CN114254904B - Method and device for evaluating operation health degree of engine room of wind turbine generator - Google Patents
Method and device for evaluating operation health degree of engine room of wind turbine generator Download PDFInfo
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Abstract
The application provides a method and a device for evaluating the operation health degree of a wind turbine generator room, wherein the method comprises the following steps: determining an expected value of the cabin temperature and upper and lower boundaries of the cabin temperature of the current wind speed interval; determining upper and lower limit values of the temperature of the cabin of the wind turbine generator in the current wind speed interval; according to the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the cabin temperature of the wind turbine generator set in the current wind speed interval, calculating the operation health degrees of the first, second and third wind turbine generator sets respectively based on the cabin temperature expected value of the current wind speed interval and the upper and lower boundaries of the cabin temperature; and determining the maximum value of the operating health degrees of the first, second and third wind turbine generator cabins as the evaluation result of the operating health degrees of the wind turbine generator cabins. The method and the device for predicting the cabin operation health state can conduct flexible prediction on the cabin operation health state, bring more quantitative auxiliary information for cabin temperature abnormal trend prediction and cabin endogenous component operation control, and have important engineering significance for guaranteeing stable and economic operation of the wind turbine generator.
Description
Technical Field
The application relates to the technical field of evaluation of operation health degree of a wind turbine generator room, in particular to a method and a device for evaluating the operation health degree of the wind turbine generator room.
Background
At present, the wind turbine basically realizes high-altitude unattended operation, but most cabins work in complicated and severe external environments for a long time, such as large temperature difference change, severe wind speed change, sand dust and acid rain pollution and the like, so that the fault rate of the wind turbine is high, and the workload of operation and maintenance personnel and the maintenance cost of a wind field are increased. Most serious accidents are caused by the fact that the interior of the engine room is over-temperature, the interior of the engine room of the wind generation set mainly generates heat due to friction, collision and electromagnetic loss in the operation process of engine room components of the wind generation set, lubricating oil and radiating fins can be lowered in the high-strength operation of the wind generation set, and in addition, the operation states of other components in the wind generation set are changed, the comprehensive effects of the factors can cause uncertain temperature rise of the engine room of the wind generation set, the abnormal temperature rise of the engine room can cause component damage, the wind generation set is stopped for maintenance, and fire accidents are caused seriously, so that the monitoring and health degree evaluation of the interior state of the engine room are of great significance.
In the prior art, a temperature prediction model of a normal working state of a main bearing is established based on a nonlinear state estimation method or a machine learning method, a statistical distribution characteristic of residual errors is calculated by adopting a sliding window method, and when a confidence interval of a mean value or a standard deviation exceeds a set threshold value, the bearing is considered to be abnormal in working.
The applicant finds in research that, different from the operation health degree analysis of a single component of a wind turbine generator, the internal environment of an engine room is influenced by multisource uncertain factors, the internal operation environment of the engine room is complex, a method similar to residual error analysis in the prior art is not suitable for an engine room system comprising a plurality of different heat source components, the potential association relation and effective information in internal temperature monitoring data of the engine room cannot be deeply mined, trend prediction of the engine room temperature is achieved, and the abnormal temperature rise trend of the engine room temperature is rapidly identified.
Disclosure of Invention
In view of the above, the present application aims to provide a method and an apparatus for evaluating the operating health degree of a nacelle of a wind turbine generator, which can make flexible prediction on the operating health state of the nacelle, bring more quantitative auxiliary information for prediction of abnormal trend of the nacelle temperature and operation control of internal components of the nacelle, and have important engineering significance for ensuring stable and economic operation of the wind turbine generator.
In a first aspect, an embodiment of the present application provides a method for evaluating an operation health degree of a nacelle of a wind turbine, including:
determining an expected value of the cabin temperature and upper and lower boundaries of the cabin temperature of the current wind speed interval;
determining upper and lower limit values of the temperature of the cabin of the wind turbine generator in the current wind speed interval;
calculating the operation health degree of the first wind turbine generator room based on the expected value of the temperature of the generator room in the current wind speed interval, the real-time monitoring value of the temperature of the generator room at the current moment and the upper and lower limit values of the temperature of the generator room of the wind turbine generator in the current wind speed interval;
calculating the operation health degree of the second wind turbine generator room based on the upper boundary of the current wind speed interval, the real-time monitoring value of the current-time generator room temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
calculating the operation health degree of a third wind turbine generator room based on the lower boundary of the current wind speed interval, the real-time monitoring value of the current-time generator room temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
and determining the maximum value of the operating health degree of the first wind turbine generator room, the operating health degree of the second wind turbine generator room and the operating health degree of the third wind turbine generator room as an evaluation result of the operating health degree of the wind turbine generator room.
