Electrical Alarm Filtering for Nuisance Event Reduction
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Solution Overview
Problem
Existing electrical systems face challenges in efficiently analyzing and mitigating alarm nuisance behaviors, which can lead to increased downtime, equipment damage, and higher energy costs due to power quality issues.
Innovation Solution
The system processes electrical measurement data from intelligent electronic devices (IEDs) to identify events and alarms in the electrical system, aggregates this information, and analyzes it to automatically distinguish between noise and relevant signals, thereby reducing alarm nuisance behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm systems are used in electrical systems, then all alarm events are captured and recorded, but alarm nuisance behaviors increase causing data clutter and making it difficult to identify relevant issues
Solution Approach 1:
The patent introduces an intermediary analysis layer between alarm generation and alarm presentation to operators. This intermediary system processes raw alarm data, identifies patterns, and filters out nuisance alarms while preserving meaningful alerts. The intermediary performs contextual analysis using historical data and system state information to distinguish between significant alarms and noise, thereby maintaining reliable detection while improving information quality.
Solution Approach 2:
The system implements feedback mechanisms where alarm filtering decisions are continuously refined based on operator responses and system performance data. When operators mark certain alarms as nuisance or investigate specific patterns, this feedback is used to adjust filtering algorithms and improve future alarm differentiation. The feedback loop enables the system to learn from actual operational contexts and refine its ability to distinguish relevant signals from noise over time.
2Reliability
If comprehensive alarm monitoring is implemented, then all electrical system events are tracked, but the complexity of analyzing and responding to alarms increases
Solution Approach 1:
The patent segments the alarm monitoring system into distinct functional modules: data collection, pattern recognition, filtering, prioritization, and presentation. Each module handles a specific aspect of alarm processing, transforming a monolithic complex system into manageable segments. This segmentation allows comprehensive monitoring while distributing analytical complexity across specialized components, making the overall system more tractable and maintainable.
Solution Approach 2:
The system performs preliminary analysis and filtering of alarm data before presenting it to operators. By pre-processing alarm information, identifying patterns, and filtering nuisance alarms in advance, the system reduces the complexity of real-time decision-making. Operators receive pre-analyzed, prioritized alarm information rather than raw unprocessed data, significantly reducing the cognitive load and response complexity.
3Measurement precision
If alarm thresholds are set to be highly sensitive, then more electrical issues are detected, but false alarms and nuisance behaviors increase
Solution Approach 1:
The patent implements dynamic alarm thresholds that adapt based on system operating conditions, historical data, and contextual information rather than using fixed static thresholds. The thresholds dynamically adjust to distinguish between normal operational variations and genuine anomalies. This dynamic approach maintains high detection sensitivity while reducing false alarms by contextualizing alarm triggers within the actual system state and operational patterns.
Solution Approach 2:
The system changes multiple parameters simultaneously when evaluating alarm conditions, not just single threshold values. It considers temporal patterns, frequency of occurrences, system load conditions, and historical baseline data together as a multidimensional assessment. This multi-parameter approach allows highly sensitive detection of genuine issues while filtering out false alarms that may trigger on single parameter deviations alone.
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.