CN117703690A - Wind generating set health state assessment method and system - Google Patents
Wind generating set health state assessment method and system Download PDFInfo
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Abstract
The invention discloses a health state evaluation method of a wind generating set, which comprises the steps of dividing acquired monitoring data of the wind generating set according to label quantity, analysis variables and working condition variables, calculating correlation between the analysis variables and the working condition variables, and acquiring a working condition variable set corresponding to the analysis variables according to the correlation between the analysis variables and the working condition variables; dividing the working condition variable values into a plurality of sections by adopting a box diagram method according to the working condition variables of the obtained monitoring data of the wind generating set, and taking the working condition variable values as judgment bases of different working conditions of the running state of the set; the invention adopts the equipment combination working condition analysis method to analyze the health state of the real-time monitoring data of the unit under different working condition combination conditions, and can solve the problems of discrete health state, coupling of operation working conditions, variable operation and maintenance decisions and the like of the equipment.
Description
Technical Field
The invention belongs to the field of generator state monitoring and evaluation, and particularly relates to a method and a system for evaluating the health state of a wind generating set.
Background
Wind energy plays an increasingly important role in improving the structure of Chinese energy as clean energy at present, but the problems of safety and economic benefit of wind power plants are also gradually attracting attention. The wind generating set is used as a typical of a distributed complex electromechanical system, has typical characteristics of physical dispersion, logical integration, multiple bodies and multiple modes, and the service health state of the wind generating set is comprehensively influenced by multiple strong coupling uncertain factors such as the health state of discrete unit equipment in an area, individual operation working conditions, differential production scheduling, subjective operation and maintenance decision and the like, and presents complex coupling and aggregation characteristics in time and space. The SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control system) is a computer-based DCS and power automation monitoring system, has a wide application field, and can be applied to various fields such as data acquisition and monitoring control in the fields of electric power, metallurgy, petroleum, chemical industry, fuel gas, railways and the like, process control and the like. As a main subsystem of an energy management system (EMS system), the system has the advantages of complete information, improved efficiency, correct grasping of the running state of the system, speeding up decision making, helping to quickly diagnose the fault state of the system and the like, and is an indispensable tool for power dispatching. The method has irreplaceable effects on improving the reliability, safety and economic benefit of power grid operation, reducing scheduling load, realizing power scheduling automation and modernization and improving scheduling efficiency and level.
The alarm mechanism of the SCADA system commonly used by the wind generating set plays an important role in safe and stable operation of the set, but the current SCADA alarm mechanism depends on the experience judgment of specific monitoring variables to a great extent, and the alarm has hysteresis and one-sided performance, and has weaker early warning capability on abnormality. Meanwhile, most of the analysis of the monitoring system of the wind generating set is qualitative analysis, only the normal and the abnormality can be judged, and only a single alarm is needed, so that the degradation process and the trend of the monitoring variable of the system cannot be provided, the alarms caused by different abnormalities cannot be classified, the alarms are difficult to be sent out timely at the moment when the abnormality really happens or even before, and a method and a system capable of accurately and quantitatively analyzing the health state from the monitoring variable to the whole wind generating set are needed.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the health state of a wind generating set, which are used for overcoming the defects of the traditional fan health state analysis and early warning method, and accurately analyzing the wind generating set before SCADA warning so as to realize early warning of monitoring variables and the health state of the whole machine.
A health state evaluation method of a wind generating set comprises the following steps:
s1, dividing the obtained monitoring data of the wind generating set according to the label quantity, the analysis variable and the working condition variable, calculating the correlation between the analysis variable and the working condition variable, and obtaining a working condition variable set corresponding to the analysis variable according to the correlation between the analysis variable and the working condition variable;
s2, dividing the working condition variable values into a plurality of sections by adopting a box diagram method according to the working condition variable of the obtained monitoring data of the wind generating set, and taking the working condition variable values as judgment bases of different working conditions of the running state of the set;
s3, obtaining different working condition combinations according to the intervals divided in the step S2, screening monitoring data of each analysis variable under the corresponding working condition combination, determining a variable threshold center, and obtaining the health grade of the analysis variable at each moment, and further obtaining the health grade of the whole machine at different moments and the health grade of the whole machine at each day.
