Automated Utility Hierarchy Classification for Multi-Source Systems
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Solution Overview
Problem
Existing utility systems with multiple sources lack an automated method to determine hierarchical structures effectively, as current algorithms fail to differentiate among various types of multi-source models, limiting their ability to provide contextual data for troubleshooting, efficiency improvement, and failure prediction.
Innovation Solution
An automated multi-source hierarchy algorithm that uses data from discrete monitoring devices with minimal user input to determine the hierarchical arrangement of utility systems, incorporating additional monitoring points and utilizing statistical methods and rules to establish accurate relationships among monitoring devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing hierarchy algorithms are used for utility systems with multiple sources, then the system can determine a hierarchical arrangement, but the algorithm fails to differentiate among various types of multi-source models, reducing measurement precision
Solution Approach 1:
The patent segments the utility system into distinct operational models (radial, non-radial, islanding, generation, cogeneration, load reduction) based on the relationship between multiple utility sources. Each model represents a specific configuration pattern that the algorithm can identify and classify separately, enabling precise hierarchy determination for each model type while maintaining adaptability across diverse multi-source configurations.
2Loss of information
If manual configuration is used to establish hierarchical context, then the system can provide detailed contextual data, but the process requires significant user input and time
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine hierarchical arrangements and identify utility source relationships using algorithms that process monitoring device data autonomously. The system performs model differentiation and hierarchy classification without requiring manual user input, thereby preventing loss of hierarchical context information while eliminating the time cost of manual configuration.
Solution Approach 2:
The patent applies preliminary action by pre-establishing multiple utility source models (radial, non-radial, islanding, generation, cogeneration, load reduction) that represent common multi-source configurations. These pre-defined models enable the algorithm to quickly match and classify the actual system configuration against known patterns, providing hierarchical context immediately without requiring time-consuming manual analysis or configuration.
3Quantity of substance
If discrete monitoring device data is used without hierarchical context, then the system can collect electrical operating parameters, but the data lacks contextual information for troubleshooting and analysis
Solution Approach 1:
The patent introduces an intermediary hierarchical structure that sits between discrete monitoring device data and the final analysis output. The algorithm determines hierarchical arrangements and identifies utility source relationships, creating a contextual framework that links individual monitoring points to the overall system architecture. This intermediary hierarchy transforms raw data into contextualized information, enabling effective troubleshooting and analysis while preserving all monitoring data.
Data Source
AI summary
A method for automatically determining how monitoring devices in an electrical system having a main source of energy and at least one alternative source of energy (e.g., another utility source, a generator, or UPS system) are connected together to form a hierarchy. The end-user inputs identification information about the monitoring device(s) monitoring the alternative source of energy. The method receives time-series data from the monitoring devices and determines a model type of the electrical system by analyzing the monitoring device's time-series data. Once the model type is known, the method builds the complete monitoring system hierarchy in which the monitoring devices that are monitoring the main and alternative sources are placed properly. The method can also validate polarity nomenclature of the time-series data to account for end-user's varying polarity configurations.