Power System Fault Prediction and Diagnosis Based on Big Data Analysis
In modern power system management,with the increasing complexity and scale of power grid,traditional fault detection methods can no longer meet the requirements of efficient and real-time monitoring.Based on the method of big data analysis,an advanced solution is provided by using data mining and machine learning technology,which makes the prediction and diagnosis of power system faults more accurate and timely.This method can analyze and process a large number of power system operation data,such as voltage,current,frequency,etc.,to identify potential faults and anomalies,so as to optimize the operation stability and security of the power grid.This paper summarizes big data technology,discusses how to use time series analysis and feature recognition prediction system to predict faults,describes the realization of real-time fault monitoring system,automatic fault early warning and system integration,and verifies the effectiveness and practicability of this method through application tests.
big data analysispower systemfault predictiondiagnosis method
NETL
NSTL
万方数据
