Automated Voltage Analysis Using Contextual IED Data
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Solution Overview
Problem
End-users in electrical systems face challenges in identifying and addressing voltage deviations due to the lack of contextualized voltage data, leading to inefficient operation, equipment damage, and increased costs, as existing monitoring systems provide non-contextualized and disorganized data, making it difficult to determine the sources and impacts of voltage anomalies.
Innovation Solution
An automated method that aligns voltage data from intelligent electronic devices (IEDs) in spatial and pseudo-temporal context, using an algorithm to analyze voltage conditions and provide recommendations for mitigating anomalies, including tap changes for transformers, load redistribution, and capacitor bank evaluations, to maintain optimal voltage levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If voltage data is collected from multiple monitoring points throughout the electrical system, then the quantity of voltage information increases, but the data becomes incoherent and disorganized, making it difficult to identify voltage deviations and their sources
Solution Approach 1:
The patent segments voltage data by organizing it according to the electrical system hierarchy (utility system, service transformer, distribution transformers, loads). This segmentation transforms the incoherent mass of voltage data into structured, manageable segments that can be easily analyzed and interpreted, directly resolving the contradiction between data quantity and organizational complexity
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically analyzes voltage data from multiple monitoring points and presents it in a contextualized manner. This intermediary layer acts as a mediator between the raw data collection system and the end-user, transforming disorganized data into meaningful information without requiring complex user-side data management
2Ease of operation
If end-users manually analyze voltage data without contextual information, then the analysis process can be simple, but the ability to identify voltage deviations and their impacts is severely limited
Solution Approach 1:
The patent performs preliminary action by automatically organizing and contextualizing voltage data before presentation to the end-user. The system pre-processes the data to establish relationships between voltage deviations and affected equipment, so that when users view the data, it is already in an easily analyzable format with contextual information intact, eliminating the need for complex manual analysis
3Quantity of substance
If extensive power monitoring systems are deployed to track voltage levels, then the quantity of monitoring data increases, but end-users remain unaware of damaging voltage levels due to lack of experience and knowledge
Solution Approach 1:
The patent implements feedback by automatically analyzing voltage data against known equipment requirements and providing actionable insights to end-users. The system continuously monitors voltage levels, compares them to optimal ranges for specific equipment, and provides feedback about deviations and their potential impacts, enabling users to take corrective action without requiring specialized electrical knowledge
Solution Approach 2:
The patent enables self-service by providing an automated system that performs the complex task of voltage analysis and interpretation. The system serves itself by automatically collecting, organizing, analyzing, and presenting voltage data with contextual information about affected equipment, eliminating the need for users to have expert knowledge to interpret monitoring data reliably
Data Source
AI summary
A voltage analysis algorithm for automatically determining anomalous voltage conditions in an electrical system monitored by a plurality of intelligent electronic devices (IEDs) and automatically making recommendations for ameliorating or eliminating the anomalous voltage conditions. The electrical system hierarchy is determined automatically or manually, and the algorithm receives voltage data from all capable IEDs. The voltage data is temporally aligned or pseudo-aligned to place the voltage data in both spatial and temporal context. The algorithm determines anomalous voltage conditions systemically by comparing measured voltage values against nominal or expected ones across the system. Based on the spatial and temporal context of the IEDs, the algorithm automatically identifies a source of the voltage deviation in the hierarchy, and recommends a modification associated with the source for mitigating the anomalous voltage condition. The algorithm checks its recommendation to determine any adverse effects on the electrical system and adjusts the recommendation accordingly.