Streaming Power Measurement Anomaly Detection for Real-Time Grid Events
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Solution Overview
Problem
Modern power systems face challenges in automating the identification of anomalies in electrical and physical parameters, as the volume of data generated can be overwhelming, making it difficult for operators to detect mechanical failures or real-time events in a timely and efficient manner.
Innovation Solution
The system employs techniques to analyze streaming data by decomposing raw measurements into statistically anomalous behavior, grouping anomalies by similarity, and implementing control actions based on detected conditions, using software modules that can be distributed across a network to identify and flag anomalies in parameters like frequency, voltage, and temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated anomaly detection systems are implemented to identify mechanical failures and real-time events, then detection efficiency and timeliness are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex task of anomaly detection into multiple specialized software modules including data acquisition modules, preprocessing modules, anomaly detection modules, and analysis modules. Each module handles specific aspects of the detection process, allowing the system to manage complexity through functional decomposition while maintaining high detection efficiency.
Solution Approach 2:
The patent creates a universal anomaly detection platform that can monitor multiple parameters (frequency, voltage, temperature, etc.) across different power system components using the same software architecture. This multi-functional approach reduces overall system complexity by using standardized modules rather than separate specialized systems for each parameter.
2Measurement precision
If comprehensive streaming measurements are collected from multiple parameters to improve anomaly detection accuracy, then measurement precision is improved, but data volume and processing burden increase
Solution Approach 1:
The patent extracts only the most relevant features and parameters from the comprehensive streaming measurements using preprocessing modules that filter and select critical data points. This extraction process maintains detection accuracy by focusing on key indicators while reducing the overall data volume that requires intensive processing.
Solution Approach 2:
The patent applies partial processing to data streams by continuously monitoring all parameters but performing detailed analysis only on data points that exhibit anomalous characteristics. This approach maintains high detection accuracy for critical events while reducing processing burden by avoiding exhaustive analysis of all data points at full depth.
3Loss of time
If real-time analysis of streaming measurements is performed to enable immediate anomaly detection, then response time is improved, but computational load and energy consumption increase
Solution Approach 1:
The patent implements periodic processing cycles where data is continuously acquired and preprocessed, with full anomaly detection analysis performed at regular intervals or triggered by specific conditions. This periodic approach enables timely detection of anomalies while reducing computational energy consumption by avoiding continuous full-depth analysis of all data streams.
Solution Approach 2:
The system includes automated alert generation and notification modules that self-trigger when anomalies are detected, eliminating the need for continuous manual monitoring. This self-service capability maintains rapid response times while reducing overall computational load by automating the notification process and allowing the system to enter lower-power states between detection cycles.
Data Source
AI summary
The present disclosure pertains to detection of anomalous conditions in a variety of types of systems. In one embodiment, a system may be configured to identify anomalous conditions in a stream of measurements. The system may include a communications interface configured to receive a stream of measurements. An archive subsystem may maintain a data archive comprising a statistical representation of the stream of measurements. A pre-processing subsystem may divide the stream of measurements into a plurality of data windows. The plurality of data windows may be analyzed by an analysis subsystem configured to generate a plurality of normalized representations based on the data archive. The plurality of normalized representations may be grouped into a plurality of ranges. An anomaly detection subsystem may perform a comparison of the plurality of normalized representations to at least one threshold and may determine that the comparison indicates an anomalous condition.