Introduction

In the renewable energy sector, optimizing the performance of wind turbines is crucial for maximizing energy output and efficiency. XMPro’s solution for Wind Turbine Performance Optimization leverages advanced data analytics and intelligent digital twin technology to enhance the operational efficiency of wind turbines.

The Challenge

Wind turbines operate under varying environmental conditions, which can significantly impact their efficiency and energy output. Key challenges involve:

Optimizing Turbine Performance: Adjusting turbine operations to maximize energy output under different wind conditions.

Reducing Wear and Tear: Minimizing unnecessary stress on turbine components to extend their lifespan.

Maximizing Energy Yield: Ensuring turbines operate at peak efficiency to maximize the energy yield.

The Solution: XMPro iBOS Wind Turbine Performance Optimization

Our solution features real-time data monitoring through IoT sensors, predictive analytics for optimal turbine settings, and digital twin modeling for performance simulations. Automated recommendations help adjust turbine settings for maximum efficiency, while customizable dashboards provide operators with key performance metrics for informed decision-making.

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Key Features

Real-Time Data Monitoring:

Utilizing IoT sensors to continuously monitor wind speed, direction, turbine rotation speed, and other critical parameters.

Predictive Analytics for Performance Tuning:

Analyzing sensor data with advanced algorithms to predict optimal turbine settings for different wind conditions.

Digital Twin Modeling:

Creating digital twins of wind turbines to simulate and analyze performance under various scenarios, aiding in decision-making for performance optimization.

Automated Adjustment Recommendations:

Providing automated recommendations for adjusting turbine settings such as blade pitch and rotation speed to optimize performance and energy output.

Customizable Dashboards and Reporting:

Offering customizable dashboards that display key performance metrics, enabling operators to monitor turbine efficiency and make informed decisions.

How XMPro iBOS Modules Work Together To Create This Condition Monitoring Solution

Data Integration & Transformation

Intelligence & Decision Making

Visualization & Event Response

Artificial Intelligence & Generative Agents

Integration & Transformation

Intelligence & Decision Making

Visualization & Event Response

Artificial Intelligence &
Generative Agents

See this Solution In Action In Our Product Tour

Figure 1. Wind Turbine Condition Monitoring Data Stream

This renewable condition monitoring data stream for a wind turbine begins by reading all records from various data sources, including an OPC UA server. The data is then joined and contextualized with sensor data to provide a comprehensive view. Calculated metrics, such as oil levels, are derived from the sensor data. The processed data is broadcasted to multiple endpoints for further analysis. A failure recommendation rule is applied to identify potential issues, and the data is rounded and filtered specifically for wind turbines. The final processed data is then made available for viewing and further action through a visualization platform, ensuring continuous monitoring and proactive maintenance of the wind turbine.

Figure 2. Wind Energy Asset Data Stream

This wind energy asset monitoring data stream collects and processes telemetry data from various sources, including rotor and gearbox, power, yaw, pitch, operational telemetry, and weather and wind services. The data is contextualized with metadata and operational signals, then broadcasted for different calculations. Calculations include yaw error and efficiency, asset health and utilization scores, and predicting the likelihood of failure. Anomalies in operating time series data are identified, and asset reliability and operations data are combined. The system checks spares on location, runs recommendation rules, and stores time series data in an InfluxDB historian. Recommendations and control data are displayed on the XMPro app, and set points are sent to the control unit, ensuring comprehensive monitoring and proactive maintenance of wind energy assets.

Figure 3: Wind Turbine Unity Model For Condition Monitoring

This pump predictive maintenance data stream ingests pressure, flow, temperature, vibration, and sensor health data from multiple sources before it is normalized and combined with contextual data from SAP and Azure Digital Twin. The integrated data is then used to calculate performance metrics, run predictive models, and update Azure Digital Twin and ADX, enabling the identification of pumps likely to fail and the estimation of their remaining useful life.

