Power Quality Monitoring System for Outage Cause Identification
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Solution Overview
Problem
Power quality disturbances cause significant business downtime, equipment damage, and increased operational costs due to issues like overheating, premature aging, and false circuit breaker tripping, with existing methods failing to effectively detect and analyze the impact of these disturbances on electrical power systems.
Innovation Solution
A processor-implemented method and system for detecting power quality events and determining their impact, which involves monitoring power quality events, identifying associated power outages, and providing a reliability index to determine the confidence level of the outage's cause, using a network of power quality monitoring devices and a processor to analyze data from these devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power quality monitoring is implemented to detect disturbances, then reliability of power system operation is improved, but device complexity increases due to need for multiple monitoring devices and analysis systems
Solution Approach 1:
The patent combines multiple monitoring functions into a single integrated system. The power quality monitoring device and outage detection system are merged into one unified apparatus that simultaneously monitors both power quality parameters and outage conditions, reducing the need for separate monitoring systems and simplifying the overall device architecture.
Solution Approach 2:
The monitoring device is designed with multi-functionality, capable of detecting both power quality disturbances and power outages using the same hardware platform. This universal approach allows a single device to perform multiple monitoring tasks, reducing device complexity while maintaining comprehensive monitoring coverage.
2Measurement precision
If correlation analysis between power quality events and outages is performed, then measurement precision of outage cause is improved, but loss of time increases due to additional data processing requirements
Solution Approach 1:
The system performs preliminary correlation analysis by establishing temporal relationships between power quality events and outages as they occur. By analyzing the timing and sequence of events in real-time or near-real-time, the system identifies causal relationships without requiring extensive post-processing, thus maintaining measurement precision while minimizing time loss.
Solution Approach 2:
The system creates simplified representations of power quality events and outage data that capture essential causal relationships. By working with these condensed data copies rather than full raw datasets, the system achieves accurate cause identification while reducing computational time and processing requirements.
Data Source
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AI summary
A method comprises detecting a power quality event, determining if one or more power outages occurs in a defined time period extending from a beginning of the power quality event to an end of the power quality event, and if one or more power outages occurs in the defined time period, then performing an analysis, where performing the analysis comprises determining if the one or more power outages is associated with the power quality event. The method may also comprise outputting the information regarding the analysis to a display device.