Online Failure Forecasting for Overhead Distribution Lines
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Solution Overview
Problem
Existing outage prediction methods for electric power distribution systems face challenges such as data unavailability, imbalance in service status reporting, and the inability to accurately predict individual overhead distribution line failures due to uncertainties in weather forecasting and varying environmental conditions, leading to delayed power restoration and customer dissatisfaction.
Innovation Solution
An online failure forecasting algorithm that dynamically updates a forecasting model using real-time reported outage data, representing weather conditions in tabular form to identify relationships between weather variables and utilizing independent failure forecasting models for each cell to predict component status as 'in-service' or 'outage', incorporating Bernoulli bandit problems and maximum likelihood or maximum a posteriori estimation for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional outage prediction methods using historical data are used, then the prediction process is simple, but the prediction accuracy is low due to data unavailability and imbalance in service status reporting
Solution Approach 1:
The patent implements dynamic model updating by continuously incorporating real-time outage data into the prediction model. The model transitions from static historical analysis to dynamic adaptive learning, adjusting predictions based on newly observed outage patterns and weather conditions, thereby improving accuracy without requiring complete model redesign
Solution Approach 2:
The system establishes a feedback loop where real-time outage data from the field is continuously fed back into the prediction model. This feedback mechanism allows the model to learn from actual outage occurrences, correct previous prediction errors, and improve future predictions, resolving the accuracy issue while maintaining manageable complexity through iterative refinement
2Measurement precision
If individual failure models are derived for each distribution line using historical data, then the model can capture line-specific characteristics, but the model cannot accurately predict outages because most days are labeled as 'in service' creating severe data imbalance
Solution Approach 1:
The patent introduces weather conditions as an intermediary variable that mediates between distribution line characteristics and outage occurrence. Instead of directly modeling the rare outage event from imbalanced service status data, the model uses weather data as a proxy indicator that triggers outages, thereby circumventing the data imbalance problem while maintaining individual line prediction capability
Solution Approach 2:
The system changes the predictive parameters from direct service status labels (which are imbalanced) to weather condition parameters combined with line-specific vulnerability factors. This parameter transformation allows the model to predict outages based on weather-triggered events rather than relying on scarce labeled outage data, improving both accuracy and reliability
3Ease of manufacture
If prediction models are based on past knowledge and historical data, then the model development is straightforward, but the predictions are inaccurate because past knowledge is not sufficient evidence to support current decisions due to weather uncertainties and environmental variations
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing detailed weather data, line characteristics, and historical outage patterns before prediction is needed. This preparatory work creates a comprehensive knowledge base that can be quickly queried during prediction, maintaining ease of model development while improving accuracy through access to relevant pre-organized information
Solution Approach 2:
The system transitions from static historical models to dynamic predictions by continuously updating the model with real-time data. The model adapts to changing weather patterns and environmental conditions, allowing it to maintain accuracy despite uncertainties, while the incremental update approach keeps development manageable
4Measurement precision
If real-time data collection and dynamic model updating are implemented, then prediction accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the prediction system into modular components: data collection modules, weather data processing modules, line-specific prediction modules, and model updating modules. This segmentation allows each component to be developed and maintained independently, improving prediction accuracy through specialized processing while managing overall system complexity through modular architecture
Data Source
AI summary
A system for a region including a processor to receive data including weather and component data. Generate a dataset for the region to identify weather events and a plurality of model forcings converted into tabular form. A machine learning (ML) model for each model forcing for each component uses dataset. Iteratively, for each weather event: identify for each component a corresponding model forcing with weather variables. Generate an output value for ML model corresponding to the identified components to the set of weather variables and update ML model. Receive observed data over time periods of an impending weather (IW) event. Iteratively, for each time period: identify, for each component a corresponding model forcing with weather variables and update ML model. Generate for the updated ML model, an output value predicting a component status as a failed or not failed for the time period.