Overview / Usage

Wind Turbine Fault Detection and Energy Generation Prediction

Objective

The primary objective of this project is to develop a predictive model that can forecast wind turbine energy generation and detect potential faults. The model utilizes historical data to identify patterns and correlations between various factors affecting energy production.

Problem Statement

Wind turbine energy generation is influenced by multiple factors, including wind speed, direction, and turbine characteristics. Unexpected changes in these factors can lead to reduced energy production, equipment damage, and increased maintenance costs. A predictive model can help identify potential issues, optimize energy production, and reduce maintenance costs.

Methodology

1. Data Collection: Historical data on wind turbine energy generation, wind speed, direction, and theoretical power curves are collected.

2. Data Preprocessing: Data is cleaned, and features are scaled using pandas, NumPy, and Scikit-learn libraries.

3. Model Development: A Multiple Linear Regression (MLR) model is developed to predict wind turbine energy generation based on the collected data.

4. Model Evaluation: The performance of the MLR model is evaluated using metrics such as mean squared error, mean absolute error, and R-squared.

5. Visualization: The predicted energy generation values are visualized using Matplotlib to facilitate easy interpretation of the results.

Key Features

• Wind Speed: The wind speed at the hub height of the turbine.

• Wind Direction: The wind direction at the hub height of the turbine.

• Theoretical Power Curve: The theoretical power values that the turbine generates with a given wind speed.

• Active Power: The actual power generated by the turbine.

Methodology / Approach

IoT-Based Data Collection

1. Sensor Deployment: Various sensors (e.g., anemometers, wind vanes, and power meters) are installed on the wind turbine to collect data on wind speed, direction, and power generation.

2. IoT Gateway: The sensors transmit the collected data to an IoT gateway, which aggregates and processes the data.

3. Cloud Connectivity: The IoT gateway sends the processed data to the cloud for storage, analysis, and visualization.

Data Preprocessing and Analysis

1. Data Ingestion: The cloud-based platform ingests the collected data and stores it in a scalable database.

2. Data Cleaning: The data is cleaned, and any inconsistencies or missing values are handled.

3. Feature Engineering: Relevant features are extracted from the data, such as wind speed, direction, and power generation.

4. Data Visualization: The preprocessed data is visualized using dashboards and charts to facilitate easy interpretation.

Machine Learning Model Development

1. . Model Selection: A suitable machine learning algorithm (e.g., Multiple Linear Regression) is selected based on the problem requirements and data characteristics.

2. Model Training: The selected algorithm is trained using the preprocessed data to learn patterns and relationships.

3. Model Evaluation: The trained model is evaluated using metrics such as mean squared error, mean absolute error, and R-squared.

4. Model Deployment: The trained model is deployed in the cloud-based platform to make predictions on new, unseen data.

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Standards

1. Data Security: The system adheres to data security standards such as GDPR, HIPAA, and PCI-DSS.

2. Data Quality: The system ensures data quality by following standards such as ISO 8000 and ISO 9001.

3. IoT Security: The system follows IoT security standards such as OWASP IoT Security and IETF CoAP.

Technologies Used

Frameworks, Standards, and Techniques

1. IoT Framework: The IoT framework used is based on the Open Systems Interconnection (OSI) model.

2. Cloud Platform: The cloud platform used is Amazon Web Services (AWS) or Microsoft Azure.

3. Machine Learning Framework: The machine learning framework used is Scikit-learn or TensorFlow.

4. Data Visualization Tools: The data visualization tools used are Tableau, Power BI, or D3.js.

5. Programming Languages: The programming languages used are Python, Java, or C++.

Repository

https://www.kaggle.com/code/zengamer/wind-turbine-energy-model-prediction