WO2018121668A1 - A method and system for evaluating wind turbine generator performance - Google Patents

A method and system for evaluating wind turbine generator performance Download PDF

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Publication number
WO2018121668A1
WO2018121668A1 PCT/CN2017/119358 CN2017119358W WO2018121668A1 WO 2018121668 A1 WO2018121668 A1 WO 2018121668A1 CN 2017119358 W CN2017119358 W CN 2017119358W WO 2018121668 A1 WO2018121668 A1 WO 2018121668A1
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wtg
wtgs
power production
specific
data
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PCT/CN2017/119358
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English (en)
French (fr)
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Matthew SUMMERS
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Envision Energy (Jiangsu) Co., Ltd.
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Application filed by Envision Energy (Jiangsu) Co., Ltd. filed Critical Envision Energy (Jiangsu) Co., Ltd.
Priority to CN201780081758.4A priority Critical patent/CN110168220B/zh
Publication of WO2018121668A1 publication Critical patent/WO2018121668A1/en

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/84—Modelling or simulation
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/328—Blade pitch angle
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present invention relates to a method of evaluating performance of a specific wind turbine generator (WTG) amongst a set of wind turbine generators (WTGs) .
  • the method may comprise one or more of the following acts. There may be an act of measuring actual power production of the specific WTG. There may be an act of receiving a set of data from the specific WTG and at least one set of data from at least one of the other WTGs in the set of WTGs. There may be an act of estimating power production of the specific WTG as a function of the received set of data from the specific WTGs and the received at least one set of data from at least one of the other WTGs. There may be an act of comparing the actual power production to the estimated power production. The act of estimating power production may be based on a computer implemented evaluation.
  • the present invention also relates to a system for evaluating performance of a WTG amongst a set of WTGs.
  • WTGs wind turbine generators
  • Traditional methods to predict power performance of wind turbine generators rely on a “power curve” describing the expected power generation as a function of wind speed measured by an anemometer mounted on a nacelle or a met mast, along with other atmospheric properties like density and possibly even humidity and temperature among others. The actual power is then compared to values looked up from the power curve to evaluate the WTG performance.
  • Such methods are subject to a variety of problems associated with anemometer measurements.
  • Pairs may be pre-selected to account for spatial differences to some extent.
  • US 2011/0270450 describes a method and system for evaluation of a wind turbine performance for a specific wind turbine in a wind farm.
  • the measurement of actual power production (wind turbine performance) of a specific wind turbine and similar wind turbines within the pair is performed.
  • the suggested pairing and handling of paired wind turbines have shown to be problematic.
  • Patent application US20160084233 discloses the application of segmented regression to model the power produced by an entire farm, using data from a subset of WTGs as input variables.
  • An objective is achieved by continuously evaluating performance of a specific wind turbine generator (WTG) amongst a set of wind turbine generators (WTGs) .
  • the method may comprise one or more of the following acts.
  • the act of estimating power production may be based on a computer implemented evaluation.
  • Such method allows WTGs to be continuously compared relatively to each other and thereby giving the operator or manufacturer valuable information about the performance of one or more wind turbines by using data empirically available.
  • the method gives an easy and reliable indication of the performance of a specific WTG.
  • the computer implemented evaluation may be implemented as computer instructions representing one or more of the following types of models.
  • a regression model There may be an implementation of a support vector regression model. There may be an implementation of machine learning such as a neural network. Machine learning may be under a category of models known as ” supervised learning” .
  • Pre-processing, normalisation and general preparation of data may be required and depend on the actual model implementation.
  • a person skilled in the art will appreciate a variety of models chosen amongst the mentioned types of models.
  • a person skilled in the art will also appreciate varieties of naming of models.
  • a model may be found amongst models known as supervised learning or regression. Implementing such models will give the power for the specific WTG.
  • a person skilled in the art and familiar with such classes of algorithms will readily know where to find literature or software libraries to implement the methods.
  • One starting point of supervised learning method may be the so-called “Random Forest Regression” [Ho, Tin Kam (1998) . "The Random Subspace Method for Constructing Decision Forests” .
  • Another starting point may be “IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8) : 832–844] ” .
  • k-Nearest Neighbors may be Altman, N.S. (1992) . "An introduction to kernel and nearest-neighbor nonparametric regression” . The American Statistician. 46 (3) : 175–185.
