Nested Microgrid State Estimation for False Data Mitigation
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Solution Overview
Problem
Existing state estimation systems in nested microgrids face challenges in accurately detecting false data, which can lead to erroneous control commands and security vulnerabilities due to faulty field devices or cyber attacks, affecting the entire power network.
Innovation Solution
A multi-layered state estimation system is implemented, where both microgrid and central control systems work together to detect false data by comparing local and global state estimations, using equations to estimate bus voltages and perform observability analysis, and employing software-defined networking to block malicious data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional state estimation systems are used in nested microgrids, then system monitoring is implemented, but false data from faulty field devices or cyber attacks cannot be reliably detected
Solution Approach 1:
The system divides the microgrid into multiple nested control areas (e.g., distribution automation centers and substation automation systems), each performing local state estimation independently. This segmentation allows each layer to detect false data within its scope while maintaining overall system monitoring, resolving the contradiction by enabling reliable false data detection without compromising state estimation accuracy through centralized processing bottlenecks.
Solution Approach 2:
The patent introduces an intermediary false data detection mechanism that operates between field devices and the state estimator. This intermediary layer validates measurements before they enter the state estimation process, using consistency checks and cross-validation techniques. This mediator enables reliable false data detection while preserving the accuracy of state estimation by filtering out corrupted data points.
2Loss of information
If more field devices are deployed to improve monitoring coverage, then system awareness increases, but the risk of false data injection and communication interference increases
Solution Approach 1:
The system performs preliminary false data detection and validation before measurements are fully integrated into state estimation. By pre-screening data from multiple field devices using consistency checks and cross-validation, the system maintains comprehensive monitoring coverage while preventing false data from propagating through the network, thus reducing injection risk.
Solution Approach 2:
The patent implements feedback mechanisms where state estimation results are continuously compared against expected physical constraints and historical patterns. When anomalies are detected, the system provides feedback to identify and isolate potentially compromised field devices. This feedback loop enables the system to maintain wide monitoring coverage while dynamically adjusting trust levels for individual devices based on their data quality.
3Measurement precision
If centralized state estimation is used, then system-wide monitoring is achieved, but detection of false data attacks is delayed and less accurate
Solution Approach 1:
The patent implements a nested architecture where state estimation is performed at multiple hierarchical levels (local distribution automation centers and central substation automation systems). Each layer performs rapid local estimation and false data detection on its subset of measurements, providing quick detection of local anomalies. The central system then performs coordinated estimation using results from all layers, achieving both fast local detection and accurate system-wide monitoring simultaneously.
Solution Approach 2:
Local control systems perform preliminary state estimation and false data detection before results are aggregated centrally. This preliminary action at the edge enables rapid detection of false data injections before they can propagate through the entire system, while the central system subsequently performs comprehensive validation to ensure system-wide estimation accuracy.
Data Source
AI summary
Systems, methods, techniques and apparatuses of nested microgrids are disclosed. One exemplary embodiment is a method for removing false data in a nested microgrid system, the method comprising: calculating a first local state estimation using a first plurality of local measurements and a second local state estimation using a second plurality of local measurements; calculating, with a central control system, a plurality of global state estimations including a first global state estimation a second global state estimation; performing a first false data detection test using the first local state estimation and one global state estimation of the plurality of global state estimations; performing a second false data detection test using the first global state estimation and the second global state estimation; detecting false data; and updating the first global state estimation, the second global state estimation, or the first local state estimation in response to detecting the false data.