In one possible embodiment, determining the desired value of the nacelle temperature and the upper and lower boundaries of the nacelle temperature for the current wind speed interval comprises:
obtaining a cabin temperature fixed value prediction result of a cabin of the wind turbine generator at the current moment and an interval temperature prediction result in a preset confidence interval;
determining an expected cabin temperature value at the current moment based on the prediction result of the fixed cabin temperature value;
determining the upper and lower boundaries of the cabin temperature at the current moment based on the interval temperature prediction result;
determining a wind speed interval at the current moment;
and determining the cabin temperature expected value and the cabin temperature upper and lower boundaries of the current wind speed interval based on the cabin temperature expected value, the cabin temperature upper and lower boundaries and the wind speed interval at the current moment.
In one possible embodiment, determining the wind speed interval at the current time comprises:
dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the cabin of the wind turbine generator;
constructing a wind speed time series model based on Gaussian process regression according to the prediction time interval and the prediction time period of the cabin temperature of the wind turbine generator, and performing ultra-short-term wind speed fixed value prediction on the wind speed at the current moment based on the prediction of the wind speed time series model to obtain a wind speed prediction value at the current moment;
determining a wind speed interval corresponding to the wind speed predicted value based on the divided different wind speed intervals;
and determining the wind speed interval at the current moment based on the wind speed interval corresponding to the wind speed predicted value.
In a possible embodiment, obtaining a prediction result of a nacelle temperature fixed value of a nacelle of a wind turbine at a current time includes:
carrying out preprocessing including data cleaning and standardization on an operation parameter data set of a wind turbine generator cabin to obtain a standardized temperature parameter data set;
screening out the types of a plurality of target input variables from the standardized temperature parameter dataset based on a maximum information coefficient method; the maximum information coefficient between the target input variable and the temperature of the cabin of the wind turbine generator is larger than a preset value;
constructing a multivariable wind turbine generator cabin temperature prediction model based on a long-term and short-term memory network by taking the target input variables as model input variables and the wind turbine generator cabin temperature as model output variables;
selecting the super parameters of the long-term and short-term memory network by adopting a k-fold cross validation method to obtain a target super parameter combination by taking the lowest prediction error of the multivariable wind turbine generator cabin temperature prediction model as a target;
dividing the standardized temperature parameter data set into a test set and a training set, inputting the training set into the multivariable wind turbine generator cabin temperature prediction model containing the target hyperparameter combination for training, inputting the test set into the trained multivariable wind turbine generator cabin temperature prediction model containing the target hyperparameter combination for prediction, and obtaining a cabin temperature constant value prediction result of the wind turbine generator cabin at the current moment.
In one possible embodiment, the plurality of target input variables are of the type including at least two of power, gearbox oil temperature, ambient temperature and main bearing temperature.
In one possible embodiment, obtaining the prediction result of the interval temperature at the current time comprises:
determining a wind turbine generator room temperature prediction residual sequence according to the room temperature fixed value prediction result of the wind turbine generator room at the current moment and the actual measurement result of the wind turbine generator room temperature;
and constructing an engine room temperature interval prediction model based on the wind turbine generator engine room temperature prediction residual sequence by adopting a conditional kernel density estimation method, and predicting to obtain an interval temperature prediction result at the current moment based on the engine room temperature interval prediction model.
In one possible embodiment, determining upper and lower limit values of the temperature of the nacelle of the wind turbine in the current wind speed interval includes:
and dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the wind turbine generator cabin, and determining the upper and lower limit values of the wind turbine generator cabin temperature corresponding to each wind speed interval.
