Residual Load Signal Analysis for Generator Class Estimation
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Solution Overview
Problem
Power distribution networks face challenges in accurately estimating and managing contributions from various energy generators, particularly renewable energy sources, due to lack of direct measurement data, which affects utility operations and energy balancing.
Innovation Solution
A method and system that analyze residual load data using physical and generalized additive models to identify and quantify contributions from different energy generator classes, enabling dynamic adjustment of energy production and consumption within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If residual load data is analyzed using physical and generalized additive models to identify energy generator contributions, then measurement precision of generator contributions is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of measuring generator contributions by dividing it into multiple model components: physical models for specific generator types and generalized additive models for remaining generators. This segmentation allows precise measurement of individual generator contributions while managing overall system complexity through modular analysis.
Solution Approach 2:
The patent introduces residual load data as an intermediary measurement that indirectly captures generator contributions without requiring direct measurement devices at each generator. By analyzing residual load at measurement points, the system mediates between unavailable direct measurements and the need for accurate generator contribution data.
2Measurement precision
If direct measurement devices are installed at each energy generator, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
Instead of installing physical measurement devices at each generator, the patent creates a virtual copy of the measurement function through mathematical models. The physical and generalized additive models replicate the measurement capability by analyzing residual load patterns, eliminating the need for expensive direct measurement hardware at every generator location.
Solution Approach 2:
The patent replaces mechanical measurement devices with a computational system based on physical and generalized additive models. This substitution transitions from physical measurement infrastructure to information processing, reducing device complexity while maintaining measurement precision through mathematical analysis of residual load data.
3Reliability
If comprehensive monitoring of all energy generators is implemented, then reliability of utility operations is improved, but loss of information increases due to data complexity
Solution Approach 1:
The patent segments generator contributions into distinct categories (specific generator types modeled with physical models and remaining generators with generalized additive models). This segmentation organizes complex data into manageable groups, preventing information loss by structuring comprehensive monitoring data in an analyzable format.
Solution Approach 2:
The patent transforms complex generator monitoring data into meaningful parameters through model analysis, extracting key contribution metrics from residual load signals. By changing the parameter representation from raw complex data to modeled contribution values, the system maintains reliability while preventing information loss through effective data transformation and organization.
Data Source
AI summary
Various embodiments manage energy generation in a power generation and distribution system. In one embodiment, a set of residual load data is obtained for a given period of time measured at one or more nodes within a power generation and distribution system. The set of residual load data encodes a set of power flow signals. The set of residual load data is analyzed. An amount of power contributed to the set of residual load data by at least one energy generator class is determined based on the analysis of the set of residual load data.