Renewable Energy System Security Hardening with Machine Learning
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Solution Overview
Problem
The manual process of system hardening in renewable energy systems, particularly wind power plants, is inefficient and prone to partial upgrades, leading to inconsistent compliance with security standards, which is critical for maintaining system stability and safety.
Innovation Solution
A computer-implemented method using machine learning models to determine a target security standard, identify deviations, and recommend actions to automate the security hardening process, ensuring incremental updates that maintain system reliability and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
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 especially in large and complicated systems with multiple and distributed components
Solution Approach 1:
The system enables automated self-assessment of security compliance through machine learning models that automatically evaluate security posture, identify deviations from target standards, and recommend hardening actions without requiring manual intervention from skilled engineers at each location
Solution Approach 2:
The patent replaces the mechanical manual process of engineer assessment and documentation with an automated digital system using machine learning models to perform security evaluation, compliance checking, and recommendation generation
2Reliability
If cautious manual updating is performed to maintain system stability and safety, then system reliability is preserved, but security standards are not consistently adhered to and upgrades are rarely completed in one go
Solution Approach 1:
The system continuously monitors security posture and provides feedback on compliance status against target standards, enabling iterative improvements while maintaining system stability through automated validation at each step
Solution Approach 2:
The machine learning models perform preliminary assessment and planning of hardening actions before implementation, ensuring that compliance requirements are fully understood and can be consistently applied across the entire system
3Measurement precision
If comprehensive compliance checks are performed to assess security posture, then thorough security assessment is achieved, but the complexity of managing the state of each component becomes a huge task
Solution Approach 1:
The patent implements a universal automated assessment framework that can evaluate multiple security standards and compliance requirements across diverse system components using the same machine learning models, eliminating the need for separate management processes for each component type
Solution Approach 2:
The system transforms complex security assessment data into simplified compliance metrics and recommendations that can be easily interpreted and acted upon, changing the parameters from detailed technical configurations to high-level compliance status indicators
Data Source
AI summary
A computer implemented method for security hardening of a renewable energy device system including: 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. The method also includes ii) identifying one or more deviations in the security posture of the system from the target security standard, and 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.