Preferably, a copula nonlinear analysis method is adopted to calculate nonlinear correlation between each analysis variable and each working condition variable, a correlation coefficient matrix between the analysis variable and the working condition variable is constructed, a set threshold is judged according to strong correlation, and a working condition variable set most correlated with each analysis variable is identified.
Preferably, a box diagram method is adopted, characteristic values of each box diagram of the working condition variable monitoring data are calculated, each group of data is arranged from small to large, the number of quarters of the group of data is found, the first quartile, the median and the third quartile are used as endpoints of interval division, and the working condition variable numerical intervals are initially divided.
Preferably, the data of one analysis variable under a certain combined working condition is set as x 1 ,x 2 ,…,x N Where N is the data length, then
Wherein C is the central value of the combined working condition at the moment, and M is the standard deviation of the combined working condition at the moment.
Preferably, analyzing variable and working condition variable monitoring data of the moment to be analyzed are collected, the deviation degree from the monitoring value of the moment analyzing variable to the central value is quantitatively calculated according to the determined corresponding working condition threshold center, and the health grade of the moment analyzing variable is determined; and setting a threshold according to the normal working condition analysis variable health grade, and taking the threshold as a basis index for monitoring variable alarming.
Preferably, after the health grade of different analysis variables at each moment is obtained, the health grade of each day of all analysis variables is fused, the health grade of the whole machine at different moments is obtained, the health grade of each day of the whole machine is further obtained, and a change curve of the health grade of the whole machine is constructed.
A health state evaluation system of a wind generating set comprises a variable dividing module, an interval dividing module and an evaluation module;
the variable dividing module divides the obtained monitoring data of the wind generating set according to the label quantity, the analysis variable and the working condition variable, calculates the correlation between the analysis variable and the working condition variable, and obtains a working condition variable set corresponding to the analysis variable according to the correlation between the analysis variable and the working condition variable;
the interval dividing module divides the working condition variable values into a plurality of intervals by adopting a box diagram method according to the working condition variable of the obtained monitoring data of the wind generating set, and the working condition variable values are used as judging basis of different working conditions of the running state of the set;
the evaluation module obtains different working condition combinations according to the divided intervals, screens the monitoring data of each analysis variable under the corresponding working condition combination, and determines a variable threshold center, so that the health grade of the analysis variable at each moment can be obtained, and the health grade of the whole machine at different moments and the health grade of the whole machine at each day are further obtained.
Preferably, a copula nonlinear analysis method is adopted to calculate nonlinear correlation between each analysis variable and each working condition variable, a correlation coefficient matrix between the analysis variable and the working condition variable is constructed, a set threshold is judged according to strong correlation, and a working condition variable set most correlated with each analysis variable is identified.
Preferably, a box diagram method is adopted, characteristic values of each box diagram of the working condition variable monitoring data are calculated, each group of data is arranged from small to large, the number of quarters of the group of data is found, the first quartile, the median and the third quartile are used as endpoints of interval division, and the working condition variable numerical intervals are initially divided.
Preferably, the data of one analysis variable under a certain combined working condition is set as x 1 ,x 2 ,…,x N Where N is the data length, then
Wherein C is the central value of the combined working condition at the moment, and M is the standard deviation of the combined working condition at the moment.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a health state evaluation method of a wind generating set, which comprises the steps of dividing acquired monitoring data of the wind generating set according to label quantity, analysis variable and working condition variable, calculating correlation between the analysis variable and the working condition variable, and acquiring a working condition variable set corresponding to the analysis variable according to the correlation between the analysis variable and the working condition variable; dividing the working condition variable values into a plurality of sections by adopting a box diagram method according to the working condition variables of the obtained monitoring data of the wind generating set, and taking the working condition variable values as judgment bases of different working conditions of the running state of the set; according to the divided multiple intervals, different working condition combinations are obtained, monitoring data of each analysis variable under the corresponding working condition combination are screened, a variable threshold center is determined, the health grade of the analysis variable at each moment can be obtained, and the health grade of the whole machine at different moments and the health grade of the whole machine at each day are further obtained. According to the invention, by adopting the equipment combination working condition analysis method, the health state analysis is carried out on the real-time monitoring data of the unit under different working condition combination conditions, so that the problems of discrete health state, coupling of operation working conditions, variable operation and maintenance decisions and the like of the equipment can be solved.
