Synchronous Generator Modeling Using Unscented Kalman Filter
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Solution Overview
Problem
Current SCADA systems with low-density sampling rates fail to capture the dynamics of power systems, and existing methods for synchronous generator modeling do not effectively incorporate frequency control or estimate parameters like inertia constant, damping factor, and secondary frequency control parameters using PMU data.
Innovation Solution
The use of the Unscented Kalman Filter (UKF) for synchronous generator modeling, which estimates both state and parameter variables, including frequency control parameters, by processing high-density PMU data, overcoming limitations of linearization in Extended Kalman Filter methods and improving accuracy and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If SCADA systems with low-density sampling rates are used to monitor power systems, then device complexity is reduced, but measurement precision and ability to capture system dynamics deteriorate
Solution Approach 1:
The patent replaces the mechanical sampling approach of SCADA systems with a data-driven filtering algorithm (UKF) that processes available measurements to extract dynamic information. The UKF algorithm substitutes the need for high-density physical sampling by computationally reconstructing system dynamics from lower-rate measurements through predictive modeling and state estimation.
2Device complexity
If Extended Kalman Filter methods are used for parameter estimation, then computational complexity is reduced through linearization, but measurement precision and convergence accuracy deteriorate due to linearization errors
Solution Approach 1:
The patent replaces the linearization mechanism of EKF with an unscented transformation approach. Instead of approximating nonlinear functions with linear models, the UKF uses a set of carefully chosen sample points (sigma points) that capture the true distribution of the state variables, eliminating linearization errors while maintaining computational efficiency.
Solution Approach 2:
The patent changes the fundamental parameters of the filtering approach by transitioning from linearization-based state propagation (EKF) to unscented transformation-based propagation (UKF). This parameter change in the mathematical approach allows accurate handling of nonlinearities without the computational burden of higher-order methods.
3Measurement precision
If high-density PMU data is processed using UKF for parameter estimation, then measurement precision and parameter accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary actions by pre-defining the sigma point transformation rules and covariance propagation mechanisms of the UKF algorithm. By establishing the unscented transformation framework in advance with pre-computed weights and scaling parameters, the system reduces real-time computational burden while maintaining high estimation accuracy.
Data Source
AI summary
Various examples are related to synchronous generator modeling with frequency control, which can be achieved using unscented Kalman filtering. In one example, a method includes obtaining operational parameters associated with a generator of a power system; determining parameters of a synchronous generator model with frequency control based at least in part upon the operational parameters associated with the generator; and providing a command to a frequency control of the generator, the command updating one or more parameters of the frequency control. In another example, a system includes a generator controller for a generator of a power system; and a computing device in communication with the generator controller, where the computing device is configured to determine parameters of the synchronous generator model using operational parameters associated with the generator and provide a command updating one or more parameters of a frequency control of the generator controller.