Patent Description:
Turbine underperformance is a large contributor to a turbine's energy production loss. Conventional approaches first analyze the turbine performance data manually, which can lead to results with large uncertainty. Then, the connection between energy underproduction and the turbine operation anomaly cannot be built automatically, which makes root cause identification difficult.

Further, document <CIT> describes systems and methods for maximizing efficiency and output performance for wind turbines similarly situated and positioned in a geographical site. The system includes at least a controller operably connected to the wind turbines and sensors positioned in or proximate to the site for monitoring environmental conditions. The controller receives operation and performance information for each wind turbine and based on the received information establishes an operational parameter that is set as a baseline parameter for adjusting any underperforming wind turbines operational parameters. More particular, a stochastic dynamic game for a large number of the wind turbines operating in a given geographical site is proposed for this purpose. This approach may be comparatively numerically complex.

In view of the above, a method according to claim <NUM> and a turbine control system according to claim <NUM> are provided.

Embodying systems and methods can detect underperformance of a wind turbine power generation station, and identify root causes of the underperformance by applying operating characteristic model(s) of the wind turbine. Embodiments provide automatic detection of the turbine's underperformance and its operational anomalies. This automatic detection avoids conventional manual processes of data analysis, reduces uncertainty, and identifies root cause(s) of the underperformance. Root causes on underperformance can include incorrect control parameters, blade misalignment, sub-optimal blade pitch control, etc..

In some implementations, after identifying the root cause(s), embodiments can generate actionable information from which a turbine's control system can adjust the turbine's operating parameters to increase its energy performance, and/or provide reports indicating component maintenance/replacement recommendations.

An embodying method can simultaneously be applied to the real-time turbine operational data for turbines co-located at a particular wind farm and/or across turbines at a fleet level. Implementation at a broad level can facilitate an asset performance management strategy and largely increase energy productivity across an enterprise's facilities. The ability to identify root causes enables more efficient maintenance planning and recovery from lost energy production.

<FIG> depicts system <NUM> to detect turbine underperformance and identify root cause(s) contributing to the underperformance in accordance with embodiments. System <NUM> can be incorporated within an individual turbine, implemented locally at a wind turbine farm, or at a remote server. System <NUM> can include control processor <NUM> (having a processor unit <NUM>) that communicates with other components of the system across data/control bus <NUM>. The system can communicate with remote servers and other devices across an electronic communication network via communication interface unit <NUM>.

Control processor <NUM> can access executable program instructions <NUM>, which cause the processor to perform embodying operations. The executable instructions can be stored in memory unit <NUM>, or in data store <NUM>. Memory unit <NUM> can provide the control processor with local cache memory <NUM>.

Data filtering/ preprocessing unit <NUM> receives sensor/monitor data containing information on the turbine operational status (rotation, blade pitch, energy output, etc.) and ambient environmental conditions (wind speed, wind direction, air density, temperature, humidity, etc.). This operational status and environmental condition data is correlated with a time stamp representing data points' time of acquisition. In accordance with embodiments, detection of underproduction due to turbine performance uses the full-load turbine operational data. The data filtering/preprocessing unit excludes data representing periods of turbine downtime and curtailments. Data processing is performed to ensure data quality and data validity, such as the air density correction for wind speed measurements. The resulting data is stored in operational data records <NUM>.

Power production curve baseline model <NUM> predicts the turbine's expected power production at different wind speeds and other environmental conditions. To reduce the impact introduced by extreme outliers and generate a reliable power curve estimate, the baseline power curve model can include a robust smoothing technique - for example, residual error, or regression analysis calculation.

Power curve change detection model <NUM> quantifies the difference between measured power (contained within operational data records <NUM>) and the corresponding baseline power estimation (from the power curve baseline model <NUM>) at the same wind speed. Given no change, the power residual time series would follow a common distribution across the detection period. This power curve change detection model identifies changes in the power residual time series. A detected change could correspond to potential power underproduction. The detection model can implement a statistical method that identifies times when a time series changes significantly in terms of a specific metric (e.g., mean, variance, etc.). This metric can be predetermined by a user based on site conditions, production requirements, and other factors.

Operability curve baseline model <NUM> describes the relationship within one or more pairs of paired turbine operational variables (for example, generator torque vs. generator speed; blade pitch angle vs. wind speed; blade pitch angle vs. power; etc.). For each type of operability curve, a robust baseline model is provided to represent the normal turbine operation pattern.

