Power System Model Calibration Using Sequential PMU Event Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current power system model calibration methods are inadequate for accurately representing multiple events, leading to potential instability and high costs due to the need for frequent and costly staged tests, and existing methods struggle to maximize the use of multiple disturbance events for model validation.
Innovation Solution
A system utilizing a computing device with a processor to analyze multiple events sequentially, generate and update calibration values for power system model parameters, and integrate these updates into the model to enhance accuracy and reduce testing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If staged tests are performed to calibrate power system models, then model accuracy is improved, but testing costs and system downtime increase
Solution Approach 1:
The patent uses PMU recorded disturbance events as copies of actual system behavior to calibrate models, replacing the need for physical staged tests. By analyzing historical disturbance data that captures real generator responses, the system achieves calibration accuracy without taking generators offline for dedicated testing.
Solution Approach 2:
The system leverages existing PMU infrastructure and naturally occurring disturbances to perform self-calibration of power system models. The generator essentially calibrates itself by providing its own operational data during normal disturbances, eliminating the need for external testing equipment and personnel.
2Measurement precision
If multiple disturbance events are analyzed, then model calibration accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the calibration process into distinct phases: identifying informative events, selecting candidate parameters, performing parameter estimation, and validating results. This segmentation breaks down the complex task of analyzing multiple disturbances into manageable steps, reducing computational overhead while maintaining accuracy.
Solution Approach 2:
The system extracts only the most informative disturbance events and relevant model parameters from the large set of available data. By filtering out redundant information and focusing on key features, the computational complexity is reduced while preserving the essential calibration insights.
3Reliability
If frequent model validation is performed using PMU data, then model accuracy is maintained, but data processing requirements increase
Solution Approach 1:
The patent performs preliminary filtering and selection of informative disturbance events before detailed analysis. By pre-identifying which events contain calibration-relevant information, the system reduces the volume of data that requires intensive processing, enabling frequent validation without proportional increases in computational burden.
Data Source
AI summary
A system for enhanced sequential power system model calibration is provided. The system is programmed to store a model of a device. The model includes a plurality of parameters. The system is also programmed to receive a plurality of events associated with the device, receive a first set of calibration values for the plurality of parameters, generate a plurality of sets of calibration values for the plurality of parameters, for each of the plurality of sets of calibration values, analyze a first event of the plurality of events using a corresponding set of calibration values to generate a plurality of updated sets of calibration values, analyze the plurality of updated sets of calibration values to determine a current updated set of calibration values, and update the model to include the current updated set of calibration values.