In a second aspect, an embodiment of the present application further provides a wind turbine generator nacelle operation health evaluation device, including:
the first determining module is used for determining the expected value of the cabin temperature and the upper and lower boundaries of the cabin temperature of the current wind speed interval;
the second determining module is used for determining the upper limit value and the lower limit value of the cabin temperature of the wind turbine generator in the current wind speed interval;
the first calculation module is used for calculating the operation health degree of the first wind turbine generator cabin based on the cabin temperature expected value in the current wind speed interval, the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the wind turbine generator cabin temperature in the current wind speed interval;
the second calculation module is used for calculating the operation health degree of the second wind turbine generator room based on the upper boundary of the wind turbine generator room temperature in the current wind speed interval, the real-time monitoring value of the current-time wind turbine generator room temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
the third calculation module is used for calculating the operation health degree of the cabin of the third wind turbine generator set based on the lower boundary of the cabin temperature in the current wind speed interval, the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the cabin temperature of the wind turbine generator set in the current wind speed interval;
and the health degree evaluation module is used for determining the maximum value of the operation health degree of the first wind turbine generator room, the operation health degree of the second wind turbine generator room and the operation health degree of the third wind turbine generator room as the evaluation result of the operation health degree of the wind turbine generator room.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions being executable by the processor to perform the steps of the first aspect or any one of the possible implementations of the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium, where a computer program is stored, and the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
According to the evaluation method for the operating health degree of the cabin of the wind turbine generator, firstly, an expected cabin temperature value and upper and lower cabin temperature boundaries of a current wind speed interval are determined, and upper and lower limit values of the cabin temperature of the wind turbine generator in the current wind speed interval are determined; then, according to the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the cabin temperature of the wind turbine generator in the current wind speed interval, respectively calculating the operation health degree of the first wind turbine generator cabin, the operation health degree of the second wind turbine generator cabin and the operation health degree of the third wind turbine generator cabin based on the cabin temperature expected value of the current wind speed interval and the upper and lower boundaries of the cabin temperature; and finally, determining the maximum value of the operating health degree of the first wind turbine generator room, the operating health degree of the second wind turbine generator room and the operating health degree of the third wind turbine generator room as the operating health degree evaluation result of the wind turbine generator room. The method and the device for predicting the cabin operation health state can conduct flexible prediction on the cabin operation health state, bring more quantitative auxiliary information for cabin temperature abnormal trend prediction and cabin endogenous component operation control, and have important engineering significance for guaranteeing stable and economic operation of the wind turbine generator.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 shows a flowchart of a method for evaluating the operating health of a nacelle of a wind turbine provided in an embodiment of the present application;
FIG. 2 shows a schematic structural diagram of a multivariable wind turbine generator cabin temperature prediction model based on a long-short term memory network;
FIG. 3 is a schematic diagram showing cabin temperature setpoint predictions and interval temperature predictions over a preset confidence interval;
FIG. 4 shows a schematic view of the wind speed interval division;
FIG. 5 shows a schematic of the wind speed time series prediction;
FIG. 6 shows a schematic view of a cabin health quantification indicator result;
fig. 7 is a schematic structural diagram illustrating a wind turbine generator nacelle operation health evaluation device provided by an embodiment of the present application;
fig. 8 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Considering that the operation health degree analysis of a single component different from that of a wind turbine generator is different from the operation health degree analysis of a single component of the wind turbine generator, the internal environment of the cabin is influenced by multi-source uncertain factors, the operation environment in the cabin is complex, the method adopting similar residual error analysis in the prior art is not suitable for a cabin system comprising a plurality of different heat source components, the potential association relation and effective information in the internal temperature monitoring data of the cabin can not be deeply mined, the trend prediction of the cabin temperature is realized, and the abnormal temperature rising trend of the cabin temperature is rapidly identified. Based on this, the embodiment of the application provides a method and a device for evaluating the operation health degree of a cabin of a wind turbine generator system, which are described below through an embodiment.
For facilitating understanding of the embodiment, a method for evaluating the operation health of the nacelle of the wind turbine generator disclosed in the embodiment of the present application is first described in detail.
Referring to fig. 1, fig. 1 is a flowchart of a method for evaluating an operation health degree of a nacelle of a wind turbine generator provided in an embodiment of the present application. As shown in fig. 1, the method may include the steps of:
s101, determining an expected cabin temperature value and upper and lower cabin temperature boundaries of a current wind speed interval;
s102, determining upper and lower limit values of the cabin temperature of the wind turbine generator in the current wind speed interval;
s103, calculating the operation health degree of the first wind turbine generator room based on the expected value of the temperature of the wind turbine generator room in the current wind speed interval, the real-time monitoring value of the temperature of the wind turbine generator room at the current moment and the upper and lower limit values of the temperature of the wind turbine generator room in the current wind speed interval;
s104, calculating the operation health degree of a second wind turbine generator room based on the upper boundary of the room temperature in the current wind speed interval, the real-time monitoring value of the room temperature at the current moment and the upper and lower limit values of the room temperature of the wind turbine generator in the current wind speed interval;
s105, calculating the operation health degree of a third wind turbine generator room based on the lower boundary of the current wind speed interval, the real-time monitoring value of the current-time generator room temperature and the upper and lower limit values of the current wind speed interval wind turbine generator room temperature;
s106, determining the maximum value of the operation health degree of the first wind turbine generator room, the operation health degree of the second wind turbine generator room and the operation health degree of the third wind turbine generator room as an evaluation result of the operation health degree of the wind turbine generator room.
In order to more clearly understand the present invention, the above steps are described in detail below.
Step S101 may determine the desired value of the nacelle temperature and the upper and lower boundaries of the nacelle temperature for the current wind speed interval by the following sub-steps:
s1011, obtaining a cabin temperature fixed value prediction result of a cabin of the wind turbine generator at the current moment and an interval temperature prediction result in a preset confidence level interval;
s1012, determining an expected cabin temperature value at the current moment based on the prediction result of the fixed cabin temperature value;
s1013, determining the upper and lower boundaries of the cabin temperature at the current moment based on the interval temperature prediction result;
s1014, determining a wind speed interval at the current moment;
and S1015, based on the expected cabin temperature value at the current moment, the upper and lower boundaries of the cabin temperature and the wind speed interval, determining the expected cabin temperature value and the upper and lower boundaries of the cabin temperature in the current wind speed interval.