Operability curve change detection model <NUM> defines a vertical residual for any specific type of operability baseline curve. This vertical residual can be the difference between the actual value on the y-axis and the associated baseline estimation corresponding to the same value on the x-axis. Operability curve change detection model identifies significant changes (based on a predetermined metric) in the residual time series, and any detected change could correspond to potential abnormal turbine operation.

The baseline models can implement robust regression technique(s) to accurately capture the baseline turbine performance, and a change point detection algorithm can be used to identify the most significant changes for the deviation from baseline.

Underperformance detection & root cause identification unit <NUM> (herein "identification unit") monitors output from the power curve change detection model. If this monitored power curve change exceeds a predetermined metric (threshold, and/or magnitude), the identification unit analyzes one or more of the paired turbine operational variables from the operability curve change detection model to identify the root cause of the power curve change. In some implementations, the power curve change detection model can compare the power curve change to the predetermined metric and signal the identification unit of the out of tolerance condition.

By way of example, <FIG> graphically depicts time series data plot <NUM> of multiple data points <NUM>. Data plot region <NUM> precedes time t<NUM>, and contains data points having a data point mean <NUM> of about zero. Data plot region <NUM> extends between time t<NUM>-t<NUM>, and contains data points having a data point mean <NUM> with a magnitude of about Y<NUM>. Data plot region <NUM> extends after time t<NUM>, and contains data points having a data point mean <NUM> with a magnitude of about Y<NUM>. Time series data plot <NUM> can be representative of the residual time series data produced by comparing the measured operability curve to the operability curve baseline model <NUM> for one of the paired turbine operational variables.

In accordance with embodiments, identification unit <NUM> can evaluate the residuals for one or more time periods of a time series data plot (e.g., plot <NUM>). The evaluation can be a comparison of the residuals' magnitude(s) to predetermined metrics. Based on the result of the evaluation, the identification unit can determine if the paired turbine operation variable producing the particular data is a root cause of a change in the production power curve for the turbine.

Embodying systems and methods provide automatic detection of turbine underperformance and operation anomalies. Embodying methods can be simultaneously applied to real-time turbine operational data for one or more turbines of a wind farm, and/or at a fleet level, which facilitates development of improved asset performance management strategy. The improved strategy can increase power productivity. Also, the ability to identify root causes enables more efficient maintenance planning and a reduction energy production loss.

In some implementations, user-defined baseline performance models can be used, such as using user-specified engineering power curve in the power curve detection model. Furthermore, the method can be applied to a variety of turbine operating conditions and/ or parameters (low or high wind speeds, specified humidity ranges, atmospheric particulate suspension, etc.) to detect condition-specific root causes. In some implementations, these user-specified conditions and/or parameters can be used during design specification development to be predictive of expected production capabilities for a turbine, a windfarm, and/or fleet operations.

<FIG> depicts process <NUM> for identifying root causes of turbine underperformance in accordance with embodiments. Operational data containing information on the turbine operational status (rotation, blade pitch, energy output, etc.) and ambient environmental conditions (wind speed, wind direction, air density, temperature, humidity, etc.) for a turbine is accessed, step <NUM>. This operational data is acquired in real time (i.e., during turbine operation) by sensors and monitors.

At step <NUM>, data filtering of the turbine output power readings removes data representing periods of turbine downtime and curtailments. Data preprocessing correlates environmental conditions with the same time stamp as the output power reading data. The resulting filtered and processed data is stored in operational data records <NUM>.

A baseline model of the turbine's power curve is generated, step <NUM>. The baseline model represents the expected power production curve for the turbine at a variety of wind speeds and other conditions. The expected power production can be based on manufacturer's specifications, which can be supplemented with historic (individualized or fleet level) measured data for the turbine.

<FIG> graphically depicts turbine operational performance data <NUM> in accordance with embodiments. This performance data is the turbine output power (over time) for multiple wind speeds that were recorded contemporaneously with the sensed output power. Baseline curve <NUM> represents the expected output power versus wind speed. Many data samples of the power output within region <NUM> do not increase with increasing wind speed. This stagnation of power output can be indicative of underproduction.

Changes between the turbine's power production curve (e.g., from operational data records <NUM>) and the turbine's power production curve baseline model are detected, step <NUM>. The changes can be identified by examining a power residual time series, where a negative change could correspond to potential power underproduction.

<FIG> graphically depicts time series representation <NUM> of the performance data of <FIG>. Prior to t<NUM>, the time series data has about a zero mean residual, which indicates that the turbine's measured power production curve matches its baseline power production curve model. Between time t<NUM>-t<NUM>, the time series data has a negative mean residual. Subsequent to time t<NUM>, the residual has a positive bias.

