https://github.com/pranavsuriya-sr/renewableproject

Fault Diagnosis of Wind Turbines Using Machine Learning
https://github.com/pranavsuriya-sr/renewableproject

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Fault Diagnosis of Wind Turbines Using Machine Learning

• Host: GitHub
• URL: https://github.com/pranavsuriya-sr/renewableproject
• Owner: pranavsuriya-sr
• Created: 2024-05-05T07:26:37.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-11-17T11:34:42.000Z (about 2 months ago)
• Last Synced: 2024-11-17T12:29:02.237Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 3.12 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Wind Turbine Fault Diagnosis and Predictive Maintenance

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.

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## 📜 Project Overview

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.

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## ⚙️ Methodology

The proposed methodology consists of the following steps:

1. **Data Preprocessing**

   - Cleaning the dataset to remove inconsistencies.

   - Normalizing the data to ensure uniform scaling.

   - Selecting relevant features for analysis.

2. **Analysis Techniques**

   - **Statistical Analysis**: Identify trends and correlations in the data.

   - **Pattern Recognition**: Detect fault signatures and patterns.

3. **Fault Diagnosis and Predictive Maintenance**

   - Use preprocessed data to identify potential faults.

   - Predict maintenance requirements to reduce downtime.

4. **Case Study**

   - Applied the methodology to a single wind turbine to evaluate performance.

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## 🔑 Key Features

- **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.

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## 🚀 Results and Impact

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.

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