Remote Wind Farm Monitoring Application

Project Overview

Wind turbines are typically located in remote places where they are exposed to harsh weather conditions that could take a toll on machine parts and their performance. Faults need to be discovered in time or well in advance to make wind farm operations reliable, efficient, and profitable. Our integrated solution provides visibility into wind farms reaching down to specific parts of individual turbines. It enables real-time monitoring, predictive analysis, and efficient maintenance operations.

Business Requirement

The client wanted a solution that would aggregate the turbine data scattered across different vendor portals and make it easily accessible on a single dashboard. The solution would have remote monitoring and analytics capabilities, including the capability to track Service Level Agreements (SLA) on performance and uptime.

Client Profile

The client is a fast-growing renewable energy company with wind farms in multiple locations.

Solution

To address the business requirement, we developed a platform leveraging Industrial Internet of Things and big data technologies. The platform executes real-time and batch processing of data and extracts intelligence that can be accessed using a web user interface from any location. It is a highly scalable cloud-based solution capable of executing complex computations even as data grows in volume.

Remote Data Collection

We deployed an OPC client program that connects to the OPC server in the SCADA system used by the client. The program pushes data on important operation parameters to our cloud platform. The data collected includes nacelle and gearbox oil temperatures, rotor and generator speeds, pitch angle, yaw deviation, nacelle position, power output, wind speed, and wind direction. These are collected to check for deviations in parameter values and to verify power curves.

Real-Time Analysis

The data collected from each turbine is processed in real-time, and the status of all turbines in all farms are relayed to users on custom dashboards for real-time performance monitoring. A time-series graph allows users to easily track power production versus wind speeds. The system flags deviations in data, which could indicate equipment malfunction. Users can drill down and track operating parameters for each turbine, daily power production, and power lost or gained.

Predictive Maintenance

Our solution serves as a lifetime data record of turbines on each farm. Models built of the historical data support advanced analytics driving farm productivity and reliability. Most importantly, our solution supports predictive maintenance, which helps operators identify impending equipment failure and thus minimize maintenance costs and machine downtime.

Ticketing System

The solution incorporates a Computerized Maintenance Management (CMMS) system that allows all maintenance issues to be recorded on a centralized system. Issues related to machines in multiple locations can be logged, assigned, and tracked to ensure speedy and transparent resolution of issues.

Key Features

  • Unified view of multiple wind farms
  • Fast and real-time data analytics
  • Live updates on turbine and weather parameters
  • Real-time power output monitoring at turbine and farm level
  • Anomaly detection and visual alerts
  • Predictive maintenance of turbines
  • Monitoring of Key Performance Indicators (KPIs)
  • Wind and power production pattern tracking
  • Power-curve analysis to compare actual and warranted performance
  • Convenient trouble ticketing system
  • User-friendly web interface

Technologies Used

Play Scala Spark Kafka MongoDB Hadoop Node.js React Python

Business Benefits

  • The solution eliminated the hassle of logging into multiple vendor portals for turbine monitoring.
  • A unified view of the data enables the client to build critical insight into business operations and KPI tracking allows the client to make more informed business decisions.
  • Real-time monitoring and early detection of turbine malfunction reduced operation and maintenance costs.
  • The integrated trouble-ticketing system fast tracked reporting and resolution of issues.
  • Automatic anomaly detection helped optimize inspections and maintenance operations.
  • The solution improved the management of contracts and the tracking and enforcement of Service Level Agreements.