Inertia-Aware State Estimation for Asynchronous Power Network Measurements
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Solution Overview
Problem
Current state estimation methods for power distribution networks struggle with asynchronous measurements, relying on synchronized data and probabilistic models, which limits their ability to provide real-time estimates and manage complex, distributed energy systems effectively.
Innovation Solution
The implementation of inertia-aware state estimation techniques that process information as it comes in, using a linearized system model and a momentum term to ensure consistent estimates, even with asynchronous data, without relying on probabilistic models or pseudo-measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronized data and probabilistic models are used for state estimation, then measurement consistency is improved, but real-time processing capability and adaptability to asynchronous measurements deteriorate
Solution Approach 1:
The patent implements a dynamic state estimation approach that processes measurements as they arrive without requiring synchronization. The system continuously updates the power network state using incoming measurements at different times, adapting to the asynchronous nature of data arrival while maintaining estimation accuracy through iterative refinement of state variables.
Solution Approach 2:
The patent changes the estimation parameters by introducing a linearized system model that works with asynchronous measurements. Instead of using fixed probabilistic models requiring synchronized data, the system dynamically adjusts estimation parameters based on the timing and availability of measurements, enabling real-time processing while maintaining accuracy.
2Measurement precision
If complex probabilistic models are used for state estimation, then estimation accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts the essential elements needed for state estimation by using a linearized system model instead of complex probabilistic models. This extraction approach removes unnecessary computational complexity while retaining the core functionality of estimating power network state, enabling real-time processing with reduced computational burden.
Solution Approach 2:
The patent employs a simplified linearized model that can be rapidly computed and discarded, replaced by updated measurements in real-time. This approach uses computationally inexpensive calculations that can be performed frequently without the heavy computational cost of complex probabilistic models, enabling real-time state estimation.
3Stability of the object's composition
If traditional state estimation methods are used, then model consistency is improved, but real-time processing and responsiveness to new measurements deteriorate
Solution Approach 1:
The patent implements continuous state estimation that processes measurements as they arrive without interruption or waiting for synchronization. The system maintains continuous updates of the power network state, ensuring both model consistency through iterative refinement and real-time processing by continuously incorporating new measurements as they become available.
Data Source
AI summary
The present disclosure provides techniques for estimating network states using asynchronous measurements by leveraging network inertia. For example, a device configured in accordance with the techniques of the present disclosure may receive electrical parameter values corresponding to at least one first location within a power network and determine, based on the electrical parameter values and a previous estimated state of the power network, an estimated value of unknown electrical parameters that correspond to a second location within the power network. The device may further cause at least one device within the power network to modify operation based on the estimated value of the unknown electrical parameters. The leveraging of network inertia may obviate the need for probabilistic models or pseudo-measurements.