In step S1011, a prediction result of the nacelle temperature constant value of the wind turbine generator nacelle at the current time may be obtained through the following steps:
s10111, preprocessing the operation parameter data set of the wind turbine generator cabin, including data cleaning and standardization, to obtain a standardized temperature parameter data set;
s10112, screening out types of a plurality of target input variables from the standardized temperature parameter data set based on a maximum information coefficient method; the maximum information coefficient between the target input variable and the temperature of the cabin of the wind turbine generator is larger than a preset value;
s10113, constructing a multivariable wind turbine generator cabin temperature prediction model based on a long-term and short-term memory network by taking the target input variables as model input variables and the wind turbine generator cabin temperature as model output variables;
s10114, selecting the hyper-parameters of the long-term and short-term memory network by adopting a k-fold cross validation method to obtain a target hyper-parameter combination by taking the lowest prediction error of the multivariable wind turbine generator cabin temperature prediction model as a target;
s10115, dividing the standardized temperature parameter data set into a test set and a training set, inputting the training set into the multivariate wind turbine generator cabin temperature prediction model containing the target hyper-parameter combination for training, inputting the test set into the trained multivariate wind turbine generator cabin temperature prediction model containing the target hyper-parameter combination for prediction, and obtaining a cabin temperature constant value prediction result of the wind turbine generator cabin at the current moment.
In step S10111, taking a data set of three months of operation of a certain wind turbine nacelle obtained from the SCADA system of the wind turbine as an example, the wind turbine nacelle temperature parameter database is reestablished according to a certain sampling time (for example, sampling once every 5 minutes). The wind turbine generator cabin temperature parameter database comprises various temperature-related operating parameter data such as wind speed, power, cabin temperature, environment temperature, main bearing temperature, gearbox oil temperature, gearbox bearing temperature and generator bearing temperature. The operating parameter data set of the wind turbine generator room is obtained based on a wind turbine generator room temperature parameter database.
The method for cleaning the data of the operating parameter data set of the cabin of the wind turbine generator comprises the following steps: and removing and filling abnormal data and missing data in the operating parameter data set of the wind turbine generator cabin by using a data cleaning method to obtain the parameter data set of the wind turbine generator cabin.
In order to eliminate the influence of different parameter dimensions on model training, the convergence rate of the model needs to be ensured and data processing is convenient, in this embodiment, a min-max method standardization method is adopted to map data into an interval of [ -1,1], and the formula is as follows:
where x is the data of a parameter sample, x min ，x max The minimum value and the maximum value of the index data are respectively,
is normalized data.
In the embodiment, the min-max method standardization method is adopted to standardize the parameter data set of the wind turbine generator cabin obtained after data cleaning, so that a standardized temperature parameter data set is obtained.
In step S10112, the maximum information coefficient is used to characterize the degree of association between the model input variable and the model output variable. In the step, the model output variable is the wind turbine generator cabin temperature, and the maximum information coefficient is the correlation degree between the model input variable and the wind turbine generator cabin temperature. And screening a plurality of variables of which the maximum information coefficients are larger than a preset value from the standardized temperature parameter data set based on a maximum information coefficient Method (MIC) as a plurality of target input variables. Wherein the types of the plurality of target input variables include at least two of power, gearbox oil temperature, ambient temperature, and main bearing temperature. In the present embodiment, the preset value may be 0.3, and is not particularly limited herein.
In step S10113, as shown in fig. 2, the model input variables are 5 kinds of variable data related to the nacelle temperature including the nacelle temperature, that is, the nacelle temperature, the power, the gearbox oil temperature, the ambient temperature, and the main bearing temperature. And the model output variable is the cabin temperature data of the wind turbine generator set, which is separated from the input variable by 10 min. And then constructing a multivariable wind turbine generator cabin temperature prediction model based on a long-short term memory network (LSTM).
In step S10114, a k-fold cross validation method is used to select optimal parameters for multiple superparameters including the number of network layers, the number of iterations of neurons in the hidden layer, and the size of batch processing. Specifically, a data set is divided into k sub-data sets, one of the k sub-data sets is taken as a test set, and meanwhile, other k-1 sub-data sets are used for training, then the ith test set is input into a model for computing MSE, the k MSE is averaged to obtain a final model performance evaluation index CV (k), until an optimal hyper-parameter is selected, and an index computing formula is as follows:
and selecting the optimal hyper-parameter combination as the target hyper-parameter combination based on the k-fold cross verification method.