One or more baseline models of the turbine's operability curves are generated, step <NUM>. These baseline model operability curve(s) represents relationship within one or more pairs of paired turbine operational variables (for example, generator torque vs. generator speed; blade pitch angle vs. wind speed; blade pitch angle vs. power; etc.) These operability curves can be based on manufacturer's specifications, which can be supplemented with historic (individualized or fleet level) data for the turbine.

By way of example, <FIG> graphically depicts in accordance with embodiments operational performance data <NUM> for the paired operational variables blade pitch angle vs. wind speed, which can be an underlying contributor to the operational performance data of <FIG>. Curve <NUM> represents the baseline operability curve generated at step <NUM> for this pair of variables.

<FIG> graphically depicts in accordance with embodiments operational performance data <NUM> for the paired operational variables generator torque vs. generator speed, which can be an underlying contributor to the operational performance data of <FIG>. Curve <NUM> represents the baseline operability curve generated at step <NUM> for this pair of variables.

Vertical residual changes in residual time-series data between the operability curve baseline models and the turbine's actual operability curves are detected, step <NUM>. The actual operability curves can be generated from time-stamped paired operational variable sensor data <NUM> for the various parameter pairs of the turbine's paired operational variables - generator torque vs. generator speed; blade pitch angle vs. wind speed; blade pitch angle vs. power; etc. The magnitude of a detected change (based on a predetermined metric) in the residual time series could correspond to potential abnormal turbine operation.

By way of example, <FIG> graphically depicts a time series representation <NUM> of the operational performance data of <FIG>. Prior to t<NUM>, the time series data has about a zero mean residual, which indicates that these paired variables measured operation matches their baseline operability curve. Between time t<NUM>-t<NUM>, the time series data has a negative mean residual. Subsequent to time t<NUM>, the residual has a positive bias.

<FIG> graphically depicts a time series representation <NUM> of the performance data of <FIG>. The time series data has about a zero mean residual across the monitored time, which indicates that these paired variables measured operation matches their baseline operability curve.

For one or more types of root causes, the vertical mean residual changes in residual time-series data for the operability curves at times corresponding to changes in power production curve is quantified by comparison to a predetermined metric, step <NUM>.

A determination is made, step <NUM>, as to whether one or more operability curves include change in excess of its respective predetermined amount. If the change is less than the predetermined metric, process <NUM> returns to step <NUM>. If the change is in excess of its predetermined metric, feedback information regarding the root cause of underproduction is provided to the turbine control system, step <NUM>. The turbine control system can then adjust one or more turbine actuators to impact a value for a respective one of the operational variables to increase the power production. Process <NUM> can then return to step <NUM> for continued underperformance detection and root cause identification.

In accordance with some embodiments, a computer program application stored in non-volatile memory or computer-readable medium (e.g., register memory, processor cache, RAM, ROM, hard drive, flash memory, CD ROM, magnetic media, etc.) may include code or executable program instructions that when executed may instruct and/or cause a controller or processor to perform methods discussed herein such as a method of detecting turbine underproduction and identifying root cause(s), as disclosed above.

Claim 1:
A method of correcting turbine underperformance, the method comprising:
accessing monitored operational data (<NUM>) for the turbine;
calculating a power production curve using at least a portion of the monitored operational data;
predicting a baseline power production curve (<NUM>) based on expected performance of the turbine;
detecting (<NUM>) a first set of vertical residual changes in power residual time series data produced by comparing at least the portion of the monitored operational data and the baseline power production curve;
generating one or more monitored operability curves from the at least a portion of the monitored operational data, each of the one or more monitored operability curves describing a relationship between monitored values for one or more paired turbine operational variables;
generating (<NUM>) one or more baseline operability curves for the turbine, each of the one or more baseline operability curves describing an expected relationship between the one or more paired turbine operational variables;
detecting (<NUM>) a set of vertical residual changes in residual time-series data produced by comparing the one or more monitored operability curves and a corresponding one of the one or more baseline operability curves;
comparing (<NUM>) the sets of vertical residual changes to a respective predetermined metric for each of the one or more paired turbine operational variables; and
based on a determination (<NUM>) that one or more members of the sets of vertical residual changes is in excess of the respective predetermined metric, providing (<NUM>) feedback to a turbine control system identifying at least one of the one or more paired turbine operational variables that corresponds to the member of the sets of vertical residual changes in excess of the predetermined metric, the feedback comprising information regarding a corresponding root cause of a turbine underproduction and is used to adjust one or more turbine actuators to impact a value for a respective one of the one or more paired turbine operational variables to increase the power production.