This repository presents a methodology for fault diagnosis and predictive maintenance of wind turbines. The project leverages a dataset containing turbine parameters and fault types, aiming to improve the efficiency and profitability of wind energy systems.
Wind energy is a significant contributor to renewable energy systems. However, wind turbines frequently encounter faults such as:
- Generator heating faults
- Mains failure faults
- Feeding faults
- Air cooling faults
- Excitation faults
These faults result in extended downtime and high repair costs, reducing efficiency and profit margins for wind farms. To address this challenge, this project focuses on fault diagnosis and predictive maintenance using data-driven techniques.
The proposed methodology consists of the following steps:
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Data Preprocessing
- Cleaning the dataset to remove inconsistencies.
- Normalizing the data to ensure uniform scaling.
- Selecting relevant features for analysis.
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Analysis Techniques
- Statistical Analysis: Identify trends and correlations in the data.
- Pattern Recognition: Detect fault signatures and patterns.
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Fault Diagnosis and Predictive Maintenance
- Use preprocessed data to identify potential faults.
- Predict maintenance requirements to reduce downtime.
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Case Study
- Applied the methodology to a single wind turbine to evaluate performance.
- Machine Learning Integration: Techniques for fault detection and prediction.
- Data Preprocessing Pipeline: Ensures high-quality data for analysis.
- Feature Selection: Identifies critical variables for fault diagnosis.
- Scalability: Can be extended to larger wind farms.
The results demonstrate the potential of this methodology to:
- Improve the operational efficiency of wind turbines.
- Reduce repair and maintenance costs.
- Enhance the profitability of wind farms.