Adaptive Diagnostic Rules for Power Substation Primary Devices
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Solution Overview
Problem
Existing power substation monitoring systems face challenges in efficiently monitoring and maintaining primary devices, such as circuit breakers and transformers, due to remote locations, high variability in equipment, and the need for expert knowledge to set parameters, leading to potential malfunctions and delays in repair.
Innovation Solution
A method and system for monitoring primary devices that collect operational diagnostic data, adapt diagnostic rules based on health indicator data and correlated operational data, and provide diagnostic warnings, enabling automated updates and improved reliability without constant expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple monitoring devices with static thresholds are used, then device complexity is reduced, but measurement precision and reliability of diagnostic warnings deteriorate
Solution Approach 1:
The patent transforms static diagnostic rules into dynamic, adaptive rules that automatically adjust thresholds and parameters based on learned operational patterns. The monitoring system evolves its diagnostic criteria over time by analyzing historical data and identifying correlations between operational parameters and actual device failures, thereby improving measurement precision without increasing device complexity.
Solution Approach 2:
The monitoring system performs self-updates by automatically learning from operational data and adjusting its own diagnostic rules without requiring external expert intervention. The system autonomously identifies diagnostic correlations and adapts its monitoring parameters, enabling it to improve its own precision while maintaining simple device architecture.
2Measurement precision
If expert knowledge is required to set diagnostic parameters, then measurement precision improves, but ease of operation and productivity deteriorate
Solution Approach 1:
The system automatically acquires and integrates expert knowledge by learning from operational data and failure patterns. Instead of requiring manual configuration by experts, the monitoring system autonomously develops diagnostic rules through data analysis, making the system easy to operate while maintaining high diagnostic accuracy.
Solution Approach 2:
The system continuously learns from feedback provided by actual device performance and failure data. By analyzing the outcomes of diagnostic warnings and comparing them with actual device states, the system refines its diagnostic rules automatically, eliminating the need for manual expert tuning while improving accuracy over time.
3Ease of operation
If fixed diagnostic rules are used, then ease of operation is maintained, but adaptability to different device types and conditions deteriorates
Solution Approach 1:
The patent transforms fixed diagnostic rules into dynamic, adaptive rules that automatically adjust to different device types and operational conditions. The system learns specific patterns for each device type and adapts its monitoring parameters accordingly, maintaining simplicity while achieving high adaptability through automated rule evolution.
Solution Approach 2:
The system automatically modifies diagnostic parameters and thresholds based on the specific characteristics of different device types and operational conditions. By dynamically adjusting parameters such as warning thresholds, sampling rates, and correlation weights, the system achieves versatility across different devices while maintaining ease of operation through automated adaptation.
4Device complexity
If remote power substations are monitored manually, then device complexity is reduced, but loss of time and productivity deteriorate
Solution Approach 1:
The monitoring system autonomously performs continuous surveillance and automatic diagnostic analysis without requiring manual intervention. It independently detects anomalies, generates warnings, and identifies potential failures, thereby eliminating the time loss associated with manual monitoring while keeping the system architecture simple and suitable for remote deployments.
Data Source
AI summary
For monitoring diagnostic rules of a power system, an exemplary method is disclosed and includes collecting operational diagnostic data from a first primary device; identifying a change of a diagnostic indicator based on the operational diagnostic data; adapting a diagnostic rule to the identified change, wherein the diagnostic rule is applicable to the diagnostic data for determining a diagnostic warning which indicates a diagnostic state of a primary device; and providing a diagnostic warning of a second primary device by applying the adapted diagnostic rule to operational diagnostic data collected for the second primary device.