Electrical Alarm Analytics for Nuisance Behavior Reduction
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Solution Overview
Problem
Current electrical system analysis tools are time-consuming and require significant expertise to identify and troubleshoot power quality issues, leading to overwhelming alarm situations that can result in costly downtime and resource misallocation, especially in complex environments like data centers and manufacturing facilities.
Innovation Solution
The development of systems and methods that automatically process large amounts of electrical measurement data to differentiate between noise and relevant signals, identify alarm nuisance behaviors, and provide recommendations for mitigation, leveraging intelligent electronic devices (IEDs) and advanced analytics to streamline alarm analysis and reduce human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional electrical system analysis tools are used, then comprehensive monitoring coverage is achieved, but analysis time and expertise requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary processing layer between data collection and analysis that automatically performs initial filtering, aggregation, and prioritization of alarm data. This intermediary system reduces the complexity of raw data before it reaches human analysts, maintaining comprehensive monitoring coverage while significantly reducing the time required for meaningful analysis.
Solution Approach 2:
The system implements self-service capabilities through automated anomaly detection, self-diagnosis algorithms, and autonomous prioritization mechanisms. The system serves itself by automatically identifying and flagging critical issues without requiring constant human intervention, thereby reducing analysis time while maintaining reliable monitoring across the entire electrical system.
2Reliability
If comprehensive alarm monitoring is implemented, then all electrical issues are captured, but alarm nuisance and data clutter increase
Solution Approach 1:
The patent applies local quality by implementing context-aware filtering that adapts to specific electrical system conditions, locations, and operational states. Different filtering criteria and thresholds are applied locally based on the specific characteristics of each monitored component, allowing comprehensive detection while maintaining high signal-to-noise ratio by reducing irrelevant alarms in each local context.
Solution Approach 2:
The system dynamically changes parameters such as alarm thresholds, filtering criteria, and aggregation windows based on operational conditions, historical data, and system state. This allows the system to maintain comprehensive detection sensitivity while adapting to reduce nuisance alarms under different operating conditions, effectively managing the balance between detection completeness and information quality.
3Measurement precision
If expert operators are used for analysis, then accurate troubleshooting is achieved, but operational costs and resource requirements increase
Solution Approach 1:
The patent replaces the mechanical system of expert human operators with an automated intelligent system that uses algorithms, machine learning models, and expert knowledge bases. This substitution maintains high troubleshooting accuracy by encoding expert knowledge into the system while eliminating the need for expensive and scarce expert operators, thereby reducing operational resource requirements.
Solution Approach 2:
The system creates digital copies of expert knowledge, experience, and diagnostic procedures that can be replicated and applied consistently across multiple systems and operators. By copying and codifying expert troubleshooting methodologies into automated algorithms and decision support systems, the patent maintains high accuracy while reducing dependency on individual expert resources.
Data Source
AI summary
Systems and methods for reducing alarm nuisance behaviors in an electrical system are disclosed herein. In one aspect, a method for reducing alarm nuisance behaviors includes processing electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device in the electrical system to identify events in the electrical system, and alarms triggered in response to the identified events and/or other events in or related to the electrical system. Information related to at least the identified events and the identified alarms may be aggregated, and the aggregated information may be analyzed to identify at least one alarm nuisance behavior. At least one action may be taken or performed based on or in response to the at least one identified alarm nuisance behavior.