Wind Turbine Operability Curve Analysis for Underperformance Detection
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Solution Overview
Problem
Conventional methods for analyzing turbine performance data are manual and uncertain, making it difficult to automatically identify the connection between energy underproduction and operational anomalies, thereby complicating root cause identification.
Innovation Solution
An automated system that applies operating characteristic models to detect turbine underperformance and identify root causes, using data filtering, baseline models, and change detection algorithms to analyze real-time operational data from wind turbines, enabling adjustments to operating parameters and maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to analyze turbine performance data, then the analysis process can be performed with simple tools, but the results have large uncertainty and root cause identification is difficult
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated computer-based processing systems. The control processor automatically performs data filtering, baseline model comparison, and anomaly detection algorithms, substituting human manual analysis with computational processes that provide consistent, repeatable results with reduced uncertainty.
Solution Approach 2:
The patent introduces operating characteristic models as intermediary components between raw turbine data and analysis results. These models serve as mediators that translate complex turbine operational data into meaningful performance assessments and root cause identifications, bridging the gap between raw data and actionable insights.
2Productivity
If automated detection systems are implemented to identify turbine underperformance, then the detection speed and consistency improve, but the system complexity and implementation cost increase
Solution Approach 1:
The patent segments the automated detection system into distinct functional modules: data filtering unit, operating characteristic model comparison unit, anomaly detection unit, and root cause identification unit. Each module performs a specific function, allowing the complex automated detection task to be divided into manageable components that can be implemented and maintained more easily.
Solution Approach 2:
The system implements feedback mechanisms where detection results and identified anomalies are fed back into the analysis process. The control processor continuously monitors turbine data, compares it against operating characteristics, and adjusts its analysis based on detected deviations, creating a closed-loop system that improves detection accuracy over time.
Data Source
AI summary
A method of correcting turbine underperformance includes calculating a power production curve using monitored data, detecting changes between the monitored data and a baseline power production curve, generating operability curves for paired operational variables from the monitored data, detecting changes between the operability curves and corresponding baseline operability curves, comparing the changes to a respective predetermined metric, and if the change exceeds the metric, providing feedback to a turbine control system identifying at least one of the paired operational variables for each paired variable in excess of the metric. A system and a non-transitory computer-readable medium are also disclosed.