  • a distance metric is required and it will be a matter of choice for the person skilled in the art, but for the purposes here a weighted Euclidean distance metric may be used.
  • the model inputs are the data from the specific WTG and other WTGs, excluding the specific WTG’s power.
  • the model output is the specific WTG’s power.
  • the models are trained with historical data including both the inputs and outputs and then, after training, make predictions of the output using the input.
  • An alarm may be based on a more generalized criteria, e.g. an alarm may be activated if the average power for the last N-hours is a certain percentage less than the average predicted power for the last N hours. Alternatively, an alarm may be triggered using confidence intervals.
  • the act of identifying may be based on characteristics of how the underperformance in power production happens in time. Slow gradual reduction in performance can be caused by blade contamination.
  • the reason could be icing. If an abrupt decrease in actual power production is observed, the reason could be a software (controller) update, blade change or breakage.
  • the received data from the specific WTG includes at least the actual pitch setting of the specific WTG.
  • the selected model may then use pitch settings of the specific WTG as a parameter and produce an estimated power output based on the pitch settings of other WTGs with actual power outputs.
  • the method allows for the actual power production to be compared with an estimated power production based on the power production of other WTGs by fitting, modelling or predicting.
  • the model input data includes pitch from other WTGs, but not the specific WTG.
  • the selected model may thus optionally include the pitch setting of the specific WTG and/or other WTGs.
  • Other settings of the specific WTG may be included in the received data. For example, torque settings.
  • the selected model may then use torque settings of other WTGs.
  • the model input data includes torque from other WTGs, but not the specific WTG.
  • the selected model may thus optionally include the torque setting of the specific WTG and/or other WTGs.
  • received data from the other WTGs include at least pitch settings and corresponding actual power production of one or more of the other WTGs.
  • the act of receiving data may further comprise receiving a set of meteorological conditions pertinent to each WTG and wherein the act of estimating furthermore is a function of the set of meteorological conditions.
  • the collected parameters or data from the various WTGs and other devices may be delivered as independent variables.
  • the values of these parameters at each point in time may be used in the function or modelling such as used to create the regression function, which targets the power on the specific WTG.
  • the evaluation or model may be adjusted or refined. Weights or free parameters may be adjusted according to the output or other factual information available that will increase the reliability of the evaluation.
  • a regression model may show that one or more particular WTGs are outside a certain statistical threshold and the particular WTGs are given less weight or eliminated in following evaluation or modelling.
  • the set of wind turbine generators comprises at least one reference WTG.
  • One particular WTG amongst the other WTGs may be maintained or empirically observed to be performing more reliable than the remaining WTGs.
  • Such particular WTG may be considered as a reference WTG and used with a higher weight than other WTGs.
  • a model or algorithm may also automatically learn if the power output is more related with some WTGs than with others.
  • evaluating performance may be performed with more or each one of the WTGs in a set of wind turbine generators (WTGs) as the specific wind turbine generator (WTG) .
  • Such acts may be used to calibrate a model or evaluation. Furthermore, such acts may be used to classify or order the WTGs relatively to each other.
  • this subset may be identified and form the basis for a better and more precise evaluation.
  • the acts may be used to reduce the complexity by identifying the most suitable set of other WTGs and then reduce required data or computational efforts.
  • the specific WTG comprises one or more additional WTG features such as vortex generators, Gurney devices, and serrations, when compared to the other wind turbine generators (WTGs) .
  • the specific wind turbine has one or more control settings that vary compared to the other wind turbine generators (WTGs) .
  • An object may be achieved by a computer program product comprising instruction to perform one or more actions of evaluating performance according to outlined methods or actions.
  • An objective may be achieved by a wind turbine evaluating system comprising one or more of the following features.
  • There may be a data receiving system configured to receive a set of data from the specific WTG and a at least one set of data from at least one of the other WTGs in the set of WTGs.
  • “continuously” is understood more than one and preferably periodically or regularly. In principle, there may be a first (former) evaluation and later on a second (later) evaluation. In practice, a person skilled in the art will know how to adjust the regularity, but an evaluation may be performed periodically with 10 minutes intervals.
  • set is understood a collection of items or objects.
  • the set may be a non-empty set.
  • a “set” may be a group of WTGs that share some grouping features or similarities, but a “set” may also be one or more WTGs collected to form a set of other WTGs to be used for the analysis.