In step S10115, data ten days after three months in the standardized temperature parameter data set is taken as a test set, and the rest of data is taken as a training set. Inputting the training set into the multivariate wind turbine generator cabin temperature prediction model containing the target hyper-parameter combination for training, and then inputting the test set into the trained multivariate wind turbine generator cabin temperature prediction model containing the target hyper-parameter combination for prediction, so as to obtain a cabin temperature constant value prediction result of the wind turbine generator cabin at the current moment as shown in fig. 3.
In step S1011, the section temperature prediction result at the preset confidence interval at the current time may be obtained by:
s10116, determining a wind turbine generator room temperature prediction residual sequence according to the current time wind turbine generator room temperature fixed value prediction result and the wind turbine generator room temperature actual measurement result;
s10117, a cabin temperature interval prediction model based on the wind turbine generator cabin temperature prediction residual sequence is built by adopting a conditional kernel density estimation method, and an interval temperature prediction result at the current moment is obtained through prediction based on the cabin temperature interval prediction model.
In step S10117, a conditional kernel density estimation method (CKDE) is used to determine an uncertainty interval boundary of the nacelle temperature based on the prediction residual sequence of the nacelle temperature of the wind turbine generator. In the conditional kernel density estimation method (CKDE), the kernel function is a gaussian kernel function, the bandwidth is calculated by a Silverman estimation method, and the obtained interval temperature prediction result under 90% confidence is shown in fig. 3.
In step S1012, the expected cabin temperature value at the present time is determined based on the prediction result of the cabin temperature constant value.
In step S1013, the upper and lower temperature boundaries of the nacelle at the current time are determined based on the section temperature prediction result.
In step S1014, the wind speed interval at the current time may be determined by the following sub-steps:
s10141, dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the wind turbine generator cabin;
s10142, constructing a wind speed time series model based on Gaussian process regression according to the prediction time interval and the prediction time period of the cabin temperature of the wind turbine generator, and performing ultra-short-term wind speed constant value prediction on the wind speed at the current moment based on the wind speed time series model prediction to obtain a wind speed prediction value at the current moment;
s10143, determining a wind speed interval corresponding to the wind speed prediction value based on the divided different wind speed intervals;
s10144, determining a wind speed interval at the current moment based on the wind speed interval corresponding to the wind speed predicted value.
In step S10141, the temperature of the nacelle of the wind turbine has interval characteristics at different wind speeds. As shown in FIG. 4, the present embodiment uses a Bin method with a spacing of 2m/s to divide the wind speed-cabin temperature into intervals.
In step S10142, a wind speed time series model based on Gaussian Process Regression (GPR) is built according to the prediction time interval and the prediction time period of the cabin temperature of the wind turbine generator, and ultra-short-term wind speed constant value prediction is carried out on the wind speed at the current moment based on the wind speed time series model prediction, so that the wind speed prediction value at the current moment is obtained. The wind speed data volume is the same as the cabin temperature data set, and the results of simulation verification of the test set data are shown in fig. 5.
In steps S10143 and S10144, since different wind speed intervals are divided, the wind speed interval in which the wind speed predicted value at the current time is located, that is, the wind speed interval at the current time is searched.
In step S1015, the expected value of the nacelle temperature and the upper and lower boundaries of the nacelle temperature at the current time and the wind speed interval at the current time are obtained, so that the expected value of the nacelle temperature and the upper and lower boundaries of the nacelle temperature in the current wind speed interval are determined.
In step S102, as shown in fig. 4, different wind speed intervals are divided based on the wind speed data and the temperature data in the operating parameter data set of the wind turbine generator room, and the upper and lower limit values of the wind turbine generator room temperature corresponding to each wind speed interval are determined.
In step S103, the temperature of the wind turbine generator cabin is used as an evaluation index of the operating Health degree of the wind turbine generator cabin, and a Health Quantitative Index (HQI) h (t) is used to represent the operating Health degree of the first wind turbine generator cabin (i.e., the current state Health degree of the inside of the first wind turbine generator cabin). The real-time monitoring value of the temperature of the engine room at the time t when the wind turbine generator operates is set as T (t), and the current health degree of the engine room can be calculated according to the following formula:
wherein h (t) takes on the value of [0,1]In the meantime, 0 represents the optimal state of the operation of the cabin, and 1 represents the early warning state of the operation health degree of the cabin;
and
respectively the upper and lower limit values of the cabin temperature of the wind turbine generator in the current wind speed interval,
the expected value of the cabin temperature in the current wind speed interval is obtained.
In step S104, the expected value of the cabin temperature in the current wind speed interval in the above-mentioned h (t) expression is calculated
And replacing the upper boundary of the cabin temperature of the current wind speed interval, and calculating the operation health degree of the second wind turbine cabin.
