Dynamic State Estimation for Natural Gas Networks Using Frequency-Domain Linearization
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Solution Overview
Problem
Current state estimation methods for natural gas networks are inadequate, particularly in considering dynamic characteristics and constraints, limiting their application in providing real-time, reliable, and complete operating data for integrated energy systems.
Innovation Solution
A method for dynamic state estimation of natural gas networks is developed, involving the establishment of time-domain and frequency-domain windows, construction of measurement and state vectors, formulation of an objective function, and application of constraints to solve for the state vector using Lagrange or interior point methods, considering topological and pressure constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic state estimation methods based on Kalman filtering are used for a single natural gas pipeline, then real-time state estimation can be achieved, but the method fails to consider constraints of the natural gas network and requires an initial state to be known, greatly limiting its application
Solution Approach 1:
The patent segments the natural gas network into multiple pipelines and nodes, establishing separate state estimation models for each pipeline while considering the coupling relationships between them. This allows the complex network to be broken down into manageable segments that can be estimated independently yet collectively, improving both reliability and adaptability.
Solution Approach 2:
The patent transitions from static to dynamic state estimation by incorporating time-varying parameters and dynamic constraints. The estimation model updates states in real-time based on changing operating conditions, allowing the system to adapt to dynamic network conditions without requiring steady-state assumptions.
Solution Approach 3:
The patent changes the estimation parameters from requiring known initial states to using measurable boundary conditions. By transforming the mathematical formulation to use observable parameters (pressures, flows at boundaries) instead of unobservable initial states, the method becomes applicable to real-world scenarios where initial conditions are unknown.
2Measurement precision
If a step of bad data identification is added to the iterative solution format of the Kalman filtering, then data quality can be improved, but the complexity of the solution process increases greatly
Solution Approach 1:
The patent performs preliminary data validation and constraint checking before the main iterative estimation process. By pre-screening measurements against known physical constraints (pressure bounds, flow directions, energy conservation), potentially bad data is identified and handled before complicating the iterative solution, thus improving data quality without significantly increasing overall complexity.
Solution Approach 2:
The patent introduces constraint equations as intermediary elements that mediate between raw measurements and the state estimation. These constraints act as filters that automatically eliminate inconsistent measurements during the estimation process, improving measurement precision while maintaining a relatively simple solution structure through unified mathematical formulation.
3Loss of information
If state estimation technology that considers dynamic natural gas is developed, then real-time operating state information can be provided, but research is still in its infancy and lacks consideration of network constraints
Solution Approach 1:
The patent implements feedback mechanisms where the estimated states are continuously checked against network constraints (mass conservation, energy conservation, pressure bounds). When estimates violate constraints, the model adjusts through iterative refinement, ensuring that real-time information remains consistent with physical laws. This feedback loop maintains both completeness and reliability of the estimation.
Solution Approach 2:
The patent creates a composite estimation model that combines dynamic state estimation techniques with constraint satisfaction methods. By merging the real-time capabilities of dynamic estimation with the reliability of constraint-based validation, the model achieves both complete real-time information and consistent, physically plausible results.
Data Source
AI summary
Provided is a method for a dynamic state estimation of a natural gas network considering dynamic characteristics of natural gas pipelines. The method can obtain a result of the dynamic state estimation of the natural gas network by establishing an objective function of the dynamic state estimation of the natural gas network, a state quantity constraint of a compressor, a state quantity constraint of the natural gas pipeline and a topological constraint of the natural gas network, and using a Lagrange method or an interior point method to solve a state estimation model of the natural gas network. The method takes the topological constraint of the natural gas network into consideration, and employs a pipeline pressure constraint in a frequency domain to implement linearization of the pipeline pressure constraint, thereby obtain a real-time, reliable, consistent and complete dynamic operating state of the natural gas network.