According to the invention, by adopting the equipment combination working condition analysis method, the health state analysis is carried out on the real-time monitoring data of the unit under different working condition combination conditions, so that the problems of discrete health state, coupling of operation working conditions, variable operation and maintenance decisions and the like of the equipment can be solved. Compared with the existing monitoring and alarming mechanism of the wind generating set, the invention provides the health state evaluation and alarming mechanism based on the distribution rule of the data, so that the monitoring and alarming of the wind generating set has continuity and traceability;
compared with the traditional SCADA alarm mechanism, the invention remarkably considers the mining and extraction of the system state characteristics from the distribution rule of the monitoring variable data, reduces the influence of personnel operation and maintenance decision on monitoring judgment, and has stronger universality and stability.
Drawings
FIG. 1 is a flow chart of a method for evaluating the health status of a wind turbine generator system according to the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, the invention provides a method for evaluating health status of a wind generating set, comprising the following steps:
s1, dividing the obtained monitoring data of the wind generating set according to the label quantity, the analysis variable and the working condition variable, calculating the correlation between the analysis variable and the working condition variable, and obtaining a working condition variable set corresponding to the analysis variable according to the correlation between the analysis variable and the working condition variable;
s2, dividing the working condition variable values into a plurality of sections by adopting a box diagram method according to the working condition variable of the obtained monitoring data of the wind generating set, and taking the working condition variable values as judgment bases of different working conditions of the running state of the set;
s3, obtaining different working condition combinations according to the intervals divided in the step S2, screening monitoring data of each analysis variable under the corresponding working condition combination, determining a variable threshold center, and obtaining the health grade of the analysis variable at each moment, and further obtaining the health grade of the whole machine at different moments and the health grade of the whole machine at each day.
According to the invention, by adopting the equipment combination working condition analysis method, the health state analysis is carried out on the real-time monitoring data of the unit under different working condition combination conditions, so that the problems of discrete health state, coupling of operation working conditions, variable operation and maintenance decisions and the like of the equipment can be solved.
In step S1, the system monitors classification of data variable: and collecting monitoring variables of the wind generating set, and according to actual indexes and physical meanings of the monitoring variables of the system, combining monitoring data with monitoring analysis experience of the running state of the fan, distinguishing the running state quantity and the working condition quantity of the set, and dividing the monitoring values into label quantity, analysis variables and working condition variables.
Working condition variable identification based on copula nonlinear analysis: and calculating the nonlinear correlation between each analysis variable and each working condition variable by adopting a copula nonlinear analysis method, constructing a correlation coefficient matrix between the analysis variable and the working condition variable, judging a set threshold according to the strong correlation, and identifying a working condition variable set most correlated with each analysis variable.
Set X 1 ,X 2 ,…X N N random variables, their respective edge distributions being F 1 (x 1 ),F 2 (x 2 ),…,F N (x N ) Their joint distribution is H (x 1 ,x 2 ,…,x N ) The Copula nonlinear correlation calculation can be obtained from the following equation,
C(u 1 ,u 2 ,…,u N )＝H[F 1 -1 (u 1 ),F 2 -1 (u 2 ),…,F N -1 (u N )]
wherein C (u) 1 ,u 2 ,…,u N ) As a Copula function, F -1 (u) represents the inverse of F (u).
Dividing the operation working conditions of the wind generating set: and dividing the working condition variable value into a plurality of sections by adopting a box diagram method for each working condition variable obtained through identification, and taking the working condition variable value as a judging basis of different working conditions of the unit operation state.
Working condition interval division based on box line diagrams: calculating characteristic values of each box diagram of the working condition variable monitoring data by adopting a box diagram method, arranging each group of data from small to large, finding out the number of quartering the group of data, and dividing the first quartile into Q 1 Median Q 2 Third quartile Q 3 And as the end point of the interval division, the working condition variable numerical interval is initially divided.
On-site working condition interval division adjustment: based on the working condition intervals divided in the method, the working condition intervals are divided and adjusted according to expert experience, monitoring system logic and actual working conditions of the unit.
In step S3, the analysis variable threshold center determines: and screening the monitoring data of each analysis variable under the working condition combination according to the working condition combination, and calculating the central value and standard deviation of each analysis variable under each working condition to be used as the judgment basis of the variables and the unit state under different working conditions.
