Power Vector Analyzer with Tracking Null for Grid Transient Detection
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Solution Overview
Problem
Existing power grid monitoring systems, such as Phase Measurement Units (PMUs), struggle to accurately detect and analyze transient power events like blackouts and voltage sags, as they fail to provide clear insights into power direction, magnitude, and oscillation between inductive and capacitive reactance.
Innovation Solution
Implementing a power vector analyzer (PVA) with tracking null and tracking gates that normalize quiescent power using a tracking limit test circle and tracking null normalization, isolating transient events by removing continuous background power fluctuations, and employing AI embedding to classify these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PMUs display phasor diagrams to show voltage and current phase angles, then the system can monitor power grid disturbances, but the system cannot readily tell power direction, magnitude, or oscillation characteristics of transient events
Solution Approach 1:
The patent transforms the traditional two-dimensional phasor diagram into a three-dimensional power vector representation that includes magnitude, phase angle, and power direction as separate dimensions. This dimensional expansion enables simultaneous display of transient event characteristics, power direction, and magnitude that were previously inaccessible in conventional PMU displays.
Solution Approach 2:
The patent segments the power measurement into distinct components: active power, reactive power, and apparent power, each represented as separate vectors in the power vector diagram. This segmentation allows independent analysis of each power component and its transient characteristics, providing detailed insights into power direction and magnitude separate from the traditional combined phasor representation.
2Productivity
If PMUs continuously update phasor diagrams to track phase angle changes, then the system can monitor reactive loads, but the system cannot isolate transient events from continuous background power fluctuations
Solution Approach 1:
The patent extracts transient event information from the continuous power signal by comparing current power vectors against a learned quiescent power model. This extraction process isolates transient events from background fluctuations, allowing the system to identify and characterize transient characteristics without being overwhelmed by continuous operational variations.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously learns and updates the quiescent power model based on normal operational patterns. This feedback loop enables the system to distinguish between expected operational variations and actual transient events, improving transient detection accuracy while maintaining continuous monitoring capability.
3Measurement precision
If the system provides detailed transient event analysis, then the system can classify and characterize power events, but the system complexity increases
Solution Approach 1:
The patent creates a simplified representation of complex power transients through the power vector diagram, which copies the essential characteristics of three-phase power into a normalized two-dimensional representation. This copying process preserves transient event information while reducing computational complexity compared to full three-phase analysis.
Solution Approach 2:
The patent transforms complex three-phase power measurements into normalized power vector parameters (magnitude, phase angle, power direction) that are easier to process and classify. This parameter transformation simplifies the data structure while maintaining the essential information needed for transient event characterization and classification.
Data Source
AI summary
A power monitoring system includes one or more power vector analyzers, and a power controller having one or more ports to receive transient event data comprising one or more power images and associated metadata for a transient event from the one or more power vector analyzers, and one or more processors configured to execute code to cause the one or more processors to convert the one or more power images from the one or more power vector analyzers and the associated metadata to one or more transient event vectors, and store the one or more transient event vectors in a vector database.


