Power Distribution Network Event Correlation Analysis
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Solution Overview
Problem
Power distribution networks face challenges in identifying seemingly unrelated events, such as weather conditions or catastrophes, which affect equipment and personnel requirements, repair crew positioning, and event probability estimation, making it difficult to plan and manage network structures effectively.
Innovation Solution
A method and system for correlating and analyzing power distribution network events using a querying engine, correlation engine, and root-cause analysis engine, which receive and normalize data from various sources, identify events of interest, and discover patterns to determine root causes across multiple networks and external factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple sources is collected and correlated to identify root causes, then the ability to identify seemingly unrelated events and plan network structure is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: data collection module that gathers events from multiple sources, normalization module that standardizes data formats, correlation engine that identifies relationships between events, and root cause analysis module that determines causal links. This segmentation allows complex multi-source data correlation to be handled through specialized subsystems, improving reliability without overwhelming system complexity
Solution Approach 2:
A normalization layer acts as an intermediary between diverse data sources and the correlation engine. This intermediary standardizes event data from weather services, catastrophes, and power distribution networks into a common format, enabling accurate correlation while isolating the complexity of data integration from the core analysis functions
2Measurement precision
If comprehensive event data is collected from multiple sources, then the estimation of equipment and personnel requirements is improved, but the data processing time and computational resources increase
Solution Approach 1:
Event data from multiple sources is collected, normalized, and pre-processed before correlation analysis is performed. The system maintains a normalized event database that is ready for rapid querying, allowing accurate resource estimation without time-consuming data processing during critical analysis phases
Solution Approach 2:
The correlation engine applies filtering criteria to focus on the most relevant events and relationships, processing only the necessary subset of data required for accurate resource estimation rather than analyzing every piece of collected information, thus reducing processing time while maintaining precision
3Productivity
If root cause analysis is performed across multiple power distribution networks and external factors, then the ability to position repair crews and estimate event probabilities is improved, but the computational complexity increases
Solution Approach 1:
The correlation engine identifies and analyzes relationships specific to each event type and network context, applying appropriate correlation methods and weightings based on the local characteristics of the data being analyzed. This allows efficient root cause determination for repair crew positioning without requiring uniform complex processing across all networks
Solution Approach 2:
The system dynamically adjusts correlation parameters and analysis depth based on event severity, network importance, and available resources. For critical events requiring rapid repair crew positioning, the system intensifies analysis on relevant parameters while reducing complexity elsewhere, improving productivity without excessive computational burden
Data Source
AI summary
A method for power distribution network correlation and analysis includes receiving event data from a plurality of data sources, identifying an event of interest, retrieving, in a querying engine, the event data, correlating the event data and the event of interest and identifying one or more root causes of the event of interest.