Random Matrix Analysis for Regional Energy Internet Load Abnormality Detection
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Solution Overview
Problem
Current load forecasting and abnormality data recognition in power grids rely on traditional physical modeling methods, which fail to handle the complexity and require real-time analysis accurately, leading to low accuracy in recognizing active load abnormalities due to the influence of independent meteorological factors.
Innovation Solution
A method and system that analyze regional energy Internet load behavior using a random matrix, combining meteorological data with active load data to calculate Pearson correlation coefficients and perform matrix transformation, resulting in improved abnormality recognition accuracy by generating a coupled meteorological factor index and using the Pearson correlation coefficient matrix for abnormality detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional physical modeling methods are used for load forecasting and abnormality data recognition, then the system is simple to implement, but the accuracy of recognizing active load abnormalities is low and cannot meet real-time analysis requirements
Solution Approach 1:
The patent transforms the analysis approach by changing from traditional physical modeling parameters to random matrix parameters (characteristic values, probability density distribution). This parameter transformation enables the system to capture complex correlations between meteorological factors and active load while achieving real-time analysis through mathematical transformation rather than complex physical modeling.
Solution Approach 2:
The patent replaces traditional physical modeling methods with a mathematical statistics approach based on random matrix theory. By substituting the mechanical/physical modeling system with a statistical analysis system that uses Pearson correlation coefficients and random matrix characteristic values, the method achieves higher accuracy in recognizing active load abnormalities without being constrained by simplified physical assumptions.
2Measurement precision
If singular scattered meteorological factors are used independently for analysis, then the data processing is simple, but the correspondence with power active load cannot be revealed and recognition accuracy is low
Solution Approach 1:
The patent merges multiple scattered meteorological factors (temperature, humidity, wind speed, etc.) into a unified random matrix framework. By combining these independent factors into a single augmented data source matrix that incorporates both basic state data and influence factor data, the system reveals the collective correspondence between meteorological conditions and power active load, achieving high recognition accuracy through integrated analysis.
Solution Approach 2:
The patent introduces Pearson correlation coefficients as an intermediary between meteorological factors and active load data. This intermediary quantifies the linear relationship between coupled meteorological factor indices and active load, enabling the system to reveal correspondences that are not apparent when analyzing singular factors independently, while maintaining a systematic and manageable analysis framework.
3Reliability
If data from power grid measurement terminals is directly introduced and transformed into a random matrix, then the processing is straightforward, but abnormality of single or single-batch data causes state recognition omission or misrecognition
Solution Approach 1:
The patent performs preliminary actions by first calculating Pearson correlation coefficients between meteorological factors and active load data, and by constructing an augmented data source matrix that integrates multiple data sources before forming the random matrix. This preliminary processing ensures that single or single-batch abnormal data points are contextualized within a broader framework, preventing recognition omission or misrecognition while maintaining reliable state identification.
Data Source
AI summary
A method and system for analyzing a regional energy Internet load behavior based on a random matrix. Obtaining a coupled meteorological factor index according to acquired meteorological data; obtaining an influence factor matrix according to coupled meteorological index data; obtaining a basic state matrix according to active load data; obtaining an augmented data source matrix according to the basic state matrix and the influence factor matrix, and obtaining a Pearson correlation coefficient matrix by calculating Pearson correlation coefficients of the coupled meteorological factor index and the active load data; obtaining a source matrix according to the matrices; obtaining the random matrix after performing matrix transformation on the source matrix; obtaining probability density distribution after performing spectrum analysis on characteristic values of the random matrix, and obtaining an abnormality recognition result of the active load data according to comparison between the probability density distribution and historical probability density distribution.