Figure 1. Real-Time Renewable Asset Overview Dashboard for Wind and Solar Farms

This advanced dashboard is specifically designed for operators of wind farms, providing a comprehensive view of wind turbine performance and optimization. It features an interactive map that dynamically updates with the operational status of different wind farms, offering a clear visual representation of their performance efficiency and health. Each wind farm on the map is marked with a color-coded status icon, indicating its current operational state, including active status and any alerts or error messages related to performance optimization or maintenance needs of individual turbines within that wind farm.

Overview of Renewable Asset Health:

The dashboard displays the overall performance status of wind turbines, highlighting areas with potential efficiency issues or optimization opportunities. It includes critical alerts such as suboptimal wind direction alignment, blade pitch adjustments, and gearbox efficiency.

Performance Optimization Alerts:

Utilizing data from existing sensors and advanced analytics, the dashboard provides real-time insights into optimization opportunities. It highlights turbines requiring adjustments for issues like wind direction misalignment or blade pitch inefficiencies.

Maintenance Planning and Scheduling:

A detailed graph tracks maintenance and performance optimization requirements across the wind farm. It prioritizes turbines based on their needs for maintenance or performance adjustments, facilitating efficient and proactive scheduling.

Drill-Down Capability for In-Depth Analysis:

Users can explore specific assets for detailed information, including historical performance data, recent maintenance activities, and predictive maintenance recommendations. This level of detail enables targeted actions based on the system’s predictive analytics.

Customizable Alerts and Recommendations:

The dashboard highlights active recommendations generated by the system’s smart rule logic and machine learning algorithms. This includes suggestions for enhancing turbine performance, addressing gearbox oil viscosity issues, and other optimization actions.

Overall Asset Status Summary:

At the bottom of the screen, there’s a summary of the status of different assets, including the number of active and inactive assets across various facilities like Wind Farm 1 and 2, Photovoltaic Plant 1 and 2, and Biomass Plant 1.

Search Functionality:

A search bar at the top allows users to search for specific data across the platform.

This Real-Time Wind Turbine Performance Optimization Dashboard is an essential tool for wind farm operators, enabling them to effectively monitor and optimize the performance of their turbines. By providing real-time data, predictive insights, and actionable recommendations, it ensures informed decision-making and enhances the operational efficiency and energy output of wind turbines.

Figure 2a. Real-Time Wind Farm Performance Management View

This XMPro digital dashboard, designed for Wind Farm Management, equips operators with essential tools for optimizing turbine operations and enhancing overall efficiency.

Immediate Energy Output Assessment:

The real-time power gauge showing current power generation in megawatts (MW) is crucial for assessing the farm’s immediate energy output. This feature allows operators to quickly identify any deviations from expected performance levels, which is key to maintaining optimal energy production.

Long-Term Performance Analysis:

The historical power chart displaying monthly power output data enables operators to analyze long-term performance trends. This insight is vital for strategic planning, identifying underperformance periods, and making informed decisions about maintenance and operational adjustments. Data Range: Monthly data from May to April.

Targeted Turbine Maintenance and Optimization:

The status table for individual wind turbines provides detailed information on each turbine’s status, power output, and performance. This targeted approach helps in pinpointing turbines that require maintenance or optimization, directly influencing the overall efficiency and reliability of the wind farm. Details: Asset name, status, power output, and performance percentage.

Visual Management of Wind Farm Operations:

The farm overview visualization offers a geographical representation of the wind farm, with clear indicators for each turbine’s status. This visual management tool is essential for large-scale operations, enabling quick identification and prioritization of turbines for performance optimization or maintenance.

Optimization Based on Wind Conditions:

The wind details section, showing real-time wind speed and direction, is critical for adjusting turbine operations to maximize energy capture. This real-time data ensures turbines are optimally aligned with current wind conditions, enhancing energy production efficiency. Metrics: Wind speed (m/s) and direction (degrees).

Proactive Maintenance and Performance Alerts:

The dashboard’s recommendations and alerts section provides actionable insights for proactive maintenance and performance optimization. These alerts address issues that can significantly impact energy output, ensuring timely interventions for optimal turbine performance.

