Microgrid Island Detection Using Consensus Across DER Sources
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Solution Overview
Problem
Existing UI detection methods for microgrids suffer from non-detection zones, nuisance tripping, and vulnerability to cyber-attacks, which can lead to inaccurate islanding responses and potential damage to the power system.
Innovation Solution
A consensus-based UI detection method that utilizes multiple UI detection sources and employs redundancy to build a consensus before triggering a UI response, thereby reducing non-detection zones, preventing false positives, and enhancing cybersecurity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single UI detection source is used, then the device complexity is reduced, but the reliability and measurement precision of UI detection deteriorate due to non-detection zones and vulnerability to cyber-attacks
Solution Approach 1:
The detection system is segmented into multiple independent UI detection sources (e.g., multiple DERs or monitoring points) that each independently monitor for islanding conditions. This segmentation allows the system to overcome the limitations of single-point detection by distributing detection capabilities across multiple locations, thereby eliminating non-detection zones and improving reliability without requiring a single complex centralized system.
Solution Approach 2:
Multiple UI detection sources are merged through a consensus mechanism where detection results from different sources are combined and evaluated collectively. The system requires a predetermined number of detection sources to confirm an islanding event before triggering a response, which merges the information from multiple sources to achieve higher reliability and cyber-security while maintaining manageable individual component complexity.
2Reliability
If multiple UI detection sources with redundancy are used, then the reliability and sensitivity of UI detection is improved, but the device complexity increases
Solution Approach 1:
The system implements partial redundancy by requiring only a predetermined number of detection sources (less than the total available) to confirm an islanding event. This partial action approach provides sufficient reliability improvement without requiring all possible detection sources to be fully integrated and operational, thereby limiting the increase in system complexity while still achieving enhanced detection reliability and sensitivity.
3Measurement precision
If a consensus mechanism is implemented, then false positives and nuisance tripping are prevented, but the response time may increase due to the need to build consensus
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and pre-evaluating data from multiple UI detection sources before an islanding event occurs. This preliminary monitoring establishes baseline conditions and prepares the consensus mechanism, so that when an actual islanding event happens, the system can quickly compare against pre-established criteria and reach consensus faster, thereby maintaining high detection accuracy without excessive delay in response time.
Data Source
AI summary
Unintentional islanding (UI) of a circuit of distributed energy resources (DERs) may leave area electrical power systems (EPS), external to the DER circuit, energized. Thus, UI detection methods have been developed to detect unintentional islanding and trigger a UI response. However, individual UI detection methods have various deficiencies. Thus, a consensus-based UI detection process is disclosed that builds a consensus from multiple UI detection sources, optionally implementing different UI detection methods. The redundancy in this consensus-based UI detection process provides robust, sensitive, selective, and cybersecure UI detection for the entire DER circuit. For example, the consensus-based UI detection process may eliminate or reduce non-detection zones, avoid false positives, thwart cyber-attacks, and/or the like.