Setting the data of an analysis variable under a certain combined working condition as x 1 ,x 2 ,…,x N Where N is the data length, then
Wherein C is the central value of the combined working condition at the moment, and M is the standard deviation of the combined working condition at the moment.
Analysis variable health grade determination: collecting analysis variable and working condition variable monitoring data of moment to be analyzed, quantitatively calculating the deviation degree from the moment analysis variable monitoring value to a central value according to the determined corresponding working condition threshold center, and determining the health grade of the moment analysis variable; and setting a threshold according to the normal working condition analysis variable health grade, and taking the threshold as a basis index for monitoring variable alarming.
Wherein D is monitoring data, C is a central value of the combined working condition at the moment, M is a standard deviation of the combined working condition at the moment, result represents the health grade of the analysis variable at the moment, and the physical meaning is that the greater the monitoring value is deviated from the central value, the more deviated the value is from the central value, and the worse the health condition is.
And (3) determining the health grade of the whole machine: after the health grade of different analysis variables at each moment is obtained, the health grade of each day of all analysis variables is fused, the health grade of the whole machine at different moments is obtained, the health grade of each day of the whole machine is further obtained, and a change curve of the health grade of the whole machine is constructed.
Compared with the existing monitoring and alarming mechanism of the wind generating set, the invention provides the health state evaluation mechanism of the wind generating set based on the distribution rule of the data, so that the monitoring and alarming of the wind generating set has continuity and traceability;
compared with the traditional SCADA alarm mechanism, the invention remarkably considers the mining and extraction of the system state characteristics from the distribution rule of the monitoring variable data, reduces the influence of personnel operation and maintenance decision on monitoring judgment, and has stronger universality and stability;
the working condition division and threshold setting are provided by an algorithm, can be adjusted at any time by combining with the actual on-site conditions, and have stronger adaptability to complex and changeable unit operation conditions and operation and maintenance decisions;
the invention has reasonable scheme and easy realization, provides a flexible algorithm system, can fully exert the advantages based on data analysis, and provides a good foundation for subsequent correction and improvement
The invention provides a health state assessment method of a wind generating set, which aims at the problems of discrete equipment health state, coupling of operation working conditions and variable operation and maintenance decisions. Because real-time monitoring data of the unit during operation is often influenced by working condition changes and presents different distributions under different working conditions, the invention focuses on distinguishing and analyzing the distribution characteristics of the unit operation state variables under different working conditions, thereby eliminating the influence of different working conditions on variable analysis. Meanwhile, aiming at the characteristics of hysteresis, discontinuity and the like of the traditional alarm mechanism, the invention provides a quantitative unit health state evaluation mode, adopts a method for calculating the health grade of the wind generating set from a variable to the whole machine, constructs a health state change curve from the variable to the whole machine according to the health grade, and is used as a criterion for monitoring the system alarm.
The method specifically comprises the following steps:
s1, identifying monitoring variables of a wind generating set: the method comprises the steps of collecting historical monitoring data of the wind generating set, dividing the historical monitoring data into label quantity, analysis variables and working condition variables, calculating correlation among the variables, and obtaining a working condition variable set corresponding to the analysis variables. The tag quantity is used to distinguish between unit numbers and time.
(2) Dividing the operation condition of the wind generating set. And (3) dividing the working condition variable value into a plurality of sections by adopting a box diagram method for each working condition variable obtained in the step (1), taking the working condition variable value as a judging basis of different working conditions of the unit operation state, and adjusting the working condition sections according to the field reality.
(3) And (5) quantitatively analyzing and alarming the health state of the unit. Screening the monitoring data of each analysis variable under the corresponding working condition combination according to the different working condition combinations obtained in the step 2), determining a variable threshold center, quantitatively calculating the health grade of the analysis variable at each moment, and further obtaining the health grade of the whole machine at different moments and the health grade of the whole machine at each day.
The method comprises the following steps:
1) And a step of identifying monitoring variables of the wind generating set. And collecting monitoring data of the SCADA system of the wind generating set, classifying the monitoring variable types into tag quantities, analysis variables and working condition variables, calculating the correlation among the variables, and obtaining a working condition variable set corresponding to the analysis variables.
