IED Disturbance Analysis for Substation Fault Root Cause
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Solution Overview
Problem
Fault analysis in electrical substations is intricate, burdensome, and time-consuming, relying heavily on manual processes, especially with larger and more complex systems, making it difficult to accurately judge and control faults.
Innovation Solution
A method and system utilizing an intelligent electronic device (IED) to receive disturbance records, extract variable time series data, generate a causality matrix through causal analysis, predict fault types using machine learning, and retrieve probable causes from a knowledge database, with incremental learning and expert feedback for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual fault analysis techniques are used, then specialists can analyze faults using system data, but the process becomes intricate, burdensome, and time-consuming as system scale increases
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated computer-based system that uses machine learning models and causal analysis algorithms to automatically analyze fault data, extract causal relationships, and generate diagnostic reports, thereby eliminating the time-consuming manual review process while maintaining or improving accuracy
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a bridge between raw fault data and expert interpretation, using causal analysis algorithms and machine learning models to process and interpret data before presenting results to specialists, thereby reducing the time burden on experts while preserving analytical accuracy
2Reliability
If traditional manual fault analysis techniques are used, then specialists can analyze faults using system data, but the process becomes intricate and burdensome as system scale increases
Solution Approach 1:
The patent segments the complex fault analysis process into distinct automated modules: data collection from multiple sources, causal relationship extraction using algorithms, machine learning-based fault type prediction, and report generation. This segmentation simplifies the overall complexity by breaking down the intricate manual process into manageable automated components that can be executed systematically
Solution Approach 2:
The patent replaces the complex manual analytical process with an automated computer-based system that uses standardized algorithms and machine learning models, thereby reducing the complexity burden on specialists while maintaining or improving the reliability of fault judgments through consistent automated analysis
3Loss of information
If larger and more extensive datasets are processed for larger faults, then more comprehensive analysis is achieved, but the analysis process becomes more complex and time-consuming
Solution Approach 1:
The patent replaces manual data processing with automated computational systems that can efficiently handle large and extensive datasets using machine learning algorithms and causal analysis, thereby maintaining data completeness while reducing the complexity burden on human analysts who would otherwise struggle to process such voluminous information
Solution Approach 2:
The patent transforms the processing approach by changing from manual inspection of individual data points to automated algorithmic processing that can simultaneously analyze multiple parameters and variables, thereby maintaining comprehensive data analysis while reducing the perceived complexity through systematic automated handling of large datasets
4Reliability
If specialists manually co-relate and analyze multiple system data sources, then fault conclusions can be reached, but the difficulty in correctly judging faults and root causes increases
Solution Approach 1:
The patent replaces manual correlation and analysis of multiple data sources with automated computational algorithms that systematically process alarm data, event logs, and system parameters to identify causal relationships and root causes, thereby maintaining accurate fault identification while reducing the difficulty of detecting and measuring complex fault patterns across multiple data sources
Data Source
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AI summary
The present disclosure describes a system and method performing fault and event analysis in electrical substations is disclosed. The method comprises the step of receiving a disturbance record triggered by an intelligent electronic device (IED) at an electrical substation, preprocessing the received disturbance record to extract at least one variable time series data of plurality of electrical parameters, generating a causality matrix based on the extracted at least one variable time series data by applying causal analysis, predicting, using a Machine learning (ML) module, a fault type at least based on the causality matrix, retrieving, from a knowledge database, a plurality of probable causes corresponding to the predicted fault type, determining at least one exact cause from the plurality of probable causes based on the causal pattern, and providing the fault type, the plurality of probable causes, and the at least one exact cause to a user.