Power System Signal Compression via Linear Prediction
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Solution Overview
Problem
High-speed monitoring of electric power systems generates large volumes of data, making storage and transmission challenging due to limited connectivity and bandwidth constraints, particularly in substation devices with security concerns.
Innovation Solution
The use of linear prediction and Golomb coding for compressing power system signals, allowing for lossless compression and efficient data representation, enabling more information to be stored in a given capacity and transmitted quickly over lower bandwidth channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-speed monitoring is implemented to improve power system stability and fault detection, then measurement precision and reliability are improved, but data volume increases causing storage and transmission difficulties
Solution Approach 1:
The patent extracts only the essential information from the high-speed monitoring data by using linear prediction to model the signal and encoding only the prediction errors and relevant parameters (coefficients, excitation signal estimates) rather than storing the complete raw data stream. This extraction approach maintains measurement precision while dramatically reducing data volume for storage and transmission.
2Quantity of substance
If data compression is applied to reduce storage requirements and enable faster transmission, then loss of information may occur, but compression algorithms can preserve data integrity
Solution Approach 1:
The patent replaces direct storage of raw monitoring data with a mathematical modeling approach using linear prediction. Instead of mechanically storing every data point, the system substitutes a computational model that reconstructs the original signal from compressed parameters (prediction coefficients, excitation signal), thereby reducing data size while preserving information fidelity through mathematical relationships rather than raw data replication.
Data Source
AI summary
The present disclosure pertains to systems and methods to compress an input signal representing a parameter in an electric power system. In one embodiment, a system includes a data acquisition subsystem to receive an input signal comprising a plurality of high-speed representations of electrical conditions. A linear prediction subsystem generates an excitation signal estimate based on the input signal, a plurality of linear prediction coefficients based on the input signal, and an estimated signal based on the excitation signal estimate and the plurality of linear prediction coefficients. An error encoding subsystem may generate an encoding of an error signal based on a difference between the input signal and the estimated signal. A non-transitory computer-readable storage medium may store an encoded and compressed representation of the input signal comprising the excitation signal estimate, the plurality of linear prediction coefficients, and the encoding of the error signal.