Power Grid Dynamic Model Estimation for Stable Converter Control
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Solution Overview
Problem
The integration of intermittent renewable energy sources and advanced power electronics into power grids introduces variability and complexity, affecting stability and frequency predictability, necessitating an accurate control scheme that models the grid dynamics.
Innovation Solution
A method for estimating a dynamical model of a power grid involves determining a characteristic matrix from input data, performing singular value decomposition (SVD) to identify the model order, and representing the dynamics with a dynamical model function using adaptive algorithm-adjusted coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If intermittent renewable energy sources and power electronics are integrated into the grid, then energy generation capacity and flexibility are improved, but grid stability and frequency predictability deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the controller continuously monitors grid behavior through measured input-output signals and adjusts the dynamical model accordingly. This closed-loop approach allows the system to adapt to changing grid conditions caused by renewable energy integration while maintaining stability through real-time model updates and control adjustments.
Solution Approach 2:
The patent employs a dynamical model that captures the time-varying behavior of the grid, transitioning from static to dynamic representation. By using measured signals to identify and update model parameters, the system adapts to changing grid conditions, allowing the controller to respond appropriately to variability introduced by renewable energy sources while maintaining stability.
2Ease of operation
If a control scheme is designed based on an approximate grid model, then implementation complexity is reduced, but control accuracy and effectiveness deteriorate
Solution Approach 1:
The patent implements a self-identifying control system where the controller automatically determines grid dynamics through measured input-output signals during normal operation. This self-service approach eliminates the need for external system identification procedures or complex manual modeling, providing accurate grid models while maintaining ease of operation through automated adaptation.
Solution Approach 2:
The patent performs preliminary system identification by determining the dynamical model structure and parameters before implementing the final control strategy. By using measured signals to pre-characterize grid behavior, the controller is equipped with accurate model information in advance, enabling precise control without requiring complex real-time identification during operation.
3Reliability
If the grid dynamics are modeled with high accuracy, then control performance is improved, but the complexity of the modeling and control system increases
Solution Approach 1:
The patent extracts only the essential dynamic characteristics of the grid that are relevant for control purposes, rather than attempting to model every detail of the complex power system. By focusing on the dominant dynamics captured through input-output signal analysis, the system achieves sufficient modeling accuracy for effective control while avoiding unnecessary complexity in the model structure and implementation.
Data Source
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AI summary
A method (100) of estimating a dynamical model of a power grid (10) is provided. The method comprises determining (106) a characteristic matrix from input data, the characteristic matrix representing the system dynamics of the grid (10) and having a rank r; performing (108) a singular value decomposition, SVD, on the characteristic matrix, thereby identifying an order q<r of the dynamical model; and representing (110) the dynamical model by a dynamical model function with a number p of model coefficients being dependent on q.