Enhanced Operational Efficiency and User Experience:

The user-friendly interface with easy navigation and quick access to various functionalities enhances operational efficiency. This feature allows operators to manage complex wind farm operations effectively, ensuring optimal performance and maintenance scheduling. The Real-Time Wind Farm Performance Management Dashboard is a comprehensive tool that provides wind farm operators with the necessary data and insights for optimizing turbine performance and overall wind farm efficiency. Its combination of real-time monitoring, historical analysis, and actionable recommendations plays a crucial role in enhancing the operational efficiency and energy output of wind farms.

Figure 2.b Real-Time Wind Farm Performance Management View – Individual Wind Turbine

This XMPro dashboard view provides a detailed perspective on individual wind turbines within a wind farm, enhancing the ability to monitor and optimize each turbine’s performance.

Figure 3. Asset Analysis View – Wind Turbine WT-10 Health

This Asset Analysis View on the XMPro dashboard offers detailed insights into a specific wind turbine within a renewable energy system, focusing on turbine WT-10.

Comprehensive Production and Performance Data:

The left section of the dashboard displays crucial production data for WT-10, including kilowatt-hours (2107 kWh), average power (1837 kW), and performance (63.7%). A green line graph illustrates the power output fluctuation over time, providing a visual representation of the turbine’s energy production efficiency. This data is essential for assessing the turbine’s current output and identifying trends or deviations in performance.

Detailed Turbine Information:

Below the production data, detailed information about WT-10 is listed, including turbine ID, wind farm location (West Rock), total power generated (7.8 GWh), operational hours (5345), and the turbine model (GE Haliade-X 14 MW). Geographical coordinates are also provided. This comprehensive profile is vital for understanding the turbine’s operational context and history, aiding in maintenance planning and performance analysis.

Weather Forecast for Operational Planning:

A 3-day weather forecast presents predictions for wind top speed and temperature highs, along with expected weather conditions. This forecast is crucial for anticipating environmental factors that could impact turbine performance and planning appropriate operational responses.

Blade Damage Analysis:

A detailed table outlines the damage to the turbine’s blades, including blade side, severity, damage type (LE Erosion), and the affected area in square meters. This information is critical for prioritizing maintenance activities and addressing blade health, which directly impacts turbine efficiency.

Interactive 3D Turbine Visualization:

The central 3D visualization of WT-10 highlights different parts, such as the rotor hub, and shows an alert symbol indicating issues. This interactive model allows for a deeper understanding of the turbine’s condition and aids in identifying areas requiring attention.

Wind Speed and Direction Monitoring:

On the right, a gauge displays the current wind speed (8.7 m/s) and direction (237°), along with an average wind speed indicator. Monitoring these conditions is essential for optimizing turbine alignment and settings to maximize energy capture.

Targeted Recommendations and Alerts:

Below the wind details, specific recommendations and alerts for WT-10 are listed, including high wind speed, suboptimal wind direction, and low wind speed warnings. These alerts, complete with timestamps, are key for proactive maintenance and operational adjustments.

User Interface and Navigation:

The dashboard includes a search function, user profile, and other interface icons for easy navigation and settings adjustments. This enhances the user experience, allowing for efficient management of turbine data and settings.

This Asset Analysis View for Wind Turbine WT-10 on the XMPro dashboard provides a specialized and comprehensive analysis of the turbine’s performance, condition, and environmental factors. It is an invaluable tool for maintenance planning, operational decision-making, and optimizing the turbine’s energy output and efficiency.

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Why XMPro iDTS?

XMPro’s Intelligent Digital Twin Suite (iDTS) offers several unique solutions for optimizing the performance of wind turbines, particularly in the context of the Wind Turbine Performance Optimization use case. Here’s how XMPro iDTS effectively addresses this challenge:

In summary, XMPro iDTS addresses the Wind Turbine Performance Optimization use case by providing a comprehensive, real-time, predictive, and integrated solution. Its capabilities in digital twin technology, advanced data integration, predictive analytics, and interactive dashboards make it a powerful tool for enhancing the performance, safety, and efficiency of wind turbines.

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