Wind Farm Remote Monitoring With Work Tickets for O&M Planning
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Solution Overview
Problem
Managing wind farms is challenging due to accessibility issues related to weather conditions, supply and demand of parts and equipment, and the number of workers, especially in offshore locations, which complicates operation and maintenance (O&M) and increases costs.
Innovation Solution
A wind farm supervision monitoring method that utilizes a system integrating SCADA data and CMS for real-time status monitoring, abnormality detection, and automated alarm systems, enabling the creation of operation and maintenance plans, work ticket management, and cooperative work through a web-based interface to minimize downtime and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and centralized management systems are implemented, then operational control and maintenance efficiency are improved, but system complexity and initial costs increase
Solution Approach 1:
The patent implements a centralized server as an intermediary that receives data from multiple wind turbines via SCADA systems and provides a web-based interface for remote monitoring and control. This intermediary architecture allows distributed wind farms to be managed centrally without requiring direct complex connections between each turbine and the control center, thus improving maintenance efficiency while managing system complexity through modular data flow management.
Solution Approach 2:
The patent replaces physical on-site monitoring and manual maintenance coordination with automated electronic systems including SCADA data collection, web-based user interfaces, and automated alert notification systems. This substitution of mechanical/physical processes with electronic and software-based solutions improves operational efficiency while the modular architecture keeps system complexity manageable through standardized communication protocols.
2Reliability
If workers are deployed to remote offshore wind farms for maintenance, then equipment reliability is improved, but accessibility challenges and operational costs increase
Solution Approach 1:
The patent implements self-service capabilities through automated monitoring systems that continuously collect SCADA data, detect anomalies, and generate maintenance alerts without requiring constant human presence. The system can identify equipment failures and notify relevant personnel automatically, allowing the wind farm infrastructure to monitor and report its own status, thereby improving reliability while reducing the need for workers to physically access remote locations.
Solution Approach 2:
The centralized server acts as an intermediary between the remote wind turbines and maintenance personnel. It aggregates data from multiple turbines, processes information, and presents consolidated maintenance needs through a web interface, eliminating the need for workers to travel to each individual turbine for routine monitoring and enabling more efficient deployment when physical intervention is required.
3Reliability
If comprehensive data collection from multiple wind farms is implemented, then early failure detection capability is improved, but data processing complexity and communication requirements increase
Solution Approach 1:
The patent merges data from multiple wind farms into a single centralized database on the server. By consolidating SCADA data, maintenance records, and operational parameters from various locations into one unified system, the patent enables comprehensive failure detection across the entire fleet while simplifying data processing through centralized storage and standardized data formats, rather than requiring separate complex processing systems for each farm.
Solution Approach 2:
The system implements feedback mechanisms where collected data is continuously analyzed, compared against normal operational parameters, and used to generate alerts when deviations indicate potential failures. This feedback loop enables early detection of equipment issues across multiple wind farms, while the automated nature of the feedback process reduces manual data processing complexity through algorithmic analysis and automated threshold-based alerting.
Data Source
AI summary
A wind farm supervision monitoring method includes: displaying, by a user interface (UI) unit, a location of the site server on a map along with a dashboard menu, a navigation menu, and an alarm & event menu; collecting, by a data collection unit, data about status monitoring of each wind turbine from the site server; detecting, by an abnormality status detection unit, an abnormality status of each wind turbine based on the data about the status monitoring of each wind turbine and issuing an alarm based on the detecting the abnormality status; providing, by a supervision unit, a turbine operation status of each wind turbine using the dashboard menu, managing operation and maintenance; and establishing, by the supervision unit, an operation and maintenance plan for the detected abnormality status of the wind turbine.