Security-Aware Distributed Control Framework for Power Systems
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Solution Overview
Problem
Distributed power system control architectures face challenges in detecting and mitigating cyber attacks, such as false data injection and denial of service attacks, due to their reliance on limited peer-to-peer information broadcast, which can compromise global objectives like power sharing and voltage stabilization.
Innovation Solution
A security-aware distributed control framework utilizing a multi-resolution morphological gradient algorithm (MMGA) to analyze incoming information states, detect anomalies, identify infected agents, and update consensus protocols, ensuring resilient operation by excluding malicious agents and maintaining control system objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed control algorithms are used to enable local objectives, then system scalability and local autonomy are improved, but vulnerability to cyber attacks and inability to detect infected agents worsen
Solution Approach 1:
The patent implements a feedback mechanism where each agent monitors incoming information states from neighbors and uses multi-resolution morphological gradient algorithms to detect anomalies. The system continuously compares expected information patterns with actual received data, and when deviations exceed thresholds, it triggers attack detection and isolation procedures, creating a closed-loop security system that maintains both distributed control and reliability
Solution Approach 2:
The patent introduces an intermediary detection layer between distributed agents that uses morphological gradient analysis to mediate information exchange. This intermediary mechanism filters and validates incoming data before it affects the distributed control objectives, allowing the system to maintain local autonomy while adding security verification without requiring centralized control
2Reliability
If multi-resolution morphological gradient algorithm is applied to detect attacks, then detection accuracy and reliability are improved, but computational complexity and processing time worsen
Solution Approach 1:
The patent segments the attack detection process into multiple resolution levels, where a first-level morphological gradient algorithm performs initial anomaly detection with lower computational requirements, and a second-level algorithm provides more detailed analysis only when needed. This hierarchical segmentation reduces overall computational complexity while maintaining high detection accuracy through progressive refinement
Solution Approach 2:
The patent applies partial action by using simplified morphological operations at the first resolution level for routine monitoring, and only activates the more computationally intensive second-level analysis when anomalies are detected. This approach performs sufficient detection action at each level without applying excessive computational resources continuously, balancing accuracy with complexity
3Reliability
If infected agents are isolated by setting weighting factors to zero, then system security and data integrity are improved, but loss of information from infected agents worsens
Solution Approach 1:
The patent extracts only the essential healthy information from isolated agents before completely excluding them by setting weighting factors to zero. The system identifies and preserves critical data that contributes to global objectives while removing compromised information, thereby maintaining data integrity without complete loss of potentially useful information from previously infected agents
4Reliability
If centralized control algorithms are used to achieve global objectives, then system coordination and global optimization are improved, but system scalability and flexibility to distributed resources worsen
Solution Approach 1:
The patent creates a universal distributed control framework where agents can simultaneously achieve local objectives through peer-to-peer communication and contribute to global objectives through consensus-based information aggregation. The morphological gradient detection mechanism serves multiple functions: it validates local data exchange, ensures global data integrity, and coordinates agent behavior, making the system adaptable to various distributed resource configurations while maintaining global optimization
Data Source
AI summary
Systems and methods for security in power systems are provided. A security-aware distributed control framework for resilient operation of power systems can detect and mitigate different types of attacks that might target power systems. The framework can discover a change in the features of transmitted data from neighbor agents, discard an infected agent, and achieve an updated consensus protocol agreement while satisfying a control system objective.