  • the set may change over time or the WTGs in the “set of WTGs” may change or be changed.
  • a set of wind turbines may be a wind turbine farm located in the same area.
  • a set of wind turbines may also be similar models of wind turbines located at different locations.
  • a set of wind turbines may also be wind turbines of different models.
  • a set of “WTG” include at least one specific WTG i.e. a WTG that is to be compared or evaluated against the other WTGs.
  • a set may comprise of WTG “A, B, C” .
  • “A” may be the specific WTG and “B and C” the other “WTGs” .
  • “B” may also be the specific WTG and “A and C” the other “WTGs” .
  • a set may be “A, B” and “A” , or “B” may either be the specific WTG that is evaluated against the other WTG, i.e. “B” or “A” or vice versa.
  • model is understood a principle expressed as either a formula, a set of actions or steps performed in one or more algorithms.
  • the model may also be considered a neural network.
  • the methods, actions and system disclosed may be used as a method for evaluating the performance of a wind turbine generator as generally illustrated in figure 1.
  • the method may employ a regression model to calculate the power output of the wind turbine generator based on some parameters of the specific, or target, wind turbine generator, and one or more other wind turbine generators located relative to the target wind turbine generator.
  • the specific wind turbine and the other wind turbines may be as generally illustrated in figure 2.
  • the regression model may include any of the following models: machine learning or statistical methods including neural networks, decision trees, support vector regression, nearest neighbors, etc.
  • the parameters include power output from the other wind turbine generators, blade pitch from the other wind turbine generators, torque from the other wind turbine generators, air properties, wind direction, temperature, wind vane, time of day, precipitation, or density measurements, or wind turbine generators status codes, etc.
  • the data and evaluation may be as illustrated in figure 2.
  • the data collected may be processed, evaluated and compared as generally illustrated in figure 3, where calculated power of the target wind turbine generator is compared to the actual (measured) power of the target wind turbine generator to evaluate the performance of the target wind turbine generator.
  • Performance metrics is calculated on the basis of the comparison of the calculated power and the actual power in form, a difference between the powers, or a ratio of the powers.
  • An underperforming wind turbine can hence be identified on the basis of the comparison.
  • the method can be used to detect any errors on the wind turbine, including the yaw error or wind turbine blade cracking or breaking, etc. Also, this method can be used to evaluate the performance impact of any upgrades or add-ons added to the wind turbine generator or any change in the control settings of the wind turbine generator.
  • a method for evaluating performance of a wind turbine generator may comprise evaluating the performance of a wind turbine generator using a regression model wherein a regression model fits the wind turbine generator power as a function of the one or more other parameters measured in the wind farm.
  • the method may be comparing the measured wind turbine generator power (s) to (a) value (s) computed from parameters measured on this wind turbine generator and one or more other wind turbine generator located relative to this wind turbine generator.
  • the data may include power output from the other wind turbine generators, blade pitch from all wind turbine generators, or torque output from other wind turbine generators.
  • the data may include air properties, wind direction, temperature, wind vane, time of day, precipitation, or density measurements.
  • an indicator of wind turbine generator performance which includes the actual power relative to the regressed power expressed in the form of performance metrics which includes:
  • wind turbine generator status codes may be used as the parameters.
  • Periodic aggregates e.g. daily or hourly means or medians, potentially with confidence intervals or standard deviations
  • a rotor RPM may be substituted for power to reduce statistical uncertainty.
  • This method may be applied in a software product to generate alarms when performance is too low.
  • This method may be used to evaluate the performance impact of wind turbine generator add-ons like vortex generators, Gurney devices, and serrations.
  • the method may be used to detect yaw error or suboptimal control settings if the associated things are varied while monitoring performance i.e. different operating wind vane, pitch, or torque schedule.
  • This method may be applied to determine when a blades geometry has been unintentionally compromised (e.g. blade soiling, leading edge erosion, cracked/broken, etc. ) .
  • the disclosed actions or systems describe an improved methodology for evaluating the performance of a WTG.
  • the method or principles are based on comparing the measured WTG power (s) to a value/values computed from parameters measured on this WTG and one or more other WTGs.
  • the other WTG may be located relative to this WTG.
  • the data or parameters typically include power output from the other WTGs, blade pitch from all the WTGs, and air properties such as wind direction, temperature, TI, among others.
