Wind Turbine Performance Analytics by Dheepsaran VivekananthWind Turbine Performance Analytics by Dheepsaran Vivekananth

Wind Turbine Performance Analytics

Dheepsaran Vivekananth

Data Analyst

Data Visualizer

Microsoft Power BI

SQL

Energy

Wind Turbine Performance Analytics

End-to-end Power BI and SQL analytics solution for wind turbine SCADA performance monitoring, power curve analysis, underperformance detection, and operational insights.

Project Overview

This project focuses on analyzing wind turbine SCADA (Supervisory Control and Data Acquisition) data using SQL and Power BI to monitor turbine operational performance, power generation efficiency, underperformance trends, and time-based operational behavior.

The solution provides interactive dashboards for executive reporting, power curve monitoring, root cause analysis, and operational performance optimization.

Tools & Technologies Used

Power BI

SQL

DAX

Power Query

Data Modeling

Data Visualization

SCADA Analytics

Key Features

Executive KPI Dashboard

Power Curve Analysis

Underperformance Detection

Time-Based Performance Monitoring

Wind Category Analysis

Root Cause Breakdown

Interactive Slicers and Filters

Operational Trend Analysis

Key KPIs

KPI Description Total Actual Power Total generated turbine power Total Theoretical Power Expected turbine power generation Efficiency % Operational efficiency percentage Power Loss % Total operational power loss Underperformance Count Number of underperforming records Average Wind Speed Average turbine wind speed

Dashboard Pages

1. Executive Overview

Provides a high-level operational summary of turbine performance including KPIs, monthly power trends, wind category analysis, and performance distribution.

2. Power Curve Analysis

Analyzes actual vs theoretical turbine power curves across wind speed ranges and identifies underperformance behavior.

3. Underperformance Analysis

Tracks operational underperformance trends, hourly breakdowns, and root cause analysis of power loss.

4. Time-Based Performance

Monitors hourly efficiency, monthly efficiency trends, and time-block-based power generation patterns.

SQL Work Included

The project includes SQL scripts for:

Data cleaning

Feature engineering

Performance categorization

Wind speed binning

Time block creation

Power loss analysis

Operational trend analysis

Key transformations performed:

Efficiency calculation

Power loss calculation

Wind category creation

Performance status classification

Monthly and hourly aggregations

Repository Structure

```text
Wind-Turbine-Performance-Analytics/
│
├── Dashboards/
├── Datasets/
├── SQL/
├── Screenshots/
├── Insights/
└── README.md
```

Business Insights

Actual turbine power generation remained below theoretical power generation across multiple periods.

Optimal wind conditions generated the highest operational output.

Underperformance spikes were observed during specific months and operational hours.

Power loss was significantly concentrated during underperforming operational states.

Evening and afternoon operational windows generated higher power output.

Efficiency fluctuations across months indicated operational variability.

Root cause analysis identified wind category and performance status as key contributors to operational losses.

Detailed insights can be found in:

Future Enhancements

Real-time SCADA integration

Predictive maintenance analytics

Machine learning anomaly detection

Turbine-level drill-through analysis

Automated alert systems

Author

Dheepsaran Vivekananth

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Posted Jun 5, 2026

Developed a Power BI and SQL analytics solution for wind turbine performance monitoring.

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