In step S105, the expected value of the cabin temperature in the current wind speed interval in the above-mentioned h (t) expression is calculated
And replacing the lower boundary of the cabin temperature of the current wind speed interval, and calculating the operation health degree of the third wind turbine cabin.
In step S106, the maximum value of the operation health degree of the first wind turbine generator room, the operation health degree of the second wind turbine generator room and the operation health degree of the third wind turbine generator room is selected and determined as the operation health degree evaluation result of the wind turbine generator room.
The quantized health index of the operation of the cabin of the wind turbine generator is shown in fig. 6, the change trend of the HQI index is basically similar to the change trend of the temperature of the cabin, and the HQI index can truly reflect the abnormal change of the temperature of the cabin, has no obvious great increase or decrease trend and indicates that the operation condition of the wind turbine generator is in a normal range.
According to the evaluation method for the operation health degree of the cabin of the wind turbine generator, firstly, an expected cabin temperature value and upper and lower cabin temperature boundaries of a current wind speed interval are determined, and upper and lower limit values of the cabin temperature of the wind turbine generator in the current wind speed interval are determined; then, according to the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the cabin temperature of the wind turbine generator in the current wind speed interval, respectively calculating the operation health degree of the first wind turbine generator cabin, the operation health degree of the second wind turbine generator cabin and the operation health degree of the third wind turbine generator cabin based on the cabin temperature expected value of the current wind speed interval and the upper and lower boundaries of the cabin temperature; and finally, determining the maximum value of the operating health degree of the first wind turbine generator room, the operating health degree of the second wind turbine generator room and the operating health degree of the third wind turbine generator room as the operating health degree evaluation result of the wind turbine generator room. The method and the device for predicting the cabin operation health state can conduct flexible prediction on the cabin operation health state, bring more quantitative auxiliary information for cabin temperature abnormal trend prediction and cabin endogenous component operation control, and have important engineering significance for guaranteeing stable and economic operation of the wind turbine generator.
Based on the same technical concept, the embodiment of the application also provides a wind turbine generator cabin operation health degree evaluation device, electronic equipment, a computer storage medium and the like, and the following embodiments can be specifically referred to.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an evaluation device for an operation health degree of a nacelle of a wind turbine generator provided in an embodiment of the present application. As shown in fig. 7, the apparatus may include:
the first determining module 10 is used for determining an expected value of the cabin temperature and upper and lower boundaries of the cabin temperature of the current wind speed interval;
the second determining module 20 is configured to determine an upper limit value and a lower limit value of the cabin temperature of the wind turbine generator in the current wind speed interval;
the first calculation module 30 is configured to calculate an operation health degree of the first wind turbine generator room based on a desired value of the wind turbine generator room temperature in the current wind speed interval, a real-time monitoring value of the current-time wind turbine generator room temperature, and upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
the second calculation module 40 is used for calculating the operation health degree of the second wind turbine generator room based on the upper boundary of the current wind speed interval room temperature, the real-time monitoring value of the current time room temperature and the upper and lower limit values of the current wind speed interval room temperature of the wind turbine generator;
the third calculation module 50 is used for calculating the operation health degree of the third wind turbine generator room based on the lower boundary of the current wind speed interval, the real-time monitoring value of the current-time cabin temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
and the health degree evaluation module 60 is configured to determine the maximum value of the first wind turbine generator room operation health degree, the second wind turbine generator room operation health degree and the third wind turbine generator room operation health degree as a wind turbine generator room operation health degree evaluation result.
In a possible implementation, the first determining module 10 comprises:
the acquiring unit is used for acquiring the cabin temperature fixed value prediction result of the wind turbine generator cabin at the current moment and the interval temperature prediction result in the preset confidence level interval;
the first determining unit is used for determining an expected cabin temperature value at the current moment based on the prediction result of the fixed cabin temperature value;
the second determining unit is used for determining the upper and lower boundaries of the cabin temperature at the current moment based on the interval temperature prediction result;
a third determining unit, configured to determine a wind speed interval at the current time;
and the fourth determining unit is used for determining the cabin temperature expected value and the cabin temperature upper and lower boundaries of the current wind speed interval based on the cabin temperature expected value, the cabin temperature upper and lower boundaries and the wind speed interval at the current moment.
In a possible implementation manner, the third determining unit is specifically configured to:
dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the cabin of the wind turbine generator;
constructing a wind speed time series model based on Gaussian process regression according to the prediction time interval and the prediction time period of the cabin temperature of the wind turbine generator, and performing ultra-short-term wind speed fixed value prediction on the wind speed at the current moment based on the prediction of the wind speed time series model to obtain a wind speed prediction value at the current moment;
determining a wind speed interval corresponding to the wind speed predicted value based on the divided different wind speed intervals;
and determining the wind speed interval at the current moment based on the wind speed interval corresponding to the wind speed predicted value.