  • Other data or parameters may include, but not limited to torque, wind vane, time of day, precipitation, or density measurements.
  • data may include WTG status codes.
  • blade pitch and torque can be included from several WTGs.
  • the method includes a regression model to fit WTG power as a function of the one or more other parameters measured in the wind farm.
  • the actual power relative to the regressed power is an indicator of WTG performance.
  • Performance metrics may include e.g. the ratio of the actual power to the regressed value or the difference between the actual power and the regressed value.
  • Periodic aggregates e.g. daily or hourly means or medians, potentially with confidence intervals or standard deviations, may be used to provide additional statistical certainty of the conclusions.
  • a rotor RPM may be substituted for power to reduce statistical uncertainty.
  • This method may be applied in a software product to generate alarms when performance is too low.
  • This method may be used to evaluate the performance impact of WTG add-ons like vortex generators, Gurney devices, serrations among others.
  • the method may be used to detect yaw error or suboptimal control settings if the associated things are varied while monitoring performance (i.e. different operating wind vane, pitch, or torque schedule) .
  • the outlined methods or actions may be applied to determine when a blade’s geometry has been unintentionally compromised (e.g. blade soiling, leading edge erosion, cracked/broken, etc. )
  • the method may be applied to real data and to detect when a software update changed the WTG torque schedules, significantly affecting the production.
  • the method may also be applied to detect yaw error on several WTGs. It may be applied to detect when a blade cracks or breaks;. in general, anything that would cause a WTG to produce less power.
  • the method may also be applied to study the effect of changes.
  • changes may include, but are not limited to, control changes of pitch, torque, or yaw or even blade geometry changes such as VGs, gurney devices, tip extensions, boundary layer fences, or other geometry changes.
  • the collected data or parameters from the various WTGs and other devices (e.g. met mast) in the farm (excluding the target WTG power) are the independent variables.
  • the values of these parameters at each point in time are used to create the regression function which targets the power on the target WTG.
  • the regression may be performed using any machine learning or statistical methods including neural networks, decision trees, or nearest neighbors. Such regression model may optionally be preloaded with historical data. Regression model may or may not update (i.e. continuous learning) as new data is received. Regression may update periodically, e.g. daily or monthly. Regression model may update as a function of user input, e.g. in some conditions a user may command the module to stop updating for a period of time to prevent learning known bad data.
  • the choice of which independent variables to use depends on the application.
  • One system may have several regression functions, e.g. one for detecting blade geometry problems, one for detecting torque problems, and one for detecting yaw error problems.
  • torque investigation is a goal, then the torque on the target machine may not be used. In this case, the performance as a function of torque can subsequently be investigated to detect potential issues.
  • Fig. 1 illustrates a general wind turbine generator (WTG) ;
  • Fig. 2 illustrates an exemplary arrangement of WTGs with a specific WTG and other WTGs
  • Fig. 3 illustrates a result of comparing actual performance with estimated performance over time
  • Fig. 4 illustrates a set of WTGs having a specific WTG and other WTGs
  • Fig. 5 illustrates a method of evaluating WTG performance
  • Fig. 6 illustrates a computer implemented evaluation based on one or more models
  • Fig. 7 illustrates further acts when evaluating WTG performance
  • Fig. 8 illustrates further inclusion of metrological data into evaluating WTG performance
  • Fig. 9 illustrates an act of updating a model.
  • Regression model 210 Support vector regression model 215 Machine learning Model 220 Neural Network model 225 Decision Tree model 230 Nearest Neighbor’s model 240 Updating computer implemented evaluation 400 Evaluating system 1000 Power production measuring system 1100 Data receiving system 1200 Computer 2000 Computer program product 2200
  • FIG. 1 illustrates a generic wind turbine generator, a WTG, 10 having a tower 2 supporting a nacelle 3 with a rotor 4 having blades 5.
  • the WTG 10 represent a typical wind turbine generator referred to in the following.
  • the WTG 10 may have a controller 6 for controlling the WTG 10.
  • the controller 6 will generally be configured to communicate externally.
  • Various operational data will generally be available based on monitoring systems. There may be a Supervisory Control and Data Acquisition (SCADA) implementation.
  • SCADA Supervisory Control and Data Acquisition
  • Figure 2 illustrates a wind turbine evaluating system 1000 comprising a power production measuring system 1100 configured to measure 110 actual power production of a specific WTG 11 and other WTGs 12.
