SCADA Voltage Outage Prediction for Grid Reliability Insights
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Solution Overview
Problem
Existing systems struggle to efficiently integrate and analyze momentary and sustained outages in electrical grids, leading to inefficiencies and customer dissatisfaction due to the complexity of data relationships across multiple systems without AMI infrastructure, and the independent treatment of these outages hinders comprehensive power reliability insights.
Innovation Solution
A system that captures sensor data from SCADA-enabled devices, applies custom algorithms to identify momentary outages, and predicts possible sustained outages using machine learning models, providing a unified view with dynamic visualizations and automated insights for various user personas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customer complaints are used to initiate outage investigations, then outage detection can be triggered, but the process becomes inefficient and resource intensive
Solution Approach 1:
The system performs preliminary automated analysis of outage data using machine learning models before human investigation. The predictive analytics engine pre-processes outage patterns, identifies potential causes, and prioritizes cases, so that when investigations are initiated, the workload is already partially completed and organized.
Solution Approach 2:
The system enables self-service through automated outage detection and classification. The machine learning models automatically analyze outage data, identify patterns, and generate initial assessments without requiring human intervention for every case, freeing up resources for more complex investigations.
2Device complexity
If momentary and sustained outages are treated independently, then analysis of each outage type can be simplified, but comprehensive power reliability insights are hindered
Solution Approach 1:
The system merges momentary and sustained outage analyses into a unified predictive analytics framework. The machine learning models process both outage types simultaneously, examining their interrelationships and patterns to generate comprehensive power reliability insights that would be missed if analyzed separately.
Solution Approach 2:
The predictive analytics engine serves multiple functions: it analyzes momentary outages, sustained outages, and their relationships; identifies patterns across different outage types; and generates comprehensive reliability assessments. This multi-functional approach maintains analytical simplicity while providing holistic insights.
3Reliability
If automation devices are deployed to restore outages, then sustained outages are reduced, but momentary interruptions increase
Solution Approach 1:
The system implements feedback by continuously monitoring both momentary and sustained outage patterns and using this information to optimize automation device performance. The machine learning models analyze the relationship between automation actions and momentary interruptions, providing feedback that helps adjust settings to minimize harmful momentary interruptions while maintaining sustained outage reduction benefits.
Solution Approach 2:
The predictive analytics engine identifies patterns that precede problematic momentary interruptions caused by automation devices. By detecting these patterns in advance, the system can take preventive actions or adjust automation device parameters before the harmful interruptions occur, cushioning against their negative effects.
4Device complexity
If data relationships across multiple systems are undefined, then system complexity is reduced, but the ability to develop predictive models is significantly challenged
Solution Approach 1:
The system introduces an intermediary layer of predictive analytics that sits between multiple data systems. This intermediary layer standardizes and integrates data from diverse sources with undefined relationships, transforming raw data into structured insights that can be used by machine learning models without requiring direct integration between the underlying systems.
Solution Approach 2:
The system segments the complex data integration task into manageable components: data collection from multiple systems, data standardization and relationship definition, pattern recognition, and predictive modeling. This segmentation allows each component to be developed and maintained independently while still achieving comprehensive predictive capabilities.
Data Source
AI summary
According to an embodiment, a method includes receiving sensor data measured by a plurality of SCADA-enabled sensors associated with an electrical grid and identifying a plurality of momentary outages associated with the electrical grid based on the sensor data. The method further includes predicting (1) a plurality of possible sustained outages associated with the electrical grid based on the plurality of momentary outages and (2) a sustained outage likelihood from a plurality of sustained outage likelihoods for each possible sustained outage from the plurality of possible sustained outages based on the plurality of momentary outages.