1.1 The system monitors the data variable class classification. Collecting monitoring variables of a wind generating set, and dividing the monitoring values into three parts according to actual indexes and physical meanings of the monitoring variables of the system and combining monitoring data existence conditions and fan running state monitoring analysis experience to distinguish running state quantity and working condition quantity of the set: the label quantity, the analysis variable and the working condition variable.
1.2 Operating condition variable identification based on copula nonlinear analysis. And calculating the nonlinear correlation between each analysis variable and each working condition variable by adopting a copula nonlinear analysis method, constructing a correlation coefficient matrix between the analysis variable and the working condition variable, judging a set threshold according to the strong correlation, and identifying a working condition variable set most correlated with each analysis variable.
2) Dividing the operation condition of the wind generating set. And 1.2) dividing the working condition variable value into a plurality of sections by adopting a box diagram method for each working condition variable obtained through identification, taking the working condition variable value as a judging basis of different working conditions of the unit operation state, and adjusting the working condition sections according to the field reality.
2.1 Based on the operating mode interval division of the box diagram. And calculating characteristic values of each box diagram of the working condition variable monitoring data by adopting a box diagram method, taking the characteristic values as endpoints of interval division, and carrying out initial division on the working condition variable numerical intervals.
2.2 The working condition interval of the site is divided and adjusted. Based on the working condition interval divided in 2.1), the working condition interval division is adjusted according to expert experience, monitoring system logic and the actual working condition of the unit.
3) And (5) quantitatively analyzing and alarming the health state of the unit. Screening the monitoring data of each analysis variable under the corresponding working condition combination according to the different working condition combinations, determining a variable threshold center, quantitatively calculating the health grade of the analysis variable at each moment, and further obtaining the health grade of the whole machine at different moments and the health grade of the whole machine at each day.
3.1 Analysis variable threshold center determination. And screening the monitoring data of each analysis variable under the working condition combination according to the working condition combination, and calculating the central value and standard deviation of each analysis variable under each working condition to be used as the judgment basis of the variables and the unit state under different working conditions.
3.2 Analytical variable health class determination. Collecting analysis variable and working condition variable monitoring data of the moment to be analyzed, quantitatively calculating the deviation degree from the monitoring value of the analysis variable to the central value of the moment according to the corresponding working condition threshold center determined by 3.1), and determining the health grade of the analysis variable of the moment; and setting a threshold according to the normal working condition analysis variable health grade, and taking the threshold as a basis index for monitoring variable alarming.
3.3 Determining the health grade of the whole machine. After the health grade of different analysis variables at each moment is obtained, the health grade of each day of all analysis variables is fused, the health grade of the whole machine at different moments is obtained, the health grade of each day of the whole machine is further obtained, and a change curve of the health grade of the whole machine is constructed.
According to the invention, by adopting the equipment combination working condition analysis method, the health state analysis is carried out on the real-time monitoring data of the unit under different working condition combination conditions, so that the problems of discrete health state, coupling of operation working conditions, variable operation and maintenance decisions and the like of the equipment can be solved. Compared with the existing monitoring and alarming mechanism of the wind generating set, the invention provides the health state evaluation and alarming mechanism based on the distribution rule of the data, so that the monitoring and alarming of the wind generating set has continuity and traceability;
compared with the traditional SCADA alarm mechanism, the invention remarkably considers the mining and extraction of the system state characteristics from the distribution rule of the monitoring variable data, reduces the influence of personnel operation and maintenance decision on monitoring judgment, and has stronger universality and stability;
the working condition division and threshold setting are provided by an algorithm, can be adjusted at any time by combining with the actual on-site conditions, and have stronger adaptability to complex and changeable unit operation conditions and operation and maintenance decisions;
the invention has reasonable scheme and easy realization, provides a flexible algorithm system, can fully exert the advantages based on data analysis, and provides a good foundation for subsequent correction and improvement.
Claims (10)
1. The method for evaluating the health state of the wind generating set is characterized by comprising the following steps of:
s1, dividing the obtained monitoring data of the wind generating set according to the label quantity, the analysis variable and the working condition variable, calculating the correlation between the analysis variable and the working condition variable, and obtaining a working condition variable set corresponding to the analysis variable according to the correlation between the analysis variable and the working condition variable;
s2, dividing the working condition variable values into a plurality of sections by adopting a box diagram method according to the working condition variable of the obtained monitoring data of the wind generating set, and taking the working condition variable values as judgment bases of different working conditions of the running state of the set;
s3, obtaining different working condition combinations according to the intervals divided in the step S2, screening monitoring data of each analysis variable under the corresponding working condition combination, determining a variable threshold center, and obtaining the health grade of the analysis variable at each moment, and further obtaining the health grade of the whole machine at different moments and the health grade of the whole machine at each day.
