CN116146434A - Real-time evaluation method for health state of wind turbine generator based on wind power actual measurement data - Google Patents
Real-time evaluation method for health state of wind turbine generator based on wind power actual measurement data Download PDFInfo
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Abstract
The invention discloses a real-time evaluation method of the health status of a wind turbine based on wind power actual measurement data, which is based on analysis of operation data in a SCADA system of the wind turbine, intercepts a data set through a periodically updated sliding window, establishes a sample set, completes data cleaning and screening, and draws an actual wind speed-wind power scatter diagram; equally-spaced division is carried out on the wind speed intervals, and the average value of wind power in each sub-wind speed interval is calculated; obtaining theoretical wind power corresponding to wind speed at the midpoint of the subinterval; leading in a health evaluation index H of the wind turbine, and carrying out grading evaluation on the health condition of the wind turbine; every fixed time, the time window moves forward, the sample set is updated, and the health state evaluation index H is recalculated. The evaluation method provided by the invention starts from the running purpose of the wind turbine generator, can reflect the power generation performance and wind energy conversion capability of the wind turbine generator, and provides effective help for the running maintenance of the wind turbine generator.
Description
Technical Field
The invention relates to a real-time evaluation method for the health status of a wind turbine generator based on wind power actual measurement data, and belongs to the technical field of wind power.
5. Background art
Wind energy is increasingly valued by all countries of the world as a clean renewable energy source under the conditions of energy shortage and increasingly severe environmental pollution. In recent years, under the guidance of 'carbon neutralization' policies in China, the wind power generation industry can develop healthily and stably, the installed capacity is frequently created and increased, and the accumulated installed capacity is stably located in the world. Because the running environment of the wind turbine generator is severe, and the installation position of the wind turbine generator is special, the maintenance cost of the wind turbine generator is high, and once the damage has a large influence on a power grid, the reliability of the wind turbine generator is highly required. The real-time health monitoring of the wind turbine generator is beneficial to reasonably adjusting the operation strategy and reducing the maintenance cost.
At present, the health of the wind turbine is monitored, and multi-source data such as vibration signals, current signals, temperature and the like are comprehensively analyzed to evaluate the health condition of key components of the wind turbine. For example, chinese patent CN113417810 discloses a method and apparatus for monitoring and evaluating health of a driving chain of a wind turbine, which monitors the driving chain of the wind turbine in real time; chinese patent CN 114254904 discloses a method and apparatus for evaluating the operational health of a nacelle of a wind turbine generator. Chinese patent CN113420509 discloses a method, a device and a storage medium for evaluating the state of a wind turbine, which are disadvantageous in that it is difficult to ensure real-time monitoring of the health status of the wind turbine.
Through the search discovery of the prior art, research on comprehensive evaluation of the power generation performance of the wind turbine generator is lacking in the current market, the power generation performance is monitored through analysis of wind power data acquired in real time, so that evaluation is performed on the health state, the faults of the wind turbine generator are early warned in advance, reliable guarantee can be provided for operation of a wind farm through the research, power grid dispatching is matched better, and market development space is large.
6. Summary of the invention
The invention aims to provide a real-time evaluation method for the health state of a wind turbine based on wind power actual measurement data, and an introduced health evaluation index H of the wind turbine can reflect actual running efficiency and provide data support for running maintenance of a wind power plant.
