Wind Farm State Prediction for Unauthorized Access Detection
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Solution Overview
Problem
Wind farms, classified as Critical National Infrastructure, face challenges in detecting unauthorized access and responding to cyber threats due to the difficulty in identifying software incidents, especially when traditional security solutions are bypassed, and the need for rapid identification and response to minimize damage.
Innovation Solution
A method using a trained data-driven model, such as a neural network, to predict the operation and states of a wind farm by processing operational and environmental data, allowing for real-time comparison with actual conditions to detect unauthorized access, and providing alerts through a user interface for immediate action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rule-based security systems are used to detect unauthorized access, then known threats can be identified, but the system cannot detect new or bypassed threats and cannot keep pace with evolving cyber criminal strategies
Solution Approach 1:
The patent transforms the security detection approach from rule-based parameters to behavior-based parameters. Instead of checking against fixed security rules, the system monitors operational parameters (power output, rotor speed, yaw angle) and environmental parameters (wind speed, wind direction) to establish baseline behavior patterns, then detects deviations from these patterns indicating potential unauthorized access.
Solution Approach 2:
The system implements dynamic adaptation by continuously learning and updating expected behavior patterns for each wind turbine under different operating conditions. The behavior model adjusts to changing environmental conditions and operational states, enabling detection of new threat types without requiring pre-programmed rules for each specific threat scenario.
2Ease of manufacture
If wind farms remain unmanned to reduce operational costs, then maintenance and monitoring expenses are reduced, but the ability to respond to intrusions is delayed until alarm notification reaches the control center
Solution Approach 1:
The system enables the wind farm to monitor and detect security threats autonomously without requiring continuous human presence. The automated behavior analysis system continuously compares actual turbine operations against expected patterns and can identify unauthorized access in real-time, eliminating the need for on-site security personnel while maintaining rapid detection capabilities.
Solution Approach 2:
The system implements continuous feedback loops where operational data from sensors is constantly fed into the behavior model, which generates real-time assessments of turbine behavior. This automated feedback mechanism enables immediate detection and alerting of anomalies, providing timely response capability without human operators physically present at the wind farm.
3Reliability
If more traditional security measures are implemented to protect against known threats, then detection of established attack patterns improves, but the system complexity increases and scalability becomes difficult
Solution Approach 1:
The behavior-based security system serves multiple functions simultaneously: it monitors turbine operational efficiency, detects mechanical anomalies, identifies unauthorized access attempts, and provides predictive maintenance capabilities. This single unified approach replaces multiple specialized security systems, reducing overall system complexity while maintaining comprehensive security coverage.
Solution Approach 2:
The patent replaces complex mechanical and electronic security systems (physical barriers, access control devices, surveillance equipment) with a software-based behavioral analysis system. By substituting physical security infrastructure with intelligent data processing and pattern recognition algorithms, the system achieves enhanced security detection with reduced hardware complexity and easier scalability.
Data Source
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AI summary
The invention refers to a method for computer-implemented identifying an unauthorized access to a wind farm (100) by obtaining environmental and operational data (ED, OD) of the wind farm (100) from a repository (REP), which comprise technical data (SSI) and organizational data (MTL, UI) of the wind farm indicating tasks to be dealt with in the wind farm (100) in the future. Based on the environmental and operational data (ED, OD), for a predetermined time interval, a prediction of the operation and/or states of the wind farm (100) is determined by a trained data driven model (MO). The trained data driven model (MO) provides a prediction of the operation and/or states of the wind farm (100) as a digital output. The prediction is compared to operational conditions (OC) of the wind farm (100) resulting from current or past user machine interactions of a user. An unauthorized access is identified in case of a predetermined deviation of the obtained operational conditions (OC) from the prediction of the operation and/or states of the wind farm (100).