  • a data receiving system 1200 configured to receive a set of data 40, 41 from the specific WTG 11 and a at least one set of data 40, 42 from at least one of the other WTGs 12 in the set of WTGs 23.
  • the data may comprise control settings 50 such as pitch settings 52.
  • a computer 2000 (with a computer program product 2200) configured to estimate 130 power production of the specific WTG 11 as a computer implemented evaluation 200 function or model of the received 120 set of data 40, 41 from the specific WTG 11 and the received at least one set of data 40, 42 from at least one of the other WTGs 12 and for comparing 140 the actual power production 31 to the estimated power production 33.
  • the actual power production 31 of the specific WTG 11 is “normal” or within a level of the estimated power production 33, then no action is required. If the actual power production is not normal, or e.g. below the estimated power production 33, then further action may be performed. There may be an act of identifying 150 the reason or/and there may be an act of triggering an alarm 160, which may also be an act of informing the operator.
  • Figure 3 illustrates an example of evaluating a set of WTGs as outlined in figure 2 and the result estimating 130 and comparing 140 the actual power production 31 to the estimated power production 33 over time.
  • the dots represent actual power production 31 of a specific WTG 11 compared to the estimated 130 WTG power obtained by a model such as a regression model.
  • Underperforming may be defined as when the act of comparing 140 results in a measure where the actual power 31 is statistical or significantly below a certain value.
  • An alarm may be triggered.
  • There may be produced a notification which may include a list of potential problems, and the produced notification may be sent to the operator or manufacturer.
  • Figure 4 illustrates a specific wind turbine generator WTG 11 amongst a set of wind turbine generators WTGs 23.
  • the set of WTGs 23 comprises the specific WTG 11 and other WTGs 12.
  • the selection or choice of specific WTG may be changed or cycled.
  • the disclosed evaluation of performance may be performed with more WTGs or with each one of the WTGs in the set of wind turbine generators WTGs as the specific wind turbine generator WTG 11.
  • Figure 5 illustrates, with reference to the previous figures, a method of continuously evaluating 100 performance of a specific wind turbine generator WTG 11 amongst a set of wind turbine generators WTGs 23.
  • the method comprises an act of measuring 110 actual power production 31 of the specific WTG 11.
  • the act of estimating 130 the estimated power production 33 is based on a computer implemented evaluation 200.
  • the received data 40 from the specific WTG 11 includes at least actual pitch settings 51 of the specific WTG 11.
  • the received data 40 from the other WTGs 12 include at least actual pitch settings 51 and corresponding actual power production 31 of one or more of the other WTGs 12.
  • the outlined methods may also be performed where the specific WTG 11 comprises one or more additional WTG features, such as vortex generators, Gurney devices, and serrations, as compared to the other wind turbine generators WTGs 12. Also, the specific wind turbine WTG 11 may have one or more control settings 50 varied or altered as compared to control settings 50 of the other wind turbine generators WTGs 12.
  • additional WTG features such as vortex generators, Gurney devices, and serrations
  • Figure 6 illustrates that the computer implemented evaluation 200 is implemented as computer instructions representing one or more of a regression model 210, a support vector regression model 215, a machine learning 220 such as a neural network 225, a decision tree model 230, and/or a nearest neighbor’s model 240.
  • a regression model 210 a support vector regression model 215
  • a machine learning 220 such as a neural network 225, a decision tree model 230, and/or a nearest neighbor’s model 240.
  • Figure 7 illustrates the method outlined in figure 5 and further comprises an act of identifying 150 the reason why the actual power production 31 is below the estimated power production 33.
  • Figure 8 illustrates, with reference to figure 2, the method wherein the act of receiving 120 further comprises receiving 122 a set of meteorological conditions 60 pertinent to each WTG 10 and wherein the act of estimating 130 furthermore is a function of the set of meteorological conditions 60.
  • Figure 9 illustrates an act of updating or adjusting 400 the computer implemented evaluation 200 after performing the act of comparing 140.

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PCT/CN2017/119358 2016-12-30 2017-12-28 A method and system for evaluating wind turbine generator performance WO2018121668A1 (en)

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WO2020046358A1 (en) * 2018-08-31 2020-03-05 Siemens Aktiengesellschaft Methods and systems for performance loss estimation of single input systems
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