In order to solve the technical problems, the invention adopts the following technical scheme: a real-time evaluation method for the health status of a wind turbine based on wind power actual measurement data comprises the following steps:
s1, sliding interception is carried out on an operation data set in a wind turbine data acquisition and monitoring control (SCADA) system through a fixed time window, wherein the operation data set mainly comprises wind speed and wind power corresponding to the wind speed, and a real-time data stream is obtained;
s2, preprocessing the intercepted data stream, and drawing an actual wind speed-wind power scatter diagram;
s3, determining the length of a sub-wind speed interval, equally dividing the effective wind speed interval of the running wind turbine generator, and calculating the average value of the actually measured wind power in each sub-interval;
s4, obtaining the corresponding theoretical wind power at the middle point of each sub wind speed interval;
s5, introducing a health state evaluation index H of the wind turbine generator to represent the degradation rate of the wind turbine generator, judging the health state of the wind turbine generator according to the health state evaluation index H of the wind turbine generator, and submitting an evaluation result;
s6, moving the time window forwards every fixed time, updating the data stream, and repeating the steps S2-S6;
preferably, the specific implementation manner of the step S1 is as follows: acquiring data flow about wind speed and corresponding wind power in SCADA system of wind turbine generator set, and recording as P= { (v) 1 ,p 1 ,t 1 ),(v 2 ,p 2 ,t 2 ),(v 3 ,p 3 ,t 3 ),……,(v i ,p i ,t i ) … … }, where (v) i ,p i ,t i ) Representing t i Data points collected at moment, wherein the corresponding wind speed of the data points is v i The wind power is p i . Intercepting the data stream P through a time window, wherein the expression of the sliding window is as follows:
P[t-T:t]＝{(v t-T ,p t-T ,t t-T ),(v t-T+1 ,p t-T+1 ,t t-T+1 ),……,(v t ,p t ,t t )}
wherein, P [ T-T: T ] represents a subsequence of the data flow P from T-T moment to T moment, namely a wind speed-wind power sample set, and the corresponding acquisition time length is T (T >14 days).
Preferably, the specific implementation method of step S2 is as follows: cleaning abnormal data and invalid data, and removing repeated data, wherein the abnormal data and the invalid data mainly comprise: (1) data outside the standard wind speed interval; (2) data collected when the wind turbine generator is shut down, failed or manually maintained; (3) measurement error data caused by sensor failure, etc.; (4) fan load-limiting operation data; (5) and data acquired when wake effects of the wind generating set are serious. And judging and eliminating abnormal data by adopting the Laiyida criterion in the coarse error theory.
The wind speed v-wind power P sample set after data preprocessing is P' = { [ v 1 ',p 1 ']，[v 2 ',p 2 ']，[v 3 ',p 3 ']，……，[v m ',p m '][ v ]' i ,p' i ]Representing the ith valid acquisition sample; there are m valid collection points in total.
And drawing an actual wind speed-power scatter diagram by taking the wind speed v as an abscissa and the wind power p as an ordinate.
According to the method, the data set is cleaned, abnormal data are removed, the reserved sample set can truly and effectively reflect the running state of the fan, data support is provided for subsequent health state evaluation of the wind turbine, and the accuracy of the evaluation method is improved.
Preferably, the specific implementation method of step S3 is as follows:
the effective wind speed interval of the wind turbine generator is [ a, b ], the effective wind speed interval is divided into r subintervals according to interval length lm/s, the wind power average value of effective collection points in each subinterval is calculated, and the calculation formula is as follows:
wherein W is i R values are taken as the actual wind power of the ith subinterval; n (N) i The number of effective acquisition points falling into the ith subinterval; p is p i,j The actual wind power value of the jth effective point in the ith subinterval is represented.
Preferably, the specific implementation method of step S4 is as follows:
taking a standard wind power curve provided by a manufacturer when a fan is tested in a factory as a theoretical wind power curve, obtaining theoretical wind power values corresponding to wind speeds at midpoints of wind speed intervals, and recording an ith theoretical wind power value as L i There are a total of r values.
Preferably, the specific implementation method of step S5 is as follows:
the calculation formula of the health evaluation index of the wind turbine generator is as follows:
dividing the health status of the wind turbine into five grades: health, well, attention, warning, failure, the values of the indicators H corresponding respectively are: 100 to 90, 90 to 80, 80 to 60 and 60 to 0.
Preferably, the specific implementation method of step S6 is as follows:
after the kth time interval Δt (Δt < T), the real-time data stream P in the SCADA system is intercepted with a sliding window, and a new sample set is established. The expression of the sliding window is:
P[t+k△t-T:t+k△t]＝{(v t+k△t-T ,p t+k△t-T ,t t+k△t-T ),(v t+k△t-T+1 ,p t+k△t-T+1 ,t t+k△t-T+1 ),……,(v t+k△t ,p t+k△t ,t t+k△t )}
wherein, pt+Deltat-T: t+Deltat represents a sliding window separated from Pt-T: T by a time Deltat; the initial value of k is 0, and k=k+1 every time a cycle passes.
