FPGA Derivative Processing for Transient Event Characterization
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Solution Overview
Problem
High-speed data acquisition devices face processor, bus, and memory bandwidth-intensive challenges in post-processing all received data to characterize transient events in power systems, leading to performance impacts and increased costs, especially in real-time power quality monitoring applications where quick identification and characterization of power quality issues are crucial to minimize losses.
Innovation Solution
The method involves converting a digital data stream to its first derivative representation on a Field Programmable Gate Array (FPGA) and determining zero crossings to derive information about local minima and maxima, reducing the data needed to characterize transient events, which can be processed in real-time on the device or transmitted to upstream devices for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all received data is post-processed to characterize transient events, then comprehensive event characterization is achieved, but processor, bus, and memory bandwidth resources are excessively consumed
Solution Approach 1:
The system performs preliminary processing by converting the data stream to its first derivative representation and determining zero crossings before full event characterization. This preliminary extraction of derivative information and zero crossing points prepares the data in advance, allowing downstream processors to work with pre-processed information rather than raw data, thus reducing their computational burden while maintaining characterization accuracy
Solution Approach 2:
The invention extracts only the essential features needed for transient event characterization - specifically the first derivative representation and zero crossing information - from the complete data stream. By taking out only these critical elements rather than processing all raw data, the system reduces the data volume requiring intensive post-processing while preserving the information necessary for accurate event characterization
2Reliability
If comprehensive data is transmitted for transient event analysis, then accurate power quality issue identification is achieved, but transmission bandwidth and processing time increase
Solution Approach 1:
The system extracts and transmits only the critical derivative information and zero crossing data required for power quality issue identification, rather than transmitting complete raw data streams. This selective extraction maintains the reliability of transient event detection and characterization while significantly reducing transmission bandwidth requirements and processing time, enabling real-time power quality monitoring
3Speed
If high-speed data acquisition is implemented, then transient event detection capability is improved, but data volume requiring processing increases
Solution Approach 1:
The system changes the parameter representation of the acquired data by computing the first derivative of the voltage and current signals. This parameter transformation converts the original high-volume time-series data into derivative representations that capture the essential transient characteristics. The zero crossing detection further compresses this derivative data into discrete event points, reducing the quantity of data requiring storage and processing while preserving the high-speed detection capability
Data Source
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AI summary
A method of deriving information from sampled data, for example, in a digital data stream, includes processing the sampled data, for example, in the high-speed data acquisition device to detect an event in the sampled data. The sampled data is converted/transformed to its first derivative representation, and zero crossing information from the first derivative representation of the sampled data is used to determine local minima and maxima and their relative offset in time to a common point in time. Information from, or derived from, the local minima and maxima and the relative offset are provided to an upstream device. The upstream device may process the local minima and maxima and the relative offset, for example, to characterize the event.