Smart Meter Frequency Domain Anomaly Detection
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Solution Overview
Problem
Current methods for detecting anomalies in resource consumption, such as energy usage, are inefficient and require extensive human intervention, making it difficult to identify and address issues like downed lines, fraud, or theft in a timely and cost-effective manner.
Innovation Solution
A system utilizing smart meters to collect and transform time series data into the frequency domain, employing cluster analysis to detect anomalous behavior, and automatically initiating responsive actions based on identified anomalies, with the ability to classify and label anomalies for automated or human-confirmed corrective measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual meter reading and monitoring methods are used, then device complexity and operational costs are reduced, but detection precision and response time to anomalies deteriorate
Solution Approach 1:
The patent replaces manual mechanical meter reading and visual inspection with automated electronic smart meters that continuously measure consumption data. The system substitutes human operators with algorithm-based anomaly detection that analyzes frequency domain characteristics of consumption patterns, achieving precise automated detection without manual intervention.
Solution Approach 2:
The patent introduces frequency domain analysis as an intermediary layer between raw consumption data and anomaly detection. By transforming time-domain consumption data into frequency-domain representations, the system creates a mediating representation that reveals hidden patterns and anomalies not visible in traditional aggregate measurements.
2Speed
If aggregate monthly usage values are compared to detect anomalies, then device complexity is minimized, but detection speed and ability to identify specific problems deteriorate
Solution Approach 1:
The patent performs preliminary transformation of consumption data into frequency domain representations continuously, preparing the data structure in advance for rapid anomaly detection. This preliminary processing enables quick comparison and identification of anomalies when needed, rather than performing complex analysis only when anomalies are suspected.
Solution Approach 2:
The patent transitions from analyzing consumption data in the time domain (aggregate monthly values) to the frequency domain, adding a new dimensional perspective. This dimensional transformation reveals periodic patterns, harmonics, and transient events that are invisible in traditional time-based aggregate comparisons, enabling faster and more accurate anomaly detection.
3Productivity
If human operators manually investigate reported problems, then measurement precision can be maintained through expert judgment, but productivity and response time deteriorate
Solution Approach 1:
The patent implements self-service anomaly detection where the system automatically monitors, analyzes, and identifies consumption anomalies without requiring human operators. The smart metering system performs self-diagnosis by comparing frequency domain characteristics against established patterns, enabling autonomous detection and alerting that eliminates manual investigation time.
Solution Approach 2:
The patent establishes continuous feedback loops where consumption data is constantly monitored, analyzed in the frequency domain, and compared against normal patterns. When deviations are detected, the system immediately generates alerts and can trigger automated responses, creating a closed-loop feedback system that rapidly identifies and responds to anomalies without human intervention delays.
4Measurement precision
If smart meters collect detailed granular consumption data, then detection precision and anomaly identification improve, but data processing complexity and computational requirements worsen
Solution Approach 1:
The patent extracts only the essential frequency domain characteristics from the detailed granular consumption data that are necessary for anomaly detection. Rather than processing all raw data points, the system identifies and extracts key frequency components, harmonics, and pattern features that indicate anomalies, reducing processing complexity while maintaining detection precision.
Data Source
AI summary
Analysis of smart meter and/or similar data based on frequency content is disclosed. In various embodiments, for each of a plurality of resource consumption nodes a time series data including for each of a series of observation times a corresponding resource consumption data associated with that observation time is received. At least a portion of the time series data, for each of at least a subset of the plurality of resource consumption nodes, is transformed into a frequency domain. A feature set based at least in part on the resource consumption data as transformed into the frequency domain is used to detect that resource consumption data associated with a particular resource consumption node is anomalous.