The invention has the following beneficial effects: the SCADA system real-time data is obtained on line and combined with offline theoretical power and curves, the data are deeply mined, powerful data analysis provides powerful guarantee for researching and evaluating methods, health state evaluation indexes of wind turbines are introduced, operators of the wind turbines can be helped to find faults in time, wind power is reasonably distributed, overall performance of the wind turbines is improved, and stable and reliable operation of the wind turbines is guaranteed.
7. Description of the drawings
FIG. 1 is a step chart of a health state evaluation method of a wind turbine generator set provided by the invention
FIG. 2 is a flow chart of data preprocessing
8. Detailed description of the preferred embodiments
The invention is further described below with reference to the accompanying drawings.
The invention provides a real-time evaluation method for the health status of a wind turbine generator based on wind power actual measurement data, and the implementation steps of the method are shown in figure 1. The method is based on wind speed and corresponding wind power data obtained in real time in a SCADA system of the wind turbine. Firstly, acquiring real-time wind speed and wind power in a wind field through measuring equipment, uploading the real-time wind speed and the wind power to an SCADA system and storing the SCADA system; intercepting a data stream through a periodically updated sliding window, establishing a sample set, and obtaining an effective sample set after pretreatment; and analyzing the effective sample set, calculating a health evaluation index H of the wind turbine generator, carrying out grading evaluation on the health evaluation index H, realizing early fault early warning, and dynamically adjusting a wind field scheduling strategy according to a real-time evaluation result.
The method comprises the following specific implementation processes:
step one: installing wind measuring equipment near a wind generating set to be measured, and determining the real-time wind speed for driving the wind generating set; and (3) measuring the net electric power of the wind turbine generator by adopting a power measuring device (such as a power transmitter), uploading the measurement result to the SCADA system through a field optical fiber network by adopting an OPC technology, and storing. The SCADA system mainly comprises a data acquisition module, a network construction module, a centralized monitoring module and the like.
Step two: and acquiring real-time data flow about wind speed and wind power in the SCADA system of the wind turbine, and intercepting the data flow through a sliding window to obtain an operation data set. The wind speed and wind power acquired by the SCADA system are 10min data, so that the accuracy of wind power group state evaluation can be ensured. Thus, the flow rate of the data stream was 144 points per day. Setting the length of the sliding window to be t 1 Day (according to IEC61400 standard promulgated by International Electrotechnical Commission (IEC), wind power curve should be established based on operation data over 14 days, therefore, t 1 Should be greater than 14 days).
Step three: preprocessing the intercepted data stream, cleaning abnormal data and invalid data, and removing repeated data, wherein the abnormal data and the invalid data mainly comprise: (1) data outside the standard wind speed interval; (2) data collected when the wind turbine generator is shut down, failed or manually maintained; (3) measurement error data caused by sensor failure, etc.; (4) fan load-limiting operation data; (5) and data acquired when wake effects of the wind generating set are serious.
Abnormal data is judged and removed by adopting the Leida criterion in the coarse error theory, as shown in figure 2, the specific steps are as follows:
the sub wind speed interval length is set as DeltaV=0.1m/s, the actual wind power standard deviation in the cell is calculated, and sampling points which fall outside + -3σ are removed. And calculating the data cleaning rate, ending the data preprocessing when the data cleaning rate is less than 0.5%, otherwise, repeating the cleaning process until the requirements are met.
The wind speed v-wind power P sample set after data preprocessing is P' = { [ v 1 ',p 1 ']，[v 2 ',p 2 ']，[v 3 ',p 3 ']，……，[v m ',p m '][ v ]' i ,p' i ]Representing the ith valid acquisition sample; there are m valid collection points in total.
And drawing an actual wind speed-power scatter diagram by taking the wind speed v as an abscissa and the wind power p as an ordinate.
Step four: the effective wind speed interval of the running of the wind turbine generator is [ a, b ], the cut-in wind speed of the fan is a m/s, and the cut-out wind speed is b m/s. Determining the length of a sub-wind speed interval, dividing the effective wind speed interval into r subintervals according to the length l m/s, and calculating the wind power average value of effective acquisition points in each subinterval, wherein the calculation formula is as follows:
wherein W is i R values are taken as the average value of the actual wind power in the ith subinterval; n (N) i The number of effective acquisition points falling into the ith subinterval; p is p i,j The actual wind power value of the jth effective point in the ith subinterval is represented.