2. The method for evaluating the health status of a wind turbine generator system according to claim 1, wherein a copula nonlinear analysis method is adopted, nonlinear correlations between each analysis variable and each working condition variable are calculated, a correlation coefficient matrix between the analysis variable and the working condition variable is constructed, a threshold is set according to strong correlation judgment, and a working condition variable set most correlated with each analysis variable is identified.
3. The method for evaluating the health status of a wind generating set according to claim 1, wherein the characteristic values of each box diagram of the condition variable monitoring data are calculated by adopting a box diagram method, each group of data is arranged from small to large, the number of quarters of the group of data is found, the first quartile, the median and the third quartile are used as endpoints of interval division, and the condition variable value interval is initially divided.
4. The method for evaluating the health status of a wind turbine generator system according to claim 1, wherein the data of an analysis variable under a certain combined condition is defined as x 1 ,x 2 ,…,x N Where N is the data length, then
Wherein C is the central value of the combined working condition at the moment, and M is the standard deviation of the combined working condition at the moment.
5. The method for evaluating the health status of a wind generating set according to claim 1, wherein the analyzing variable and the working condition variable monitoring data of the moment to be analyzed are collected, the deviation degree from the analyzing variable monitoring value to the central value of the moment is quantitatively calculated according to the determined corresponding working condition threshold center, and the health grade of the analyzing variable of the moment is determined; and setting a threshold according to the normal working condition analysis variable health grade, and taking the threshold as a basis index for monitoring variable alarming.
6. The method for evaluating the health status of a wind generating set according to claim 1, wherein after the health grade of different analysis variables at each moment is obtained, the health grade of each day of all analysis variables is fused to obtain the health grade of the whole machine at different moments, the health grade of each day of the whole machine is further obtained, and a change curve of the health grade of the whole machine is constructed.
7. The system for evaluating the health state of the wind generating set is characterized by comprising a variable dividing module, an interval dividing module and an evaluating module;
the variable dividing module divides the obtained monitoring data of the wind generating set according to the label quantity, the analysis variable and the working condition variable, calculates the correlation between the analysis variable and the working condition variable, and obtains a working condition variable set corresponding to the analysis variable according to the correlation between the analysis variable and the working condition variable;
the interval dividing module divides the working condition variable values into a plurality of intervals by adopting a box diagram method according to the working condition variable of the obtained monitoring data of the wind generating set, and the working condition variable values are used as judging basis of different working conditions of the running state of the set;
the evaluation module obtains different working condition combinations according to the divided intervals, screens the monitoring data of each analysis variable under the corresponding working condition combination, and determines a variable threshold center, so that the health grade of the analysis variable at each moment can be obtained, and the health grade of the whole machine at different moments and the health grade of the whole machine at each day are further obtained.
8. The system for evaluating the health status of a wind turbine generator system according to claim 7, wherein a copula nonlinear analysis method is adopted to calculate nonlinear correlations between each analysis variable and each working condition variable, a correlation coefficient matrix between the analysis variable and the working condition variable is constructed, a threshold is set according to the strong correlation determination, and a working condition variable set most correlated with each analysis variable is identified.
9. The system for evaluating the health status of a wind turbine generator system according to claim 7, wherein the characteristic values of each box diagram of the condition variable monitoring data are calculated by adopting a box diagram method, each group of data is arranged from small to large, the number of quarters of the group of data is found, the first quartile, the median and the third quartile are used as endpoints of interval division, and the condition variable value interval is initially divided.
10. A system for evaluating the health of a wind turbine according to claim 7, wherein an analytical variable is providedThe data under a certain combined working condition is x 1 ,x 2 ,…,x N Where N is the data length, then
Wherein C is the central value of the combined working condition at the moment, and M is the standard deviation of the combined working condition at the moment.
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| CN118150893B (en) * | 2024-05-08 | 2024-09-24 | 工业富联(杭州)数据科技有限公司 | Device health state evaluation method and storage medium |
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