Grid Outage Prediction Using Data-Driven Customer Impact Forecasts
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Solution Overview
Problem
Existing power grid outage prediction methods lack versatility and accuracy, particularly in providing quantitative information for proactive or reactive actions to mitigate the impact of outages caused by external factors, and often require pre-classification of hazardous weather conditions.
Innovation Solution
A data-driven processing system that utilizes historical and forecasted data of variables affecting outages, employing techniques like XGboost, to predict the number or fraction of customers expected to experience outages, without requiring specific information about the power grid configuration, and provides actionable insights through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional outage prediction methods are used, then the system is simpler to implement, but the prediction accuracy and quantitative information provision are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/mathematical prediction models with a data-driven processing technique (machine learning model). The system ingests multiple input signals including weather forecasts, historical outage data, and grid conditions, processes them through a trained model, and outputs quantitative predictions of outage impact. This substitution enables higher prediction accuracy by leveraging patterns in historical data rather than relying on simplified physical models.
Solution Approach 2:
The data-driven processing technique is designed to be versatile and applicable to different power grid configurations without requiring retraining or modification. The model processes various input signal types (weather data, historical outages, grid conditions) and provides universal outage predictions that can be applied across transmission and distribution grids, eliminating the need for grid-specific customization.
2Adaptability or versatility
If pre-classification of hazardous weather conditions is required, then the prediction process becomes more targeted, but the versatility and ease of operation are reduced
Solution Approach 1:
The system performs self-service by automatically processing multiple input signals through the data-driven model without requiring manual pre-classification of weather conditions. The model independently evaluates all input data (weather forecasts, historical patterns, grid conditions) and generates predictions, eliminating the need for operators to manually categorize or prioritize different weather hazards before analysis.
Solution Approach 2:
The prediction process is segmented into distinct input signal categories (weather data, historical outage data, grid conditions) that are processed independently by the data-driven model. This segmentation allows the system to handle diverse data types separately while integrating them into a unified prediction output, maintaining versatility without requiring manual pre-classification of hazardous conditions.
3Loss of information
If quantitative prediction information is provided, then proactive and reactive actions can be better planned, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by continuously processing input signals and generating outage predictions in advance of actual outages. The data-driven model is pre-trained on historical data and can rapidly evaluate new input data to provide quantitative predictions, enabling grid operators to take proactive measures before outages occur. The model's pre-training allows it to quickly process new data without requiring extensive computational resources at prediction time.
Data Source
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AI summary
To perform a grid outage prediction, a processing system (30) uses a data-driven processing technique (32) to determine a grid outage indicator that quantifies a number or fraction of customers in an area predicted to experience a grid outage over a predictive horizon. The processing system (30) provides input signals, based on input data (28) received by the processing system (30), to the data-driven processing technique (32).