Step five: taking a standard wind power curve provided by a manufacturer when a fan is tested in factory as a theoretical wind power curve, and obtaining the wind speed at the midpoint of each wind speed intervalThe theoretical wind power value is applied, and the wind speed at the midpoint of the ith subinterval is denoted as d i The corresponding theoretical wind power is recorded as L i R values in total.
Step six: the health state evaluation index H of the wind turbine generator is introduced to represent the degradation rate of the wind turbine generator, and the calculation formula is as follows:
w in the formula i For the actual wind power average value of the ith subinterval, L i The theoretical wind power corresponding to the wind speed at the midpoint of the ith subinterval; r is the number of subintervals.
Dividing the health status of the wind turbine into five grades: health, well, attention, warning, failure. Judging the health state of the wind turbine generator according to the health state evaluation index H of the wind turbine generator, wherein the values of the indexes H respectively corresponding to the health state evaluation index H are as follows: [1 to x ] 1 )，[x 1 ～x 2 )，[x 2 ～x 3 )，[x 3 ～x 4 )，[x 4 ～0]Submitting an evaluation result, and adjusting an operation strategy by the monitoring center according to the health condition of the unit to ensure safe and stable operation of the power grid. The specific correspondence between the health status and the index of the wind turbine generator is shown in table 1:
TABLE 1 health status and index correspondence of wind turbines
Step seven: after the lapse of the time interval Δt (t 2 <t 1 ) Intercepting the data flow in the SCADA system by using the same time window, establishing a new sample set, and repeating the steps three to seven.
The method for evaluating the health state of the wind turbine generator set has good universality, the operation state of the wind turbine generator set is judged by effectively and quickly excavating the operation data of the wind turbine generator set deeply, wind farm operators can be helped to grasp the health state of the wind turbine generator set in time, the wind farm operators can maintain the wind turbine generator set in time before serious faults occur, the operation strategy is adjusted, and the maintenance cost is reduced.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and it should be noted that it is possible for those skilled in the art to make several improvements and modifications without departing from the technical principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.
Claims (7)
1. A real-time evaluation method for the health status of a wind turbine generator based on wind power actual measurement data is characterized by comprising the following steps:
s1, intercepting real-time data flow in a wind turbine generator data acquisition and monitoring control (SCADA) system through a periodically updated sliding window, wherein the real-time data flow mainly comprises three parameters of time, wind speed and wind power, and an operation data set is obtained;
s2, preprocessing the intercepted data set, and drawing an actual wind speed-wind power scatter diagram;
s3, determining the length of a sub-wind speed interval, equally dividing the effective wind speed interval of the running wind turbine generator, and calculating the average value of the actually measured wind power in each sub-interval;
s4, obtaining the corresponding theoretical wind power at the middle point of each wind speed interval;
s5, introducing a health state evaluation index H of the wind turbine generator, representing the degradation rate of the wind turbine generator, judging the health state of the wind turbine generator according to the health state evaluation index H of the wind turbine generator, and submitting a judgment result;
and S6, moving the window forwards every fixed time, updating the data set, and repeating the steps S2-S6.
2. The method for evaluating the health status of the wind turbine generator in real time according to claim 1, wherein the specific implementation method of the step S1 comprises the following steps:
acquiring real-time data flow in SCADA system of wind turbine generator set, and recording as P= { (v) 1 ,p 1 ,t 1 ),(v 2 ,p 2 ,t 2 ),(v 3 ,p 3 ,t 3 ),……,(v i ,p i ,t i ) … … }, where (v) i ,p i ,t i ) Representing t i Data points collected at moment, wherein the corresponding wind speed of the data points is v i The wind power is p i 。
Intercepting the real-time data stream P through a sliding window to obtain an operation data set, wherein the expression of the sliding window is as follows:
P[t-T:t]＝{(v t-T ,p t-T ,t t-T ),(v t-T+1 ,p t-T+1 ,t t-T+1 ),……,(v t ,p t ,t t )}
wherein, P [ T-T: T ] represents a subsequence of the data flow P from T-T moment to T moment, namely a wind speed-wind power sample set, and the corresponding acquisition time length is T (T >14 days).