In a possible implementation manner, the obtaining unit is specifically configured to:
carrying out preprocessing including data cleaning and standardization on an operation parameter data set of a cabin of a wind turbine generator to obtain a standardized temperature parameter data set;
screening out the types of a plurality of target input variables from the standardized temperature parameter dataset based on a maximum information coefficient method; the maximum information coefficient between the target input variable and the temperature of the cabin of the wind turbine generator is larger than a preset value;
constructing a multivariable wind turbine generator cabin temperature prediction model based on a long-term and short-term memory network by taking the target input variables as model input variables and the wind turbine generator cabin temperature as model output variables;
selecting the hyper-parameters of the long-short term memory network by adopting a k-fold cross validation method to obtain a target hyper-parameter combination by taking the lowest prediction error of the multivariable wind turbine generator cabin temperature prediction model as a target;
and dividing the standardized temperature parameter data set into a test set and a training set, inputting the training set into the multivariable wind turbine generator cabin temperature prediction model containing the target hyperparameter combination for training, inputting the test set into the trained multivariable wind turbine generator cabin temperature prediction model containing the target hyperparameter combination for prediction, and obtaining a cabin temperature fixed value prediction result of the wind turbine generator cabin at the current moment.
In one possible embodiment, the plurality of target input variables are of the type including at least two of power, gearbox oil temperature, ambient temperature and main bearing temperature.
In a possible implementation manner, the obtaining unit is specifically configured to:
determining a wind turbine generator room temperature prediction residual sequence according to the room temperature fixed value prediction result of the wind turbine generator room at the current moment and the actual measurement result of the wind turbine generator room temperature;
and constructing an engine room temperature interval prediction model based on the wind turbine generator engine room temperature prediction residual sequence by adopting a conditional kernel density estimation method, and predicting to obtain an interval temperature prediction result at the current moment based on the engine room temperature interval prediction model.
In a possible implementation, the second determining module 20 is specifically configured to:
and dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the wind turbine generator cabin, and determining the upper and lower limit values of the wind turbine generator cabin temperature corresponding to each wind speed interval.
The embodiment of the application discloses an electronic device, as shown in fig. 8, including: a processor 801, a memory 802, and a bus 803, the memory 802 storing machine readable instructions executable by the processor 801, the processor 801 communicating with the memory 802 via the bus 803 when the electronic device is in operation. The machine readable instructions are executed by the processor 801 to perform the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
The computer program product of the method for evaluating the operating health of the nacelle of the wind turbine generator provided by the embodiment of the present application includes a computer-readable storage medium storing a nonvolatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and will not be described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present application and are intended to be covered by the appended claims. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A wind turbine generator system cabin operation health degree evaluation method is characterized by comprising the following steps:
determining an expected value of the cabin temperature and upper and lower boundaries of the cabin temperature of the current wind speed interval;
determining upper and lower limit values of the temperature of the cabin of the wind turbine generator in the current wind speed interval;
calculating the operation health degree of the first wind turbine generator room based on the expected value of the room temperature in the current wind speed interval, the real-time monitoring value of the room temperature at the current moment and the upper and lower limit values of the room temperature of the wind turbine generator in the current wind speed interval;
calculating the operation health degree of the second wind turbine generator room based on the upper boundary of the current wind speed interval, the real-time monitoring value of the current-time generator room temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
calculating the operation health degree of a third wind turbine generator room based on the lower boundary of the current wind speed interval, the real-time monitoring value of the current-time generator room temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
determining the maximum value of the operating health degree of the first wind turbine generator room, the operating health degree of the second wind turbine generator room and the operating health degree of the third wind turbine generator room as an evaluation result of the operating health degree of the wind turbine generator rooms;
determining the expected value of the cabin temperature and the upper and lower boundaries of the cabin temperature of the current wind speed interval, comprising the following steps:
obtaining a cabin temperature fixed value prediction result of a cabin of the wind turbine at the current moment and an interval temperature prediction result in a preset confidence level interval;
determining an expected cabin temperature value at the current moment based on the prediction result of the fixed cabin temperature value;
determining the upper and lower boundaries of the cabin temperature at the current moment based on the interval temperature prediction result;
determining a wind speed interval at the current moment;
based on the expected cabin temperature value at the current moment, the upper and lower boundaries of the cabin temperature and the wind speed interval, determining the expected cabin temperature value and the upper and lower boundaries of the cabin temperature of the current wind speed interval;
determining the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval, including:
and dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the wind turbine generator cabin, and determining the upper and lower limit values of the wind turbine generator cabin temperature corresponding to each wind speed interval.
