Renewable Energy Security Hardening for Uniform Compliance Updates
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Solution Overview
Problem
The manual process of system hardening in renewable energy systems, particularly wind power plants, is inefficient and often results in partial upgrades and non-uniform adherence to security standards, posing risks due to the critical nature of these systems.
Innovation Solution
A computer-implemented method using machine learning models to determine a target security standard, identify deviations, and recommend actions to align the system's security posture with the standard, incorporating reinforcement learning for stable updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual system hardening is performed by deploying skilled Engineers onsite, then security compliance can be assessed and updated, but the process becomes highly time-consuming and inefficient for large distributed systems
Solution Approach 1:
The system performs self-assessment of its own security compliance status through automated agents that collect device profiles, configuration data, and operational logs. The machine learning models then analyze this self-collected data to determine compliance gaps and recommend hardening actions, eliminating the need for external engineers to manually assess each device.
Solution Approach 2:
The patent replaces the mechanical manual process of engineers physically visiting sites with an automated electronic system. Machine learning models analyze security compliance data and generate hardening recommendations automatically, substituting human expertise with algorithmic analysis that can process distributed device data simultaneously across the entire system.
2Reliability
If cautious manual updating is performed to ensure system stability and safety, then system reliability is maintained, but security standards are rarely upgraded in one go and the process becomes incremental and prolonged
Solution Approach 1:
The system performs preliminary analysis by using machine learning models to assess the current security posture against target standards before any updates are applied. It identifies all compliance gaps and simulates the impact of recommended hardening actions, allowing planners to understand the full scope of changes needed before implementation begins.
Solution Approach 2:
The patent implements dynamic, iterative hardening where the system continuously monitors security posture changes as updates are applied. The machine learning models adjust recommendations in real-time based on observed system responses, allowing the hardening process to adapt to actual system behavior rather than following a fixed incremental schedule.
3Reliability
If incremental manual upgrades are performed to maintain system stability, then system reliability is preserved, but adherence to security standards becomes non-uniform across different parts of the system
Solution Approach 1:
The system employs universal machine learning models that can assess and recommend hardening actions across multiple device types and security standards simultaneously. The same analytical framework is applied uniformly to all devices in the distributed system, ensuring consistent evaluation criteria and uniform adherence to security standards across the entire infrastructure.
Solution Approach 2:
The system implements continuous feedback loops where automated agents monitor security posture across all devices and report back to the central analysis system. The machine learning models use this feedback to identify and correct non-uniformities in security adherence, ensuring all devices progress toward the target security standard at consistent rates.
4Productivity
If automated machine learning models are used to determine target security standards and recommend actions, then the hardening process becomes efficient and scalable, but the system complexity increases
Solution Approach 1:
The system segments the complex security hardening process into distinct functional modules: automated data collection agents, machine learning analysis models, recommendation generation engines, and implementation monitoring components. Each module handles a specific aspect of the hardening process, making the overall complex system manageable through clear functional decomposition.
Data Source
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AI summary
A computer implemented method for security hardening of a renewable energy device system comprises: i) determining a target security standard for the system using a first model trained using a first machine learning process that predicts the target security standard from a security posture of the system. In a second step ii) identifying one or more deviations in the security posture of the system from the target security standard, and in a third step iii using a second model trained using a second machine learning process to recommend an action to perform on the system to move the security posture of the system toward the determined target security standard.