3. The method for evaluating the health status of the wind turbine generator set in real time based on the wind power actual measurement data according to claim 1, wherein the specific implementation method of the step S2 comprises the following steps:
cleaning abnormal data and invalid data, and removing repeated data, wherein the abnormal data and the invalid data mainly comprise: (1) data outside the standard wind speed interval; (2) data collected when the wind turbine generator is shut down, failed or manually maintained; (3) measurement error data caused by sensor failure, etc.; (4) fan load-limiting operation data; (5) and data acquired when wake effects of the wind generating set are serious. And judging and eliminating abnormal data by adopting the Laiyida criterion in the coarse error theory.
The wind speed v-wind power P sample set after data preprocessing is P' = { [ v 1 ',p 1 ']，[v 2 ',p 2 ']，[v 3 ',p 3 ']，……，[v m ',p m '][ v ]' i ,p' i ]Representing the ith valid acquisition sample; there are m valid collection points in total.
And drawing an actual wind speed-power scatter diagram by taking the wind speed v as an abscissa and the wind power p as an ordinate.
4. The method for evaluating the health status of the wind turbine generator set in real time based on the wind power actual measurement data according to claim 1, wherein the specific implementation method of the step S3 comprises the following steps:
the effective wind speed interval of the wind turbine generator is [ a, b ], the effective wind speed interval is divided into r subintervals according to interval length lm/s, the wind power average value of effective collection points in each subinterval is calculated, and the calculation formula is as follows:
wherein W is i R values are taken as the actual wind power of the ith subinterval; n (N) i The number of effective acquisition points falling into the ith subinterval; p is p i,j The actual wind power value of the jth effective point in the ith subinterval is represented.
5. The method for evaluating the health status of the wind turbine generator set in real time based on the wind power actual measurement data according to claim 1, wherein the specific implementation method of the step S4 comprises the following steps:
taking a standard wind power curve provided by a manufacturer when a fan is tested in a factory as a theoretical wind power curve, obtaining theoretical wind power values corresponding to wind speeds at midpoints of wind speed intervals, and recording an ith theoretical wind power value as L i R values in total.
6. The method for evaluating the health status of the wind turbine generator set in real time based on the wind power actual measurement data according to claim 1, wherein the specific implementation method of the step S5 comprises the following steps:
the calculation formula of the health evaluation index of the wind turbine generator is as follows:
dividing the health status of the wind turbine into five grades: health, well, attention, warning, failure, the values of the indicators H corresponding respectively are: [1 to x ] 1 )，[x 1 ～x 2 )，[x 2 ～x 3 )，[x 3 ～x 4 )，[x 4 ～0]。
7. The method for evaluating the health status of the wind turbine generator set in real time based on the wind power actual measurement data according to claim 1, wherein the specific implementation method of the step S6 comprises the following steps:
after the kth time interval Δt (Δt < T), the real-time data stream P in the SCADA system is intercepted with a sliding window, and a new sample set is established. The expression of the sliding window is:
P[t+k△t-T:t+k△t]＝{(v t+k△t-T ,p t+k△t-T ,t t+k△t-T ),(v t+k△t-T+1 ,p t+k△t-T+1 ,t t+k△t-T+1 ),……,(v t+k△t ,p t+k△t ,t t+k△t )}
wherein, pt+Deltat-T: t+Deltat represents a sliding window separated from Pt-T: T by a time Deltat; the initial value of k is 1, and k=k+1 every time a cycle passes.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116447089A (en) * | 2023-06-19 | 2023-07-18 | 华电电力科学研究院有限公司 | Running state detection method, device and medium for wind turbine generator |
| CN117591987A (en) * | 2024-01-18 | 2024-02-23 | 北京国旺盛源智能终端科技有限公司 | Electric equipment monitoring method and system based on artificial intelligence |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116447089A (en) * | 2023-06-19 | 2023-07-18 | 华电电力科学研究院有限公司 | Running state detection method, device and medium for wind turbine generator |
| CN116447089B (en) * | 2023-06-19 | 2023-08-25 | 华电电力科学研究院有限公司 | Running state detection method, device and medium for wind turbine generator |
| CN117591987A (en) * | 2024-01-18 | 2024-02-23 | 北京国旺盛源智能终端科技有限公司 | Electric equipment monitoring method and system based on artificial intelligence |
| CN117591987B (en) * | 2024-01-18 | 2024-04-26 | 北京国旺盛源智能终端科技有限公司 | Electric equipment monitoring method and system based on artificial intelligence |
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