2. The method of claim 1, wherein determining a wind speed interval for a current time comprises:
dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the cabin of the wind turbine generator;
constructing a wind speed time series model based on Gaussian process regression according to the prediction time interval and the prediction time period of the cabin temperature of the wind turbine generator, and performing ultra-short-term wind speed fixed value prediction on the wind speed at the current moment based on the wind speed time series model to obtain a wind speed prediction value at the current moment;
determining a wind speed interval corresponding to the wind speed predicted value based on the divided different wind speed intervals;
and determining the wind speed interval at the current moment based on the wind speed interval corresponding to the wind speed predicted value.
3. The method of claim 1, wherein obtaining a prediction of a nacelle temperature setpoint for the wind turbine nacelle at the current time comprises:
carrying out preprocessing including data cleaning and standardization on an operation parameter data set of a wind turbine generator cabin to obtain a standardized temperature parameter data set;
screening out the types of a plurality of target input variables from the standardized temperature parameter dataset based on a maximum information coefficient method; the maximum information coefficient between the target input variable and the temperature of the cabin of the wind turbine generator is larger than a preset value;
establishing a multivariable wind turbine generator cabin temperature prediction model based on a long-term and short-term memory network by taking the target input variables as model input variables and the wind turbine generator cabin temperature as model output variables;
selecting the super parameters of the long-term and short-term memory network by adopting a k-fold cross validation method to obtain a target super parameter combination by taking the lowest prediction error of the multivariable wind turbine generator cabin temperature prediction model as a target;
dividing the standardized temperature parameter data set into a test set and a training set, inputting the training set into the multivariable wind turbine generator cabin temperature prediction model containing the target hyperparameter combination for training, inputting the test set into the trained multivariable wind turbine generator cabin temperature prediction model containing the target hyperparameter combination for prediction, and obtaining a cabin temperature constant value prediction result of the wind turbine generator cabin at the current moment.
4. A method according to claim 3, wherein the types of the plurality of target input variables comprise at least two of power, gearbox oil temperature, ambient temperature and main bearing temperature.
5. The method of claim 1, wherein obtaining the interval temperature prediction at the current time comprises:
determining a wind turbine generator room temperature prediction residual sequence according to the room temperature fixed value prediction result of the wind turbine generator room at the current moment and the actual measurement result of the wind turbine generator room temperature;
and constructing an engine room temperature interval prediction model based on the wind turbine generator engine room temperature prediction residual sequence by adopting a conditional kernel density estimation method, and predicting to obtain an interval temperature prediction result at the current moment based on the engine room temperature interval prediction model.
6. The utility model provides a wind turbine generator system cabin operation health degree evaluation device which characterized in that includes:
the first determining module is used for determining the cabin temperature expected value and the upper and lower boundaries of the cabin temperature of the current wind speed interval;
the second determining module is used for determining the upper limit value and the lower limit value of the cabin temperature of the wind turbine generator in the current wind speed interval;
the first calculation module is used for calculating the operation health degree of the first wind turbine generator cabin based on the cabin temperature expected value in the current wind speed interval, the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the wind turbine generator cabin temperature in the current wind speed interval;
the second calculation module is used for calculating the operation health degree of the second wind turbine generator room based on the upper boundary of the wind turbine generator room temperature in the current wind speed interval, the real-time monitoring value of the current-time wind turbine generator room temperature and the upper and lower limit values of the wind turbine generator room temperature in the current wind speed interval;
the third calculation module is used for calculating the operation health degree of the cabin of the third wind turbine generator set based on the lower boundary of the cabin temperature in the current wind speed interval, the real-time monitoring value of the cabin temperature at the current moment and the upper and lower limit values of the cabin temperature of the wind turbine generator set in the current wind speed interval;
the health degree evaluation module is used for determining the maximum value of the operation health degree of the first wind turbine generator room, the operation health degree of the second wind turbine generator room and the operation health degree of the third wind turbine generator room as the evaluation result of the operation health degree of the wind turbine generator room;
the first determining module includes:
the acquiring unit is used for acquiring the cabin temperature fixed value prediction result of the wind turbine generator cabin at the current moment and the interval temperature prediction result in the preset confidence level interval;
the first determining unit is used for determining an expected cabin temperature value at the current moment based on the prediction result of the fixed cabin temperature value;
a second determination unit, configured to determine an upper and lower boundary of the cabin temperature at the current time based on the interval temperature prediction result;
the third determining unit is used for determining the wind speed interval at the current moment;
a fourth determining unit, configured to determine an expected cabin temperature value and an upper and lower cabin temperature boundary of the current wind speed interval based on the expected cabin temperature value, the upper and lower cabin temperature boundaries, and the wind speed interval at the current time;
the second determining module is specifically configured to:
and dividing different wind speed intervals based on wind speed data and temperature data in the operating parameter data set of the wind turbine generator cabin, and determining the upper and lower limit values of the wind turbine generator cabin temperature corresponding to each wind speed interval.
7. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method of any one of claims 1 to 5.
8. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 5.
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