Network Estimator for High RoCoF Ride-Through
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Solution Overview
Problem
Modern electric power systems, with a decrease in rotating masses due to renewable energy integration, are less tolerant of generating unit loss and experience instability from high rate-of-change-of-frequency (RoCoF) events, which can lead to further unit loss and system instability.
Innovation Solution
An electric power system with a network estimator and event estimator that receives and analyzes status information to determine system characteristics, enabling real-time control and corrective actions for generating units to ride-through high RoCoF events by correlating RoCoF values with system characteristics using a look-up table.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If renewable energy technologies are integrated into electric power systems, then the use of renewable energy increases, but the number of rotating masses decreases, reducing system tolerance to generating unit loss
Solution Approach 1:
The patent replaces mechanical rotating masses (physical inertia from synchronous generators) with an electronic control system (network estimator, event estimator, and controller) that digitally models system inertia and generates corrective control signals. This substitution allows renewable energy integration while maintaining stability through computational rather than mechanical means.
Solution Approach 2:
The patent introduces an intermediary control system consisting of network estimators and event estimators that mediate between the renewable energy sources and the grid. These intermediaries calculate system characteristics, detect RoCoF events, and generate corrective signals to synchronous generators, enabling renewable integration while preserving system stability.
2Productivity
If the number of rotating masses decreases in electric power systems, then renewable energy capacity increases, but system stability during high RoCoF events deteriorates
Solution Approach 1:
The patent implements preliminary action by having network estimators continuously calculate system characteristics (total inertia, damping) and store them before RoCoF events occur. Event estimators use these pre-calculated values to quickly detect and respond to frequency changes, enabling rapid stabilization without relying on physical rotating masses.
Solution Approach 2:
The patent employs feedback mechanisms where controllers continuously monitor frequency changes, compare them against thresholds determined by system characteristics, and adjust generator output accordingly. This closed-loop feedback system maintains stability during RoCoF events by constantly adapting generator response based on real-time system conditions.
3Reliability
If synchronous generating units are used to provide inertia, then system stability improves, but the complexity of the control system increases due to real-time monitoring and correction requirements
Solution Approach 1:
The patent segments the control system into distinct functional modules: network estimators that calculate system-wide characteristics, event estimators that detect local frequency changes, and controllers that execute corrective actions. This segmentation distributes computational complexity across multiple specialized components rather than requiring a single complex system.
Solution Approach 2:
The patent implements self-service through autonomous operation of the estimation and control system. The network and event estimators automatically calculate system characteristics and detect RoCoF events without human intervention, and controllers autonomously generate corrective signals based on predetermined criteria, reducing the need for complex manual control systems.
Data Source
AI summary
An electric power system includes a generating unit, which includes a controller for controlling an operational mode of the generating unit. The electric power system also includes an event estimator communicatively coupled to the controller of the generating unit and a network estimator communicatively coupled to the event estimator. The network estimator includes a processor configured to receive status information associated with the electric power system, determine, based upon the status information, at least one characteristic of the electric power system, and transmit the at least one characteristic to the event estimator.