relation model between the physical sign level factors and the physical sign quality indexes, a second relation model between the whole machine performance factors and the whole machine quality indexes and a third relation model between the unit performance factors and the unit quality indexes in the failure time;
the quality index system construction module is used for constructing a service quality index system of the wind power equipment to be evaluated according to the first association relationship, the second association relationship, the third association relationship, the first relationship model, the second relationship model, the third relationship model and the performance degradation target model.
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