Dynamic Power Flow Modeling for Utility Grid Signal Prediction
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Solution Overview
Problem
Utility grid management faces challenges in efficiently and reliably controlling components due to the complexity of evaluating utility supply or consumption characteristics across numerous connection points, which existing power flow models struggle to address effectively.
Innovation Solution
A data-driven, real-time dynamic power flow model using machine learning to predict and control utility grid components by processing signals from various devices and external data sources, synchronizing sampling rates, cleaning data, and applying filters to generate an input matrix for predicting future grid conditions and optimizing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional power flow models are used to control utility grid components, then the control process becomes manageable, but the ability to accurately evaluate utility supply or consumption characteristics across numerous connection points deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/mathematical power flow models with a machine learning-based dynamic power flow model. The ML model learns complex nonlinear relationships from historical data, enabling accurate evaluation of utility supply and consumption characteristics across numerous connection points without requiring complex explicit calculations, thus resolving the contradiction between ease of operation and measurement precision.
2Measurement precision
If a data-driven machine learning model is used to predict utility grid signals, then the accuracy of predicting future grid conditions improves, but the complexity of processing and synchronizing multiple signals deteriorates
Solution Approach 1:
The patent performs preliminary actions by synchronizing sampling rates of multiple signals and cleaning data before inputting them to the machine learning model. This preprocessing step organizes the complex multi-source data into a standardized format, reducing the computational burden during prediction while maintaining high prediction accuracy.
3Adaptability or versatility
If real-time dynamic power flow modeling is implemented, then the ability to adapt to changes in load and exogenous factors improves, but the computational resources required deteriorates
Solution Approach 1:
The patent implements a dynamic power flow model that adapts to changing grid conditions by continuously learning from new data. The model updates its parameters and predictions in real-time based on varying load conditions and exogenous factors, enabling adaptability while the learned model structure efficiently manages computational resources compared to repeated complex calculations.
Data Source
AI summary
Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.