Power Grid Maintenance Task Prediction Using Simulated Training Data
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Solution Overview
Problem
Existing power grid maintenance plans rely heavily on subjective operational specifications and personal experience, leading to inefficiencies and errors due to the complexity and variability of power grid environments, resulting in potential failures and increased maintenance costs.
Innovation Solution
A task prediction model training method that involves collecting and simulating power grid data, performing pre-processing, extracting features, annotating with labels, and training a deep learning model to predict maintenance tasks, enhancing data quality and model generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance plans are developed based on operational specifications and personal experience, then maintenance decisions can be made using existing knowledge, but the subjectivity leads to errors and omissions in complex power grid environments
Solution Approach 1:
The patent replaces the manual, experience-based maintenance planning system with an automated deep learning model system. The model automatically processes power grid data, extracts features, and generates maintenance predictions, eliminating the need for subjective human judgment while handling complex power grid environments effectively.
Solution Approach 2:
The deep learning model performs self-learning from historical power grid data and maintenance records. Through automatic feature extraction and iterative training, the system improves its own accuracy over time without requiring manual reprogramming or expert intervention, enabling it to adapt to complex and varying power grid conditions.
2Productivity
If traditional maintenance methods are used, then implementation is simple based on existing specifications, but efficiency is lower due to subjectivity and inability to handle complex scenarios
Solution Approach 1:
The patent replaces traditional manual maintenance planning with an automated deep learning-based system. The model automatically analyzes power grid data, identifies maintenance needs, and generates predictions, significantly improving maintenance efficiency while handling complex scenarios that traditional methods cannot address.
Solution Approach 2:
The deep learning model performs preliminary analysis of power grid data to predict maintenance needs before actual maintenance is required. By proactively identifying potential issues and optimizing maintenance schedules, the system improves efficiency by preventing failures rather than reacting to them.
3Quantity of substance
If operational data is collected from actual maintenance tasks only, then data collection is straightforward, but data quantity and diversity are insufficient for comprehensive model training
Solution Approach 1:
The patent uses simulation technology to generate synthetic operational data that copies real power grid maintenance scenarios. The simulation system creates virtual power grid environments that reproduce various operating conditions and maintenance tasks, generating abundant training data without requiring extensive real-world data collection.
Solution Approach 2:
The system performs preliminary data generation through simulation before actual model training. By pre-generating diverse operational data covering various power grid conditions and maintenance scenarios, the system ensures sufficient training data availability without the complexity of collecting data from numerous real-world sources.
Data Source
AI summary
A task prediction model training method includes: when a maintenance task is performed in a power grid, collecting, for the power grid, system running data and operational data of the maintenance task; performing a simulation of the maintenance task for the power grid to expand the operational data under a variety of the system running data; if the expanding is completed, performing pre-processing and feature data extraction on the system running data and the operational data; annotating the feature data with label data based on a maintenance result of the simulation of the maintenance task; and under supervision of the label data, training a preset deep learning model to be a task prediction model based on the feature data. A maintenance task determination method is executed based on the trained task prediction model. A device and a storage medium are also disclosed. Thereby improving the efficiency of power grid maintenance.